From 075360f9f4bb874fc9a1c769f95ad853f9d17fe2 Mon Sep 17 00:00:00 2001 From: MajestyHenius <3051797499@qq.com> Date: Wed, 2 Sep 2026 10:25:40 +0800 Subject: [PATCH 1/3] feat: harness + CV funnel (from feature/esp32-ocr) --- extensions/assistive_harness/.gitignore | 4 + .../OPENGLASS_MIGRATION_MAP.md | 25 + extensions/assistive_harness/README.md | 60 + extensions/assistive_harness/__init__.py | 14 + extensions/assistive_harness/asr/__init__.py | 6 + extensions/assistive_harness/asr/base.py | 30 + .../assistive_harness/asr/energy_vad.py | 92 + .../assistive_harness/asr/funasr_engine.py | 84 + .../config/skills.example.yaml | 128 + extensions/assistive_harness/cv/README.md | 171 ++ extensions/assistive_harness/cv/__init__.py | 14 + extensions/assistive_harness/cv/base.py | 44 + extensions/assistive_harness/cv/noop.py | 60 + extensions/assistive_harness/cv/pipeline.py | 159 ++ extensions/assistive_harness/cv/registry.py | 89 + .../assistive_harness/cv/requirements-cv.txt | 2 + extensions/assistive_harness/cv/yolo_onnx.py | 220 ++ extensions/assistive_harness/echo_guard.py | 75 + extensions/assistive_harness/model_log.py | 72 + .../assistive_harness/package_phase_a.ps1 | 69 + .../assistive_harness/phase_b/README.md | 114 + .../assistive_harness/phase_b/__init__.py | 5 + .../assistive_harness/phase_b/bridge_ui.py | 337 +++ .../phase_b/cam_pipeline_v2.py | 2283 +++++++++++++++++ .../phase_b/esp32_runtime.py | 1851 +++++++++++++ .../assistive_harness/phase_b/funnel_gate.py | 218 ++ .../phase_b/realtime_session.py | 306 +++ .../phase_b/recorder_live.py | 860 +++++++ .../phase_b/requirements-phase-b.txt | 4 + .../assistive_harness/phase_b/rerun_source.py | 368 +++ .../phase_b/rokid_runtime.py | 1703 ++++++++++++ .../phase_b/session_recorder.py | 90 + .../phase_b/templates/live.html | 620 +++++ .../phase_b/templates/replay.html | 566 ++++ .../phase_b/templates/replay_index.html | 321 +++ .../assistive_harness/phase_b/timing_probe.py | 163 ++ .../assistive_harness/prompts/README.md | 43 + .../prompts/describe_scene_zh.txt | 8 + .../prompts/find_object_zh.txt | 9 + .../prompts/idle_chat_zh.txt | 8 + .../prompts/obstacle_avoidance_zh.txt | 9 + .../prompts/read_text_zh.txt | 7 + extensions/assistive_harness/registry.py | 117 + extensions/assistive_harness/router.py | 262 ++ extensions/assistive_harness/schemas.py | 67 + extensions/assistive_harness/server.py | 604 +++++ extensions/assistive_harness/state_machine.py | 115 + .../assistive_harness/summarize_reset_run.py | 97 + extensions/assistive_harness/telemetry.py | 67 + .../assistive_harness/tests/__init__.py | 0 .../assistive_harness/tests/test_core.py | 402 +++ .../assistive_harness/tests/test_cv_yolo.py | 41 + .../tests/test_phase_b_rokid.py | 352 +++ 53 files changed, 13435 insertions(+) create mode 100644 extensions/assistive_harness/.gitignore create mode 100644 extensions/assistive_harness/OPENGLASS_MIGRATION_MAP.md create mode 100644 extensions/assistive_harness/README.md create mode 100644 extensions/assistive_harness/__init__.py create mode 100644 extensions/assistive_harness/asr/__init__.py create mode 100644 extensions/assistive_harness/asr/base.py create mode 100644 extensions/assistive_harness/asr/energy_vad.py create mode 100644 extensions/assistive_harness/asr/funasr_engine.py create mode 100644 extensions/assistive_harness/config/skills.example.yaml create mode 100644 extensions/assistive_harness/cv/README.md create mode 100644 extensions/assistive_harness/cv/__init__.py create mode 100644 extensions/assistive_harness/cv/base.py create mode 100644 extensions/assistive_harness/cv/noop.py create mode 100644 extensions/assistive_harness/cv/pipeline.py create mode 100644 extensions/assistive_harness/cv/registry.py create mode 100644 extensions/assistive_harness/cv/requirements-cv.txt create mode 100644 extensions/assistive_harness/cv/yolo_onnx.py create mode 100644 extensions/assistive_harness/echo_guard.py create mode 100644 extensions/assistive_harness/model_log.py create mode 100644 extensions/assistive_harness/package_phase_a.ps1 create mode 100644 extensions/assistive_harness/phase_b/README.md create mode 100644 extensions/assistive_harness/phase_b/__init__.py create mode 100644 extensions/assistive_harness/phase_b/bridge_ui.py create mode 100644 extensions/assistive_harness/phase_b/cam_pipeline_v2.py create mode 100644 extensions/assistive_harness/phase_b/esp32_runtime.py create mode 100644 extensions/assistive_harness/phase_b/funnel_gate.py create mode 100644 extensions/assistive_harness/phase_b/realtime_session.py create mode 100644 extensions/assistive_harness/phase_b/recorder_live.py create mode 100644 extensions/assistive_harness/phase_b/requirements-phase-b.txt create mode 100644 extensions/assistive_harness/phase_b/rerun_source.py create mode 100644 extensions/assistive_harness/phase_b/rokid_runtime.py create mode 100644 extensions/assistive_harness/phase_b/session_recorder.py create mode 100644 extensions/assistive_harness/phase_b/templates/live.html create mode 100644 extensions/assistive_harness/phase_b/templates/replay.html create mode 100644 extensions/assistive_harness/phase_b/templates/replay_index.html create mode 100644 extensions/assistive_harness/phase_b/timing_probe.py create mode 100644 extensions/assistive_harness/prompts/README.md create mode 100644 extensions/assistive_harness/prompts/describe_scene_zh.txt create mode 100644 extensions/assistive_harness/prompts/find_object_zh.txt create mode 100644 extensions/assistive_harness/prompts/idle_chat_zh.txt create mode 100644 extensions/assistive_harness/prompts/obstacle_avoidance_zh.txt create mode 100644 extensions/assistive_harness/prompts/read_text_zh.txt create mode 100644 extensions/assistive_harness/registry.py create mode 100644 extensions/assistive_harness/router.py create mode 100644 extensions/assistive_harness/schemas.py create mode 100644 extensions/assistive_harness/server.py create mode 100644 extensions/assistive_harness/state_machine.py create mode 100644 extensions/assistive_harness/summarize_reset_run.py create mode 100644 extensions/assistive_harness/telemetry.py create mode 100644 extensions/assistive_harness/tests/__init__.py create mode 100644 extensions/assistive_harness/tests/test_core.py create mode 100644 extensions/assistive_harness/tests/test_cv_yolo.py create mode 100644 extensions/assistive_harness/tests/test_phase_b_rokid.py diff --git a/extensions/assistive_harness/.gitignore b/extensions/assistive_harness/.gitignore new file mode 100644 index 0000000..e607f1c --- /dev/null +++ b/extensions/assistive_harness/.gitignore @@ -0,0 +1,4 @@ +__pycache__/ +*.pyc +runs/ +test_artifacts/ diff --git a/extensions/assistive_harness/OPENGLASS_MIGRATION_MAP.md b/extensions/assistive_harness/OPENGLASS_MIGRATION_MAP.md new file mode 100644 index 0000000..1110736 --- /dev/null +++ b/extensions/assistive_harness/OPENGLASS_MIGRATION_MAP.md @@ -0,0 +1,25 @@ +# OpenGlass Migration Map + +Phase A deliberately separates portable policy from browser-specific transport. + +| Phase A component | OpenGlass-side destination | +|---|---| +| `schemas.py` | shared structured control-event schema | +| `router.py` | local deterministic voice-command router | +| `registry.py` + prompts | configurable Skill registry/prompt bundle | +| `echo_guard.py` | output/playback-aware ASR echo filter | +| `state_machine.py` | device-independent control priority and dedupe | +| `asr/` | phone/host local ASR service behind the same interface | +| `cv/base.py` | future advisory perception provider interface | +| `server.py` WS messages | replaceable host/phone transport adapter | +| `browser-session-adapter.js` | reference semantics for an OpenGlass session adapter | + +OpenGlass migration should keep `ControlEvent`, `SkillRegistry`, prompt SHA, +generation fence and STOP priority stable. Replace only AudioMirror capture, +session lifecycle calls, and the control transport. No dependency on ESP32, Rokid, +DOM layout or the MiniCPM private wire format exists in the Python core. + +The first Rokid implementation of this boundary now lives in +[`phase_b/`](phase_b/README.md). It consumes the existing APK JPEG/PCM endpoints, +reuses this Core and the existing Gateway, and implements the browser adapter's +STOP/RESUME/RESET/Skill generation contract in Python. diff --git a/extensions/assistive_harness/README.md b/extensions/assistive_harness/README.md new file mode 100644 index 0000000..8be88a5 --- /dev/null +++ b/extensions/assistive_harness/README.md @@ -0,0 +1,60 @@ +# Assistive Voice Skill Harness (Phase A) + +This directory is an optional, local sidecar for the MiniCPM-o browser Demo. It is +disabled by default and does not replace the native microphone/video path. + +Start the sidecar explicitly: + +```powershell +python -m extensions.assistive_harness.server --enabled ` + --model-path "C:\path\to\a\local\FunASR\model" +``` + +Then opt the browser tab in with `?assistive_harness=1`. Test-only transcript +injection additionally requires `--allow-test-injection`; it is never enabled by +the normal command above. + +The browser hook is deliberately thin. Control, ASR, prompt registry, echo guard, +state machine, metrics, and CV shadow interfaces live under this directory so the +module can later be moved behind another transport (for example OpenGlass). + +The first Rokid/OpenGlass transport is implemented in +[`phase_b/`](phase_b/README.md). It preserves this Core and the existing 8040 +Gateway while replacing browser microphone, camera, playback, and Session calls +with a Python device adapter. + +## User-editable Skills + +System prompts live in `extensions/assistive_harness/prompts/`. Existing prompt +files are read on every activation, so users can replace a prompt and activate +the Skill again without restarting the sidecar. Skill IDs, enable flags and +voice phrases are configured in `config/skills.example.yaml`; changing that YAML +does require restarting the sidecar. + +Enabled by default: + +- `帮我找<物体>` -> `find_object` -> hot Session restart with `{{target}}` +- `读一下` / `帮我识字` -> `read_text` -> hot Session restart +- `描述一下` / `看看周围` -> `describe_scene` -> hot Session restart +- `帮我避障` / `前面有障碍吗` -> `obstacle_avoidance` -> hot Session restart +- `回到聊天` / `恢复普通聊天` -> `idle_chat` -> hot Session restart + +`obstacle_avoidance` contains the frozen AAAI_SI prompt and is enabled only for +stationary, supervised validation. Its mobility safety has not been accepted; +never treat this Demo as a navigation or safety device. + +With the sidecar running, `GET http://127.0.0.1:8021/skills` reports the active +configuration and absolute prompt path for every registered Skill. + +## CV V1 shadow pipeline + +Browser JPEG mirrors enter a capacity-one latest-frame queue and are analyzed +on a dedicated single-thread worker. `cv_mode: disabled` drops frames before +inference; `cv_mode: shadow` writes `CVObservation` records without changing a +Skill or taking ownership of MiniCPM output. Slow, timed-out, or failed plugins +remain outside the audio/control receive path. + +`find_object` uses the local `yolo_onnx` reference provider. Other Skills keep +the `noop` provider until a task-specific plugin is registered. Provider setup, +the observation schema, and an OCR implementation template are documented in +[`cv/README.md`](cv/README.md). diff --git a/extensions/assistive_harness/__init__.py b/extensions/assistive_harness/__init__.py new file mode 100644 index 0000000..339e7b9 --- /dev/null +++ b/extensions/assistive_harness/__init__.py @@ -0,0 +1,14 @@ +"""Portable voice-controlled Skill Harness core for Phase A.""" + +from .router import RuleIntentRouter +from .registry import SkillRegistry +from .schemas import ASREvent, ControlEvent, ControlIntent + +__all__ = [ + "ASREvent", + "ControlEvent", + "ControlIntent", + "RuleIntentRouter", + "SkillRegistry", +] + diff --git a/extensions/assistive_harness/asr/__init__.py b/extensions/assistive_harness/asr/__init__.py new file mode 100644 index 0000000..6d88083 --- /dev/null +++ b/extensions/assistive_harness/asr/__init__.py @@ -0,0 +1,6 @@ +from .base import ASREngine, ASRResult +from .energy_vad import EnergyVAD, UtteranceAudio +from .funasr_engine import FunASREngine + +__all__ = ["ASREngine", "ASRResult", "EnergyVAD", "FunASREngine", "UtteranceAudio"] + diff --git a/extensions/assistive_harness/asr/base.py b/extensions/assistive_harness/asr/base.py new file mode 100644 index 0000000..a6fc73d --- /dev/null +++ b/extensions/assistive_harness/asr/base.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Protocol + +import numpy as np + + +@dataclass(frozen=True, slots=True) +class ASRResult: + text: str + confidence: float + model: str + device: str + + +class ASREngine(Protocol): + def transcribe(self, audio: np.ndarray, sample_rate: int) -> ASRResult: + ... + + +class ScriptedASREngine: + def __init__(self, transcripts: list[str]): + self.transcripts = list(transcripts) + + def transcribe(self, audio: np.ndarray, sample_rate: int) -> ASRResult: + del audio, sample_rate + text = self.transcripts.pop(0) if self.transcripts else "" + return ASRResult(text=text, confidence=1.0, model="scripted", device="cpu") + diff --git a/extensions/assistive_harness/asr/energy_vad.py b/extensions/assistive_harness/asr/energy_vad.py new file mode 100644 index 0000000..58cec2e --- /dev/null +++ b/extensions/assistive_harness/asr/energy_vad.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +from collections import deque +from dataclasses import dataclass + +import numpy as np + + +@dataclass(frozen=True, slots=True) +class UtteranceAudio: + audio: np.ndarray + started_at_ms: float + ended_at_ms: float + + +class EnergyVAD: + """Small streaming endpoint detector; it does not perform recognition.""" + + def __init__( + self, + sample_rate: int = 16_000, + rms_threshold: float = 0.012, + min_speech_ms: int = 180, + end_silence_ms: int = 450, + max_utterance_ms: int = 8_000, + preroll_ms: int = 200, + ): + self.sample_rate = sample_rate + self.rms_threshold = rms_threshold + self.min_speech_ms = min_speech_ms + self.end_silence_ms = end_silence_ms + self.max_utterance_ms = max_utterance_ms + self.preroll_ms = preroll_ms + self._preroll: deque[tuple[np.ndarray, float]] = deque() + self._active: list[np.ndarray] = [] + self._started_at_ms: float | None = None + self._last_voice_ms: float | None = None + + def _duration_ms(self, audio: np.ndarray) -> float: + return float(audio.size) * 1000.0 / self.sample_rate + + def feed(self, audio: np.ndarray, frame_started_at_ms: float) -> UtteranceAudio | None: + frame = np.asarray(audio, dtype=np.float32).reshape(-1).copy() + if frame.size == 0: + return None + duration_ms = self._duration_ms(frame) + frame_end_ms = frame_started_at_ms + duration_ms + rms = float(np.sqrt(np.mean(np.square(frame, dtype=np.float64)))) + voiced = rms >= self.rms_threshold + + if self._started_at_ms is None: + self._preroll.append((frame, frame_started_at_ms)) + while self._preroll and frame_end_ms - self._preroll[0][1] > self.preroll_ms: + self._preroll.popleft() + if not voiced: + return None + self._started_at_ms = self._preroll[0][1] if self._preroll else frame_started_at_ms + self._active = [item[0] for item in self._preroll] + self._preroll.clear() + self._last_voice_ms = frame_end_ms + else: + self._active.append(frame) + if voiced: + self._last_voice_ms = frame_end_ms + + active_ms = frame_end_ms - float(self._started_at_ms) + silence_ms = frame_end_ms - float(self._last_voice_ms or frame_end_ms) + if active_ms >= self.max_utterance_ms or silence_ms >= self.end_silence_ms: + return self._finish(frame_end_ms) + return None + + def _finish(self, ended_at_ms: float) -> UtteranceAudio | None: + if self._started_at_ms is None or not self._active: + self.reset() + return None + audio = np.concatenate(self._active).astype(np.float32, copy=False) + started = self._started_at_ms + voiced_duration = max(0.0, float(self._last_voice_ms or ended_at_ms) - started) + self.reset() + if voiced_duration < self.min_speech_ms: + return None + return UtteranceAudio(audio=audio, started_at_ms=started, ended_at_ms=ended_at_ms) + + def flush(self, ended_at_ms: float) -> UtteranceAudio | None: + return self._finish(ended_at_ms) + + def reset(self) -> None: + self._preroll.clear() + self._active = [] + self._started_at_ms = None + self._last_voice_ms = None + diff --git a/extensions/assistive_harness/asr/funasr_engine.py b/extensions/assistive_harness/asr/funasr_engine.py new file mode 100644 index 0000000..c7a126d --- /dev/null +++ b/extensions/assistive_harness/asr/funasr_engine.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +import threading +from pathlib import Path +from typing import Any + +import numpy as np + +from .base import ASRResult + + +class FunASREngine: + """Lazy local FunASR adapter. It never downloads a model implicitly.""" + + def __init__(self, model_path: str, device: str = "cpu", model_kwargs: dict[str, Any] | None = None): + path = Path(model_path).expanduser().resolve() + if not path.is_dir(): + raise FileNotFoundError(f"FunASR model path does not exist: {path}") + self.model_path = str(path) + self.device = device + self.model_kwargs = dict(model_kwargs or {}) + self._model: Any = None + self._load_lock = threading.Lock() + self._infer_lock = threading.Lock() + + @property + def loaded(self) -> bool: + return self._model is not None + + def ensure_loaded(self) -> None: + if self._model is not None: + return + with self._load_lock: + if self._model is not None: + return + from funasr import AutoModel + + self._model = AutoModel( + model=self.model_path, + device=self.device, + disable_update=True, + **self.model_kwargs, + ) + + def warm_up(self) -> None: + """Load the model and run one short silent inference before serving clients.""" + self.ensure_loaded() + silence = np.zeros(8_000, dtype=np.float32) + with self._infer_lock: + self._model.generate( + input=silence, + cache={}, + is_final=True, + batch_size_s=0, + ) + + def transcribe(self, audio: np.ndarray, sample_rate: int) -> ASRResult: + if sample_rate != 16_000: + raise ValueError(f"FunASR Phase A requires 16 kHz audio, got {sample_rate}") + self.ensure_loaded() + waveform = np.asarray(audio, dtype=np.float32).reshape(-1) + with self._infer_lock: + results = self._model.generate( + input=waveform, + cache={}, + is_final=True, + batch_size_s=0, + ) + text = "" + confidence = 1.0 + if isinstance(results, list) and results: + first = results[0] + if isinstance(first, dict): + text = str(first.get("text") or "").strip() + if isinstance(first.get("confidence"), (int, float)): + confidence = float(first["confidence"]) + else: + text = str(first).strip() + return ASRResult( + text=text, + confidence=confidence, + model=self.model_path, + device=self.device, + ) diff --git a/extensions/assistive_harness/config/skills.example.yaml b/extensions/assistive_harness/config/skills.example.yaml new file mode 100644 index 0000000..4e589ff --- /dev/null +++ b/extensions/assistive_harness/config/skills.example.yaml @@ -0,0 +1,128 @@ +version: 2 +default_skill: idle_chat + +harness: + enabled: false + host: 127.0.0.1 + port: 8021 + allow_test_injection: false + +asr: + engine: funasr + model_path: "" + device: cpu + rms_threshold: 0.012 + min_speech_ms: 180 + end_silence_ms: 450 + max_utterance_ms: 8000 + preroll_ms: 200 + +echo_guard: + window_ms: 20000 + similarity_threshold: 0.86 + +cv: + mode: shadow + max_fps: 1 + queue_size: 1 + inference_timeout_ms: 2000 + providers: + noop: + type: noop + yolo_onnx: + type: yolo_onnx + # Resolved relative to this YAML. Keep weights outside the Core so the + # same plugin can be reused by the later OpenGlass Adapter. + model_path: ../../../../OmniHarness/mini_omni_harness/models/yolo26n.onnx + device: cpu + confidence: 0.25 + image_size: 640 + +control: + cooldown_ms: 1200 + stop_speech: + # `同一下` / `等一(一)下` are bounded aliases observed from the local + # FunASR model when the user clearly says the short command `停一下`. + # They are command-only aliases, not arbitrary substring anchors. + phrases: [停一下, 同一下, 等一下, 等一一下, 别说了, 闭嘴, 停止播报, 安静] + # Literal substring protocol: any transcript containing this anchor stops. + embedded_phrases: [停一下] + resume_speech: + # Literal substring protocol: any transcript containing this anchor resumes. + embedded_phrases: [恢复对话] + reset_session: + phrases: [重新开始] + # Literal substring protocol for real voice RESET. Product deployments can + # later prepend a wake name (for example "乐奇重新开始") without changing + # the deterministic router. + embedded_phrases: [重新开始] + return_to_chat: + phrases: [回到聊天, 回到普通聊天, 退出技能, 普通聊天, 恢复普通聊天] + cancel_skill: + phrases: [取消任务, 不找了, 不读了] + +skills: + idle_chat: + enabled: true + prompt_file: ../prompts/idle_chat_zh.txt + requires_session_restart: true + cooldown_ms: 1200 + cv_mode: disabled + cv_provider: noop + activation_phrases: [普通聊天, 回到聊天] + + find_object: + enabled: true + description: 使用第一视角画面寻找语音中指定的物体 + prompt_source: AAAI_SI/C1_OBJECT_FINDING + prompt_file: ../prompts/find_object_zh.txt + requires_session_restart: true + cooldown_ms: 1500 + cv_mode: shadow + cv_provider: yolo_onnx + task_trigger: '请立即根据当前画面寻找“{{target}}”，只回答它的位置；如果没看到就说没看到。' + slot_schema: + target: {type: string, required: true} + activation_patterns: + - "帮我找(?:一下)?(?:我的)?(?P.+)" + - "帮我找(?P.+)" + - "(?P.+)在哪" + - "(?P.+)在哪里" + - "看到(?P.+)了吗" + - "有没有(?P.+)" + + read_text: + enabled: true + description: 读取当前画面中清晰可见的文字 + prompt_source: AAAI_SI/C1_TEXT_READING + prompt_file: ../prompts/read_text_zh.txt + requires_session_restart: true + cooldown_ms: 1500 + cv_mode: shadow + cv_provider: noop + task_trigger: 请立即读取当前画面中最明显的文字，只读看到的内容。 + activation_phrases: [读一下, 念一下, 帮我读一下, 帮我识字, 帮我读字, 上面写了什么, 这是什么字, 读文字] + + describe_scene: + enabled: true + prompt_file: ../prompts/describe_scene_zh.txt + requires_session_restart: true + cooldown_ms: 1500 + cv_mode: shadow + cv_provider: noop + task_trigger: 请立即用一两句话简短描述当前画面。 + activation_phrases: [描述一下, 帮我描述一下, 帮我描述场景, 前面有什么, 看看周围, 画面里有什么] + + obstacle_avoidance: + # Experimental: enabled only for stationary, supervised browser validation. + # It is not a mobility-safety guarantee and must not be used for navigation. + enabled: true + description: 识别前方可见障碍并给出保守提示（实验） + prompt_source: AAAI_SI/C1_OBSTACLE_AVOID_DIRECT_V1 + prompt_file: ../prompts/obstacle_avoidance_zh.txt + requires_session_restart: true + cooldown_ms: 1500 + cv_mode: shadow + cv_provider: noop + task_trigger: 请立即判断当前画面前方是否有明显障碍，并给出简短、保守的安全提示。 + activation_phrases: [帮我避障, 前面有障碍吗, 前面有没有障碍, 看一下前方障碍, 前面安全吗] diff --git a/extensions/assistive_harness/cv/README.md b/extensions/assistive_harness/cv/README.md new file mode 100644 index 0000000..6c986c2 --- /dev/null +++ b/extensions/assistive_harness/cv/README.md @@ -0,0 +1,171 @@ +# Lightweight CV provider framework (V1) + +This directory is the transport-neutral CV shadow path used by the Assistive +Harness. Browser and future OpenGlass Adapters only provide JPEG frames. They do +not call YOLO, OCR, depth estimation, or control rules directly. + +## V1 data flow + +```text +Browser / ESP32 Adapter + | + | FrameEnvelope(JPEG, timestamp, skill_id, slots) + v +CVPipeline.submit() # returns immediately + | + | capacity-one latest-frame queue (old queued frame is dropped) + v +single background worker + | + | provider.analyze() in a dedicated CPU thread + v +CVObservation -> cv_events.jsonl # shadow only; no control decision +``` + +The Router, state machine, Session Adapter, and MiniCPM audio/video path do not +depend on a concrete CV model. + +## Guarantees and deliberate limits + +- `submit()` never waits for inference. +- The queue is bounded and latest-frame wins; overload cannot grow memory + without limit. +- Only one inference call runs at a time per client pipeline. +- Provider errors become `CVObservation.error` and never escape to the control + socket. +- A timeout is recorded without blocking STOP/RESET. Python cannot kill an + already-running native ONNX call safely, so that one worker lane remains + occupied until the native call returns; new queued frames continue to + collapse to the latest frame. +- V1 accepts only `disabled` and `shadow`. Shadow observations never switch a + Skill, suppress MiniCPM, or speak to the user. +- Temporal voting, result fusion, navigation decisions, and accuracy tuning are + intentionally outside V1. + +## Configuration + +Providers are declared once under `cv.providers`. Each Skill selects one with +`cv_provider`: + +```yaml +cv: + max_fps: 1 + queue_size: 1 + inference_timeout_ms: 2000 + providers: + noop: + type: noop + yolo_onnx: + type: yolo_onnx + model_path: path/to/yolo.onnx + device: cpu + confidence: 0.25 + image_size: 640 + +skills: + find_object: + cv_mode: shadow + cv_provider: yolo_onnx +``` + +Relative model paths are resolved against the YAML directory. Weights are not +owned by the Core. The current local sample points to the previously validated +`OmniHarness/mini_omni_harness/models/yolo26n.onnx` file. + +## Provider contract + +A provider is synchronous. `CVPipeline` is responsible for running it outside +the asyncio/control loop: + +Provider instances are shared so model weights load only once. If a backend is +not safe for concurrent calls from multiple connected clients, the provider +must protect that backend with its own lock, as `YoloOnnxProvider` does. + +```python +from typing import Any +from extensions.assistive_harness.cv.base import CVObservation + + +class OcrOnnxProvider: + def __init__(self, model_path): + self.model_path = model_path + self._session = None # lazy-load on first frame + + def analyze( + self, + frame: bytes | None, + frame_id: str, + timestamp_ms: float, + skill_id: str, + slots: dict[str, Any], + ) -> CVObservation: + if not frame: + raise ValueError("OCR requires a JPEG frame") + # 1. Decode JPEG. + # 2. Lazy-load and run the local model. + # 3. Return JSON-serializable values only. + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider="ocr_onnx", + values={ + "status": "ok", + "text": "recognized text", + "regions": [], + "latency_ms": 12.3, + "model": self.model_path.name, + "device": "cpu", + }, + ) +``` + +To add OCR: + +1. Put the implementation in `cv/ocr_onnx.py` and keep model-specific imports + inside that plugin. +2. Add one `ocr_onnx` factory branch in `build_provider_registry()` in + `cv/registry.py`. +3. Add its config under `cv.providers`. +4. Set `read_text.cv_provider: ocr_onnx`; leave `cv_mode: shadow` during tests. +5. Add a blank/synthetic inference smoke plus one recorded real-frame test. + +No changes are required in `router.py`, `state_machine.py`, the browser Adapter, +or the future ESP32 Frame Adapter. + +## Observation schema + +Every plugin produces the same envelope: + +```json +{ + "frame_id": "browser_123", + "timestamp_ms": 123.0, + "skill_id": "find_object", + "provider": "shadow:yolo_onnx", + "values": { + "status": "ok", + "found": true, + "detections": [], + "latency_ms": 31.1, + "pipeline_latency_ms": 42.0 + }, + "error": null +} +``` + +Plugin-specific data belongs under `values`; the outer keys stay stable for +logging, replay, and later OpenGlass migration. + +## Validation + +```powershell +python -m unittest ` + extensions.assistive_harness.tests.test_core ` + extensions.assistive_harness.tests.test_cv_yolo +``` + +The tests cover failure isolation, latest-frame dropping, timeout reporting, +non-blocking submission, and a real ONNX Runtime YOLO inference. A browser +acceptance run should additionally verify that `find_object` produces +`provider=shadow:yolo_onnx` records while STOP/RESET remain responsive. diff --git a/extensions/assistive_harness/cv/__init__.py b/extensions/assistive_harness/cv/__init__.py new file mode 100644 index 0000000..dec2042 --- /dev/null +++ b/extensions/assistive_harness/cv/__init__.py @@ -0,0 +1,14 @@ +from .base import CVObservation, FrameEnvelope, PerceptionProvider +from .noop import NoOpCVProvider, ShadowCVProvider +from .pipeline import CVPipeline +from .registry import CVProviderRegistry + +__all__ = [ + "CVObservation", + "CVPipeline", + "CVProviderRegistry", + "FrameEnvelope", + "NoOpCVProvider", + "PerceptionProvider", + "ShadowCVProvider", +] diff --git a/extensions/assistive_harness/cv/base.py b/extensions/assistive_harness/cv/base.py new file mode 100644 index 0000000..5c1e153 --- /dev/null +++ b/extensions/assistive_harness/cv/base.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from typing import Any, Protocol + + +@dataclass(slots=True) +class FrameEnvelope: + """Transport-neutral frame handed from an Adapter to the CV worker.""" + + frame: bytes | None + frame_id: str + timestamp_ms: float + skill_id: str + slots: dict[str, Any] = field(default_factory=dict) + mode: str = "shadow" + provider_id: str = "noop" + + +@dataclass(slots=True) +class CVObservation: + frame_id: str + timestamp_ms: float + skill_id: str + provider: str + values: dict[str, Any] = field(default_factory=dict) + error: str | None = None + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class PerceptionProvider(Protocol): + """Synchronous plugin contract; CVPipeline always calls it off-loop.""" + + def analyze( + self, + frame: bytes | None, + frame_id: str, + timestamp_ms: float, + skill_id: str, + slots: dict[str, Any], + ) -> CVObservation: + ... diff --git a/extensions/assistive_harness/cv/noop.py b/extensions/assistive_harness/cv/noop.py new file mode 100644 index 0000000..1770c62 --- /dev/null +++ b/extensions/assistive_harness/cv/noop.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +from typing import Any + +from .base import CVObservation, PerceptionProvider + + +class NoOpCVProvider: + def analyze( + self, + frame: bytes | None, + frame_id: str, + timestamp_ms: float, + skill_id: str, + slots: dict[str, Any], + ) -> CVObservation: + del frame, slots + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider="noop", + values={}, + ) + + +class ShadowCVProvider: + """Failure-isolated observer. Its result is never a control decision.""" + + def __init__( + self, + provider: PerceptionProvider | None = None, + provider_id: str | None = None, + ): + self.provider = provider or NoOpCVProvider() + self.provider_id = provider_id or type(self.provider).__name__ + + def analyze( + self, + frame: bytes | None, + frame_id: str, + timestamp_ms: float, + skill_id: str, + slots: dict[str, Any], + ) -> CVObservation: + try: + result = self.provider.analyze( + frame, frame_id, timestamp_ms, skill_id, slots + ) + result.provider = f"shadow:{result.provider}" + return result + except Exception as exc: # shadow failures must not escape + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider=f"shadow:{self.provider_id}", + values={}, + error=f"{type(exc).__name__}: {exc}", + ) diff --git a/extensions/assistive_harness/cv/pipeline.py b/extensions/assistive_harness/cv/pipeline.py new file mode 100644 index 0000000..f17f9cf --- /dev/null +++ b/extensions/assistive_harness/cv/pipeline.py @@ -0,0 +1,159 @@ +from __future__ import annotations + +import asyncio +import time +from concurrent.futures import ThreadPoolExecutor +from typing import Callable + +from .base import CVObservation, FrameEnvelope +from .registry import CVProviderRegistry + + +ObservationCallback = Callable[[CVObservation], None] +MetricCallback = Callable[[str, float], None] + + +class CVPipeline: + """Latest-frame-only worker that keeps CV inference off the control loop.""" + + def __init__( + self, + providers: CVProviderRegistry, + *, + on_observation: ObservationCallback, + on_metric: MetricCallback | None = None, + queue_size: int = 1, + inference_timeout_ms: float = 2000.0, + worker_name: str = "assistive-cv", + ) -> None: + if int(queue_size) != 1: + raise ValueError("CV V1 requires queue_size=1 (latest-frame-only)") + self.providers = providers + self.on_observation = on_observation + self.on_metric = on_metric + self.queue: asyncio.Queue[FrameEnvelope] = asyncio.Queue(maxsize=1) + self.inference_timeout_ms = max(1.0, float(inference_timeout_ms)) + self.executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix=worker_name) + self.worker_task: asyncio.Task[None] | None = None + self.closed = False + self.submitted_frames = 0 + self.processed_frames = 0 + self.dropped_frames = 0 + self.timeout_count = 0 + + def submit(self, envelope: FrameEnvelope) -> bool: + """Enqueue without awaiting inference; replace the oldest queued frame.""" + if self.closed or envelope.mode == "disabled": + return False + self._ensure_worker() + if self.queue.full(): + try: + self.queue.get_nowait() + self.queue.task_done() + self.dropped_frames += 1 + self._metric("cv_frames_dropped", 1.0) + except asyncio.QueueEmpty: + pass + self.queue.put_nowait(envelope) + self.submitted_frames += 1 + return True + + def snapshot(self) -> dict[str, int | bool]: + return { + "closed": self.closed, + "queued_frames": self.queue.qsize(), + "submitted_frames": self.submitted_frames, + "processed_frames": self.processed_frames, + "dropped_frames": self.dropped_frames, + "timeout_count": self.timeout_count, + } + + async def close(self) -> None: + if self.closed: + return + self.closed = True + if self.worker_task is not None: + self.worker_task.cancel() + await asyncio.gather(self.worker_task, return_exceptions=True) + self.executor.shutdown(wait=False, cancel_futures=True) + + def _ensure_worker(self) -> None: + if self.worker_task is None or self.worker_task.done(): + self.worker_task = asyncio.create_task(self._run()) + + async def _run(self) -> None: + loop = asyncio.get_running_loop() + while True: + envelope = await self.queue.get() + started = time.perf_counter() + future = loop.run_in_executor(self.executor, self.providers.analyze, envelope) + try: + observation = await asyncio.wait_for( + asyncio.shield(future), self.inference_timeout_ms / 1000.0 + ) + except asyncio.TimeoutError: + self.timeout_count += 1 + self._metric("cv_inference_timeout", 1.0) + self._emit( + CVObservation( + frame_id=envelope.frame_id, + timestamp_ms=envelope.timestamp_ms, + skill_id=envelope.skill_id, + provider=f"{envelope.mode}:{envelope.provider_id}", + values={ + "status": "timeout", + "timeout_ms": self.inference_timeout_ms, + }, + error=( + "TimeoutError: CV inference exceeded " + f"{self.inference_timeout_ms:.0f} ms" + ), + ) + ) + # Python cannot safely kill an in-flight native inference call. + # Keep this one-worker lane occupied until it exits so timeouts + # never create concurrent calls or an unbounded executor queue. + try: + await future + except Exception: + pass + except asyncio.CancelledError: + raise + except Exception as exc: + self._emit( + CVObservation( + frame_id=envelope.frame_id, + timestamp_ms=envelope.timestamp_ms, + skill_id=envelope.skill_id, + provider=f"{envelope.mode}:{envelope.provider_id}", + values={"status": "error"}, + error=f"{type(exc).__name__}: {exc}", + ) + ) + else: + observation.values.setdefault( + "pipeline_latency_ms", round((time.perf_counter() - started) * 1000, 1) + ) + self.processed_frames += 1 + self._metric( + "cv_pipeline_latency_ms", + float(observation.values["pipeline_latency_ms"]), + ) + self._emit(observation) + finally: + self.queue.task_done() + + def _emit(self, observation: CVObservation) -> None: + try: + self.on_observation(observation) + except Exception: + # Telemetry/consumer failures are also outside the control path. + pass + + def _metric(self, name: str, value: float) -> None: + if self.on_metric is None: + return + try: + self.on_metric(name, value) + except Exception: + pass diff --git a/extensions/assistive_harness/cv/registry.py b/extensions/assistive_harness/cv/registry.py new file mode 100644 index 0000000..dbe3b4e --- /dev/null +++ b/extensions/assistive_harness/cv/registry.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from .base import CVObservation, FrameEnvelope, PerceptionProvider +from .noop import NoOpCVProvider, ShadowCVProvider + + +class CVProviderRegistry: + """Small explicit registry shared by browser and future device Adapters.""" + + def __init__(self, providers: dict[str, PerceptionProvider] | None = None): + self.providers: dict[str, PerceptionProvider] = { + "noop": NoOpCVProvider(), + **(providers or {}), + } + self._shadow = { + provider_id: ShadowCVProvider(provider, provider_id) + for provider_id, provider in self.providers.items() + } + + def register(self, provider_id: str, provider: PerceptionProvider) -> None: + normalized = provider_id.strip() + if not normalized: + raise ValueError("provider_id cannot be empty") + self.providers[normalized] = provider + self._shadow[normalized] = ShadowCVProvider(provider, normalized) + + def analyze(self, envelope: FrameEnvelope) -> CVObservation: + if envelope.mode != "shadow": + return CVObservation( + frame_id=envelope.frame_id, + timestamp_ms=envelope.timestamp_ms, + skill_id=envelope.skill_id, + provider=f"{envelope.mode}:{envelope.provider_id}", + values={"status": "skipped", "reason": "unsupported_cv_mode"}, + error=f"Unsupported cv_mode: {envelope.mode}", + ) + provider = self._shadow.get(envelope.provider_id) + if provider is None: + return CVObservation( + frame_id=envelope.frame_id, + timestamp_ms=envelope.timestamp_ms, + skill_id=envelope.skill_id, + provider=f"shadow:{envelope.provider_id}", + values={"status": "skipped", "reason": "unknown_provider"}, + error=f"Unknown CV provider: {envelope.provider_id}", + ) + return provider.analyze( + envelope.frame, + envelope.frame_id, + envelope.timestamp_ms, + envelope.skill_id, + envelope.slots, + ) + + +def build_provider_registry( + config: dict[str, Any], *, config_dir: Path +) -> CVProviderRegistry: + registry = CVProviderRegistry() + providers = dict((config.get("cv") or {}).get("providers") or {}) + for provider_id, raw_spec in providers.items(): + spec = dict(raw_spec or {}) + provider_type = str(spec.get("type") or provider_id) + if provider_type == "noop": + registry.register(str(provider_id), NoOpCVProvider()) + continue + if provider_type == "yolo_onnx": + from .yolo_onnx import YoloOnnxProvider + + raw_path = Path(str(spec.get("model_path") or "")) + model_path = raw_path if raw_path.is_absolute() else config_dir / raw_path + registry.register( + str(provider_id), + YoloOnnxProvider( + model_path.resolve(), + device=str(spec.get("device") or "cpu"), + confidence=float(spec.get("confidence", 0.25)), + image_size=int(spec.get("image_size", 640)), + target_aliases=dict(spec.get("target_aliases") or {}), + ), + ) + continue + raise ValueError( + f"Unsupported CV provider type {provider_type!r} for {provider_id!r}" + ) + return registry diff --git a/extensions/assistive_harness/cv/requirements-cv.txt b/extensions/assistive_harness/cv/requirements-cv.txt new file mode 100644 index 0000000..72c2319 --- /dev/null +++ b/extensions/assistive_harness/cv/requirements-cv.txt @@ -0,0 +1,2 @@ +opencv-python-headless +onnxruntime==1.21.0 diff --git a/extensions/assistive_harness/cv/yolo_onnx.py b/extensions/assistive_harness/cv/yolo_onnx.py new file mode 100644 index 0000000..01ca1c8 --- /dev/null +++ b/extensions/assistive_harness/cv/yolo_onnx.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +import threading +import time +from pathlib import Path +from typing import Any + +import numpy as np + +from .base import CVObservation + + +COCO_NAMES = ( + "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", + "truck", "boat", "traffic light", "fire hydrant", "stop sign", + "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", + "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", + "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", + "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", + "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", + "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", + "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", + "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", + "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", + "teddy bear", "hair drier", "toothbrush", +) + +DEFAULT_TARGET_ALIASES = { + "手机": "cell phone", "电话": "cell phone", "智能手机": "cell phone", + "phone": "cell phone", "cellphone": "cell phone", "cell phone": "cell phone", + "书": "book", "书本": "book", "图书": "book", "book": "book", + "杯子": "cup", "水杯": "cup", "茶杯": "cup", "cup": "cup", + "瓶子": "bottle", "水瓶": "bottle", "bottle": "bottle", + "椅子": "chair", "chair": "chair", "人": "person", "行人": "person", + "person": "person", "电脑": "laptop", "笔记本电脑": "laptop", + "laptop": "laptop", "遥控器": "remote", "remote": "remote", + "键盘": "keyboard", "keyboard": "keyboard", "鼠标": "mouse", + "mouse": "mouse", "背包": "backpack", "书包": "backpack", + "backpack": "backpack", +} + + +class YoloOnnxProvider: + """CPU-first YOLO ONNX reference plugin with no control-side effects.""" + + def __init__( + self, + model_path: Path, + *, + device: str = "cpu", + confidence: float = 0.25, + image_size: int = 640, + target_aliases: dict[str, str] | None = None, + ) -> None: + if device.lower() != "cpu": + raise ValueError("CV V1 only accepts device=cpu") + self.model_path = Path(model_path).resolve() + self.device = "cpu" + self.confidence = float(confidence) + self.image_size = int(image_size) + self.target_aliases = { + **DEFAULT_TARGET_ALIASES, + **{str(k).lower(): str(v) for k, v in (target_aliases or {}).items()}, + } + self._session: Any = None + self._input_name: str | None = None + self._cv2: Any = None + self._lock = threading.Lock() + + def analyze( + self, + frame: bytes | None, + frame_id: str, + timestamp_ms: float, + skill_id: str, + slots: dict[str, Any], + ) -> CVObservation: + if not frame: + raise ValueError("YOLO requires a non-empty JPEG frame") + self._load() + target = str(slots.get("target") or "").strip() + target_label = self._resolve_target(target) if target else None + if target and target_label is None: + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider="yolo_onnx", + values={ + "status": "skipped", + "reason": "unsupported_target", + "target": target, + }, + ) + + encoded = np.frombuffer(frame, dtype=np.uint8) + image = self._cv2.imdecode(encoded, self._cv2.IMREAD_COLOR) + if image is None: + raise ValueError("YOLO could not decode the JPEG frame") + height, width = image.shape[:2] + started = time.perf_counter() + tensor, scale, pad_x, pad_y = self._prepare_input(image) + with self._lock: + output = self._session.run(None, {self._input_name: tensor})[0] + latency_ms = (time.perf_counter() - started) * 1000.0 + if output.ndim != 3 or output.shape[0] != 1 or output.shape[2] != 6: + raise ValueError(f"Unexpected YOLO output shape: {output.shape}") + + target_id = COCO_NAMES.index(target_label) if target_label else None + detections: list[dict[str, Any]] = [] + for row in output[0]: + x1, y1, x2, y2, confidence, raw_class = row + class_id = round(float(raw_class)) + if float(confidence) < self.confidence or not 0 <= class_id < len(COCO_NAMES): + continue + if target_id is not None and class_id != target_id: + continue + left = max(0.0, min(width, (float(x1) - pad_x) / scale)) + top = max(0.0, min(height, (float(y1) - pad_y) / scale)) + right = max(0.0, min(width, (float(x2) - pad_x) / scale)) + bottom = max(0.0, min(height, (float(y2) - pad_y) / scale)) + if right <= left or bottom <= top: + continue + center_x = ((left + right) / 2.0) / width + center_y = ((top + bottom) / 2.0) / height + detections.append( + { + "label": COCO_NAMES[class_id], + "confidence": round(float(confidence), 4), + "bbox_xyxy": [round(left), round(top), round(right), round(bottom)], + "bbox_normalized": [ + round(left / width, 4), round(top / height, 4), + round(right / width, 4), round(bottom / height, 4), + ], + "center_normalized": [round(center_x, 4), round(center_y, 4)], + "position": self._position(center_x, center_y), + } + ) + detections.sort(key=lambda item: float(item["confidence"]), reverse=True) + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider="yolo_onnx", + values={ + "status": "ok", + "found": bool(detections), + "target": target or None, + "canonical_label": target_label, + "best_detection": detections[0] if detections else None, + "detections": detections, + "image": {"width": width, "height": height}, + "latency_ms": round(latency_ms, 1), + "frame_age_ms": round(max(0.0, time.time() * 1000.0 - timestamp_ms), 1), + "model": self.model_path.name, + "device": self.device, + }, + ) + + def _load(self) -> None: + if self._session is not None: + return + if not self.model_path.is_file(): + raise FileNotFoundError(f"YOLO weights not found: {self.model_path}") + import cv2 + import onnxruntime as ort + + session = ort.InferenceSession( + str(self.model_path), providers=["CPUExecutionProvider"] + ) + model_input = session.get_inputs()[0] + if model_input.shape[-2:] != [self.image_size, self.image_size]: + raise ValueError( + f"YOLO input is {model_input.shape[-2:]}, expected " + f"[{self.image_size}, {self.image_size}]" + ) + self._cv2 = cv2 + self._session = session + self._input_name = model_input.name + + def _prepare_input( + self, image: np.ndarray + ) -> tuple[np.ndarray, float, float, float]: + height, width = image.shape[:2] + scale = min(self.image_size / width, self.image_size / height) + resized_width, resized_height = round(width * scale), round(height * scale) + resized = self._cv2.resize(image, (resized_width, resized_height)) + pad_x = (self.image_size - resized_width) / 2.0 + pad_y = (self.image_size - resized_height) / 2.0 + left, top = round(pad_x - 0.1), round(pad_y - 0.1) + right = self.image_size - resized_width - left + bottom = self.image_size - resized_height - top + padded = self._cv2.copyMakeBorder( + resized, top, bottom, left, right, + self._cv2.BORDER_CONSTANT, value=(114, 114, 114), + ) + rgb = self._cv2.cvtColor(padded, self._cv2.COLOR_BGR2RGB) + tensor = np.ascontiguousarray(rgb.transpose(2, 0, 1), dtype=np.float32) + return np.expand_dims(tensor / 255.0, axis=0), scale, float(left), float(top) + + def _resolve_target(self, target: str) -> str | None: + normalized = target.lower().replace(" ", "") + for alias in sorted(self.target_aliases, key=len, reverse=True): + if alias.lower().replace(" ", "") in normalized: + label = self.target_aliases[alias] + return label if label in COCO_NAMES else None + return None + + @staticmethod + def _position(center_x: float, center_y: float) -> str: + horizontal = "左" if center_x < 1 / 3 else "右" if center_x > 2 / 3 else "中" + vertical = "上" if center_y < 1 / 3 else "下" if center_y > 2 / 3 else "中" + return { + ("左", "上"): "左上方", ("中", "上"): "正上方", + ("右", "上"): "右上方", ("左", "中"): "左侧", + ("中", "中"): "中央", ("右", "中"): "右侧", + ("左", "下"): "左下方", ("中", "下"): "正下方", + ("右", "下"): "右下方", + }[(horizontal, vertical)] diff --git a/extensions/assistive_harness/echo_guard.py b/extensions/assistive_harness/echo_guard.py new file mode 100644 index 0000000..fc3d25e --- /dev/null +++ b/extensions/assistive_harness/echo_guard.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import time +from collections import deque +from dataclasses import dataclass +from difflib import SequenceMatcher + +from .router import normalize_text +from .schemas import ControlIntent + + +@dataclass(frozen=True, slots=True) +class EchoDecision: + allow: bool + reason: str + similarity: float = 0.0 + + +class EchoGuard: + def __init__(self, window_ms: int = 20_000, similarity_threshold: float = 0.86): + self.window_ms = int(window_ms) + self.similarity_threshold = float(similarity_threshold) + self._model_text: deque[tuple[float, str]] = deque() + self.ai_speaking = False + + def note_model_text(self, text: str, at_ms: float | None = None) -> None: + normalized = normalize_text(text) + if not normalized: + return + now = at_ms if at_ms is not None else time.time() * 1000 + self._model_text.append((now, normalized)) + self._prune(now) + + def set_ai_speaking(self, active: bool) -> None: + self.ai_speaking = bool(active) + + def _prune(self, now_ms: float) -> None: + cutoff = now_ms - self.window_ms + while self._model_text and self._model_text[0][0] < cutoff: + self._model_text.popleft() + + def evaluate( + self, + utterance: str, + intent: ControlIntent, + at_ms: float | None = None, + ) -> EchoDecision: + now = at_ms if at_ms is not None else time.time() * 1000 + self._prune(now) + normalized = normalize_text(utterance) + if len(normalized) < 2: + return EchoDecision(False, "too_short") + + best = 0.0 + for _, model_text in self._model_text: + if normalized in model_text or model_text in normalized: + best = 1.0 + break + best = max(best, SequenceMatcher(None, normalized, model_text).ratio()) + if best >= self.similarity_threshold: + return EchoDecision(False, "matches_recent_model_echo", best) + + if self.ai_speaking and intent not in { + ControlIntent.STOP_SPEECH, + ControlIntent.RESUME_SPEECH, + ControlIntent.RESET_SESSION, + ControlIntent.CANCEL_SKILL, + ControlIntent.RETURN_TO_CHAT, + # A positively routed Skill command is also a control-plane + # interruption. Recent-model-text similarity is checked above, + # so model echo is still rejected before this exception applies. + ControlIntent.ACTIVATE_SKILL, + }: + return EchoDecision(False, "ordinary_skill_suppressed_while_ai_speaking", best) + return EchoDecision(True, "allowed", best) diff --git a/extensions/assistive_harness/model_log.py b/extensions/assistive_harness/model_log.py new file mode 100644 index 0000000..43045e4 --- /dev/null +++ b/extensions/assistive_harness/model_log.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + + +@dataclass(slots=True) +class ModelTurnAccumulator: + """Aggregate streamed Gateway fragments into auditable model turns.""" + + turn_index: int = 0 + _key: tuple[Any, ...] | None = None + _text: list[str] = field(default_factory=list) + _audio_ms: float = 0.0 + _metadata: dict[str, Any] = field(default_factory=dict) + + def _flush(self) -> dict[str, Any] | None: + if self._key is None: + return None + self.turn_index += 1 + record = { + "type": "model.turn", + "turn_index": self.turn_index, + **self._metadata, + "text": "".join(self._text), + "audio_ms": round(self._audio_ms, 1), + } + self._key = None + self._text.clear() + self._audio_ms = 0.0 + self._metadata = {} + return record + + def feed(self, message: dict[str, Any]) -> list[dict[str, Any]]: + state = str(message.get("state") or "unknown") + key = ( + message.get("session_id"), + int(message.get("generation") or 0), + str(message.get("skill_id") or ""), + state, + ) + completed: list[dict[str, Any]] = [] + if self._key is not None and key != self._key: + record = self._flush() + if record is not None: + completed.append(record) + + text = str(message.get("text") or "") + audio_ms = float(message.get("audio_ms") or 0.0) + end_of_turn = bool(message.get("end_of_turn")) + if self._key is None and (text or audio_ms > 0): + self._key = key + self._metadata = { + "role": "assistant" if state == "speak" else "listen", + "state": state, + "session_id": message.get("session_id"), + "generation": int(message.get("generation") or 0), + "skill_id": str(message.get("skill_id") or ""), + "slots": dict(message.get("slots") or {}), + } + if self._key is not None: + if text: + self._text.append(text) + self._audio_ms += max(0.0, audio_ms) + if end_of_turn: + record = self._flush() + if record is not None: + completed.append(record) + return completed + + def flush(self) -> dict[str, Any] | None: + return self._flush() diff --git a/extensions/assistive_harness/package_phase_a.ps1 b/extensions/assistive_harness/package_phase_a.ps1 new file mode 100644 index 0000000..8264580 --- /dev/null +++ b/extensions/assistive_harness/package_phase_a.ps1 @@ -0,0 +1,69 @@ +param( + [string]$RepoRoot = (Resolve-Path (Join-Path $PSScriptRoot '..\..')).Path, + [string]$Date = '2026-08-11' +) + +$ErrorActionPreference = 'Stop' +$deliverables = Join-Path $RepoRoot 'deliverables' +$packageName = "VOICE_SKILL_HARNESS_PHASE_A_CODEX_RETURN_$Date" +$stage = Join-Path $deliverables $packageName +$zipPath = Join-Path $deliverables "$packageName.zip" + +$resolvedRepo = (Resolve-Path $RepoRoot).Path.TrimEnd('\') +New-Item -ItemType Directory -Force -Path $deliverables | Out-Null +$resolvedDeliverables = (Resolve-Path $deliverables).Path +if (-not $resolvedDeliverables.StartsWith($resolvedRepo, [StringComparison]::OrdinalIgnoreCase)) { + throw "Unsafe delivery target: $resolvedDeliverables" +} +if (Test-Path $stage) { Remove-Item -LiteralPath $stage -Recurse -Force } +if (Test-Path $zipPath) { Remove-Item -LiteralPath $zipPath -Force } +New-Item -ItemType Directory -Force -Path $stage | Out-Null + +function Copy-TreeFiltered([string]$Source, [string]$Destination) { + Get-ChildItem -LiteralPath $Source -Recurse -File | Where-Object { + $_.FullName -notmatch '[\\/]runs[\\/]' -and + $_.FullName -notmatch '[\\/]__pycache__[\\/]' -and + $_.FullName -notmatch '[\\/]test_artifacts[\\/]' -and + $_.Extension -ne '.pyc' + } | ForEach-Object { + $relative = $_.FullName.Substring($Source.TrimEnd('\').Length).TrimStart('\') + $target = Join-Path $Destination $relative + New-Item -ItemType Directory -Force -Path (Split-Path $target) | Out-Null + Copy-Item -LiteralPath $_.FullName -Destination $target + } +} + +Copy-TreeFiltered (Join-Path $RepoRoot 'extensions\assistive_harness') (Join-Path $stage 'extensions\assistive_harness') +Copy-TreeFiltered (Join-Path $RepoRoot 'static\assistive_harness') (Join-Path $stage 'static\assistive_harness') +New-Item -ItemType Directory -Force -Path (Join-Path $stage 'static\omni') | Out-Null +Copy-Item -LiteralPath (Join-Path $RepoRoot 'static\omni\omni-app.js') -Destination (Join-Path $stage 'static\omni\omni-app.js') + +$docNames = @( + 'VOICE_SKILL_HARNESS_AUDIT.md', + 'VOICE_SKILL_HARNESS_DESIGN.md', + 'VOICE_SKILL_HARNESS_EXECUTION_REPORT.md', + 'VOICE_SKILL_HARNESS_GO_NO_GO.md', + 'VOICE_SKILL_HARNESS_RERUN_COMMANDS.md', + 'VOICE_SKILL_HARNESS_CHANGED_FILES.md' +) +$docTarget = Join-Path $stage '_codex_context' +New-Item -ItemType Directory -Force -Path $docTarget | Out-Null +foreach ($name in $docNames) { + $text = Get-Content -Raw -Encoding UTF8 (Join-Path $RepoRoot "_codex_context\$name") + $text = $text.Replace($env:USERPROFILE, '%USERPROFILE%') + Set-Content -Encoding UTF8 -NoNewline -Path (Join-Path $docTarget $name) -Value $text +} + +$manifest = [ordered]@{ + project = 'Voice-Controlled Skill Harness' + phase = 'A_MINICPMO_BROWSER' + date = $Date + ready_for_phase_b = $false + feature_default = 'off' + excluded = @('model weights', 'raw audio/video', 'runs', 'credentials', 'absolute user paths') +} +$manifest | ConvertTo-Json -Depth 5 | Set-Content -Encoding UTF8 (Join-Path $stage 'MANIFEST.json') + +Compress-Archive -Path (Join-Path $stage '*') -DestinationPath $zipPath -CompressionLevel Optimal +$hash = (Get-FileHash -Algorithm SHA256 -LiteralPath $zipPath).Hash +[pscustomobject]@{Zip=$zipPath; SHA256=$hash} diff --git a/extensions/assistive_harness/phase_b/README.md b/extensions/assistive_harness/phase_b/README.md new file mode 100644 index 0000000..b7ffe10 --- /dev/null +++ b/extensions/assistive_harness/phase_b/README.md @@ -0,0 +1,114 @@ +# Rokid Phase B Adapter + +This adapter replaces the Phase A browser transport while preserving the +frozen Harness policy and the existing MiniCPM backend: + +```text +Rokid APK --JPEG/PCM over Wi-Fi--> Phase B Adapter :18080 + |-- audio.mirror/frame.shadow --> Harness :8021 + |-- audio_chunk + JPEG --------> Gateway :8040 + `-- MiniCPM audio -------------> PC speaker +``` + +The backend remains `llama.cpp-omni -> Worker -> Gateway :8040 -> MiniCPM-o +4.5`. The old ESP32 and Rokid bridge scripts remain reference implementations; +`demo_rokid_phase_b_harness.py` is the Phase B entry point. + +## Frozen control contract + +- `STOP`: block and flush PC playback immediately. Device PCM continues to + reach Harness ASR and Gateway with `force_listen=true`. +- `RESUME`: release the playback gate without replacing the Session. +- `RESET`: stop the old Gateway Session with light cleanup, increment the + generation, create a new Session using the idle prompt, and reject stale + output. After `restart_complete`, the PC plays a short two-note ready cue; + initial startup stays silent. Use `--no-session-ready-chime` to disable it, + or `--session-ready-chime-volume` (default `0.32`) to tune it. +- Skill activation, cancellation, and return-to-chat use the same replacement + flow with the prompt and slots supplied by Harness. +- After a Skill Session reaches `restart_complete`, Harness injects the + original one-shot task into that new Session. This makes read-text and + experimental obstacle activation answer once instead of only changing the + system prompt. +- Rokid PCM is repacketized into 100 ms float32 `audio.mirror` frames, matching + Phase A. A bounded drop-oldest queue prevents device capture from being + blocked by Gateway latency. +- PC speaker queue depth plus `--playback-echo-tail-s` (default `0.80`) keeps + EchoGuard active through buffered playback and its short acoustic tail. + +## Prerequisites + +Run the existing Worker/Gateway on port 8040 and the Assistive Harness on port +8021. Install optional adapter dependencies from `requirements-phase-b.txt` in +the same Python environment. + +The current Rokid APK must target the host computer on port 18080 and provide: + +- `POST /rokid/image` with raw JPEG bytes; +- `WS /rokid/audio` with 16 kHz mono signed PCM16 little-endian packets. + +## Start + +From the repository root: + +```powershell +python .\demo_rokid_phase_b_harness.py ` + --gateway localhost:8040 ` + --harness-url ws://127.0.0.1:8021/ws/control ` + --input-gain 12 ` + --image-rotate-cw 270 ` + --session-ready-chime-volume 0.32 ` + --playback-echo-tail-s 0.80 +``` + +The local Gateway currently uses plain `ws://`. Use `--gateway-tls` only when +the deployed Gateway endpoint actually serves `wss://`. First-round model audio +plays on the computer; `--no-play` disables playback for diagnostics. + +The Rokid SDK PCM observed on the current device is materially quieter than the +browser microphone signal. `--input-gain 12` is the initial device calibration; +gain is applied before both Harness and Gateway and saturates instead of wrapping +PCM16. Recalibrate from real-device RMS logs rather than lowering the shared +Phase A VAD threshold. + +The ready cue briefly suppresses Rokid PCM forwarding for the cue plus a short +guard interval so the laptop speaker notification is not immediately recycled +into Harness or MiniCPM-o. + +Streaming model text is aggregated by Session, generation and end-of-turn. It +is printed as `[AssistiveHarness][MODEL]` and written under the current run: + +- `model_events.jsonl` for structured records; +- `model_transcript.txt` for quick human review. + +This is generated model text, not ASR over the final TTS waveform. Keep a +session recording when exact spoken audio must be audited. + +Health and the normalized latest frame are available at: + +- `GET http://127.0.0.1:18080/health` +- `GET http://127.0.0.1:18080/capture` + +Healthy input is approximately 25 audio packets/s and 1 JPEG/s, with one audio +client, zero sustained queue drops, `harness_connected=true`, and +`gateway_status=running`. `device_input_ready=true` confirms that at least one +PCM packet or JPEG has reached this process. If `[ROKID][NO_INPUT]` appears, +restart Sensor Mode on the glasses (`STOP -> RUN`) after the PC listener is up; +an APK WebSocket disconnected by a PC restart may not reconnect by itself. + +Stop the adapter with `Ctrl+C` (or `Ctrl+Break` on Windows). The HTTP listener, +Rokid WebSocket handler, Gateway Session, Harness connection, and PC speaker are +closed with bounded waits. A 10-second process watchdog is the final fallback, +so a native audio cleanup stall cannot leave port 18080 occupied indefinitely. + +## First device acceptance order + +1. Ordinary speech produces a MiniCPM response on the PC speaker. +2. While it speaks, say `停一下`; playback must stop and Harness must ACK + `stop_speech`. +3. Say `恢复对话`; the same Session resumes. +4. Say `重新开始`; the Session ID and generation must change. +5. Activate `找物`, `识字`, `场景描述`, and supervised experimental `避障`; + each switch must complete with a new Session and no old-generation output. + +Experimental obstacle output is not a navigation or safety guarantee. diff --git a/extensions/assistive_harness/phase_b/__init__.py b/extensions/assistive_harness/phase_b/__init__.py new file mode 100644 index 0000000..80d861b --- /dev/null +++ b/extensions/assistive_harness/phase_b/__init__.py @@ -0,0 +1,5 @@ +"""Device adapters for the Phase B glasses runtime.""" + +from .rokid_runtime import PhaseBRokidRuntime, RokidRuntimeConfig + +__all__ = ["PhaseBRokidRuntime", "RokidRuntimeConfig"] diff --git a/extensions/assistive_harness/phase_b/bridge_ui.py b/extensions/assistive_harness/phase_b/bridge_ui.py new file mode 100644 index 0000000..c3940f8 --- /dev/null +++ b/extensions/assistive_harness/phase_b/bridge_ui.py @@ -0,0 +1,337 @@ +"""bridge_ui.py — ESP32 duplex bridge 的 Web 观测/回放服务 + +URL: + / 实时观测(摄像头 + 字幕 + 状态) + /replay session 列表 + /replay/ session 回放(直接播 live_session.mp4,字幕跟随) + +依赖 recorder_live.py v5+ 写到 session_dir 的成品: + live_session.mp4 ← 播放主体(ffmpeg 已对齐) + live_session.m4a ← 没有视频帧时的纯音频回退 + live_user.wav ← 诊断下载 + live_ai.wav ← 诊断下载 + events.jsonl ← 字幕/事件源 + meta.json ← 会话元信息 +""" + +from __future__ import annotations +from typing import Optional, Set, Callable +import json +import logging +from pathlib import Path +from typing import Optional, Set + +from aiohttp import web + +LOGGER = logging.getLogger("bridge_ui") + +# 模板目录(和 bridge_ui.py 同级的 templates/) +TEMPLATES_DIR = Path(__file__).parent / "templates" + +# 开发期想"改完刷新浏览器就生效"就设 True;生产环境设 False 只读一次 +HOT_RELOAD_TEMPLATES = True + +_template_cache: dict[str, str] = {} + +def _load_template(name: str) -> str: + """读取 templates/。HOT_RELOAD_TEMPLATES=True 时每次都重读。""" + if not HOT_RELOAD_TEMPLATES and name in _template_cache: + return _template_cache[name] + path = TEMPLATES_DIR / name + try: + text = path.read_text(encoding="utf-8") + except FileNotFoundError: + LOGGER.error("[UI] template not found: %s", path) + return f"

Template not found: {name}

" + _template_cache[name] = text + return text + + +# ============================================================ +# Server +# ============================================================ + +class WebUIServer: + def __init__(self, port: int = 8080, host: str = "0.0.0.0", + sessions_root: Path = Path("./sessions"), + stop_callback: Optional[Callable[[], None]] = None, + mode_info: Optional[dict] = None): + self.port = port + self.host = host + self.sessions_root = Path(sessions_root) + self.live_clients: Set[web.WebSocketResponse] = set() + self._runner: Optional[web.AppRunner] = None + self._stop_callback = stop_callback # ← 新增 + self._stop_fired = False # ← 新增,防重入 + self.mode_info = mode_info or {"mode": "live"} + + async def start(self) -> None: + app = web.Application() + app.router.add_get('/', self._h_live) + app.router.add_get('/replay', self._h_replay_index) + app.router.add_get('/replay/{sid}', self._h_replay) + app.router.add_get('/live_ws', self._h_live_ws) + app.router.add_get('/api/sessions', self._h_list) + app.router.add_get('/api/session/{sid}/events', self._h_events) + app.router.add_get('/api/session/{sid}/meta', self._h_meta) + # —— v2: 直接吐成品文件,FileResponse 自带 HTTP Range 支持 —— + app.router.add_get('/api/session/{sid}/video', self._h_video) + app.router.add_get('/api/session/{sid}/audio', self._h_audio_only) + app.router.add_get('/api/session/{sid}/user_audio.wav', self._h_user_wav) + app.router.add_get('/api/session/{sid}/ai_audio.wav', self._h_ai_wav) + # 兼容老路由(可能旧版 events.jsonl 引用 images/xxx.jpg) + app.router.add_get('/api/session/{sid}/{path:images/.+}', self._h_image_legacy) + app.router.add_get('/api/session/{sid}/{path:live_images/.+}', self._h_image_live) + app.router.add_post('/api/stop', self._h_stop) + self._runner = web.AppRunner(app) + await self._runner.setup() + site = web.TCPSite(self._runner, self.host, self.port) + await site.start() + LOGGER.info("[UI] http://%s:%d (live + replay)", self.host, self.port) + + async def stop(self) -> None: + for ws in list(self.live_clients): + try: await ws.close() + except Exception: pass + if self._runner: + await self._runner.cleanup() + + async def emit(self, evt: dict) -> None: + if not self.live_clients: + return + msg = json.dumps(evt, ensure_ascii=False) + dead = [] + for ws in list(self.live_clients): + try: await ws.send_str(msg) + except Exception: dead.append(ws) + for ws in dead: + self.live_clients.discard(ws) + + # ---- handlers ---- + async def _h_live(self, request): + return web.Response(text=_load_template("live.html"), content_type='text/html') + + async def _h_stop(self, request): + if self._stop_fired: + return web.json_response({"ok": True, "already": True}) + self._stop_fired = True + LOGGER.info("[UI] /api/stop triggered by browser") + # 通知所有 live 客户端"要关了",前端可以把按钮改成"Saving..." + try: + await self.emit({"type": "stopping"}) + except Exception: + pass + if self._stop_callback is not None: + try: + self._stop_callback() + except Exception as e: + LOGGER.warning("[UI] stop_callback err: %s", e) + return web.json_response({"ok": True}) + + async def _h_replay_index(self, request): + return web.Response(text=_load_template("replay_index.html"), content_type='text/html') + + async def _h_replay(self, request): + return web.Response(text=_load_template("replay.html"), content_type='text/html') + + async def _h_live_ws(self, request): + ws = web.WebSocketResponse(heartbeat=30) + await ws.prepare(request) + self.live_clients.add(ws) + + LOGGER.info("[UI] live client +1 (total=%d)", len(self.live_clients)) + try: + await ws.send_str(json.dumps( + {"type": "mode", **self.mode_info}, ensure_ascii=False)) + except Exception: + pass + + try: + async for _ in ws: pass + finally: + self.live_clients.discard(ws) + LOGGER.info("[UI] live client -1 (total=%d)", len(self.live_clients)) + return ws + + async def _h_list(self, request): + sessions = [] + if self.sessions_root.exists(): + for d in sorted(self.sessions_root.iterdir(), reverse=True): + if not d.is_dir(): + continue + meta_p = d / "meta.json" + meta = {} + if meta_p.exists(): + try: + meta = json.loads(meta_p.read_text(encoding='utf-8')) + except Exception: + pass + if (d / "live_session.mp4").exists(): + media = "mp4" + elif (d / "live_session.m4a").exists(): + media = "m4a" + else: + media = None + sessions.append({ + "id": d.name, + "tag": meta.get("session_tag", d.name), + "start_time": meta.get("start_time"), + "end_time": meta.get("end_time"), + "stats": meta.get("stats", {}), + "media": media, + }) + return web.json_response({"sessions": sessions}) + + def _safe(self, sid: str) -> Optional[Path]: + if "/" in sid or ".." in sid: + return None + p = (self.sessions_root / sid).resolve() + try: + p.relative_to(self.sessions_root.resolve()) + except ValueError: + return None + if not p.is_dir(): + return None + return p + + async def _h_meta(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.json_response({"error": "not found"}, status=404) + meta = p / "meta.json" + if not meta.exists(): + return web.json_response({"error": "no meta"}, status=404) + return web.json_response(json.loads(meta.read_text(encoding='utf-8'))) + + async def _h_events(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.json_response({"error": "not found"}, status=404) + ev = p / "events.jsonl" + if not ev.exists(): + return web.json_response({"events": []}) + out = [] + for line in ev.read_text(encoding='utf-8').splitlines(): + line = line.strip() + if not line: + continue + try: + out.append(json.loads(line)) + except Exception: + pass + return web.json_response({"events": out}) + + # ---- 媒体:直接 FileResponse(支持 Range,可拖动进度条)---- + + async def _h_video(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.Response(status=404) + mp4 = p / "live_session.mp4" + if not mp4.exists(): + return web.Response(status=404, text="no live_session.mp4") + return web.FileResponse(mp4, headers={"Content-Type": "video/mp4"}) + + async def _h_audio_only(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.Response(status=404) + m4a = p / "live_session.m4a" + if not m4a.exists(): + return web.Response(status=404, text="no live_session.m4a") + return web.FileResponse(m4a, headers={"Content-Type": "audio/mp4"}) + + async def _h_user_wav(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.Response(status=404) + w = p / "live_user.wav" + if not w.exists(): + return web.Response(status=404) + return web.FileResponse(w, headers={"Content-Type": "audio/wav"}) + + async def _h_ai_wav(self, request): + p = self._safe(request.match_info['sid']) + if not p: + return web.Response(status=404) + w = p / "live_ai.wav" + if not w.exists(): + return web.Response(status=404) + return web.FileResponse(w, headers={"Content-Type": "audio/wav"}) + + async def _h_image_live(self, request): + return await self._serve_image(request, subdir="live_images") + + async def _h_image_legacy(self, request): + return await self._serve_image(request, subdir="images") + + async def _serve_image(self, request, subdir: str): + p = self._safe(request.match_info['sid']) + if not p: + return web.Response(status=404) + rel = request.match_info['path'] + # rel 形如 "live_images/img_00001.jpg" 或 "images/xxx.jpg" + # 这里只取末尾文件名,防穿越 + name = Path(rel).name + if "/" in name or ".." in name or not name: + return web.Response(status=400) + img = p / subdir / name + if not img.exists(): + return web.Response(status=404) + return web.FileResponse(img, headers={"Content-Type": "image/jpeg"}) + + +# ============================================================ +# Standalone entry +# ============================================================ + +def _main() -> None: + import argparse + import asyncio + + parser = argparse.ArgumentParser( + description="Bridge UI — standalone replay server (no ESP32 / no model)" + ) + parser.add_argument("--port", type=int, default=8080) + parser.add_argument("--host", default="0.0.0.0") + parser.add_argument("--sessions", default="./sessions", + help="sessions 根目录(默认 ./sessions)") + args = parser.parse_args() + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + + sessions_root = Path(args.sessions).resolve() + if not sessions_root.exists(): + LOGGER.warning("[UI] sessions dir 不存在: %s(之后录到这里就能看见)", + sessions_root) + + server = WebUIServer( + port=args.port, + host=args.host, + sessions_root=sessions_root + ) + + async def _run(): + await server.start() + LOGGER.info("[UI] standalone mode — replay only") + LOGGER.info("[UI] sessions root: %s", sessions_root) + LOGGER.info("[UI] open http://localhost:%d/replay", args.port) + try: + while True: + await asyncio.sleep(3600) + except (KeyboardInterrupt, asyncio.CancelledError): + pass + finally: + await server.stop() + + try: + asyncio.run(_run()) + except KeyboardInterrupt: + print("\n[UI] bye.") + + +if __name__ == "__main__": + _main() \ No newline at end of file diff --git a/extensions/assistive_harness/phase_b/cam_pipeline_v2.py b/extensions/assistive_harness/phase_b/cam_pipeline_v2.py new file mode 100644 index 0000000..fdd2b8c --- /dev/null +++ b/extensions/assistive_harness/phase_b/cam_pipeline_v2.py @@ -0,0 +1,2283 @@ +# -*- coding: utf-8 -*- +"""cam_tuner.py — ESP32 相机调参台 (形态1: 纯调参, 不接模型) + +目的: 一边看 TCP live 画面, 一边热调分辨率/对焦/quality, 实时看每帧的 + 拉普拉斯清晰度分 + 亮度 —— 为三级漏斗一级标定阈值。 + +用法: + python cam_tuner.py --ip 10.100.7.68 + 浏览器开 http://localhost:8080 + +注意 (硬约束): + 固件 TCP 5000 是单客户端串行。本工具独占 TCP —— 调参时不要同时跑主程序 + (pc_vlm / demo_esp32), 否则抢同一条 TCP 连接会错位。 + +依赖: aiohttp, opencv-python(cv2), numpy, requests +""" + +from __future__ import annotations + +import argparse +import asyncio +import socket +import struct +import threading +import time +from pathlib import Path +from typing import Optional + +import cv2 +import numpy as np +import requests +from aiohttp import web + + +# ============================================================ +# TCP 5000 抓帧 (复用已验证协议; 单连接 + 锁, 后台线程持有) +# ============================================================ +class TCPImageClient: + def __init__(self, ip: str, port: int = 5000, timeout: float = 2.0): + self.ip, self.port, self.timeout = ip, port, timeout + self._sock = None + self._lock = threading.Lock() + + def _connect_locked(self) -> bool: + try: + self._sock = socket.create_connection((self.ip, self.port), timeout=self.timeout) + self._sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) + self._sock.settimeout(self.timeout) + return True + except Exception: + self._sock = None + return False + + def _recv_exactly(self, n: int) -> Optional[bytes]: + buf = b"" + while len(buf) < n: + chunk = self._sock.recv(n - len(buf)) + if not chunk: + return None + buf += chunk + return buf + + def capture(self) -> Optional[bytes]: + with self._lock: + for attempt in (1, 2): + if self._sock is None and not self._connect_locked(): + return None + try: + self._sock.sendall(b"\x01") + hdr = self._recv_exactly(20) + if not hdr: + raise ConnectionError("no header") + magic = struct.unpack_from(" bool: + try: + r = self.sess.get(f"{self.base}/control", + params={"var": var, "val": val}, timeout=self.timeout) + return r.status_code == 200 + except Exception: + return False + + def set_resolution(self, name: str) -> bool: + v = FRAMESIZE.get(name.upper()) + return self.control("framesize", v) if v is not None else False + + def set_quality(self, q: int) -> bool: + return self.control("quality", max(0, min(63, int(q)))) + + def trigger_af(self) -> bool: + # 单次对焦: 直接写 OV5640 寄存器 0x3022=0x03 (single auto focus)。 + # 固件用标准 esp32-camera web server, 没有实现 /control?var=af (af 非标准变量), + # 之前先试 control("af") 会"假成功"(固件忽略)而不真对焦 —— 故直接走 /reg。 + # 固件端应设 g_auto_af=false, 关掉每秒定时硬对焦, 对焦完全由此按需触发。 + try: + r = self.sess.get(f"{self.base}/reg", + params={"reg": 0x3022, "mask": 0xff, "val": 0x03}, + timeout=self.timeout) + return r.status_code == 200 + except Exception: + return False + + +def rotate_jpeg(jpg: bytes, deg: int) -> bytes: + """后端真旋转 (顺时针 deg∈{0,90,180,270})。返回旋转后的 JPEG bytes。 + 真转而非只转显示: 因为最终自动旋转策略判断后会真喂给模型, 数据路径要一致; + 且清晰度分基于转后图算, 可验证'旋转不改变清晰度'(锐利度与朝向正交)。""" + d = deg % 360 + if d == 0: + return jpg + arr = np.frombuffer(jpg, dtype=np.uint8) + img = cv2.imdecode(arr, cv2.IMREAD_COLOR) + if img is None: + return jpg + if d == 90: + img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + elif d == 180: + img = cv2.rotate(img, cv2.ROTATE_180) + elif d == 270: + img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) + else: + return jpg + ok, enc = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 90]) + return enc.tobytes() if ok else jpg + + +def _decode_gray(jpg: bytes): + arr = np.frombuffer(jpg, dtype=np.uint8) + return cv2.imdecode(arr, cv2.IMREAD_GRAYSCALE) + + +def frame_motion(jpg_a: bytes, jpg_b: bytes) -> float: + """两帧灰度平均绝对差 (0-255)。大 = 画面在动 (快速移动/转头)。 + **先高斯模糊再算差**: 抹掉密集文字的高频细节, 只保留宏观位移 —— + 否则密集文字面(成分面)手持轻微移动时每个小字边缘都产生大量像素差, motion 虚高, + 把可读的图误判成"在动"(真机+OCR校准证实成分面可读却被全拒)。 + 漏斗二级用: 一批帧相邻 diff 都大 -> 画面不稳 -> 拒绝(念字必错, 让用户停稳)。""" + a, b = _decode_gray(jpg_a), _decode_gray(jpg_b) + if a is None or b is None: + return 0.0 + if a.shape != b.shape: + b = cv2.resize(b, (a.shape[1], a.shape[0])) + a = cv2.GaussianBlur(a, (7, 7), 0) + b = cv2.GaussianBlur(b, (7, 7), 0) + return float(np.abs(a.astype(np.int16) - b.astype(np.int16)).mean()) + + +def optical_flow_motion(jpg_a: bytes, jpg_b: bytes, downscale: float = 0.35) -> float: + """两帧稠密光流(Farneback)的平均位移幅度(像素)。物理含义明确、可解释: + 画面整体移动了多少像素。用于【严重晃动一刀切】—— 相机大幅位移时光流幅度大。 + 比 frame_motion(逐像素差)更鲁棒: 光流看运动矢量场, 不被密集文字的高频细节干扰 + (密集字轻微移动 -> 光流一致小位移; 逐像素差却虚高)。降采样提速(判"严重"够用)。""" + a, b = _decode_gray(jpg_a), _decode_gray(jpg_b) + if a is None or b is None: + return 0.0 + if a.shape != b.shape: + b = cv2.resize(b, (a.shape[1], a.shape[0])) + if downscale != 1.0: + a = cv2.resize(a, None, fx=downscale, fy=downscale, interpolation=cv2.INTER_AREA) + b = cv2.resize(b, None, fx=downscale, fy=downscale, interpolation=cv2.INTER_AREA) + flow = cv2.calcOpticalFlowFarneback(a, b, None, + pyr_scale=0.5, levels=3, winsize=15, + iterations=3, poly_n=5, poly_sigma=1.2, flags=0) + mag = np.sqrt(flow[..., 0] ** 2 + flow[..., 1] ** 2) + # 位移幅度按降采样比例还原到原图尺度, 便于用原图像素单位设阈值 + return float(mag.mean() / max(downscale, 1e-6)) + + +def bg_signals(jpg_bytes): + """候选'背景干扰'信号(都可解释, 检测画面有额外背景 -> 提示拿近/对准)。 + 干净白纸标签: 大片均匀白 + 中间少量黑字; 有背景(灯管/工位/天花板)时信号异常。 + 也含清晰度类(local_sharp/worst_block/sharp_ratio)供评分。返回 dict。""" + arr = np.frombuffer(jpg_bytes, np.uint8) + g = cv2.imdecode(arr, cv2.IMREAD_GRAYSCALE) + if g is None: + return {} + h, w = g.shape + # 信号1: 亮度直方图熵 —— 干净标签(白底+黑字)分布集中熵低; 背景杂->熵高 + hist = cv2.calcHist([g], [0], None, [32], [0, 256]).flatten() + p = hist / (hist.sum() + 1e-9) + entropy = float(-np.sum(p * np.log2(p + 1e-12))) + # 信号2: 最大均匀(低方差)区占比 —— 白纸有大片均匀白; 占比低=画面杂乱 + blur = cv2.GaussianBlur(g, (5, 5), 0) + localvar = cv2.blur((g.astype(np.float32) - blur.astype(np.float32)) ** 2, (15, 15)) + lowvar = (localvar < 30).astype(np.uint8) + n, labels, stats, _ = cv2.connectedComponentsWithStats(lowvar) + max_uniform = float(stats[1:, cv2.CC_STAT_AREA].max() / g.size) if n > 1 else 0.0 + # 信号3: 边缘空间分布 —— 干净标签边缘集中中间(文字); 背景使边缘散布/落外围 + edges = cv2.Canny(g, 50, 150) + ys, xs = np.where(edges > 0) + if len(xs) > 10: + cx, cy = w / 2, h / 2 + d = np.sqrt(((xs - cx) / w) ** 2 + ((ys - cy) / h) ** 2) + edge_spread = float(d.mean()) + edge_periph = float((d > 0.35).mean()) # 边缘落外围(远离中心)比例 + else: + edge_spread = 0.0; edge_periph = 0.0 + # 信号4: local_sharp / worst_block —— 有高对比背景(灯管)时 local虚高而整体糊 + lap = cv2.Laplacian(g, cv2.CV_64F) + local_sharp = float(lap.var()) + grid = 6 + vals = [] + for i in range(grid): + for j in range(grid): + blk = g[i*h//grid:(i+1)*h//grid, j*w//grid:(j+1)*w//grid] + if blk.size == 0: + continue + e = cv2.Canny(blk, 50, 150) + if (e > 0).mean() < 0.006: + continue + vals.append(float(cv2.Laplacian(blk, cv2.CV_64F).var())) + worst = min(vals) if vals else 0.0 + sharp_ratio = float(local_sharp / (worst + 1e-6)) if worst > 0 else 0.0 + return { + "bright_entropy": round(entropy, 2), + "max_uniform": round(max_uniform, 3), + "edge_spread": round(edge_spread, 3), + "edge_periph": round(edge_periph, 3), + "sharp_ratio": round(sharp_ratio, 1), + "local_sharp": round(local_sharp, 1), + "worst_block": round(worst, 1), + } + + +# ============================================================ +# 三级漏斗 (形态2核心) +# 输入: 一批 N 帧 (bytes list) +# 一级 单帧质量筛: 太糊/太暗/空白 的帧标记不合格 +# 二级 稳定性判定: 相邻帧 motion 都过大 -> 整批拒绝 (画面不稳) +# 三级 选优: 合格帧里选 sharpness 最高的 = best +# 全不合格 / 不稳 -> 返回拒绝 (安全闸, 不喂模型) +# +# 阈值说明: 分辨率固定 HD 后针对 HD 标定。这里给的是初始默认值, +# 你戴眼镜实测后按 CSV 数据调 (尤其 SHARP_MIN / MOTION_MAX)。 +# sharp 低 + edge 低 = 空白(拒绝, 说"没看到文字"); 用 EDGE_MIN 兜。 +# ============================================================ +import math + + +def _g_sat(x: float, x0: float) -> float: + """Michaelis-Menten 饱和: x/(x+x0) -> [0,1)。x0=半饱和点。""" + x = max(0.0, x) + return x / (x + x0) if (x + x0) > 0 else 0.0 + + +def _g_light(b: float, lo: float, hi: float, soft: float) -> float: + """亮度响应: 太暗/过曝降分, 中间平台=1。两 sigmoid 相夹。""" + def sig(z): + try: + return 1.0 / (1.0 + math.exp(-z)) + except OverflowError: + return 0.0 if z < 0 else 1.0 + return sig((b - lo) / soft) * (1 - sig((b - hi) / soft)) + + +def frame_score(metrics: dict, rel_change: float, cfg: "FunnelConfig") -> dict: + """对一帧算 quality + focus_gain + 各分量 (供解释/记录)。 + rel_change: 该帧相对上一帧的 sharp 相对变化 (稳定性输入); 首帧传 0。""" + # 清晰度用 worst_block(最糊的有内容块) 而非全局 sharpness —— + # 全局拉普拉斯被大面积白底稀释(白底无边缘, 拉普拉斯低), 把"清晰的白盒子标签"误判成糊, + # 触发假的 need_focus。worst_block 只看有内容的最糊块, 不被白底拉低。 + # 真机+OCR校准: 不可读帧 worst_block<4.6, 可读>5.5 -> 分界~5, 故 x0 用 worst_x0(~5量级)。 + worst = metrics.get("worst_block", None) + if worst is None: + worst = metrics.get("sharpness", 0.0) # 兜底: 无 worst_block 时退回全局 + g_sharp = _g_sat(worst, cfg.worst_x0) + g_stable = max(0.0, 1.0 - min(1.0, rel_change)) # rel_change 已归一化到[0,1] + g_content = _g_sat(metrics.get("edge_density", 0.0), cfg.edge_x0) + g_light = _g_light(metrics.get("brightness", 0.0), cfg.light_lo, cfg.light_hi, cfg.light_soft) + + wsum = cfg.w_sharp + cfg.w_stable + cfg.w_content + cfg.w_light + base = (cfg.w_sharp * g_sharp + cfg.w_stable * g_stable + + cfg.w_content * g_content + cfg.w_light * g_light) / wsum + sharp_gate = g_sharp ** cfg.sharp_gate_gamma # 念字硬要求: 糊则压分 + quality = base * sharp_gate + # focus_gain: 稳 + 糊 时该对焦。对 content 用"温和依赖"(sqrt)而非硬乘 —— + # 重度失焦会把边缘也糊没(g_content 低), 但它恰恰最该对焦, 不能因此归零; + # sqrt 让低内容时 focus_gain 降低但保留触发机会。真空白由 g_content 极低 + + # g_stable 高的组合另行识别(见 run_funnel 拒绝归因)。 + focus_gain = g_stable * (1 - g_sharp) * math.sqrt(max(0.0, g_content)) + return { + "quality": round(quality, 3), + "focus_gain": round(focus_gain, 3), + "g_sharp": round(g_sharp, 3), + "g_stable": round(g_stable, 3), + "g_content": round(g_content, 3), + "g_light": round(g_light, 3), + } + + +class FunnelConfig: + """评分函数 f 的参数 (替代硬阈值 if)。每帧算一个连续 quality 分 + focus_gain, + 决策基于分数而非离散阈值 —— 可解释、可 ablation、可用数据校准。 + + quality = [Σ wi·gi] · g_sharp^gamma (sharp 作乘性门, 念字硬要求) + g_sharp = sharp/(sharp+sharp_x0) 清晰度饱和响应 + g_stable = 1 - min(1, rel_change/change_ref) 帧间相对变化越小越稳 + g_content = edge/(edge+edge_x0) 有无内容(区分糊/空) + g_light = 亮度双sigmoid(暗/过曝都降) + focus_gain = g_stable·(1-g_sharp)·g_content 稳+糊+有内容 -> 该对焦 + 决策: max(quality) >= ACCEPT_Q -> 选该帧; 否则看 max(focus_gain) >= FOCUS_G + -> 触发对焦再采; 都低 -> 拒绝。 + 参数是物理动机初值, 戴眼镜采数据后可校准/拟合 (方法 vs 调参的区别)。 + """ + # g 响应形状 (半饱和点/参考值) + sharp_x0 = 120.0 # (记录用) 全局清晰度半饱和点; g_sharp 现改用 worst_block + worst_x0 = 6.5 # worst_block 半饱和点。调严(原5.0)要求更清晰。真机+OCR: 不可读<4.6/可读>5.5。 + # g_sharp = worst/(worst+worst_x0): worst=5时g_sharp=0.5, 白盒清晰标签 + # worst 正常(不被白底稀释)故不误判need_focus。阈值待白标签标准数据精校。 + change_ref = 0.5 # (旧, 已弃用: 稳定性改用帧间像素差) + motion_ref = 15.0 # 帧间模糊后平均像素差达此视为"完全在动"。校准依据: OCR证实可读的 + # 成分面(密集字)模糊后 motion 达~8.4 仍可读, 须能过; 故 ref 上调到15, + # 使可读帧 stable>=0.44 通过, 同时剧烈晃动(>15)仍被拒。下界待补采晃动样本精校。 + edge_x0 = 0.01 # 边缘密度半饱和 (区分有内容/空白) + light_lo = 40.0 # 亮度下沿 + light_hi = 220.0 # 亮度上沿(过曝) + light_soft = 25.0 # 亮度软边宽度 + # 权重 (语义清晰, 可做 ablation) + w_sharp = 1.0 + w_stable = 1.0 + w_content = 0.6 + w_light = 0.4 + sharp_gate_gamma = 0.5 # sharp 乘性门强度 (0=不否决, 大=糊帧强烈压分) + # 决策阈值 (作用在归一化综合分上, 只此两个, 远少于原来一堆硬阈值) + ACCEPT_Q = 0.55 # 可用性门: 调严(原0.40太松, 放过模糊图)。0.55 要求更清晰才放行。 + # 宁拒绝不念错: 送下游的必须够清晰, 模糊的宁可让用户重拍。 + # 放宽的理由(数据得出): 全局quality分不开'能OCR'与'轻微糊', + # 那是任务层的精判(局部/文字区清晰度), 通用层不越俎代庖。 + FOCUS_G = 0.35 # (保留) focus_gain 参考 + # 稳定性优先三出口的门限 (段级中位数上判定; 待真机 rerun 校准) + STABLE_MIN = 0.35 # 出口A: g_stable 低于此 = 持续在动 -> "请拿稳" (第一闸)。 + # 从0.45降到0.35: 配合 motion_ref=15, 让OCR证实可读的密集字面通过, 修复误拒。 + SEVERE_FLOW = 8.0 # 出口A0: 段级光流位移(像素)>=此 = 严重晃动一刀切拒绝。初值8, + # 待 shake 样本校准("多大位移算严重"); 光流位移物理可解释。 + LIGHT_MIN = 0.35 # 出口D(留): g_light 低于此且为最差 -> 太暗 + CONTENT_MIN = 0.30 # 出口E(留): g_content 低于此 + SHARP_OK_FOR_E = 0.45 # 出口E(留): 判"没对准"要求 sharp 够高(清晰却没字才算对错) + MIN_QUALIFIED = 1 + + +# ============================================================ +# 场景配置 (核心: 机制一套, 参数按场景标定 —— "权重由场景物理特征+安全等级决定") +# 药盒(medicine): 小字/高危/白底 -> 偏拒绝、要求清晰、零容忍。= 默认 FunnelConfig。 +# 文具(stationery): 中大字/低危/彩色包装 -> 可放松、允许更多放行。 +# 固有机制(对焦/多帧筛选/倒置/光流晃动)完全一致, 只有下面这些判定参数不同。 +# ============================================================ +def make_scene_config(scene: str = "medicine") -> "FunnelConfig": + """按场景返回标定好的 FunnelConfig。机制代码不变, 只改参数。""" + cfg = FunnelConfig() + if scene in ("medicine", "药盒", "drug"): + return cfg # 药盒 = 现有默认(小字/高危/严) + if scene in ("stationery", "文具", "supplies", "生活用品"): + # 生活用品/文具: 安全等级低(念错关系不大) -> 放行阈值放松, 少拒绝、少打扰。 + # 参数依据: 网络摄像头 clip_windows csv (routing 分析) + 安全等级。 + cfg.worst_x0 = 5.0 # 松(药盒6.5): 拦截判据, 药盒严文具松(安全等级低)。 + # 手动定: csv里 worst_block 对ok/blur区分很弱(图都较清晰, + # AUC~0.52), 算不出可靠值, 按"文具比药盒松"手动定松一档。 + cfg.ACCEPT_Q = 0.48 # 略放松(药盒0.55): 安全低, 容错高一点。 + cfg.SEVERE_FLOW = 8.0 # 沿用药盒: csv 里 seg_flow 是最强判据(AUC~0.71), >8 后 + # ok率骤降, 与药盒一致。晃动是通用物理约束, 不随场景放松。 + return cfg + # 未知场景 -> 退回药盒默认, 并提示 + print(f"[scene] 未知场景 '{scene}', 用药盒默认配置") + return cfg + + +class FunnelResult: + def __init__(self): + self.accepted = False + self.need_focus = False # 稳但糊: 上层应触发AF再采一批 + self.reject_reason = "" + self.best_index = -1 + self.best_jpg = None + self.per_frame = [] + self.motions = [] # 这里存 rel_change 序列 + self.components = {} # 段级各 gi 分量 (归因用) + self.timings = {} + + def to_dict(self): + def _fmt(v): + if isinstance(v, (int, float)): + return round(v, 1) + return v # list 之类原样 (如 grab_per_frame_ms) + return { + "accepted": self.accepted, + "need_focus": self.need_focus, + "reject_reason": self.reject_reason, + "best_index": self.best_index, + "per_frame": self.per_frame, + "rel_changes": [round(m, 3) for m in self.motions], + "components": self.components, + "timings": {k: _fmt(v) for k, v in self.timings.items()}, + } + + +def run_funnel(frames: list[bytes], cfg: FunnelConfig = FunnelConfig()) -> FunnelResult: + """评分函数版漏斗: + 1) 每帧算 metrics(sharp/亮度/edge) + 2) 帧间相对变化 -> 每帧 quality + focus_gain (连续分, 非硬阈值) + 3) 决策: max(quality)>=ACCEPT_Q -> 选该帧(选优); + 否则 max(focus_gain)>=FOCUS_G -> 建议对焦(need_focus); + 都低 -> 拒绝。 + 返回里带 need_focus 标志, 供上层决定"稳但糊->触发AF->再采"。""" + r = FunnelResult() + if not frames: + r.reject_reason = "no_frames" + return r + + # 1) 单帧 metrics + t0 = time.monotonic() + metrics_list = [frame_metrics(j) for j in frames] + r.timings["level1_metrics_ms"] = (time.monotonic() - t0) * 1000 + + # 2) 帧间"运动"(稳定性输入) + 每帧评分 + # 稳定性用真正的帧间像素差分(frame_motion)衡量 —— 位移/转动会改变画面内容, + # 但不一定改变清晰度, 故不能用 sharp 变化代替。归一化到 [0,1] 的 rel。 + t1 = time.monotonic() + per = [] + prev_jpg = None + flow_mags = [] # 相邻帧光流位移幅度(严重晃动一刀切用) + for i, m in enumerate(metrics_list): + if prev_jpg is None: + motion = 0.0 + else: + motion = frame_motion(prev_jpg, frames[i]) # 平均绝对像素差 0-255 + flow_mags.append(optical_flow_motion(prev_jpg, frames[i])) # 光流位移(像素) + prev_jpg = frames[i] + rel = min(1.0, motion / cfg.motion_ref) # 归一: motion>=motion_ref 视为完全在动 + sc = frame_score(m, rel, cfg) + per.append({**m, "rel_change": round(rel, 3), "motion": round(motion, 2), **sc}) + r.motions.append(rel) + r.per_frame = per + # 段级光流: 用中位数抗单帧噪声。大 = 整段相机在大幅移动 = 严重晃动。 + seg_flow = float(sorted(flow_mags)[len(flow_mags) // 2]) if flow_mags else 0.0 + r.timings["level2_score_ms"] = (time.monotonic() - t1) * 1000 + + # 3) 决策: 稳定性优先的三出口 (A/B/C), argmin(gi) 归因, D/E 留接口 + t2 = time.monotonic() + qualities = [p["quality"] for p in per] + # 段级聚合各分量 (用中位数抗单帧噪声) + def med(key): + vals = sorted(p[key] for p in per) + n = len(vals) + return vals[n // 2] if n % 2 else (vals[n // 2 - 1] + vals[n // 2]) / 2 + seg_stable = med("g_stable") + seg_sharp = med("g_sharp") + seg_content = med("g_content") + seg_light = med("g_light") + # best 帧选择: 只按【清晰度】选最能读的一帧, 不用综合 quality。 + # 原因: quality 含 stable 分量, 会让"晃动某瞬间恰好帧间motion低(转折点)但画面糊"的帧 + # quality 虚高, 压过"稍动但清晰可读"的帧 -> best 选成糊的(真机验证: f0糊被选/f2清晰没选)。 + # 念字能不能读只取决于清晰度, 与该帧瞬时稳不稳无关; 稳定性只用于整段是否拒绝(出口A)。 + # 用 worst_block(最糊有内容块)选: 选"最糊处也最清晰"的那帧 = 最可读。 + def _clarity(i): + return per[i].get("worst_block", per[i].get("sharpness", 0.0)) + best_q = max(range(len(per)), key=_clarity) + # quality 仍保留供出口B的接受阈值判断(下面 qualities[best_q]>=ACCEPT_Q) + r.timings["level3_decide_ms"] = (time.monotonic() - t2) * 1000 + + # 归因分量表 (供 argmin 路由 + 记录) + r.components = {"stable": round(seg_stable, 3), "sharp": round(seg_sharp, 3), + "content": round(seg_content, 3), "light": round(seg_light, 3), + "flow": round(seg_flow, 2)} # 段级光流位移(像素) + + # ---- 出口 A0: 严重晃动一刀切 —— 光流位移过大 = 相机大幅移动, 不看别的直接拒 ---- + # 可解释: 画面整段平均移动 >SEVERE_FLOW 像素 = 明显在晃, 念字必糊, 让用户停稳。 + # 光流比逐像素差鲁棒(不被密集字伪运动骗); 阈值待 shake 样本校准。 + if seg_flow >= cfg.SEVERE_FLOW: + r.reject_reason = "severe_shake" # 出口 A0: "晃得厉害, 请拿稳" + r.need_focus = False + r.best_index = best_q + return r + + # ---- 出口 A: 稳定性优先 —— 持续在动, 不看清晰度, 直接请拿稳 ---- + # (动的时候对焦也没用, 且用户没老实, 不该浪费时间去挑图) + if seg_stable < cfg.STABLE_MIN: + r.reject_reason = "unstable" # 出口 A: "请拿稳/对准" + r.need_focus = False + r.best_index = best_q # 仅供参考, 不喂模型 + return r + + # ---- 稳定。看有没有帧够清晰 ---- + if qualities[best_q] >= cfg.ACCEPT_Q: + # ---- 出口 B: 稳 + 有清晰帧 -> 给最清晰的 ---- + r.accepted = True + r.best_index = best_q + r.best_jpg = frames[best_q] + r.need_focus = False + return r + + # ---- 稳但没有够清晰的帧: 归因 argmin, 决定是 C(对焦) 还是 D/E ---- + # 在"稳定"前提下, 看哪个分量最拖累: sharp低=失焦(C); light低=暗(D); + # content低但sharp高=清晰却没内容=没对准(E)。argmin 连续路由, 非硬堆阈值。 + cand = {"sharp": seg_sharp, "light": seg_light, "content": seg_content} + worst = min(cand, key=cand.get) + + if worst == "light" and seg_light < cfg.LIGHT_MIN: + # 出口 D (留接口, 未激活干预): 太暗 -> 未来 CLAHE 数字增强 / 提示到亮处 + r.reject_reason = "too_dark" # TODO: 接 CLAHE 后改为可救 + r.need_focus = False + r.best_index = best_q + return r + + # 出口 E（aimed_wrong）已弃用：原设计（-v/OCR）用"清晰但无内容结构"判"没对准"， + # 目的是排除背景干扰。但 -o 不受背景影响、完全没字时会老实说"没看到字"， + # 不需要漏斗拦。故这种帧直接放行，交给模型自己判断，避免误判打断。 + # （真正危险的"像字又不是字"是另一个问题，待 720p 数据用别的信号重做。） + if worst == "content" and seg_sharp >= cfg.SHARP_OK_FOR_E and seg_content < cfg.CONTENT_MIN: + r.accepted = True + r.best_index = best_q + r.best_jpg = frames[best_q] + r.need_focus = False + return r + + # ---- 出口 C: 稳但糊 (sharp 主导拖累) -> 触发AF重采 ---- + r.accepted = False + r.need_focus = True + r.reject_reason = "need_focus" # 上层触发AF+重采, 仍不行才最终拒绝 + r.best_index = best_q + return r + + +# ============================================================ +# 方向处理 & 推理 —— 留桩, 下一步填 +# ============================================================ +def _center_crop(gray, frac=0.6): + """取画面中心区, 排掉四周背景(手/桌面/墙)。用户会把目标大致对准中心, + 故中心区更可能是文字主体; 在其上算判据可显著减轻背景污染。""" + h, w = gray.shape[:2] + y0, y1 = int(h * (1 - frac) / 2), int(h * (1 + frac) / 2) + x0, x1 = int(w * (1 - frac) / 2), int(w * (1 + frac) / 2) + return gray[y0:y1, x0:x1] + + +def _binv_fg(gray): + _, b = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) + return (b > 0).astype(np.float64) + + +def _detect_sideways(gray): + """90°/270°侧向: 水平投影方差 vs 垂直投影方差。多档中心区(0.5/0.6/0.7)投票, + 比单一比例更稳(文字不在正中心时不易被单档背景污染带偏)。 + 返回 (verdict, ratio): 'upright'/'sideways'/'ambiguous'。ratio 取中位数。""" + ratios = [] + for frac in (0.5, 0.6, 0.7): + b = _binv_fg(_center_crop(gray, frac)) + hvar = float(np.var(b.sum(axis=1))) + vvar = float(np.var(b.sum(axis=0))) + ratios.append(hvar / (vvar + 1e-6)) + ratios.sort() + med = ratios[1] # 中位数, 抗单档异常 + votes = ["sideways" if r < 0.67 else ("upright" if r > 1.5 else "ambiguous") + for r in ratios] + # 多数票: 3档里≥2档一致才定, 否则 ambiguous + for v in ("sideways", "upright"): + if votes.count(v) >= 2: + return v, round(med, 2) + return "ambiguous", round(med, 2) + + +def _detect_upside_down(gray): + """180°正倒: '注水容量'判据(资料方法, 真图验证对中文有效)。 + 对每列: 最高文字点上方空白(top_cap) vs 最低文字点下方空白(bot_cap)。 + 正立版面文字整体下方留白多 -> (bot-top)>0; 倒置翻转 -> <0。 + **看版面级上下留白, 不依赖单字对称性, 绕开中文字对称死结。** + 真图验证: 正立+0.66/倒置-0.48, 抗±8°倾斜、抗裁剪比例(0.5~0.9)。必须裁剪(整图会被背景淹)。 + 返回 (verdict, score): 'upright'/'flipped'/'ambiguous'。""" + b = _binv_fg(_center_crop(gray, 0.7)) + h, w = b.shape + tc = bc = cnt = 0 + for x in range(0, w, 3): + col = np.where(b[:, x] > 0)[0] + if len(col) < 2: + continue + tc += col[0] + bc += (h - 1 - col[-1]) + cnt += 1 + if cnt < 10: + return "ambiguous", 0.0 + score = (bc - tc) / (bc + tc + 1e-9) + if score > 0.15: + return "upright", round(score, 3) + if score < -0.15: + return "flipped", round(score, 3) + return "ambiguous", round(score, 3) + + +def _detect_truncation(gray, margin=10): + # 先确认中心区有文字主体; 否则不判截断(避免背景边缘误报) + cb = _binv_fg(_center_crop(gray, 0.6)) + if cb.mean() < 0.01: + return {}, {} # 中心没内容, 不谈截断 + b = _binv_fg(gray) + edges = {"top": b[:margin, :].mean(), "bottom": b[-margin:, :].mean(), + "left": b[:, :margin].mean(), "right": b[:, -margin:].mean()} + overall = b.mean() + if overall < 1e-4: + return {}, {} + # 贴边判据收紧: 边缘密度需 >= 整图的一定比例, 且 >= 中心密度的一定比例 + # (排掉"背景纹理贴边但中心才是文字"的误报) + center_d = cb.mean() + touched = {k: (v >= overall * 0.6 and v >= center_d * 0.4 and v > 0.02) + for k, v in edges.items()} + return {k: round(v, 4) for k, v in edges.items()}, {k: t for k, t in touched.items() if t} + + +_PAN_HINT = {"top": "请向上看一点", "bottom": "请向下看一点", + "left": "请向左看一点", "right": "请向右看一点"} + + +_ORI_CLS = None # 方向分类器单例(全局只加载一次) +_ORI_CLS_TRIED = False # 是否已尝试加载(避免反复重试失败) + +def _get_orient_classifier(): + """懒加载 PaddleOCR 文档方向分类器(PP-LCNet, 6.75MB, 0/90/180/270)。 + 单例: 只在首次调用时加载一次(几秒), 之后每次推理仅几ms。 + 降级: 未安装/加载失败 -> 返回 None, process_orientation 回退到轻量CV判据, 不崩。""" + global _ORI_CLS, _ORI_CLS_TRIED + if _ORI_CLS is not None: + return _ORI_CLS + if _ORI_CLS_TRIED: + return None # 之前试过且失败, 不再重试 + _ORI_CLS_TRIED = True + # 加载方向分类器。优先用最简单的写法(实测 SmartGlasses 环境可用, 与 torch 不冲突 —— + # 当初冲突的是完整 PaddleOCR-GPU 识别, 不是这个轻量方向分类器)。 + # 若想显式指定设备, 后面的参数变体作为备选依次尝试。 + from paddleocr import DocImgOrientationClassification + last_err = None + for kw in (dict(model_name="PP-LCNet_x1_0_doc_ori"), # 最简单(原来能用的) + dict(model_name="PP-LCNet_x1_0_doc_ori", device="cpu"), # 显式CPU(可选) + dict()): # 全默认兜底 + try: + _ORI_CLS = DocImgOrientationClassification(**kw) + print(f"[orient] PaddleOCR 方向分类器已加载 (PP-LCNet, 参数={list(kw) or 'default'})") + return _ORI_CLS + except Exception as e: + last_err = e + continue # 这个参数组合不行, 换下一个 + print(f"[orient] 方向分类器加载失败, 回退到轻量CV判据: {last_err}") + _ORI_CLS = None + return None + + +# 文档方向标签(图像被顺时针旋转的角度) -> (状态, 给用户的转向提示) +# 注意: "图像旋转角"与"用户转药盒方向"相反。文案先按标准定义, 实机核对后固定。 +# 90°: 画面顺时针转了90° = 药盒被逆时针放了 -> 用户应顺时针转回? 需实机核对, 故保留 raw 标签调试。 +_ORIENT_MAP = { + "0": ("upright", None), + "90": ("sideways", "请把药盒顺时针转90°"), + "270": ("sideways", "请把药盒逆时针转90°"), + "180": ("flipped", "药盒拿反了，请上下翻转"), +} + + +def _classify_orientation(img_bgr): + """用分类器判方向。返回 (raw_label, conf) 或 (None, 0) 若不可用/低置信。""" + cls = _get_orient_classifier() + if cls is None: + return None, 0.0 + try: + res = cls.predict(img_bgr) # 传 ndarray, 免落盘 + r = res[0] + labels = r.get("label_names") or [str(x) for x in r.get("class_ids", [])] + scores = r.get("scores", [0.0]) + raw = str(labels[0]).replace("_degree", "").strip() + conf = float(max(scores)) if scores else 0.0 + return raw, conf + except Exception as e: + print(f"[orient] 推理失败: {e}") + return None, 0.0 + + +def process_orientation(best_jpg: bytes): + """第2级(方向) + 第3级(取景完整) —— 只对清晰的 best 帧做一次。 + 方向: 优先用 PaddleOCR 轻量方向分类器(PP-LCNet, 真图验证0/90/180/270全准, 置信~0.92); + 未装/低置信 -> 回退轻量CV判据。180°走多帧一致性('请确认正反'在handler触发)。 + 返回 (jpg, diag)。diag 含方向反馈 + 截断反馈 + incomplete 标志(传下游prompt)。 + [留桩] 圆柱曲面矫正 = 独立课题, 不在此处。""" + arr = np.frombuffer(best_jpg, np.uint8) + img = cv2.imdecode(arr, cv2.IMREAD_COLOR) + if img is None: + return best_jpg, {"ok": False} + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + orient_hint = None + orient_state = "upright" + ud_score = 0.0 + side_ratio = "" + orient_raw = "" + orient_conf = 0.0 + CONF_MIN = 0.60 # 置信门控: 低于此不硬判, 走uncertain/兜底 + + raw, conf = _classify_orientation(img) + orient_raw, orient_conf = (raw or ""), conf + if raw is not None and conf >= CONF_MIN and raw in _ORIENT_MAP: + # 分类器可用且置信足够 -> 直接按映射给状态+提示 + # (实测180 conf~0.93很确定, 不会与0混淆, 故即时提示; 若需滤噪声在簇内多帧投票层做) + orient_state, orient_hint = _ORIENT_MAP[raw] + elif raw is not None: + # 分类器给了结果但低置信 -> 不硬判 + orient_state = "uncertain" + else: + # 分类器不可用 -> 回退轻量CV判据(整块投影/注水容量) + side, side_ratio = _detect_sideways(gray) + if side == "sideways": + orient_state, orient_hint = "sideways", "请把药盒转90°" + else: + ud, ud_score = _detect_upside_down(gray) + orient_state = {"flipped": "flipped", "upright": "upright"}.get(ud, "uncertain") + + # 取景完整性(截断) —— 显示层暂关, 接口保留 + edges, truncated = _detect_truncation(gray) + pan_hint = None + if truncated: + worst = max(truncated, key=lambda k: edges.get(k, 0)) + pan_hint = _PAN_HINT.get(worst) + + diag = { + "ok": True, + "orient_state": orient_state, # upright/sideways/flipped/uncertain + "orient_hint": orient_hint, # 侧向即时提示; 180的"请确认"由多帧一致性在handler加 + "orient_raw": orient_raw, # 分类器原始标签(0/90/180/270), 实机核对转向用 + "orient_conf": round(orient_conf, 3), + "sideways_ratio": side_ratio, + "flipped_frame": orient_state == "flipped", # 供多帧一致性累积 + "ud_score": ud_score, + "truncated_edges": list(truncated.keys()), + "pan_hint": pan_hint, + "incomplete": bool(truncated), # 传下游prompt: 只念可见部分 + } + return best_jpg, diag # 第一版不自动转正, 只诊断+提示(方向由用户调) + + +def infer(frame_jpg: bytes) -> Optional[str]: + """[留桩] 未来: 把 best 帧喂给 -v (或 Qwen-VL) 做念字/识别。 + 现在返回 None。接推理时把这里换成真的模型调用。""" + return None + + +# ============================================================ +# CV 指标: 拉普拉斯方差(清晰度) + 平均亮度 +# 这两个就是三级漏斗一级的判据; 在这里实时显示以标定阈值。 +# ============================================================ +def frame_metrics(jpg: bytes) -> dict: + """返回一帧的 CV 指标字典。记录器会动态把所有 key 当作 CSV 列 —— + 以后 CV 那步加新指标(如帧间差分测晃动、方向检测分), 只需在这里往 + dict 里加 key, CSV 自动多列, 记录代码不用改。""" + arr = np.frombuffer(jpg, dtype=np.uint8) + img = cv2.imdecode(arr, cv2.IMREAD_GRAYSCALE) + if img is None: + return {"sharpness": 0.0, "brightness": 0.0, "kb": round(len(jpg) / 1024, 1)} + lap_var = cv2.Laplacian(img, cv2.CV_64F).var() + edges = cv2.Canny(img, 80, 160) + edge_density = float((edges > 0).mean()) + + # --- 附加指标: 先记录不判定, 采数据后用 ok/blur 标注看哪个最能对齐可读性 --- + # local_sharp: N×N 分块取最清晰块。抓"局部有清晰区"; 但会被高频背景(木纹)骗高。 + ls = _local_sharp(img, grid=4) + # text_sharp / text_ratio: MSER 类文字候选区的清晰度 + 面积占比。 + # text_ratio 说"有没有像字的区域"(高频背景 ratio≈0, 骗不过它); + # 但糊到一定程度 MSER 找不到字 -> ratio→0 (自我矛盾, 已离线证实)。 + # 与 local_sharp 组合: local高+ratio低=清晰但非字; 两者皆低=糊。 + ts, tr = _text_sharp(img) + # worst_block / block_var: 匹配"一处糊即不可用(OCR 标准)"的悲观判据。 + # worst_block = 有内容的块里最糊的清晰度 (看最差, 非最好); + # block_std = 块间清晰度标准差 (大 = 部分清晰部分糊 = 局部糊)。 + # 对 OCR: 决定成败的是最糊的文字区, 不是最清晰的 —— 故看 worst 而非 best。 + wb, bstd = _block_sharp_stats(img, grid=4) + # contrast / dark_ratio: 区分"糊"与"暗/低对比"。暗+低对比时全局sharp假性偏低, + # 但 contrast 低会揭示真因 -> 对应"数字增强(CLAHE)"而非"对焦"这条干预路径。 + contrast = float(img.std()) + dark_ratio = float((img < 50).mean()) + + # [CLAHE 接口预留] 未来暗环境念字: 若 contrast 低 / dark_ratio 高, 可在此对 img + # 做 CLAHE/gamma 增强后重算指标, 作为"数字层干预"(区别于对焦的"物理层干预")。 + # 现在不实现 —— 待有暗环境测试条件再填。示意: + # if contrast < TH: img_enh = cv2.createCLAHE(...).apply(img); 重算 lap_var ... + + return { + "sharpness": round(float(lap_var), 1), # 全局拉普拉斯方差 + "brightness": round(float(img.mean()), 1), + "edge_density": round(edge_density, 4), + "local_sharp": round(float(ls), 1), # 最清晰块 (乐观, 记录) + "worst_block": round(float(wb), 1), # 最糊的有内容块 (悲观, 匹配OCR标准) + "block_std": round(float(bstd), 1), # 块间清晰度std (大=局部糊) + "text_sharp": round(float(ts), 1), # 文字区清晰度 (记录) + "text_ratio": round(float(tr), 4), # 文字区面积占比 (记录) + "contrast": round(contrast, 1), # 对比度(std) (记录) + "dark_ratio": round(dark_ratio, 4), # 暗像素占比 (记录) + "kb": round(len(jpg) / 1024, 1), + } + + +def _block_sharp_stats(gray, grid: int = 4): + """分块清晰度的 (最糊有内容块, 块间std)。 + 只统计"有内容"的块(edge 非极低), 避免纯背景块干扰; + worst = 有内容块里最低清晰度 (对应'一处糊即失败'); + std = 块间清晰度离散度 (局部糊 -> 大)。""" + h, w = gray.shape[:2] + vals = [] + for i in range(grid): + for j in range(grid): + blk = gray[i * h // grid:(i + 1) * h // grid, j * w // grid:(j + 1) * w // grid] + if blk.size == 0: + continue + # 只算"有文字/边缘结构"的块。关键: 用【边缘密度】而非 std 判有内容 —— + # std 只反映明暗起伏, 白纸的折痕/阴影/渐变 std 也 >8, 会被误当"内容块", + # 而白纸拉普拉斯极低(~1) -> 把 worst_block 拉到假性极低 -> 清晰标签被误判糊。 + # 文字块有大量边缘, 白纸光影块几乎无边缘 -> 用 Canny 边缘占比区分。 + edges = cv2.Canny(blk, 50, 150) + edge_frac = float((edges > 0).mean()) + if edge_frac < 0.006: # 边缘太少 = 无文字结构(白纸/背景) -> 跳过 + continue + vals.append(float(cv2.Laplacian(blk, cv2.CV_64F).var())) + if not vals: + return 0.0, 0.0 + worst = min(vals) + std = float(np.std(vals)) if len(vals) > 1 else 0.0 + return worst, std + + +def _local_sharp(gray, grid: int = 4) -> float: + """N×N 分块, 取最清晰块的拉普拉斯方差。""" + h, w = gray.shape[:2] + best = 0.0 + for i in range(grid): + for j in range(grid): + blk = gray[i * h // grid:(i + 1) * h // grid, j * w // grid:(j + 1) * w // grid] + if blk.size: + best = max(best, float(cv2.Laplacian(blk, cv2.CV_64F).var())) + return best + + +def _text_sharp(gray): + """MSER 找类文字候选区, 在候选区上算清晰度。返回 (清晰度, 面积占比)。 + 找不到 -> (0,0)。占比=0 提示'没找到字'(可能糊/无字), 与 local_sharp 组合判读。""" + try: + mser = cv2.MSER_create(delta=5, min_area=60, max_area=14400) + regions, _ = mser.detectRegions(gray) + except Exception: + return 0.0, 0.0 + if not regions: + return 0.0, 0.0 + mask = np.zeros(gray.shape[:2], np.uint8) + for pts in regions: + x, y, bw, bh = cv2.boundingRect(pts.reshape(-1, 1, 2)) + ar = bw / max(bh, 1) + if 0.1 < ar < 10 and bw < gray.shape[1] * 0.9: + cv2.fillPoly(mask, [cv2.convexHull(pts.reshape(-1, 1, 2))], 255) + area_ratio = float((mask > 0).mean()) + if area_ratio < 0.001: + return 0.0, area_ratio + lap = cv2.Laplacian(gray, cv2.CV_64F) + return float(lap[mask > 0].var()), area_ratio + + +# ============================================================ +# 后台抓帧线程: 独占 TCP, 持续抓最新帧 (供 /frame 取用) +# 前端按自己的刷新率来取, 后端只维护"最新一帧+指标"。 +# ============================================================ +class Recorder: + """记录一段一段的指标序列。每段带一个 label(触发来源, 如 af / res:UXGA / manual), + 每帧一行, 动态列(跟随 frame_metrics 的 key)。存磁盘 + 供前端下载。 + + 两种触发: (a) 点控制按钮自动录 duration 秒; (b) 手动开始/停止。 + 记录挂在后端抓帧线程上(每抓一帧记一行), 与前端显示刷新率解耦, 密度=真实抓帧率。""" + + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + self._lock = threading.Lock() + self._rows: list[dict] = [] # 所有已记录的行 (跨段, 全量) + self._active = False + self._seg_label = "" + self._seg_start_mono = 0.0 + self._seg_deadline = 0.0 # >0 表示定时录; 0 表示手动录(无限直到 stop) + self._seg_index = 0 + self._columns: list[str] = [] # 动态列, 首次记录时按 metrics key 确定 + + def start_segment(self, label: str, duration_s: float = 0.0): + """duration_s>0: 定时录; =0: 手动录(直到 stop_segment)。""" + with self._lock: + self._active = True + self._seg_index += 1 + self._seg_label = label + self._seg_start_mono = time.monotonic() + self._seg_deadline = (self._seg_start_mono + duration_s) if duration_s > 0 else 0.0 + + def stop_segment(self): + with self._lock: + self._active = False + + def feed(self, metrics: dict, grab_ms: float, res: str, rotate: int): + """抓帧线程每帧调一次。仅在 active 且未超时时记录。""" + with self._lock: + if not self._active: + return + now = time.monotonic() + if self._seg_deadline > 0 and now >= self._seg_deadline: + self._active = False + return + if not self._columns: + # 首次: 固定前缀列 + metrics 的动态列 + self._columns = (["seg", "label", "t_ms", "grab_ms", "res", "rotate"] + + list(metrics.keys())) + row = { + "seg": self._seg_index, + "label": self._seg_label, + "t_ms": round((now - self._seg_start_mono) * 1000, 1), + "grab_ms": round(grab_ms, 1), + "res": res, + "rotate": rotate, + } + row.update(metrics) + self._rows.append(row) + + def status(self) -> dict: + with self._lock: + remain = 0.0 + if self._active and self._seg_deadline > 0: + remain = max(0.0, self._seg_deadline - time.monotonic()) + return { + "active": self._active, + "label": self._seg_label, + "remain_s": round(remain, 1), + "n_rows": len(self._rows), + "n_segments": self._seg_index, + } + + def to_csv(self) -> str: + import csv, io + with self._lock: + rows = list(self._rows) + cols = list(self._columns) if self._columns else ["seg"] + # 动态列可能因未来指标增删而不齐: 用所有行 key 的并集补齐 + allcols = list(cols) + for r in rows: + for k in r: + if k not in allcols: + allcols.append(k) + buf = io.StringIO() + w = csv.DictWriter(buf, fieldnames=allcols, extrasaction="ignore") + w.writeheader() + for r in rows: + w.writerow(r) + return buf.getvalue() + + def save_disk(self) -> Optional[Path]: + csv_text = self.to_csv() + stamp = time.strftime("%Y%m%d_%H%M%S") + path = self.out_dir / f"tuning_{stamp}.csv" + try: + path.write_text(csv_text, encoding="utf-8-sig") # BOM 便于 Excel 直接开 + return path + except Exception as e: + print(f"[REC] 存盘失败: {e}") + return None + + def clear(self): + with self._lock: + self._rows.clear() + self._seg_index = 0 + self._columns = [] + self._active = False + + +class FrameGrabber: + def __init__(self, tcp: TCPImageClient, recorder: "Recorder", ctl_state: dict): + self.tcp = tcp + self.recorder = recorder + self.ctl_state = ctl_state # {"res":..., "rotate":...} 供记录标注当前状态 + self._latest_jpg: Optional[bytes] = None + self._latest_metrics: dict = {} + self._latest_grab_ms: float = 0.0 + self._rotate_deg = 0 + self._lock = threading.Lock() + self._stop = threading.Event() + self._thread = threading.Thread(target=self._run, daemon=True) + + def set_rotation(self, deg: int): + with self._lock: + self._rotate_deg = deg % 360 + + def start(self): + self._thread.start() + + def _run(self): + while not self._stop.is_set(): + t0 = time.monotonic() + jpg = self.tcp.capture() + dt = (time.monotonic() - t0) * 1000 + if jpg: + with self._lock: + deg = self._rotate_deg + if deg: + jpg = rotate_jpeg(jpg, deg) # 真转, 指标基于转后图 + mtr = frame_metrics(jpg) + with self._lock: + self._latest_jpg = jpg + self._latest_metrics = mtr + self._latest_grab_ms = dt + # 喂记录器 (仅在 active 时真记); 带当前分辨率/旋转状态 + self.recorder.feed(mtr, dt, + res=self.ctl_state.get("res", "?"), + rotate=deg) + else: + time.sleep(0.05) + + def latest(self): + with self._lock: + return self._latest_jpg, dict(self._latest_metrics), self._latest_grab_ms + + def grab_batch(self, n: int, rotate_apply: bool = True): + """连抓 n 帧 (复用 TCP 连接, 串行)。返回 (frames_list, grab_ms_list)。 + 对应真实链路 ASR 后'抓 N 张'那一步。""" + frames, lats = [], [] + with self._lock: + deg = self._rotate_deg + for _ in range(n): + t0 = time.monotonic() + jpg = self.tcp.capture() + dt = (time.monotonic() - t0) * 1000 + if jpg: + if rotate_apply and deg: + jpg = rotate_jpeg(jpg, deg) + frames.append(jpg) + lats.append(dt) + return frames, lats + + def stop(self): + self._stop.set() + self._thread.join(timeout=2.0) + self.tcp.close() + + +# ============================================================ +# Web 服务 +# ============================================================ +PAGE = r""" + + + + +相机调参台 + + + +
+
+ — +
+
Laplacian variance · 清晰度
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+ +
等待画面…
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+
+
+
漏斗选出的 BEST 帧 (喂给模型的那张)
+ +
+
+ + + + + + +""" + + +class PipelineStats: + """漏斗运行统计: 接受/拒绝计数(按原因)+ best帧存盘 + 最近 best 缓存。 + 拒绝率是安全指标的雏形: 该拒的拒了多少、各什么原因。""" + + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + self._lock = threading.Lock() + self.n_total = 0 + self.n_accepted = 0 + self.reasons: dict = {} # reason -> count + self.last_best: Optional[bytes] = None + self._batch_i = 0 + self._runs: list[dict] = [] # 每次抓拍一行汇总 (供标定用) + + def add_run_row(self, scene: str, res, focused: bool, best_path: str = "", geom: dict = None): + """每次抓拍追加一行汇总, 带用户标注的场景标签 + best帧路径。 + best_path: 供你事后人工看这张 best 清不清楚, 在 manual_label 列标真值 —— + 用人工判断(而非抓拍前意图标签)当 ground truth 校准阈值, 绕开标签错位。""" + per = res.per_frame or [] + max_q = max((p.get("quality", 0) for p in per), default=0) + max_fg = max((p.get("focus_gain", 0) for p in per), default=0) + # best 帧的完整 CV 指标 (供标定: 一张汇总表就能对比各指标 vs ok/blur) + bm = {} + if 0 <= res.best_index < len(per): + bm = per[res.best_index] + best_sharp = bm.get("sharpness", 0) + t = res.timings or {} + import os as _os + row = { + "time": time.strftime("%H:%M:%S"), + "scene": scene, + "manual_label": "", # ← 你看完 best 帧后手填: ok / blur (真值) + "n_frames": len(per), + "accepted": int(res.accepted), + "need_focus": int(res.need_focus), + "focused": int(focused), + "reject_reason": res.reject_reason, + "best_index": res.best_index, + "best_sharp": round(best_sharp, 1), + "best_local": bm.get("local_sharp", ""), + "best_worst_block": bm.get("worst_block", ""), + "best_block_std": bm.get("block_std", ""), + "best_text_sharp": bm.get("text_sharp", ""), + "best_text_ratio": bm.get("text_ratio", ""), + "best_contrast": bm.get("contrast", ""), + "best_dark_ratio": bm.get("dark_ratio", ""), + "seg_stable": (res.components or {}).get("stable", ""), + "seg_sharp": (res.components or {}).get("sharp", ""), + "seg_content": (res.components or {}).get("content", ""), + "seg_light": (res.components or {}).get("light", ""), + "max_quality": round(max_q, 3), + "max_focus_gain": round(max_fg, 3), + "grab_batch_ms": round(t.get("grab_batch_ms", 0), 1), + "funnel_ms": round(t.get("level1_metrics_ms", 0) + + t.get("level2_score_ms", 0) + + t.get("level3_decide_ms", 0), 1), + "total_ms": round(t.get("total_ms", 0), 1), + "best_file": _os.path.basename(best_path) if best_path else "", + "best_path": best_path, + # 方向分类器原始输出(定位方向不准: raw标签 vs 你实际摆放) + "orient_raw": (geom or {}).get("orient_raw", ""), + "orient_conf": (geom or {}).get("orient_conf", ""), + "orient_state": (geom or {}).get("orient_state", ""), + "orient_hint": (geom or {}).get("orient_hint", "") or "", + } + with self._lock: + self._runs.append(row) + + def runs_csv(self) -> str: + import csv, io + with self._lock: + runs = list(self._runs) + cols = ["time", "scene", "manual_label", "n_frames", "accepted", "need_focus", + "focused", "reject_reason", "best_index", "best_sharp", + "best_local", "best_worst_block", "best_block_std", + "best_text_sharp", "best_text_ratio", "best_contrast", "best_dark_ratio", + "seg_stable", "seg_sharp", "seg_content", "seg_light", + "max_quality", "max_focus_gain", "grab_batch_ms", "funnel_ms", "total_ms", + "orient_raw", "orient_conf", "orient_state", "orient_hint", + "best_file", "best_path"] + # 未来新字段自动并入 + for r in runs: + for k in r: + if k not in cols: + cols.append(k) + buf = io.StringIO() + w = csv.DictWriter(buf, fieldnames=cols, extrasaction="ignore") + w.writeheader() + for r in runs: + w.writerow(r) + return buf.getvalue() + + def save_runs_csv(self) -> Optional[Path]: + stamp = time.strftime("%Y%m%d_%H%M%S") + path = self.out_dir / f"pipeline_runs_{stamp}.csv" + try: + path.write_text(self.runs_csv(), encoding="utf-8-sig") + return path + except Exception as e: + print(f"[PSTATS] runs csv 存盘失败: {e}") + return None + + def record(self, accepted: bool, reason: str): + with self._lock: + self.n_total += 1 + if accepted: + self.n_accepted += 1 + else: + self.reasons[reason] = self.reasons.get(reason, 0) + 1 + + def set_last_best(self, jpg: bytes): + with self._lock: + self.last_best = jpg + + def save_batch(self, frames: list[bytes], best_index: int, res): + """存整批 N 帧 + 标出 best, 供人工核对。返回 (目录, best帧路径)。""" + with self._lock: + self._batch_i += 1 + bi = self._batch_i + d = self.out_dir / f"batch_{time.strftime('%H%M%S')}_{bi:03d}" + best_path = "" + try: + d.mkdir(parents=True, exist_ok=True) + for i, jpg in enumerate(frames): + tag = "_BEST" if i == best_index else "" + sharp = res.per_frame[i]["sharpness"] if i < len(res.per_frame) else 0 + fp = d / f"f{i}_sharp{sharp:.0f}{tag}.jpg" + fp.write_bytes(jpg) + if i == best_index: + best_path = str(fp) + import json + (d / "funnel.json").write_text( + json.dumps(res.to_dict(), ensure_ascii=False, indent=2), encoding="utf-8") + return str(d), best_path + except Exception as e: + print(f"[PSTATS] save_batch 失败: {e}") + return "", "" + + def summary(self) -> dict: + with self._lock: + rej = self.n_total - self.n_accepted + return { + "total": self.n_total, + "accepted": self.n_accepted, + "rejected": rej, + "accept_rate": round(self.n_accepted / self.n_total, 3) if self.n_total else 0, + "reject_reasons": dict(self.reasons), + } + + +def make_app(grabber: FrameGrabber, ctl: CamControl, + recorder: Recorder, ctl_state: dict, default_dur: float, + funnel_cfg: "FunnelConfig", pstats: "PipelineStats") -> web.Application: + app = web.Application() + + async def h_index(request): + return web.Response(text=PAGE, content_type="text/html") + + async def h_frame(request): + jpg, m, grab_ms = grabber.latest() + if not jpg: + return web.Response(status=503, text="no frame") + headers = { + "X-Sharpness": str(m.get("sharpness", 0)), + "X-Brightness": str(m.get("brightness", 0)), + "X-Kb": str(m.get("kb", 0)), + "X-Grab-Ms": str(round(grab_ms, 0)), + "Cache-Control": "no-store", + } + return web.Response(body=jpg, content_type="image/jpeg", headers=headers) + + async def h_ctl(request): + op = request.query.get("op", "") + val = request.query.get("val", "") + dur = float(request.query.get("dur", default_dur) or default_dur) + loop = asyncio.get_event_loop() + label = None + if op == "res": + ok = await loop.run_in_executor(None, ctl.set_resolution, val) + ctl_state["res"] = val + label = f"res:{val}" + elif op == "af": + ok = await loop.run_in_executor(None, ctl.trigger_af) + label = "af" + elif op == "quality": + ok = await loop.run_in_executor(None, ctl.set_quality, int(val or 10)) + label = f"quality:{val}" + elif op == "rotate": + grabber.set_rotation(int(val or 0)) + ctl_state["rotate"] = int(val or 0) + ok = True + label = f"rotate:{val}" + else: + return web.json_response({"ok": False, "err": "unknown op"}, status=400) + # 控制操作自动开一段定时记录 (看该操作的效果曲线) + if label and dur > 0: + recorder.start_segment(label, dur) + return web.json_response({"ok": bool(ok), "recording": label, "dur": dur}) + + # ---- 记录控制 ---- + async def h_rec_start(request): + label = request.query.get("label", "manual") + dur = float(request.query.get("dur", 0) or 0) # 0=手动(直到 stop) + recorder.start_segment(label, dur) + return web.json_response({"ok": True, **recorder.status()}) + + async def h_rec_stop(request): + recorder.stop_segment() + return web.json_response({"ok": True, **recorder.status()}) + + async def h_rec_status(request): + return web.json_response(recorder.status()) + + async def h_rec_save(request): + path = recorder.save_disk() + return web.json_response({"ok": path is not None, + "path": str(path) if path else None, + **recorder.status()}) + + async def h_rec_download(request): + csv_text = recorder.to_csv() + recorder.save_disk() # 下载同时也存盘一份, 双保险 + return web.Response( + body=csv_text.encode("utf-8-sig"), + headers={"Content-Type": "text/csv; charset=utf-8", + "Content-Disposition": "attachment; filename=tuning.csv"}) + + async def h_rec_clear(request): + recorder.clear() + return web.json_response({"ok": True, **recorder.status()}) + + # ---- 形态2: 抓批 -> 评分漏斗 -> (稳但糊则对焦再采) -> best/拒绝 ---- + async def h_pipeline_run(request): + n = int(request.query.get("n", 5) or 5) + focus_n = int(request.query.get("focus_n", 3) or 3) # 对焦后补采几帧 + af_settle_ms = int(request.query.get("af_settle_ms", 120) or 120) # 对焦生效等待(ms) + # OV5640 单次对焦(0x3022=0x03)锁定通常在~100ms量级(目标近焦时更快), 非几百ms。 + # 默认120ms为初值, 实测用 ?af_settle_ms=N 扫描找最短有效等待。 + scene = request.query.get("scene", "") # 用户标注场景 + loop = asyncio.get_event_loop() + + def _work(): + t0 = time.monotonic() + frames, grab_lats = grabber.grab_batch(n) + grab_ms = (time.monotonic() - t0) * 1000 + if not frames: + return {"ok": False, "err": "no_frames"}, None, None + + res = run_funnel(frames, funnel_cfg) + focused = False + af_on = ctl_state.get("af_enabled", True) + # "自动对焦"开关 af_enabled 的语义(用户设计): + # ON(默认): 自主判断+按需触发对焦(路线A, 正常使用)。 + # OFF: 不自动触发对焦, 只保留 res.need_focus 的判断结果 —— + # 用于录制实验时验证"判据说该对焦"这个判断本身对不对, 不让对焦动作干扰。 + # (手动"触发单次AF"按钮走 /op?op=af, 不受此开关限制, 任何时候可手动触发。) + # 固件侧须 g_auto_af=false, 否则固件每秒硬对焦会架空此开关。 + if res.need_focus and af_on: + tf = time.monotonic() + ctl.trigger_af() # 写 /reg 0x3022=0x03 单次对焦 + # 等对焦生效再抓 —— trigger_af 后马达物理对焦需时间(OV5640单次对焦~100ms量级), + # 立刻 grab_batch 会抓到"对焦过程中"的糊帧。af_settle_ms 可调, 实测最短有效等待。 + if af_settle_ms > 0: + time.sleep(af_settle_ms / 1000.0) # _work 在 executor 线程, sleep 不阻塞事件循环 + extra, extra_lats = grabber.grab_batch(focus_n) + res.timings["focus_af_regrab_ms"] = (time.monotonic() - tf) * 1000 + if extra: + frames = frames + extra + grab_lats = grab_lats + extra_lats + res = run_funnel(frames, funnel_cfg) + focused = True + + res.timings["grab_batch_ms"] = grab_ms + res.timings["grab_per_frame_ms"] = [round(x, 1) for x in grab_lats] + res.timings["focused"] = 1 if focused else 0 + + infer_text = None + # 可读性多帧一致性: 本窗口 accepted 只是"候选送下游"; 复用方向那套跨窗口累积, + # 连续窗口多数都候选送, 才真正送下游 —— 滤掉"单窗口偶然可读"(晃动中偶抓清晰帧)。 + # 严重晃动(severe_shake)已在 run_funnel 单窗口即拒, 不进这里, 不受一致性影响。 + # 纯CV判定累积(不喂下游), 与方向一致性同机制。N=3 多数。 + rhist = ctl_state.setdefault("readable_hist", []) + rhist.append(1 if res.accepted else 0) + del rhist[:-3] # 只留最近3窗口 + readable_consistent = (len(rhist) >= 3 and sum(rhist) > 3 / 2) # 3窗口多数 + res_dict_extra = {"readable_hist": list(rhist), + "readable_consistent": readable_consistent} + if res.accepted and res.best_jpg is not None and readable_consistent: + to = time.monotonic() + oriented, geom_diag = process_orientation(res.best_jpg) + res.timings["orientation_ms"] = (time.monotonic() - to) * 1000 + # 多帧一致性(方向): 累积最近若干次抓拍的"疑似倒置"标志, 连续/多数才弹"请确认正反"。 + # 纯CV判据的多帧累积(不喂VLM/OCR), 滤单帧噪声, 避免正常图偶发误报。 + if geom_diag and geom_diag.get("ok"): + hist = ctl_state.setdefault("flip_hist", []) + hist.append(1 if geom_diag.get("flipped_frame") else 0) + del hist[:-5] # 只留最近5次 + # 5次里≥3次疑似倒置, 且本次也疑似 -> 才弹确认(多数一致) + if len(hist) >= 3 and sum(hist) >= 3 and geom_diag.get("flipped_frame"): + if not geom_diag.get("orient_hint"): + geom_diag["orient_hint"] = "请确认药盒正反" + geom_diag["flip_consistent"] = True + geom_diag["flip_hist"] = list(hist) + ti = time.monotonic() + infer_text = infer(oriented) + res.timings["infer_ms"] = (time.monotonic() - ti) * 1000 + pstats.record(accepted=True, reason="") + saved_dir, best_path = pstats.save_batch(frames, res.best_index, res) + res.timings["total_ms"] = (time.monotonic() - t0) * 1000 + rd = res.to_dict(); rd.update(res_dict_extra) + return ({"ok": True, "result": rd, "focused": focused, + "infer": infer_text, "geom": geom_diag, "saved_dir": saved_dir}, + res.best_jpg, (res, focused, best_path)) + elif res.accepted and not readable_consistent: + # 本窗口看着可读, 但连续一致性未达(可能偶然/刚开始) -> 暂不送下游, 等确认 + pstats.record(accepted=False, reason="await_consistency") + res.timings["total_ms"] = (time.monotonic() - t0) * 1000 + rd = res.to_dict(); rd.update(res_dict_extra) + rd["reject_reason"] = "await_consistency" + return ({"ok": True, "result": rd, "focused": focused, + "infer": None, "await_consistency": True}, + res.best_jpg, (res, focused, None)) + else: + # 若对焦后仍 need_focus, 归为最终拒绝(对焦也没救回来) + reason = res.reject_reason if res.reject_reason != "need_focus" else "all_blurry" + pstats.record(accepted=False, reason=reason) + res.reject_reason = reason + res.timings["total_ms"] = (time.monotonic() - t0) * 1000 + # 拒绝时也存证 + 显示本次批次里最清晰的一帧(而非留旧图), + # 让你能看到"系统这次面对的到底是什么画面", 复盘判定冤不冤。 + rej_best = res.best_index if 0 <= res.best_index < len(frames) else 0 + saved_dir, best_path = pstats.save_batch(frames, rej_best, res) + show_jpg = frames[rej_best] if frames else None + return ({"ok": True, "result": res.to_dict(), "focused": focused, + "infer": None, "geom": None, "rejected_shown": True, + "saved_dir": saved_dir}, show_jpg, (res, focused, best_path)) + + result = await loop.run_in_executor(None, _work) + if len(result) == 3: + payload, best_jpg, run_info = result + else: + payload, best_jpg, run_info = result[0], result[1], None + if best_jpg is not None: + pstats.set_last_best(best_jpg) + if run_info is not None: + res_obj, focused, best_path = run_info + pstats.add_run_row(scene, res_obj, focused, best_path, payload.get("geom")) + return web.json_response({**payload, "stats": pstats.summary()}) + + async def h_pipeline_best(request): + jpg = pstats.last_best + if not jpg: + return web.Response(status=404) + return web.Response(body=jpg, content_type="image/jpeg", + headers={"Cache-Control": "no-store"}) + + async def h_pipeline_stats(request): + return web.json_response(pstats.summary()) + + async def h_pipeline_download(request): + csv_text = pstats.runs_csv() + pstats.save_runs_csv() # 同时存盘 + return web.Response( + body=csv_text.encode("utf-8-sig"), + headers={"Content-Type": "text/csv; charset=utf-8", + "Content-Disposition": "attachment; filename=pipeline_runs.csv"}) + + # ---- 录制一段纯原始帧流(按秒, 不掺对焦), 供 rerun 看过程判定(问题1) ---- + async def h_record(request): + name = request.query.get("name", "").strip() or time.strftime("clip_%H%M%S") + dur = float(request.query.get("dur", 8) or 8) + dur = max(2.0, min(30.0, dur)) + safe = "".join(ch for ch in name if ch.isalnum() or ch in "_-") + clip_dir = pstats.out_dir.parent / "clips" / safe + loop = asyncio.get_event_loop() + try: + rec = await loop.run_in_executor( + None, record_stream, grabber, clip_dir, dur) + except Exception as e: + return web.json_response({"ok": False, "err": str(e)}, status=500) + return web.json_response({ + "ok": True, "dir": str(rec.dir), "name": safe, + "n_frames": len(rec.frames), "duration_s": dur, + }) + + app.router.add_get("/", h_index) + app.router.add_get("/frame", h_frame) + app.router.add_get("/ctl", h_ctl) + app.router.add_get("/rec/start", h_rec_start) + app.router.add_get("/rec/stop", h_rec_stop) + app.router.add_get("/rec/status", h_rec_status) + app.router.add_get("/rec/save", h_rec_save) + app.router.add_get("/rec/download", h_rec_download) + app.router.add_get("/rec/clear", h_rec_clear) + app.router.add_get("/pipeline/run", h_pipeline_run) + app.router.add_get("/pipeline/best", h_pipeline_best) + app.router.add_get("/pipeline/stats", h_pipeline_stats) + async def h_af_toggle(request): + val = request.query.get("on", "") + if val in ("1", "true", "on"): + ctl_state["af_enabled"] = True + elif val in ("0", "false", "off"): + ctl_state["af_enabled"] = False + return web.json_response({"ok": True, "af_enabled": ctl_state.get("af_enabled", True)}) + + app.router.add_get("/pipeline/download", h_pipeline_download) + app.router.add_get("/record", h_record) + app.router.add_get("/af_toggle", h_af_toggle) + return app + + +# ============================================================ +# 录制 + rerun (受控对比基础) +# 录制: 采一段(未对焦) -> 触发真AF -> 再采一段(对焦后), 两段都存, 标对焦点。 +# rerun: 从录制回放帧喂 run_funnel, 同一段反复扫参数, 输入固定=受控对比。 +# 解法一: 对焦效果也录进去(对焦后的帧真实存在), 故 C 出口的对焦可受控复现。 +# ============================================================ +class Recording: + """一段录制: 原始帧序列 + 每帧时间戳 + 对焦触发点标记。""" + + def __init__(self, clip_dir: Path): + self.dir = Path(clip_dir) + self.frames: list[bytes] = [] + self.timestamps: list[float] = [] + self.af_index = -1 # 对焦触发点: 此索引及之后的帧是"对焦后" + self.meta = {} + + def save(self): + self.dir.mkdir(parents=True, exist_ok=True) + for i, jpg in enumerate(self.frames): + (self.dir / f"frame_{i:04d}.jpg").write_bytes(jpg) + import json + meta = { + "n_frames": len(self.frames), + "timestamps": self.timestamps, + "af_index": self.af_index, # -1=无对焦点 + **self.meta, + } + (self.dir / "meta.json").write_text( + json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8") + return self.dir + + @classmethod + def load(cls, clip_dir): + import json + d = Path(clip_dir) + rec = cls(d) + meta = json.loads((d / "meta.json").read_text(encoding="utf-8")) + rec.af_index = meta.get("af_index", -1) + rec.timestamps = meta.get("timestamps", []) + rec.meta = meta + n = meta.get("n_frames", 0) + for i in range(n): + fp = d / f"frame_{i:04d}.jpg" + if fp.exists(): + rec.frames.append(fp.read_bytes()) + return rec + + def pre_af_frames(self): + """对焦前的帧 (rerun 时先用这些跑漏斗)。""" + if self.af_index < 0: + return self.frames + return self.frames[:self.af_index] + + def post_af_frames(self): + """对焦后的帧 (rerun 时 C 出口触发对焦后回放这些)。""" + if self.af_index < 0: + return [] + return self.frames[self.af_index:] + + +def record_stream(grabber: "FrameGrabber", clip_dir: Path, + duration_s: float = 8.0, gap_s: float = 0.05) -> Recording: + """按秒录一段纯原始帧流 (不掺对焦)。专为问题1: rerun 回放看整个过程的 + 判定序列, 验证 CV 判的'该对焦'对不对。af_index=-1 表示无对焦点。""" + rec = Recording(clip_dir) + rec.af_index = -1 + t0 = time.monotonic() + while (time.monotonic() - t0) < duration_s: + jpg = grabber.tcp.capture() + if jpg: + rec.frames.append(jpg) + rec.timestamps.append(time.monotonic() - t0) + time.sleep(gap_s) + rec.meta["duration_s"] = duration_s + rec.meta["kind"] = "stream" # 区别于对焦对比的 clip + rec.save() + return rec + + +def rerun_stream(clip_dir, cfg: "FunnelConfig" = None, n: int = 5, stride: int = 3): + """对一段连续录制, 滑动窗口跑判定, 输出'整个过程的判定序列'。 + 每 stride 帧滑一次, 窗口大小 n。看物品进->停->出时判定(A/B/C)怎么变。 + 对焦命令天然关闭(回放死图), 即问题1: 验证'该对焦'的判断对不对。 + + 产出(供人工核对): 每窗口存 best 帧(文件名带判定) + 一个 CSV(每窗口一行, + 带判定/各分量/best帧路径)。你筛 reason=need_focus 的行, 点开原图看是否真需调焦。""" + import csv, json + cfg = cfg or FunnelConfig() + rec = Recording.load(clip_dir) + frames = rec.frames + out_dir = Path(clip_dir) / "rerun" + out_dir.mkdir(parents=True, exist_ok=True) + print(f"\n=== 过程判定序列 {Path(clip_dir).name} " + f"({len(frames)}帧, ~{rec.meta.get('duration_s','?')}s, 窗口N={n} 步长={stride}) ===") + print(f"{'win':<5}{'t(s)':<7}{'判定':<10}{'reason':<14}{'stable':<8}{'sharp':<8}{'content':<8}{'best图'}") + print("-" * 78) + rows = [] + i = 0 + win = 0 + flip_hist = [] # 跨窗口多帧一致性: 累积"疑似倒置"标志 + while i + n <= len(frames): + window = frames[i:i + n] + res = run_funnel(window, cfg) + t = rec.timestamps[i] if i < len(rec.timestamps) else i * 0.05 + comp = res.components or {} + if res.accepted: + verdict = "B_usable" + elif res.reject_reason == "need_focus": + verdict = "C_needfocus" + elif res.reject_reason == "unstable": + verdict = "A_moving" + else: + verdict = res.reject_reason + # 存 best 帧 (窗口内 best_index; 拒绝时也存参考帧) + bidx = res.best_index if 0 <= res.best_index < len(window) else 0 + best_name = f"w{win:03d}_t{t:.1f}_{verdict}.jpg" + (out_dir / best_name).write_bytes(window[bidx]) + # best 帧的完整指标 + bm = res.per_frame[bidx] if 0 <= bidx < len(res.per_frame) else {} + # === 方向判据 (只对被接受的 best 帧跑; 拒绝的帧朝向无意义) === + orient_state = ""; ud_score = ""; side_ratio = ""; orient_hint = ""; flip_consistent = "" + if res.accepted: + _, gd = process_orientation(window[bidx]) + if gd.get("ok"): + orient_state = gd.get("orient_state", "") + ud_score = gd.get("ud_score", "") + side_ratio = gd.get("sideways_ratio", "") + orient_hint = gd.get("orient_hint") or "" + # 多帧一致性(按窗口顺序累积, 与 live 同逻辑): 5窗内≥3窗疑倒置且本窗也疑 -> 触发 + flip_hist.append(1 if gd.get("flipped_frame") else 0) + del flip_hist[:-5] + if len(flip_hist) >= 3 and sum(flip_hist) >= 3 and gd.get("flipped_frame"): + flip_consistent = 1 + if not orient_hint: + orient_hint = "请确认药盒正反" + else: + flip_consistent = 0 + print(f"{win:<5}{t:<7.1f}{verdict:<10}{res.reject_reason:<14}" + f"{comp.get('stable',0):<8.2f}{comp.get('sharp',0):<8.2f}{comp.get('content',0):<8.2f}{best_name}") + rows.append({ + "win": win, "t_s": round(t, 2), "verdict": verdict, + "reject_reason": res.reject_reason, "accepted": int(res.accepted), + "need_focus": int(res.need_focus), + "g_stable": comp.get("stable", ""), "g_sharp": comp.get("sharp", ""), + "g_content": comp.get("content", ""), "g_light": comp.get("light", ""), + "seg_flow": comp.get("flow", ""), + "best_sharp": bm.get("sharpness", ""), "best_local": bm.get("local_sharp", ""), + "best_worst_block": bm.get("worst_block", ""), "best_text_ratio": bm.get("text_ratio", ""), + "best_frame": best_name, + # 方向判据输出 (供核对方向准确性) + "orient_state": orient_state, "ud_score": ud_score, + "sideways_ratio": side_ratio, "flip_consistent": flip_consistent, + "orient_hint": orient_hint, + "manual_check": "", # ← 你看完 best 图后填: 该对焦的真该吗? ok/wrong + "manual_orient": "", # ← 你填这窗 best 的真实朝向: upright/sideways/flipped + }) + i += stride + win += 1 + # 存 CSV + cols = ["win", "t_s", "verdict", "reject_reason", "accepted", "need_focus", + "g_stable", "g_sharp", "g_content", "g_light", "seg_flow", + "best_sharp", "best_local", "best_worst_block", "best_text_ratio", + "best_frame", + "orient_state", "ud_score", "sideways_ratio", "flip_consistent", "orient_hint", + "manual_check", "manual_orient"] + csv_path = out_dir / "sequence.csv" + with open(csv_path, "w", encoding="utf-8-sig", newline="") as f: + w = csv.DictWriter(f, fieldnames=cols) + w.writeheader() + w.writerows(rows) + from collections import Counter + cnt = Counter(r["verdict"] for r in rows) + print(f"\n判定分布: {dict(cnt)}") + print(f"CSV: {csv_path}") + print(f"best图: {out_dir}/ (筛 need_focus 的窗口, 看对应 best图是否真需调焦)") + return rows + + +def record_clip(grabber: "FrameGrabber", ctl: "CamControl", clip_dir: Path, + pre_n: int = 8, post_n: int = 8, gap_s: float = 0.05) -> Recording: + """录一段: 抓 pre_n 帧(未对焦) -> 触发真AF -> 抓 post_n 帧(对焦后)。 + 含对焦点标记, 供 rerun 复现 C 出口对焦效果 (解法一)。""" + rec = Recording(clip_dir) + t0 = time.monotonic() + # 未对焦段 + for _ in range(pre_n): + jpg = grabber.tcp.capture() + if jpg: + rec.frames.append(jpg) + rec.timestamps.append(time.monotonic() - t0) + time.sleep(gap_s) + # 触发真 AF + rec.af_index = len(rec.frames) + ctl.trigger_af() + time.sleep(0.2) # 给对焦一点生效时间 + # 对焦后段 + for _ in range(post_n): + jpg = grabber.tcp.capture() + if jpg: + rec.frames.append(jpg) + rec.timestamps.append(time.monotonic() - t0) + time.sleep(gap_s) + rec.meta["pre_n"] = pre_n + rec.meta["post_n"] = post_n + rec.save() + return rec + + +def rerun_clip(rec: "Recording", cfg: "FunnelConfig", n: int, focus_n: int = 3) -> dict: + """对一段录制跑漏斗: 用前 n 帧(对焦前); 若判 need_focus, 用对焦后帧重评。 + 输入固定, 只变 cfg/n/focus_n -> 受控对比。返回判定 + 是否用了对焦。""" + pre = rec.pre_af_frames() + if len(pre) < n: + n = len(pre) + batch = pre[:n] + res = run_funnel(batch, cfg) + focused = False + if res.need_focus: + post = rec.post_af_frames() + if post: + extra = post[:focus_n] + res = run_funnel(batch + extra, cfg) # 合并重评, best 应来自对焦后帧 + focused = True + return { + "accepted": res.accepted, + "reject_reason": res.reject_reason, + "need_focus": res.need_focus, + "focused": focused, + "best_index": res.best_index, + "components": res.components, + "n": n, "focus_n": focus_n, + } + + +def rerun_sweep(clip_dir, cfg: "FunnelConfig" = None, + n_values=(3, 5, 7), focus_values=(0, 3)): + """对一段录制扫参数, 打印结果表 (找 N 拐点 / 对焦是否值得)。 + 受控对比: 同一段帧, 只变 N 和 focus_n。""" + cfg = cfg or FunnelConfig() + rec = Recording.load(clip_dir) + print(f"\n=== rerun 扫参 {Path(clip_dir).name} " + f"({len(rec.frames)}帧, 对焦点@{rec.af_index}) ===") + print(f"{'N':<5}{'focus_n':<9}{'判定':<12}{'用对焦':<8}{'best':<6}{'原因'}") + print("-" * 50) + for n in n_values: + for fn in focus_values: + r = rerun_clip(rec, cfg, n, fn) + verdict = "接受" if r["accepted"] else "拒绝" + print(f"{n:<5}{fn:<9}{verdict:<12}{'是' if r['focused'] else '否':<8}" + f"{r['best_index']:<6}{r['reject_reason']}") + return rec + + +def main(): + ap = argparse.ArgumentParser(description="ESP32 相机流水线台 (形态2: 漏斗+录制+rerun)") + ap.add_argument("--ip", help="眼镜 IP (live/record 模式必需)") + ap.add_argument("--tcp-port", type=int, default=5000) + ap.add_argument("--port", type=int, default=8080, help="网页端口") + ap.add_argument("--record-dir", default="./tuning_logs", help="CSV/批次 存盘目录") + ap.add_argument("--duration", type=float, default=5.0, help="点控制按钮后自动记录秒数") + ap.add_argument("--quality", type=int, default=4, help="启动默认 JPEG 质量(小=清晰, 默认4最高)") + # 录制 / rerun 模式 + ap.add_argument("--record", metavar="CLIP_DIR", help="录一段(含对焦点)到指定目录, 然后退出") + ap.add_argument("--pre-n", type=int, default=8, help="录制: 对焦前帧数") + ap.add_argument("--post-n", type=int, default=8, help="录制: 对焦后帧数") + ap.add_argument("--rerun", metavar="CLIP_DIR", help="对一段录制扫参数(N/focus_n), 然后退出") + args = ap.parse_args() + + # ---- rerun 模式: 纯离线, 不连眼镜 ---- + if args.rerun: + rec = Recording.load(args.rerun) + if rec.meta.get("kind") == "stream" or rec.af_index < 0: + rerun_stream(args.rerun) # 连续过程 -> 过程判定序列(问题1) + else: + rerun_sweep(args.rerun) # 对焦对比 clip -> 扫参数 + return + + # ---- record 模式: 连眼镜录一段 ---- + if args.record: + if not args.ip: + print("record 模式需要 --ip"); return + tcp = TCPImageClient(args.ip, args.tcp_port) + grabber = FrameGrabber(tcp, Recorder(Path(args.record_dir)), {"res": "HD", "rotate": 0}) + grabber.start(); time.sleep(0.3) + ctl = CamControl(args.ip) + print(f"[REC] 录制中: {args.pre_n}帧(未对焦) -> AF -> {args.post_n}帧(对焦后)...") + rec = record_clip(grabber, ctl, Path(args.record), args.pre_n, args.post_n) + print(f"[REC] 已存 {rec.dir} ({len(rec.frames)}帧, 对焦点@{rec.af_index})") + print(f"[REC] rerun: python cam_pipeline.py --rerun {rec.dir}") + grabber.stop() + return + + # ---- live 模式: 网页 ---- + if not args.ip: + print("live 模式需要 --ip"); return + recorder = Recorder(Path(args.record_dir)) + ctl_state = {"res": "HD", "rotate": 0, "af_enabled": True} + funnel_cfg = FunnelConfig() + pstats = PipelineStats(Path(args.record_dir) / "batches") + + tcp = TCPImageClient(args.ip, args.tcp_port) + grabber = FrameGrabber(tcp, recorder, ctl_state) + grabber.start() + ctl = CamControl(args.ip) + # 启动默认: HD + 最高画质(quality=4), 省得每次进网页手调 + ctl.set_resolution("HD"); ctl_state["res"] = "HD" + ctl.set_quality(args.quality) + time.sleep(0.15); ctl.trigger_af() # 切完补一次对焦 + print(f"[PIPE] 启动默认: HD + quality={args.quality} + 对焦") + + app = make_app(grabber, ctl, recorder, ctl_state, args.duration, funnel_cfg, pstats) + print(f"[PIPE] http://localhost:{args.port} (TCP {args.ip}:{args.tcp_port})") + print(f"[PIPE] 录制受控对比: python cam_pipeline.py --ip {args.ip} --record clips/case1") + print("[PIPE] 独占 TCP: 调参时不要同时跑主程序") + try: + web.run_app(app, host="0.0.0.0", port=args.port, print=None) + finally: + grabber.stop() + + +if __name__ == "__main__": + main() diff --git a/extensions/assistive_harness/phase_b/esp32_runtime.py b/extensions/assistive_harness/phase_b/esp32_runtime.py new file mode 100644 index 0000000..1a7d4cc --- /dev/null +++ b/extensions/assistive_harness/phase_b/esp32_runtime.py @@ -0,0 +1,1851 @@ +"""ESP32 Phase B Harness runtime. + +复用 rokid_runtime 的全部 harness 骨架（HarnessClient / GatewaySessionManager / +GatewayDuplexSession / PCSpeaker / OutputGate / 控制逻辑），只把设备 I/O 从 +"PC 起 web server 等 Rokid 推" 换成 "PC 主动连 ESP32 拉音视频"。 + +数据方向对比： + Rokid : 眼镜(APK) --push--> PC 的 aiohttp server + ESP32 : PC --pull--> ESP32(CameraWebServer, /ws_audio_v2 + TCP:5000) + +汇聚点完全一致： + 音频 -> audio_queue(给 gateway session) + harness.send_audio(镜像给 8021 ASR) + 图像 -> latest_frame.set() + harness.send_frame(镜像给 8021) + AI 音频输出 -> PCSpeaker +""" +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +import signal +import ssl +import struct +import base64 +import os +import time +from pathlib import Path +from typing import Any, Optional + +import aiohttp +import numpy as np + +from .timing_probe import TimingProbe +from .funnel_gate import FunnelGate +from .session_recorder import SessionRecorder +from .recorder_live import LiveRecorder +try: + from .bridge_ui import WebUIServer +except Exception: + WebUIServer = None + +# ── 复用 rokid_runtime 的 harness 骨架（一个字不改）────────────────────── +from .rokid_runtime import ( + RokidRuntimeConfig, + GatewaySessionManager, + GatewayDuplexSession, + HarnessClient, + PCSpeaker, + NullSpeaker, + OutputGate, + DropOldestAudioQueue, + AudioMirrorChunker, + LatestFrame, + RuntimeStats, + CtrlCExitWatchdog, + SkillRegistry, + default_skills_config, + pcm16le_to_float32, + apply_pcm16_gain, + now_ms, + SAMPLE_RATE_IN, +) + +LOG = logging.getLogger("assistive_harness.phase_b.esp32") + +# ESP32 音频包头： = seq(4) ts_ms(4) n_samples(2) reserved(2) +# 注意：第4字段是 reserved(pad)，固件真丢包计数 g_ring_drops 未发到 wire， +# PC 端用 seq 跳变推断真丢包。 +ESP32_PKT_HDR = 12 +# TCP 图像帧头 magic +_TCP_IMG_MAGIC = 0x55AA55AA + +# 句子边界标点（作为"保护单位"的边界） +def _install_asr_tap(harness, live_rec) -> None: + """把 8021 的 asr.transcript 截下来落盘（评分取提问时刻 t0 用）。 + + 为什么不是另开一条连接：8021 每个 client_id 有独立的 runtime 和 outbound 队列， + asr.transcript 只投递给"送音频进来的那个 client"（即 esp32-phase-b）， + 另开的 esp32-asr-tap 虽然能连上，但永远收不到任何消息。 + 所以只能挂在同一条 HarnessClient 上——rokid_runtime 里加了个可选的 on_message + 旁路（默认 None，不影响原行为），这里赋值即可。 + + 8021 推的字段（server.py 148-154）： + {"type":"asr.transcript", "asr_event_id":..., "utterance":"这上面写的是什么", + "confidence":..., "final_at_ms":..., + "suppressed": true/false, "reason": "..."} ← 被回声抑制时带这两个 + 注意：**被 echo drop 的也照样推**（抑制发生在 route/echo 之后），所以这里全都记， + 由评分侧按问句筛，不在这里过滤。 + """ + if live_rec is None or harness is None: + return + + def _on_message(payload): + if not isinstance(payload, dict): + return + if payload.get("type") != "asr.transcript": + return + utt = str(payload.get("utterance") or "").strip() + if not utt: + return + try: + live_rec.log_asr(utt, payload.get("final_at_ms"), + payload.get("confidence")) + LOG.info("[ASR] %s%s", utt, + " (suppressed)" if payload.get("suppressed") else "") + except Exception as e: + LOG.warning("[ASR] 落盘失败: %s", e) + + harness.on_message = _on_message + LOG.info("[ASR] transcript 旁路已挂在 HarnessClient 上") + + +class TurnPrinter: + """把逐 chunk 的 text 碎片聚合成整段 turn（移植自 demo_esp32_duplex_0703）。 + + -o 每个 chunk 只吐几个字，评分要的是"这次回答说了什么"。规则： + text 非空 且（不在 turn 内 或 listen/speak 发生切换）→ 开新 turn，turn_idx +1 + end_of_turn → 返回 (turn_idx, 整段文本, is_listen) 并结束本 turn + is_listen=True 的 turn 是 8021 ASR 识别出的用户说话（用于取提问结束时刻 t0）。 + """ + + def __init__(self) -> None: + self.turn_idx = 0 + self._in_turn = False + self._turn_is_listen = None + self._turn_text: list[str] = [] + + def _reset(self) -> None: + self._in_turn = False + self._turn_text.clear() + + def feed(self, is_listen: bool, end_of_turn: bool, text: str): + """返回 (turn_idx, 完整文本或"", is_listen)。 + + 除 end_of_turn 外，**listen/speak 切换时也会把上一段吐出来** —— + 原实现在切换时直接 _reset() 丢掉，模型一路流式说、end_of_turn 迟迟不来时 + 整段就没了（bare 组实测 transcript 全空）。 + """ + flushed = None + if text: + if (not self._in_turn) or (self._turn_is_listen != is_listen): + if self._in_turn and self._turn_text: + flushed = (self.turn_idx, "".join(self._turn_text), + bool(self._turn_is_listen)) + self._reset() + self.turn_idx += 1 + self._in_turn = True + self._turn_is_listen = is_listen + self._turn_text.append(text) + if flushed is not None and not end_of_turn: + return flushed + if end_of_turn: + full_text = "".join(self._turn_text) + cur_idx = self.turn_idx + cur_listen = bool(self._turn_is_listen) + self._reset() + return cur_idx, full_text, cur_listen + return self.turn_idx, "", is_listen + + +_SENT_PUNCT = "，。！？,.!?" + + +def _emit_chunk_img(web_ui, jpeg: bytes, idx: int, img_sent: bool, age_ms: int = 0): + """把一帧图推给 bridge_ui 第一视角（type=chunk, img_b64）。无客户端时零负担。""" + if web_ui is None or not getattr(web_ui, "live_clients", None): + return + try: + b64 = base64.b64encode(jpeg).decode("ascii") if jpeg else None + import asyncio as _a + _a.create_task(web_ui.emit({ + "type": "chunk", + "idx": int(idx), + "img_b64": b64, + "img_sent": bool(img_sent), + "img_age_ms": int(age_ms), + })) + except Exception: + pass + + +class SentenceTracker: + """跟踪模型输出的句子边界，供漏斗"标点保护"策略用。 + + handle_result 每个 chunk 调 update()：检测句末标点，累计标点序号。 + image_loop：reject 挂起时 snapshot() 记当前标点序号；之后 + boundary_reached(snap, speaker) 判断"自挂起后是否出现了新句末标点， + 且该段语音已经播完（speaker 队列基本清空）"。 + + 关键：模型文字生成远快于语音播放，文字标点会早于语音到达。所以"语音播到 + 标点"不能靠文字标点时刻或墙钟估算，而要看 speaker.pending_ms —— 队列里还 + 没播的语音降到很低时，才说明当前这段（含标点前的字）真的播完了。这样打断点 + 落在句末标点、语音播完那一刻，不会把句子从中间截断。 + """ + def __init__(self): + self._speaking = False + self._punct_seq = 0 # 句末标点计数（每出现一个 +1） + + def update(self, text: str, audio_ms: float, is_listen: bool): + self._speaking = not is_listen + if text and any(p in text for p in _SENT_PUNCT): + self._punct_seq += 1 + + @property + def speaking(self) -> bool: + return self._speaking + + def snapshot(self): + return {"punct_seq": self._punct_seq} + + def boundary_reached(self, snap, speaker=None): + """自 snap 之后：出现了新句末标点，且该段语音已播完（speaker 队列近空）。""" + if self._punct_seq <= snap["punct_seq"]: + return False # 还没出现新的句末标点 → 仍在保护当前句 + # 出现了新标点：等语音真的播到这里（speaker 队列基本清空） + if speaker is not None: + try: + if speaker.pending_ms() > 150.0: # 还有 >150ms 没播完 → 语音还没到标点 + return False + except Exception: + pass + return True + +# ── 强制措施：播放离线预生成的 reject 提示 wav（模型音色，运行时不碰 chat/KV）── +_REJECT_WAV_CACHE = {} # reason -> float32 ndarray（进程内缓存，只读一次盘） + + +def _load_reject_wav(wav_dir, reason): + """读预生成的 {reason}.wav（24kHz 16-bit mono）→ float32 ndarray，带缓存。 + 找不到返回 None（跳过播报，不阻断主流程）。""" + import wave as _wave + if reason in _REJECT_WAV_CACHE: + return _REJECT_WAV_CACHE[reason] + path = os.path.join(wav_dir, str(reason) + ".wav") + if not os.path.isfile(path): + _REJECT_WAV_CACHE[reason] = None + return None + try: + with _wave.open(path, "rb") as wf: + n = wf.getnframes() + raw = wf.readframes(n) + pcm16 = np.frombuffer(raw, dtype=np.int16) + pcm = (pcm16.astype(np.float32) / 32768.0) + _REJECT_WAV_CACHE[reason] = pcm + return pcm + except Exception: + _REJECT_WAV_CACHE[reason] = None + return None + +# ── 强制措施（防幻觉）：reject 时用模型音色念提示 ───────────────────────── +# 复用 test_tts_speak 验证过的链路：走 gateway 的 /ws/chat(wss)，zero-shot TTS， +# 模型音色念任意文本，返回 float32 24kHz 音频 → PCSpeaker 播。 +# 停/恢复走 8021(funnel.stop/funnel.resume)，和真人「停一下」同一条 controller。 +_TTS_SYS_PROMPT = ( + "模仿音频样本的音色并生成新的内容。请用这种声音风格来为用户提供帮助。" + "直接作答，不要有冗余内容。" +) + + +async def _speak_hint_via_chat( + gateway_host: str, + gateway_port: int, + hint_text: str, + speaker, + ssl_ctx, +) -> bool: + """用 chat zero-shot TTS 让模型用自己音色念 hint_text，播到 PCSpeaker。 + + 返回 True=念出并已入队播放；False=失败（不阻断主流程）。 + """ + url = f"wss://{gateway_host}:{gateway_port}/ws/chat" + req = { + "messages": [ + {"role": "system", "content": _TTS_SYS_PROMPT}, + {"role": "user", "content": "请朗读以下内容：" + hint_text}, + ], + "streaming": False, + "generation": {"max_new_tokens": 128, "length_penalty": 1.1}, + "tts": {"enabled": True}, + "use_tts_template": True, + "omni_mode": False, + } + try: + async with aiohttp.ClientSession() as sess: + async with sess.ws_connect(url, ssl=ssl_ctx, max_msg_size=128 * 1024 * 1024) as ws: + await ws.send_json(req) + audio_b64 = None + sr = 24000 + # 加超时：念提示走 /ws/chat,若 duplex 占着 worker 会排队。 + # 超时返回,避免永久阻塞 image_loop(否则后续抓图全停)。 + deadline = time.monotonic() + 8.0 + while True: + remaining = deadline - time.monotonic() + if remaining <= 0: + LOG.warning("[强制措施] 念提示超时(可能 /ws/chat 排队,duplex 占用 worker)") + return False + try: + msg = await asyncio.wait_for(ws.receive(), timeout=remaining) + except asyncio.TimeoutError: + LOG.warning("[强制措施] 念提示超时(等 chat 响应)") + return False + if msg.type != aiohttp.WSMsgType.TEXT: + if msg.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + return False + continue + data = json.loads(msg.data) + if data.get("type") == "done": + audio_b64 = data.get("audio_data") + sr = data.get("audio_sample_rate") or 24000 + break + if data.get("type") == "error": + LOG.warning("[强制措施] chat TTS error: %s", data.get("error")) + return False + if not audio_b64: + LOG.warning("[强制措施] chat TTS 无音频返回") + return False + # audio_data 是 float32 base64（24kHz mono）→ PCSpeaker(也是 24kHz float32) + import base64 as _b64 + pcm = np.frombuffer(_b64.b64decode(audio_b64), dtype=np.float32) + # block_and_flush 之后 PCSpeaker.blocked=True，enqueue 会被丢；先 resume 解除 + await speaker.resume() + await speaker.enqueue(pcm, generation=0) + LOG.info("[强制措施] 念提示: %r (%d samples, %.2fs)", + hint_text, pcm.size, pcm.size / float(sr)) + return True + except Exception as e: + LOG.warning("[强制措施] 念提示失败: %s", e) + return False + + +# ============================================================ +# ESP32 音频输入：PC 主动连 ESP32 的 /ws_audio_v2，收 int16 PCM +# （摘自 demo_esp32_duplex_0703 的 esp32_audio_reader，去掉 ring/live_rec， +# 直接把每包音频喂给 harness 骨架的 audio_queue + harness.send_audio） +# ============================================================ +async def esp32_audio_reader( + host: str, + port: int, + manager: GatewaySessionManager, + harness: HarnessClient, + audio_queue: DropOldestAudioQueue, + audio_mirror: AudioMirrorChunker, + stats: RuntimeStats, + input_gain: float, + stop_evt: asyncio.Event, + probe=None, + live_rec=None, +) -> None: + url = f"ws://{host}:{port}/ws_audio_v2" + LOG.info("[ESP32] audio WS: %s", url) + backoff = 1.0 + last_log = time.monotonic() + + while not stop_evt.is_set(): + try: + async with aiohttp.ClientSession() as session: + async with session.ws_connect(url, heartbeat=30, max_msg_size=0) as ws: + LOG.info("[ESP32] audio WS connected") + backoff = 1.0 + stats.audio_clients = 1 + async for msg in ws: + if stop_evt.is_set(): + break + if msg.type != aiohttp.WSMsgType.BINARY: + if msg.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + break + continue + data = msg.data + if len(data) < ESP32_PKT_HDR: + continue + # 固件包头: seq(4) ts_ms(4) n_samples(2) reserved(2) + # 注意: 第4字段是 reserved(pad)，不是丢包数；真丢包用 seq 跳变推断 + seq, ts_ms, n_samples, reserved = struct.unpack(" 0.0: + stats.non_silent_audio_packets += 1 + amplified = apply_pcm16_gain(raw, input_gain) + audio_queue.put_nowait(amplified) # -> gateway session + for frame in audio_mirror.feed(amplified): + await harness.send_audio(frame) # -> 8021 ASR 镜像 + # -o record：录 user 音（原始 samples，未放大） + if live_rec is not None: + try: + live_rec.feed_user_raw(samples) + except Exception: + pass + + if probe is not None: + probe.mark_audio(seq, n_samples, packet_rms) + + now = time.monotonic() + if now - last_log >= 5.0: + LOG.info( + "[ESP32] rx=%d pkts seq=%d ts=%d rsv=%d rms=%.4f", + stats.audio_packets, seq, ts_ms, reserved, stats.audio_rms, + ) + last_log = now + except asyncio.CancelledError: + raise + except (aiohttp.ClientError, asyncio.TimeoutError, OSError) as e: + LOG.warning("[ESP32] audio WS error: %s, retry in %.1fs", e, backoff) + finally: + stats.audio_clients = 0 + if not stop_evt.is_set(): + try: + await asyncio.wait_for(stop_evt.wait(), timeout=backoff) + except asyncio.TimeoutError: + pass + backoff = min(backoff * 2, 10.0) + + LOG.info("[ESP32] audio reader stopped") + + +# ============================================================ +# rerun 音频 reader：读 live_user.wav，按 40ms 包喂 audio_queue + harness +# （移植自 rerun_source.local_pcm_reader，下游从 ring 改为 esp32 的 +# audio_queue + harness，其余节奏/回放逻辑一致） +# ============================================================ +async def rerun_audio_reader( + session_dir: str, + manager: GatewaySessionManager, + harness: HarnessClient, + audio_queue: DropOldestAudioQueue, + audio_mirror: AudioMirrorChunker, + stats: RuntimeStats, + input_gain: float, + stop_evt: asyncio.Event, + speed: float = 1.0, + live_rec=None, + ready_evt: Optional[asyncio.Event] = None, + replay_t0=None, + hard_stop: Optional[asyncio.Event] = None, + done_evt: Optional[asyncio.Event] = None, +) -> None: + if hard_stop is None: + hard_stop = stop_evt + # 等 duplex session 就绪再推，否则排队期间的音频全丢（见 _wait_gateway_ready） + if ready_evt is not None: + await ready_evt.wait() + import wave as _wave + wav_path = os.path.join(session_dir, "live_user.wav") + if not os.path.isfile(wav_path): + LOG.error("[RERUN] live_user.wav 不存在: %s", wav_path) + stop_evt.set() + return + with _wave.open(wav_path, "rb") as w: + sr_in = w.getframerate() + pcm_i16 = np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16) + _SR = 16000 + _PKT = 640 # 40ms @16k + total = pcm_i16.size + LOG.info("[RERUN] audio: %s samples=%d dur=%.2fs sr=%d", + wav_path, total, total / _SR, sr_in) + + packet_interval = (_PKT / _SR) / max(speed, 0.01) + ts_ms = 0 + cursor = 0 + pkt = 0 + # next_tick 必须是"本函数真正开始推包的那一刻"。 + # 曾经这里写成 replay_t0()（就绪门放行的时刻），但那之后还要经过任务调度、 + # 读 wav、打日志，等跑到循环里时 next_tick 已经是过去时刻 → + # sleep_for 恒为负 → 每轮都不 sleep → 564 个包在几十毫秒内全灌进 audio_queue + # → 队列容量 96、DropOldest 把开头的提问全丢了，模型只听到最后 3.8 秒的尾音。 + # （stats 里的 audio=25.0pps 统计的是"推进队列"的数量，正好掩盖了这个问题。） + # 图那边按 frames.jsonl 的 t 等待、以 replay_t0 为零点，两边起点差几十毫秒， + # 对 1s 粒度的 chunk 没有影响。 + next_tick = time.monotonic() + # 只受 hard_stop 控制，不再被 stop_evt 掐断。 + # 原来是 `while cursor < total and not stop_evt.is_set()`： + # 图那条路径放完图会 set stop_evt，而素材的图轮数常少于音频秒数 + # （drugbox 16 轮图 vs 22.5s 音频）→ 图先放完就把音频推送掐了， + # 后半段提问根本没进模型。两条流应各自跑完自己的。 + while cursor < total and not hard_stop.is_set(): + end = min(cursor + _PKT, total) + chunk_i16 = pcm_i16[cursor:end] + cursor = end + pkt += 1 + raw = chunk_i16.astype(" 0: + # 这里等的是 hard_stop，不是 stop_evt。 + # 之前 while 条件改成了 hard_stop，但循环体里仍 await stop_evt 并 break， + # 等于没改——图放完 set stop_evt 照样把音频推送掐断。 + try: + await asyncio.wait_for(hard_stop.wait(), timeout=sleep_for) + break + except asyncio.TimeoutError: + pass + + if done_evt is not None: + done_evt.set() + LOG.info("[RERUN] audio 推完 %d 包 (%.2fs)，等模型说完…", pkt, ts_ms / 1000.0) + # 音频推完不立即 stop。注意：这里**不能** await stop_evt 就 break —— + # 图那条路径放完图也会 set stop_evt，两条互相踩，结果谁先到谁把另一条掐了 + # （图少的素材尤其明显：图很快放完 → 直接收工 → 模型话没说完）。 + # 这里独立按时间等，让模型有机会把最后一句说完；真正的结束由 image_loop + # 的静默判据 + 尾巴决定，或走这里的兜底。 + waited = 0.0 + while waited < 120.0: + if stop_evt.is_set(): + # 另一条已判定结束，再宽限一小段让 TTS 播完 + await asyncio.sleep(2.0) + break + await asyncio.sleep(0.5) + waited += 0.5 + if not stop_evt.is_set(): + stop_evt.set() + LOG.info("[RERUN] audio reader stopped") + + +# ============================================================ +# rerun 图像 loop：用 LocalImageSource 按 chunk 顺序读 best 图，直接喂模型 +# （-o rerun 不重跑 funnel——best 已是筛过的，直接 send_frame） +# ============================================================ +async def rerun_image_loop( + session_dir: str, + latest_frame, + harness: HarnessClient, + stats: RuntimeStats, + interval_s: float, + stop_evt: asyncio.Event, + web_ui=None, + live_rec=None, + ready_evt: Optional[asyncio.Event] = None, + replay_t0=None, + done_evt: Optional[asyncio.Event] = None, + peer_done: Optional[asyncio.Event] = None, + speaker=None, + sent_tracker=None, + tail_wait_s: float = 30.0, +) -> None: + if ready_evt is not None: + await ready_evt.wait() + try: + from .rerun_source import LocalImageSource + except Exception as e: + LOG.error("[RERUN] 无法导入 LocalImageSource: %s", e) + stop_evt.set() + return + from pathlib import Path as _Path + try: + src = LocalImageSource(_Path(session_dir)) + except Exception as e: + LOG.error("[RERUN] LocalImageSource 初始化失败: %s", e) + stop_evt.set() + return + # 按 chunk_idx 升序回放 + idxs = sorted(src._map.keys()) + LOG.info("[RERUN] image loop: %d 帧待回放", len(idxs)) + for cidx in idxs: + if stop_evt.is_set(): + break + jpeg = await src.capture(cidx) + if jpeg: + ts = now_ms() + latest_frame.set(jpeg, ts) + stats.image_count += 1 + await harness.send_frame(jpeg, latest_frame.sequence, ts) + LOG.info("[RERUN] send frame idx=%d (%d bytes)", cidx, len(jpeg)) + _emit_chunk_img(web_ui, jpeg, latest_frame.sequence, True) + if live_rec is not None: + try: + live_rec.on_frame(jpeg, latest_frame.sequence) + except Exception: + pass + try: + await asyncio.wait_for(stop_evt.wait(), timeout=interval_s) + break + except asyncio.TimeoutError: + pass + LOG.info("[RERUN] image loop 回放完毕") + if done_evt is not None: + done_evt.set() + # -o rerun 的收尾。之前这条路径图放完就什么都不做，结束全靠 + # rerun_audio_reader 里 `while waited < 120.0` 的兜底干等两分钟 + #（日志表现：音频推完后一串 audio=0.0pps 的 STATS，两分钟才退）， + # 而且 --rerun-tail-wait-s 对它无效。 + # 判定与 esp32_image_loop 保持一致：先等音频也推完，再看模型说完没有 + #（队列空 且 不在 speak，连续保持 2s 才算），最后加 2s 尾巴。 + if peer_done is not None and not peer_done.is_set(): + LOG.info("[RERUN] 图放完，等音频推完…") + try: + await asyncio.wait_for(peer_done.wait(), timeout=180.0) + except asyncio.TimeoutError: + LOG.warning("[RERUN] 等音频超时") + _QUIET_HOLD_S, _TAIL_S = 2.0, 2.0 + _quiet_since = None + _deadline = time.monotonic() + tail_wait_s + while time.monotonic() < _deadline and not stop_evt.is_set(): + try: + pending = speaker.pending_ms() if speaker is not None else 0.0 + except Exception: + pending = 0.0 + speaking = bool(getattr(sent_tracker, "speaking", False)) if sent_tracker else False + if pending <= 50 and not speaking: + if _quiet_since is None: + _quiet_since = time.monotonic() + elif time.monotonic() - _quiet_since >= _QUIET_HOLD_S: + break + else: + _quiet_since = None + await asyncio.sleep(0.25) + try: + await asyncio.wait_for(stop_evt.wait(), timeout=_TAIL_S) + except asyncio.TimeoutError: + pass + LOG.info("[RERUN] 结束（静默保持%.1fs + 尾巴%.1fs）", _QUIET_HOLD_S, _TAIL_S) + stop_evt.set() + + +# ============================================================ +# ESP32 图像输入：TCP 5000 持久连接，请求-响应取裸 JPEG +# （摘自 demo_esp32_duplex_0703 的 TcpImageClient，接口不变） +# 帧头(20B 小端): magic(4) frame_id(4) w(2) h(2) fmt(1) reserved(3) len(4) +# ============================================================ +_ROTATE_MAP = None + + +def _rotate_jpeg(jpeg: bytes, deg: int) -> bytes: + """把相机原始帧按顺时针 deg 转正。deg ∈ {0,90,180,270}，0 直接原样返回。 + + 为什么要在这里转：眼镜的摄像头是**物理侧装/倒装**的（devices.json 的 rotate）， + 出来的帧本身就是歪的。不转正的话，漏斗的方向分类器会把"相机侧装" + 误判成"用户把盒子拿反了"并提示用户翻转 —— 用户翻了反而更歪。 + 取景截断的上下左右语义、_PAN_HINT 的"向上看/向下看"也全都会反。 + 所以必须在**进漏斗之前**转，转法照搬 demo_esp32_duplex_0703。 + + 注意 PIL 按逆时针算：顺时针 90° 要用 ROTATE_270。 + """ + if not deg or not jpeg: + return jpeg + global _ROTATE_MAP + try: + from PIL import Image + import io + except Exception: + return jpeg + if _ROTATE_MAP is None: + _ROTATE_MAP = { + 90: Image.Transpose.ROTATE_270, # 顺时针 90° = PIL 的 ROTATE_270 + 180: Image.Transpose.ROTATE_180, + 270: Image.Transpose.ROTATE_90, + } + op = _ROTATE_MAP.get(int(deg) % 360) + if op is None: + return jpeg + try: + im = Image.open(io.BytesIO(jpeg)) + im = im.transpose(op) + buf = io.BytesIO() + im.save(buf, format="JPEG", quality=92) + return buf.getvalue() + except Exception as e: + LOG.warning("[ROTATE] 转正失败(%d°)，用原图: %r", deg, e) + return jpeg + + +class TcpImageClient: + def __init__(self, host: str, port: int = 5000, rotate: int = 0): + self.host = host + self.port = port + self.rotate = int(rotate) % 360 + self._reader: Optional[asyncio.StreamReader] = None + self._writer: Optional[asyncio.StreamWriter] = None + self._lock = asyncio.Lock() + + async def _ensure_conn(self, timeout_s: float) -> bool: + if self._reader is not None and self._writer is not None and not self._writer.is_closing(): + return True + try: + self._reader, self._writer = await asyncio.wait_for( + asyncio.open_connection(self.host, self.port), timeout=timeout_s) + sock = self._writer.get_extra_info("socket") + if sock is not None: + import socket as _s + sock.setsockopt(_s.IPPROTO_TCP, _s.TCP_NODELAY, 1) + LOG.info("[TCP-IMG] connected to %s:%d", self.host, self.port) + return True + except Exception as e: + LOG.debug("[TCP-IMG] connect failed: %s", e) + await self._close() + return False + + async def _close(self) -> None: + if self._writer is not None: + try: + self._writer.close() + except Exception: + pass + self._reader = None + self._writer = None + + async def capture(self, timeout_s: float = 1.0) -> Optional[bytes]: + async with self._lock: + if not await self._ensure_conn(timeout_s): + return None + try: + self._writer.write(b"C") + await self._writer.drain() + hdr = await asyncio.wait_for(self._reader.readexactly(20), timeout=timeout_s) + magic, frame_id, w, h = struct.unpack_from(" 4 * 1024 * 1024: + LOG.warning("[TCP-IMG] insane len=%d, reconnect", length) + await self._close() + return None + data = await asyncio.wait_for(self._reader.readexactly(length), timeout=timeout_s) + # 转正后再交给上层：latest_frame / 漏斗 / 录制拿到的都是正图， + # rerun 时不必再转（否则会转两次）。 + return _rotate_jpeg(bytes(data), self.rotate) + except asyncio.CancelledError: + raise + except Exception as e: + LOG.warning("[TCP-IMG] capture failed: %r, reconnect", e) + await self._close() + return None + + +# ============================================================ +# funnel rerun：把录制的整簇多帧当图源，复用 esp32_image_loop 重跑漏斗+播报 +# RecordedImageClient.capture() 按 frames.jsonl 每轮整簇顺序吐帧， +# funnel.run_once 调 N 次凑一簇 → 重判 send/reject → 播报/标点保护，逻辑全复用。 +# ============================================================ +class RecordedImageClient: + def __init__(self, session_dir: str, n_frames: int, no_funnel: bool = False): + import json as _json + self.dir = session_dir + self.images_dir = os.path.join(session_dir, "images") + self.n_frames = max(1, int(n_frames)) + self._rounds = [] # 每轮: [jpg_bytes, ...] + fr_path = os.path.join(session_dir, "frames.jsonl") + self._round_names = [] # 每轮的帧文件名（供 record 复用） + self._round_t = [] # 每轮录制时刻 t（秒），用于按原始时间轴回放 + with open(fr_path, encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rec = _json.loads(line) + except Exception: + continue + names = rec.get("frames") or [] + jpgs = [] + keep_names = [] + for nm in names: + p = os.path.join(self.images_dir, nm) + if os.path.isfile(p): + with open(p, "rb") as fp: + jpgs.append(fp.read()) + keep_names.append(nm) + if jpgs: + self._rounds.append(jpgs) + self._round_names.append(keep_names) + self._round_t.append(float(rec.get("t", len(self._rounds) - 1))) + LOG.info("[RERUN-IMG] 载入 %d 轮，每轮≤%d帧%s", + len(self._rounds), self.n_frames, + "（无漏斗：每轮取中间张代表帧）" if no_funnel else "（funnel：整簇逐帧）") + self._round_i = 0 + self._frame_i = 0 + self._no_funnel = no_funnel + self.exhausted = False + self._t0 = None # 回放起点（优先用外部注入的统一零点） + self.replay_t0 = None # 由 runtime 注入，与音频共用 + # 整份 record 一张图都没有（图像链路当时丢了 / 手动删空 images）： + # 这是有效素材（对照：无漏斗→模型 0 图编场景；有漏斗→全程 reject no_frames）。 + # 此时不能置 exhausted，否则会被当成"图放完了"秒退；让音频推完来结束整场。 + self._no_images = (len(self._rounds) == 0) + if self._no_images: + LOG.info("[RERUN-IMG] 这份 record 没有可用图像 → 全程无图回放" + "（由音频长度决定结束）") + + def current_round_cluster(self): + """返回当前轮的整簇 (jpg_list, name_list)，供无漏斗 rerun 存 record 用。""" + i = self._round_i + if 0 <= i < len(self._rounds): + return self._rounds[i], self._round_names[i] + return [], [] + + async def _wait_round_time(self): + """按录制时刻回放：等到墙钟走到本轮的 t 再吐这一轮的帧。 + + 录制时每轮耗时并不均匀（对焦轮 ~1.9s、reject 播报轮 2.3~4s），若 rerun 按 + image_loop 的固定 interval_s 匀速吃图，就会以数倍速把图吃光（实测 33s 的录制 + 11s 就放完）。音频是按真实时长回放的，用 frames.jsonl 的 t 对齐，两边同一条 + 时间轴。 + """ + if self._t0 is None: + # 优先用统一回放零点（与音频同源）；没有才退回"第一次 capture 那一刻" + self._t0 = self.replay_t0 if self.replay_t0 is not None else time.monotonic() + i = self._round_i + if 0 <= i < len(self._round_t): + target = self._round_t[i] + delay = target - (time.monotonic() - self._t0) + if delay > 0: + await asyncio.sleep(min(delay, 10.0)) + + async def capture(self, timeout_s: float = 1.0): + if self._no_images: + # 全程无图：一直返回 None，但不置 exhausted（不提前结束）。 + # 有漏斗 → run_once 拿到 0 帧 → reject(no_frames) → 播"没有拿到画面"； + # 无漏斗 → 不 send → 模型 0 图 → 暴露编场景。 + await asyncio.sleep(0.05) + return None + if self._round_i >= len(self._rounds): + self.exhausted = True + return None + # 只在一轮的第一帧上等待，同一轮内的多帧连续吐（模拟原始高频抓帧） + if self._frame_i == 0: + await self._wait_round_time() + rnd = self._rounds[self._round_i] + if self._no_funnel: + # 无漏斗（对照组）：每轮取一张代表帧（中间张，模拟"随手抓一张就发"）， + # 每次 capture 直接进入下一轮。含模糊图会照发 → 暴露幻觉。 + jpg = rnd[len(rnd) // 2] + self._round_i += 1 + return jpg + # funnel：一帧帧吐整簇，供 run_once 调 N 次凑一簇 + jpg = rnd[self._frame_i % len(rnd)] + self._frame_i += 1 + if self._frame_i >= self.n_frames: + self._frame_i = 0 + self._round_i += 1 + return jpg + + async def _close(self): + pass + + +# ============================================================ +# ESP32 图像轮询循环：定时用 TCP 取图 -> latest_frame + harness.send_frame +# ============================================================ +async def esp32_image_loop( + client: TcpImageClient, + latest_frame: LatestFrame, + harness: HarnessClient, + stats: RuntimeStats, + interval_s: float, + timeout_s: float, + stop_evt: asyncio.Event, + probe=None, + funnel=None, + recorder=None, + speaker=None, + gateway_host: str = "127.0.0.1", + gateway_port: int = 8040, + ssl_ctx=None, + force_measure: bool = False, + manager=None, + reject_wav_dir: str = "assets/reject_wav", + sent_tracker=None, + live_rec=None, + web_ui=None, + ready_evt: Optional[asyncio.Event] = None, + tail_wait_s: float = 30.0, + no_reject: bool = False, + peer_done: Optional[asyncio.Event] = None, + done_evt: Optional[asyncio.Event] = None, +) -> None: + if ready_evt is not None: + await ready_evt.wait() + LOG.info("[ESP32] image loop start (interval=%.2fs, funnel=%s)", + interval_s, "on" if funnel else "off") + _last_grab_mono = None + + # ── 强制措施状态 ── + _funnel_stopped = False # 是否已发过 funnel.stop（在停住态，等好图 resume） + _last_hint_mono = 0.0 # 上次念提示的时刻（节流用） + _HINT_THROTTLE_S = 5.0 # 同类连续 reject 的念提示节流间隔 + _pending_reject = None # 标点保护：挂起中的 reject（等标点/超时再复查） + # ── 「持续坏」检测：3s 窗口内 reject≥2 → 判定输出基于坏图=幻觉，直接打断+压制 ── + # 覆盖"错错错对"场景（前半也错，标点保护不适用）。窗口单位=判定/chunk(每≈1s)。 + # 依据：模型拿图→输出约 1-1.5s；2s 都坏则幻觉必已在输出，必须闭嘴。 + _judge_history = [] # 最近判定时刻+是否reject: [(mono, is_reject), ...] + _WINDOW_S = 3.0 # 窗口长度 + _BAD_THRESH = 2 # 窗口内 reject≥此 → 持续坏，直接打断(跳过标点保护) + # _funnel_active：念提示临界区标志（预留给兜底：念提示那几秒真人喊话的处理） + # TODO(兜底,下次实现): 念提示期间若收到真人 STOP/RESUME/RESET， + # 先掐掉提示音(speaker.block_and_flush)+清此标志，再执行真人指令，避免撞车。 + # 当前只占位，不实现逻辑。 + _funnel_active = False # noqa: F841 (预留) + + async def _capture_one(): + return await client.capture(timeout_s=timeout_s) + + while not stop_evt.is_set(): + t0 = time.monotonic() + # funnel rerun：录制的整簇多帧放完了 → 优雅结束（不报 no_frames） + if getattr(client, "exhausted", False): + LOG.info("[FUNNEL-RERUN] 录制图已放完") + # 图轮数常少于音频秒数（drugbox 16 轮 vs 22.5s），不能图一放完就收工， + # 否则后半段音频（可能正含提问）根本没推给模型。先等音频也推完。 + if peer_done is not None and not peer_done.is_set(): + LOG.info("[FUNNEL-RERUN] 等音频推完…") + try: + await asyncio.wait_for(peer_done.wait(), timeout=180.0) + except asyncio.TimeoutError: + LOG.warning("[FUNNEL-RERUN] 等音频超时") + LOG.info("[FUNNEL-RERUN] 两条流都完事，等模型说完后结束") + # 等模型把话说完再 stop。判据不能只看"队列空"——模型是流式产出的， + # 某一瞬间队列排空只是"下一段还没到"，不代表这轮结束（图少的素材尤其明显： + # 图很快放完，恰好撞上队列空，就把还没说完的话掐了）。 + # 改为：队列必须连续 _QUIET_HOLD_S 保持空，且 sent_tracker 不在 speak 中， + # 再加一段固定尾巴，给最后一句 TTS 留出播放时间。 + _QUIET_HOLD_S = 2.0 + _TAIL_S = 2.0 + _quiet_since = None + _deadline = time.monotonic() + tail_wait_s + while time.monotonic() < _deadline and not stop_evt.is_set(): + try: + pending = speaker.pending_ms() if speaker is not None else 0.0 + except Exception: + pending = 0.0 + speaking = False + if sent_tracker is not None: + speaking = bool(getattr(sent_tracker, "speaking", False)) + if pending <= 50 and not speaking: + if _quiet_since is None: + _quiet_since = time.monotonic() + elif time.monotonic() - _quiet_since >= _QUIET_HOLD_S: + break + else: + _quiet_since = None + await asyncio.sleep(0.25) + # 尾巴：让最后一句 TTS 播完，也给模型一点补充的机会 + try: + await asyncio.wait_for(stop_evt.wait(), timeout=_TAIL_S) + except asyncio.TimeoutError: + pass + LOG.info("[FUNNEL-RERUN] 结束（静默保持%.1fs + 尾巴%.1fs）", + _QUIET_HOLD_S, _TAIL_S) + stop_evt.set() + break + try: + if funnel is None: + # ── 无漏斗：取 1 帧直发（含模糊图，暴露幻觉）── + # record 仍存整簇（mp4 用全部采集帧，和有漏斗一致，只是无 reject 标注） + cluster_jpgs, cluster_names = ([], []) + if hasattr(client, "current_round_cluster"): + cluster_jpgs, cluster_names = client.current_round_cluster() + jpeg = await client.capture(timeout_s=timeout_s) + grab_ms = (time.monotonic() - t0) * 1000.0 + if jpeg: + ts = now_ms() + latest_frame.set(jpeg, ts) + stats.image_count += 1 + await harness.send_frame(jpeg, latest_frame.sequence, ts) + _emit_chunk_img(web_ui, jpeg, latest_frame.sequence, True) + if probe is not None: + since_last = (t0 - _last_grab_mono) * 1000.0 if _last_grab_mono else 0.0 + probe.mark_grab(latest_frame.sequence, grab_ms, len(jpeg), since_last) + _last_grab_mono = t0 + # 存 record（无漏斗：send=True 无 reason）。 + # rerun 时图源是 RecordedImageClient，有 current_round_cluster()， + # 能拿到整簇；**实机时图源是 TcpImageClient，没有这个方法**， + # cluster_jpgs 恒为空 —— 原来写 `and cluster_jpgs` 就把实机的 + # 录制整个跳过了（实测 frames.jsonl 0 rounds、images/ 空）。 + # 实机 no_funnel 本来就是每轮一张图，拿不到整簇就用这一帧当一轮。 + if live_rec is not None: + _frames = cluster_jpgs if cluster_jpgs else [jpeg] + _dec = type("D", (), {"frames": _frames, + "best_index": len(_frames) // 2, + "send": True, "reason": ""})() + try: + await asyncio.to_thread( + live_rec.on_funnel_round, _dec, latest_frame.sequence) + # best 图也存一份（events.jsonl + img_*.jpg，供 -o rerun） + await asyncio.to_thread( + live_rec.on_frame, jpeg, latest_frame.sequence) + except Exception as e: + LOG.warning("[LIVE] on_funnel_round(no-funnel) err: %s", e) + else: + # ── 有漏斗：一轮判定，合格才 send_frame ── + decision = await funnel.run_once(_capture_one) + # 统一 record：每轮整簇多帧 + 判定 → live_rec（供三种 rerun）。 + # 必须放在所有分支之前：持续坏分支末尾有 continue，放在最后会把 + # 触发压制的那些轮整簇漏录（实测 42s 只落下 8 轮）。录制是原始素材， + # 不该因为走了哪条处理路径而缺失。 + if live_rec is not None: + try: + await asyncio.to_thread( + live_rec.on_funnel_round, decision, latest_frame.sequence) + except Exception as e: + LOG.warning("[LIVE] on_funnel_round err: %s", e) + grab_ms = decision.timings.get("grab_ms", 0.0) + async def _do_reject_interrupt(reason): + """真正执行打断：停 duplex → 播 wav → 恢复。""" + wav_pcm = _load_reject_wav(reject_wav_dir, reason) + if wav_pcm is None: + LOG.warning("[强制措施] 无 %s.wav，跳过播报", reason) + return + try: + await harness.send({"type": "funnel.stop", "reason": reason}) + LOG.info("[强制措施] funnel.stop 已发 (%s)", reason) + except Exception as e: + LOG.warning("[强制措施] funnel.stop 失败: %s", e) + await asyncio.sleep(0.35) # 等 STOP 经 8021→rokid→block_and_flush + try: + await speaker.resume() + await speaker.enqueue(wav_pcm, generation=0) + dur = len(wav_pcm) / 24000.0 + LOG.info("[强制措施] 播 reject wav: %s (%.2fs)", reason, dur) + if live_rec is not None: + live_rec.log_event("HINT", f"{reason} ({dur:.2f}s)") + await asyncio.sleep(dur + 0.2) + except Exception as e: + LOG.warning("[强制措施] 播 wav 失败: %s", e) + try: + await harness.send({"type": "funnel.resume", "reason": "hint_done"}) + LOG.info("[强制措施] funnel.resume 已发（提示播完）") + except Exception as e: + LOG.warning("[强制措施] funnel.resume 失败: %s", e) + + # ── 消融用：选 best 但永远放行（关闭工作③的拒绝/播报）── + # 用途：把"多帧选 best"(工作②)的收益从"拒绝坏图"(工作③)里剥出来。 + # 对照 bare（每轮取中间帧直发），本 arm 每轮取 best 直发， + # 下游模型侧完全一致，差异只来自选图。 + if no_reject: + _b = decision.best + if _b: + ts = now_ms() + latest_frame.set(_b, ts) + stats.image_count += 1 + await harness.send_frame(_b, latest_frame.sequence, ts) + _emit_chunk_img(web_ui, _b, latest_frame.sequence, True) + if live_rec is not None: + try: + await asyncio.to_thread( + live_rec.on_frame, _b, latest_frame.sequence) + except Exception as e: + LOG.warning("[LIVE] on_frame err: %s", e) + LOG.info("[漏斗-放行] best (原判定=%s)", decision.reason) + # 消融 arm 不执行拒绝，但**判别结果要留痕**： + # 这一轮漏斗本来会不会拦、拦的理由是什么，是分析判别倾向的依据。 + if live_rec is not None and not decision.send: + live_rec.log_event( + "WOULD-REJECT", + f"{decision.reason} {decision.hint or ''}") + else: + LOG.info("[漏斗-放行] 本轮无 best（%s），跳过", decision.reason) + elapsed = time.monotonic() - t0 + try: + await asyncio.wait_for(stop_evt.wait(), + timeout=max(0.0, interval_s - elapsed)) + except asyncio.TimeoutError: + pass + continue + + # ── 「持续坏」检测（优先于标点保护）── + # 3s 窗口内"真坏"reject≥2 → 输出基于坏图=幻觉。此时不走标点保护 + # （前半也错，不值得保护），直接触发"停→播提示→恢复"。 + # need_focus 不算坏：它是系统正在对焦(run_once 已发 /reg 重抓)， + # 属于内部自愈，不是用户造成的持续坏，算进去会误判、干扰对焦流程。 + # "真坏" = reject 且 reason 不是 need_focus（severe_shake/unstable/ + # too_dark/orient 等，要用户动手的）。 + _now = time.monotonic() + _is_real_bad = (not decision.send) and (decision.reason != "need_focus") + _judge_history.append((_now, _is_real_bad)) + _judge_history[:] = [(t, r) for (t, r) in _judge_history + if _now - t <= _WINDOW_S] + _bad_in_window = sum(1 for (_t, r) in _judge_history if r) + + if _bad_in_window >= _BAD_THRESH and _is_real_bad: + _pending_reject = None # 持续坏优先，取消标点保护挂起 + # 到这里必是"真坏"(severe_shake/unstable/too_dark/orient)。 + # 直接触发完整"停→播提示→恢复"（复用 _do_reject_interrupt， + # 停模型输出不杀死、播完恢复）。耗时≈播报时长，天然间隔。 + LOG.info("[持续坏] 3s内%d次真坏reject，打断+播提示（停→播→恢复）", + _bad_in_window) + if live_rec is not None: + live_rec.log_event( + "PERSIST-BAD", + f"3s内{_bad_in_window}次真坏 → 打断 ({decision.reason})") + await _do_reject_interrupt(decision.reason) + elapsed = time.monotonic() - t0 + try: + await asyncio.wait_for(stop_evt.wait(), + timeout=max(0.0, interval_s - elapsed)) + except asyncio.TimeoutError: + pass + continue + + # ── 「标点保护」核心：突发 reject 不立即打断，保护当前段语音播放到 + # 下一个句末标点，到标点那一刻复查当时判定；仍 reject 才打断，否则无事。 + # 挂起期间的 send/reject 都不算数，只看"到标点那一刻"的判定。 + # （snapshot 记标点序号，boundary_reached 判断新标点+其语音已播完） + if force_measure and speaker is not None and _pending_reject is not None: + # 正在保护中：只判断"是否到标点(语音播完)或超时"，到了才用当时判定复查 + now_mono = time.monotonic() + reached = (sent_tracker is not None + and sent_tracker.boundary_reached( + _pending_reject["snap"], speaker)) + timed_out = (now_mono - _pending_reject["since"]) >= 1.5 + if reached or timed_out: + # 到标点/超时 → 复查此刻判定 + if decision.send: + LOG.info("[标点保护] %s，此刻已恢复(send)，无事发生", + "到标点" if reached else "超时1.5s") + _pending_reject = None + elif decision.reason == "need_focus": + _pending_reject = None # 复查是对焦，静默 + else: + # 仍 reject → 打断（受节流约束） + LOG.info("[标点保护] %s，仍 reject(%s)，打断", + "到标点" if reached else "超时1.5s", decision.reason) + if now_mono - _last_hint_mono >= _HINT_THROTTLE_S: + _last_hint_mono = now_mono + _pending_reject = None + await _do_reject_interrupt(decision.reason) + else: + _pending_reject = None # 被节流 + + if decision.send and decision.best: + ts = now_ms() + latest_frame.set(decision.best, ts) + stats.image_count += 1 + await harness.send_frame(decision.best, latest_frame.sequence, ts) + LOG.info("[漏斗] send (%s)", decision.reason) + _emit_chunk_img(web_ui, decision.best, latest_frame.sequence, True) + # -o record：只录真发送给模型的 best 图（chunk_idx = sequence） + if live_rec is not None: + try: + await asyncio.to_thread( + live_rec.on_frame, decision.best, latest_frame.sequence) + except Exception as e: + LOG.warning("[LIVE] on_frame err: %s", e) + # 注意：send 不清 _pending_reject，保护期只看到标点那一刻 + else: + LOG.info("[漏斗] reject(%s) -> hint: %s", + decision.reason, decision.hint) + if live_rec is not None: + live_rec.log_event("REJECT", f"{decision.reason} {decision.hint or ''}") + if force_measure and speaker is not None and decision.reason != "need_focus": + now_mono = time.monotonic() + if _pending_reject is not None: + pass # 已在保护中，上面已处理，不重复挂起 + elif sent_tracker is not None and sent_tracker.speaking: + # 模型正念字：挂起，保护到下一个标点再复查 + _pending_reject = { + "since": now_mono, + "reason": decision.reason, + "snap": sent_tracker.snapshot(), + } + LOG.info("[标点保护] speak中，reject(%s)挂起，等语音播到标点或超时1.5s", + decision.reason) + else: + # 模型没念字（listen/静默）→ 无需保护，直接打断（受节流） + if now_mono - _last_hint_mono >= _HINT_THROTTLE_S: + _last_hint_mono = now_mono + await _do_reject_interrupt(decision.reason) + if probe is not None: + since_last = (t0 - _last_grab_mono) * 1000.0 if _last_grab_mono else 0.0 + jb = len(decision.best) if decision.best else 0 + tm = decision.timings + probe.mark_grab( + latest_frame.sequence, grab_ms, jb, since_last, + reason=decision.reason, + judge_ms=tm.get("judge_ms"), + af_ms=tm.get("af_ms"), + n_frames=tm.get("n_frames"), + ) + _last_grab_mono = t0 + except Exception as e: + LOG.warning("[ESP32] image loop error: %s", e, exc_info=True) + # 控制取图节奏 + elapsed = time.monotonic() - t0 + try: + await asyncio.wait_for(stop_evt.wait(), timeout=max(0.0, interval_s - elapsed)) + except asyncio.TimeoutError: + pass + LOG.info("[ESP32] image loop stopped") + + +# ============================================================ +# ESP32 Runtime：继承 rokid 的 PhaseBRokidRuntime，复用全部 harness 骨架， +# 只覆盖 start()：不起 web server，改起两个 ESP32 主动拉取 task。 +# ============================================================ +from .rokid_runtime import PhaseBRokidRuntime + + +def _insecure_ssl_for_wss(url: str) -> Optional[ssl.SSLContext]: + """wss:// 且自签名证书时，返回一个不校验证书的 ssl context；ws:// 返回 None。 + + 与 rokid GatewayDuplexSession._ssl_context 同款做法（check_hostname=False, + verify_mode=CERT_NONE），用于连本地自签名的 8021。""" + if not url.lower().startswith("wss://"): + return None + ctx = ssl.create_default_context() + ctx.check_hostname = False + ctx.verify_mode = ssl.CERT_NONE + return ctx + + +class TlsHarnessClient(HarnessClient): + """与 HarnessClient 完全一致，仅在 ws_connect 时对 wss:// 传入自签名 ssl。 + + 覆盖 run()：逐行照抄父类，唯一区别是 ws_connect 多了 ssl= 参数。""" + + async def run(self) -> None: + ssl_ctx = _insecure_ssl_for_wss(self.url) + while not self._stop.is_set(): + try: + async with aiohttp.ClientSession() as client: + async with client.ws_connect( + self.url, heartbeat=30, max_msg_size=0, ssl=ssl_ctx + ) as ws: + self.ws = ws + self.connected = True + LOG.info("Harness connected: %s", self.url) + async for message in ws: + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in ( + aiohttp.WSMsgType.CLOSED, + aiohttp.WSMsgType.ERROR, + ): + break + continue + payload = json.loads(message.data) + message_type = payload.get("type") + if message_type == "harness.ready": + await self.manager.emit_recovery_sync() + elif message_type == "control.intent": + task = asyncio.create_task( + self.manager.handle_control(payload) + ) + self._control_tasks.add(task) + task.add_done_callback(self._control_tasks.discard) + except asyncio.CancelledError: + raise + except Exception as exc: + if not self._stop.is_set(): + LOG.warning("Harness connection failed: %s", exc) + finally: + self.connected = False + self.ws = None + if not self._stop.is_set(): + await asyncio.sleep(self.reconnect_s) + + +class PhaseBEsp32Runtime(PhaseBRokidRuntime): + """ESP32 版 runtime：I/O 从 'PC 起 server 被动收' 换成 'PC 主动拉 ESP32'。 + + __init__ / close / health / _initial_session_loop / _stats_loop 全部继承。 + 仅覆盖 start()：把 Rokid 的 web server 换成 esp32_audio_reader + esp32_image_loop。 + """ + + def __init__(self, config, esp32_host, esp32_port, image_tcp_port, + image_interval_s, image_timeout_s, probe=None, + funnel=None, recorder=None, force_measure=False, + gateway_host="127.0.0.1", gateway_port=8040, + reject_wav_dir="assets/reject_wav", + live_record_dir=None, rerun_from=None, + funnel_rerun_from=None, web_ui_port=None, + record_no_media: bool = False, + rerun_tail_wait_s: float = 30.0, + no_reject: bool = False, + rotate: int = 0, **kwargs): + super().__init__(config, **kwargs) + # 用支持自签名 wss 的 harness client 替换父类建好的普通 HarnessClient， + # 并同步更新 manager.harness 引用（发遥测/镜像走同一个）。 + self.harness = TlsHarnessClient( + config.harness_url, + config.harness_client_id, + self.manager, + config.reconnect_s, + ) + self.manager.harness = self.harness + self._esp32_host = esp32_host + self._esp32_port = esp32_port + self._image_tcp_port = image_tcp_port + self._image_interval_s = image_interval_s + self._image_timeout_s = image_timeout_s + self._tcp_img = TcpImageClient(esp32_host, image_tcp_port, + rotate=int(rotate) % 360) + self._stop_evt = asyncio.Event() + self._probe = probe + self._funnel = funnel + self._recorder = recorder + self._force_measure = force_measure + self._reject_wav_dir = reject_wav_dir + self._gateway_host = gateway_host + self._gateway_port = gateway_port + # 念提示走 gateway wss /ws/chat，自签名证书 → 复用同款 insecure ssl + self._chat_ssl = _insecure_ssl_for_wss(f"wss://{gateway_host}:{gateway_port}/ws/chat") + + self._rerun_from = rerun_from + self._funnel_rerun_from = funnel_rerun_from + self._rerun_tail_wait_s = rerun_tail_wait_s + self._no_reject = no_reject + self._rotate = int(rotate) % 360 + self._replay_t0 = None # rerun 统一回放零点（就绪时设） + self._rec_client = None + # 回放两条流各自的完成标志：图轮数常少于音频秒数，谁先完都不能掐死对方 + self._audio_done = asyncio.Event() + self._image_done = asyncio.Event() + # bridge_ui 第一视角（8080）。live/rerun/funnel-rerun 都可推 img_b64。 + self._web_ui = None + if web_ui_port and WebUIServer is not None: + try: + self._web_ui = WebUIServer( + port=web_ui_port, + sessions_root=Path(live_record_dir).parent if live_record_dir + else Path("live_sessions"), + mode_info={"mode": "rerun" if (rerun_from or funnel_rerun_from) + else "live"}, + ) + except Exception as e: + LOG.warning("[UI] WebUIServer 创建失败: %s", e) + self._web_ui = None + # -o record（音+图，只存真发送的 best）：和漏斗 record 并存。 + self.live_rec = (LiveRecorder(live_record_dir, no_media=record_no_media) + if live_record_dir else None) + if self.live_rec is not None: + try: + self.live_rec.attach_to_player(self.speaker) # 录 AI 音 + except Exception as e: + LOG.warning("[LIVE] attach_to_player 失败: %s", e) + + # 句子边界追踪器：包装 manager.handle_result，每个模型 chunk 更新句子/语音进度， + # 供 image_loop 的"标点保护"策略读取（reject 时保护当前段到下一个标点再复查）。 + self.sent_tracker = SentenceTracker() + self._turn_printer = TurnPrinter() + self._turn_start_ms = None # 当前 turn 的起点（session 内相对 ms） + _orig_handle_result = self.manager.handle_result + + async def _wrapped_handle_result(session, result): + try: + is_listen = bool(result.get("is_listen")) + text = str(result.get("text") or "") + end_of_turn = bool(result.get("end_of_turn")) + ab64 = str(result.get("audio_data") or "") + audio_ms = 0.0 + if ab64 and not is_listen: + n = len(base64.b64decode(ab64)) // 4 # float32 + audio_ms = n * 1000.0 / 24000.0 + self.sent_tracker.update(text, audio_ms, is_listen) + + # ── 评分用：把 chunk 碎片聚合成整段 turn，落 transcript/subtitles ── + if self.live_rec is not None: + # 先记原始逐 chunk（不依赖 end_of_turn，保证一定有东西可评分） + self.live_rec.log_model_chunk(text, is_listen, end_of_turn, audio_ms) + prev_idx = self._turn_printer.turn_idx + turn_idx_cur, full_text, turn_listen = self._turn_printer.feed( + is_listen, end_of_turn, text) + if text and self._turn_printer.turn_idx > prev_idx: + self._turn_start_ms = self.live_rec.session_ms() + # 只要拿到完整段就落盘（end_of_turn 或 listen/speak 切换冲出的） + if full_text: + end_ms = self.live_rec.session_ms() + 800 # 多挂 0.8s + start_ms = (self._turn_start_ms + if self._turn_start_ms is not None + else max(end_ms - 3000, 0)) + self.live_rec.log_turn_text(turn_idx_cur, full_text, turn_listen) + self.live_rec.log_subtitle( + start_ms=start_ms, end_ms=end_ms, text=full_text, + is_listen=turn_listen, turn_idx=turn_idx_cur) + self._turn_start_ms = None + + # 推模型文字到 bridge_ui 第一视角（type=result） + if self._web_ui is not None and getattr(self._web_ui, "live_clients", None): + asyncio.create_task(self._web_ui.emit({ + "type": "result", + "is_listen": is_listen, + "end_of_turn": end_of_turn, + "text": text, + })) + except Exception: + pass + return await _orig_handle_result(session, result) + + self.manager.handle_result = _wrapped_handle_result + + async def _gate_open_when_ready(self, ready_evt: asyncio.Event) -> None: + """等 gateway 就绪后放行回放任务。""" + try: + await self._wait_gateway_ready() + finally: + # 录制的 t0 必须和回放起点一致，否则 frames.jsonl/subtitles 的时间轴 + # 会把排队那几十秒也算进去，两个 arm 无法对齐。 + if self.live_rec is not None: + self.live_rec.start() + LOG.info("[LIVE] rerun 录制已开（就绪后启动，结束自动出 mp4）") + # 统一回放零点：音频按 40ms 绝对时钟推、图按 frames.jsonl 的 t 等待， + # 两者必须用同一个 t0，否则各自以"自己被调度到的那一刻"为零点， + # 起跑差多少全看事件循环，音图就对不齐。 + self._replay_t0 = time.monotonic() + _rc = getattr(self, "_rec_client", None) + if _rc is not None: + _rc.replay_t0 = self._replay_t0 + ready_evt.set() + + async def _wait_gateway_ready(self, timeout_s: float = 180.0) -> bool: + """等 duplex session 真正 prepared 之后再开始回放。 + + 实测：连着跑两次 rerun 时，后一次会在 gateway 排队（[GW] queue position=1） + 长达 17 秒才 prepared。而回放任务从第 0 秒就推音频和图 —— 这 17 秒的输入 + 全部推给一个还不存在的 session，直接丢掉，提问就在里面，模型自然没反应。 + 更要命的是两个 arm 被吞掉的长度不一样，配对设计直接失效，而且事后从结果上 + 看不出来（长得就像"模型没回答"）。所以回放前必须等就绪。 + """ + t0 = time.monotonic() + warned = False + while time.monotonic() - t0 < timeout_s: + if self._stop_evt.is_set(): + return False + try: + status = str(self.manager.health().get("gateway_status") or "") + except Exception: + status = "" + if status == "running": + waited = time.monotonic() - t0 + if waited > 1.0: + LOG.info("[RERUN] gateway 就绪（等了 %.1fs），开始回放", waited) + return True + if not warned and time.monotonic() - t0 > 3.0: + warned = True + LOG.info("[RERUN] 等 gateway session 就绪…（status=%s）", status or "?") + await asyncio.sleep(0.25) + LOG.warning("[RERUN] 等 gateway 就绪超时 %.0fs，仍开始回放（本次结果可能不可用）", + timeout_s) + return False + + async def start(self) -> None: + await self.speaker.start() + _install_asr_tap(self.harness, self.live_rec) + if self._web_ui is not None: + try: + await self._web_ui.start() + LOG.info("[UI] bridge_ui 第一视角已启动: http://localhost:%d", + self._web_ui.port) + except Exception as e: + LOG.warning("[UI] bridge_ui 启动失败: %s", e) + self._web_ui = None + # 方向分类器预热（原设计，-o 迁移时漏了）：首次 process_orientation 含模型加载+ + # oneDNN 编译（~2s，甚至 5s+）。不预热的话它会推迟到第一次 accept 才现场加载， + # 卡住那一轮，且在此之前链路出不了 send（模型长时间拿不到图）。 + if self._funnel is not None: + try: + ms = await asyncio.to_thread(self._funnel.warmup_orient) + LOG.info("[预热] 方向分类器就绪 (首次 %.0fms，运行时应降到 ~10ms)", ms) + except Exception as e: + LOG.warning("[预热] 方向分类器预热跳过: %s", e) + # ── rerun 模式：用录制的 session 回放（音频+best图）重跑 -o，不接实时设备 ── + if self._rerun_from: + LOG.info("[RERUN] 模式启动，回放 session: %s", self._rerun_from) + _ready = asyncio.Event() + asyncio.create_task(self._gate_open_when_ready(_ready)) + self._tasks = [ + asyncio.create_task(self.harness.run()), + asyncio.create_task(self._initial_session_loop()), + asyncio.create_task(self._stats_loop()), + asyncio.create_task(rerun_audio_reader( + self._rerun_from, + self.manager, self.harness, + self.audio_queue, self.audio_mirror, self.stats, + self.config.input_gain, self._stop_evt, + live_rec=self.live_rec, ready_evt=_ready, + replay_t0=lambda: self._replay_t0, + done_evt=self._audio_done, + )), + asyncio.create_task(rerun_image_loop( + self._rerun_from, self.latest_frame, self.harness, + self.stats, self._image_interval_s, self._stop_evt, + web_ui=self._web_ui, + live_rec=self.live_rec, ready_evt=_ready, + replay_t0=lambda: self._replay_t0, + done_evt=self._image_done, + peer_done=self._audio_done, + speaker=self.speaker, sent_tracker=self.sent_tracker, + tail_wait_s=self._rerun_tail_wait_s, + )), + ] + return + # ── funnel rerun：录制的整簇多帧重跑漏斗；不加 --funnel 则为无漏斗对照组 ── + if self._funnel_rerun_from: + no_funnel = (self._funnel is None) + _ready = asyncio.Event() + asyncio.create_task(self._gate_open_when_ready(_ready)) + LOG.info("[RERUN] %s 模式启动: %s", + "无漏斗对照组" if no_funnel else "funnel 重跑漏斗+播报", + self._funnel_rerun_from) + n_frames = getattr(self._funnel, "n_frames", 3) if self._funnel else 3 + rec_client = RecordedImageClient(self._funnel_rerun_from, n_frames, + no_funnel=no_funnel) + self._rec_client = rec_client # 就绪时注入统一零点 + self._tasks = [ + asyncio.create_task(self.harness.run()), + asyncio.create_task(self._initial_session_loop()), + asyncio.create_task(self._stats_loop()), + asyncio.create_task(rerun_audio_reader( + self._funnel_rerun_from, + self.manager, self.harness, + self.audio_queue, self.audio_mirror, self.stats, + self.config.input_gain, self._stop_evt, + live_rec=self.live_rec, ready_evt=_ready, + replay_t0=lambda: self._replay_t0, + done_evt=self._audio_done, + )), + # 复用完整 esp32_image_loop（run_once 重判 + reject 停播恢复 + 标点保护）， + # 只把图源从 TCP 换成 RecordedImageClient（读录制整簇多帧）。 + asyncio.create_task(esp32_image_loop( + rec_client, self.latest_frame, self.harness, self.stats, + self._image_interval_s, self._image_timeout_s, self._stop_evt, + probe=self._probe, funnel=self._funnel, recorder=None, + speaker=self.speaker, + gateway_host=self._gateway_host, gateway_port=self._gateway_port, + ssl_ctx=self._chat_ssl, force_measure=self._force_measure, + manager=self.manager, + reject_wav_dir=self._reject_wav_dir, + sent_tracker=self.sent_tracker, + live_rec=self.live_rec, # 开 --record-live 则录 rerun 输出→自动出 mp4 + web_ui=self._web_ui, ready_evt=_ready, + tail_wait_s=self._rerun_tail_wait_s, + no_reject=self._no_reject, + peer_done=self._audio_done, done_evt=self._image_done, + )), + ] + return + # 设 ESP32 分辨率为 HD(1280×720)。固件默认 SVGA(800×600)，但漏斗阈值是按 HD + # 标定的（cam_pipeline_v2 注释：分辨率固定 HD 后针对 HD 标定）。-o 迁移时漏了这步， + # 导致一直跑 SVGA、画质与阈值不匹配。这里启动时补上。 + if self._funnel is not None: + ok = await asyncio.to_thread(self._funnel.set_resolution_hd) + LOG.info("[ESP32] 设分辨率 1280×720(UXGA档): %s", + "成功" if ok else "失败(检查ESP32 /control)") + await asyncio.sleep(0.3) # 切分辨率后固件重配，稍等 + if self.live_rec is not None: + self.live_rec.start() + LOG.info("[LIVE] -o record started") + self._tasks = [ + # ── 继承自 rokid 的三个骨架 task ── + asyncio.create_task(self.harness.run()), + asyncio.create_task(self._initial_session_loop()), + asyncio.create_task(self._stats_loop()), + # ── ESP32 特有：主动拉取音视频 ── + asyncio.create_task(esp32_audio_reader( + self._esp32_host, self._esp32_port, + self.manager, self.harness, + self.audio_queue, self.audio_mirror, self.stats, + self.config.input_gain, self._stop_evt, + probe=self._probe, + live_rec=self.live_rec, + )), + asyncio.create_task(esp32_image_loop( + self._tcp_img, self.latest_frame, self.harness, self.stats, + self._image_interval_s, self._image_timeout_s, self._stop_evt, + probe=self._probe, funnel=self._funnel, recorder=self._recorder, + speaker=self.speaker, + gateway_host=self._gateway_host, gateway_port=self._gateway_port, + ssl_ctx=self._chat_ssl, force_measure=self._force_measure, + manager=self.manager, + reject_wav_dir=self._reject_wav_dir, + sent_tracker=self.sent_tracker, + live_rec=self.live_rec, + web_ui=self._web_ui, + no_reject=self._no_reject, + )), + ] + + async def close(self) -> None: + self._stop_evt.set() + if self._web_ui is not None: + try: + await self._web_ui.stop() + except Exception: + pass + if self.live_rec is not None: + try: + # 冲掉还没等到 end_of_turn 的那段（bare 组常见：模型一路流式说， + # 没有 stop/resume 打断，end_of_turn 迟迟不来 → 整段丢失） + tp = getattr(self, "_turn_printer", None) + if tp is not None and getattr(tp, "_in_turn", False): + pending = "".join(getattr(tp, "_turn_text", [])) + if pending: + end_ms = self.live_rec.session_ms() + start_ms = (self._turn_start_ms + if self._turn_start_ms is not None + else max(end_ms - 3000, 0)) + self.live_rec.log_turn_text(tp.turn_idx, pending, + bool(tp._turn_is_listen)) + self.live_rec.log_subtitle( + start_ms=start_ms, end_ms=end_ms, text=pending, + is_listen=bool(tp._turn_is_listen), turn_idx=tp.turn_idx) + LOG.info("[LIVE] 冲出未收尾的 turn #%d (%d字)", + tp.turn_idx, len(pending)) + except Exception as e: + LOG.warning("[LIVE] flush pending turn err: %s", e) + try: + self.live_rec.stop() + LOG.info("[LIVE] -o record stopped") + except Exception as e: + LOG.warning("[LIVE] stop err: %s", e) + await self._tcp_img._close() + if self._probe is not None: + self._probe.close() + if self._recorder is not None: + self._recorder.close() + await super().close() + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="ESP32 Phase B Harness runtime") + # ── ESP32 设备 ── + p.add_argument("--esp32-host", required=True, help="ESP32 IP address") + p.add_argument("--rotate", type=int, default=0, choices=(0, 90, 180, 270), + help="摄像头顺时针旋转角度，按眼镜侧装方向填" + "（取自 devices.json 的 rotate）。在进漏斗前转正，" + "否则方向检测会把相机侧装误判成用户拿反了。") + p.add_argument("--esp32-port", type=int, default=80, help="ESP32 HTTP/WS port") + p.add_argument("--image-tcp-port", type=int, default=5000, help="ESP32 TCP image port") + p.add_argument("--image-interval-s", type=float, default=1.0, help="取图间隔(秒)") + p.add_argument("--image-timeout-s", type=float, default=1.0, help="单次取图超时(秒)") + # ── gateway / harness（与 rokid 一致）── + p.add_argument("--gateway", default="localhost:8040") + p.add_argument("--gateway-proto", default="auto", + choices=("auto", "duplex", "realtime"), + help="gateway 协议。duplex=/ws/duplex（V1）；" + "realtime=/v1/realtime（V2，V2 gateway 只有这个端点，" + "连 /ws/duplex 会被 403 拒绝）；" + "auto=按端口猜（8006→realtime，其余→duplex）") + p.add_argument("--gateway-tls", action="store_true", default=True) + p.add_argument("--no-tls", dest="gateway_tls", action="store_false") + p.add_argument("--harness-url", default="ws://127.0.0.1:8021/ws/control") + p.add_argument("--client-id", default="esp32-phase-b") + p.add_argument("--skills-config", default=default_skills_config()) + p.add_argument("--cleanup-mode", choices=("light", "full"), default="light") + p.add_argument("--chunk-ms", type=int, default=1_000) + p.add_argument("--force-listen-count", type=int, default=3) + p.add_argument("--audio-queue-packets", type=int, default=96) + p.add_argument("--input-gain", type=float, default=12.0) + p.add_argument("--prompt", default=None, help="system prompt（可选）") + p.add_argument("--no-play", action="store_true") + p.add_argument("--log-level", default="INFO") + # ── 时序探针（纯旁路）── + p.add_argument("--timing-probe", action="store_true", help="开启时序探针，写 CSV") + p.add_argument("--timing-csv", default=None, help="探针 CSV 路径（默认自动带时间戳）") + # ── CV 漏斗 ── + p.add_argument("--funnel", action="store_true", help="开启 CV 漏斗（抓N帧判定，合格才送模型）") + p.add_argument("--scene", default="medicine", choices=("medicine", "stationery"), + help="漏斗场景参数") + p.add_argument("--funnel-frames", type=int, default=3, help="每轮抓帧数（3选2）") + p.add_argument("--no-focus", dest="funnel_focus", action="store_false", default=True, + help="关闭漏斗内的自动对焦触发") + # ── session 录制（对齐 -v 格式）── + p.add_argument("--record", action="store_true", help="录 session（整簇帧+判定到 sessions/）") + p.add_argument("--sessions-root", default="sessions", help="session 根目录") + p.add_argument("--no-reject", action="store_true", + help="消融：漏斗只选 best、永远放行，不拒绝不播报" + "（用于把工作②选图的收益与工作③拒绝分开）") + p.add_argument("--rerun-tail-wait-s", type=float, default=30.0, + help="rerun 图放完后，最多再等模型说完的秒数（默认30）") + p.add_argument("--record-no-media", action="store_true", + help="批量评分用：只写 wav+jsonl+transcript，跳过 jpg 落盘和 mp4 拼接") + p.add_argument("--record-live", action="store_true", + help="-o record：录音+图(只存真发送的best)到 live_sessions/，供 -o rerun") + p.add_argument("--rerun-from", default=None, + help="-o rerun：从 live_sessions/ 回放(音+best图)重跑 -o 模型") + p.add_argument("--funnel-rerun-from", default=None, + help="funnel rerun：从 live_sessions/ 回放(音+整簇多帧)重跑漏斗+播报+-o") + p.add_argument("--web-ui-port", type=int, default=None, + help="开 bridge_ui 第一视角(如 8080)，live/rerun/funnel-rerun 都可看") + # ── 强制措施（防幻觉）：reject 时用模型音色念提示 + 停/恢复走 8021 ── + p.add_argument("--force-measure", action="store_true", + help="开启强制措施：reject→停duplex→播预生成wav→恢复duplex") + p.add_argument("--reject-wav-dir", default="assets/reject_wav", + help="预生成的 reject 提示 wav 目录（gen_reject_wavs.py 生成）") + return p.parse_args() + + +def main() -> None: + args = parse_args() + logging.basicConfig( + level=getattr(logging, args.log_level.upper(), logging.INFO), + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + force=True, + ) + config = RokidRuntimeConfig( + gateway=args.gateway, + gateway_tls=args.gateway_tls, + harness_url=args.harness_url, + harness_client_id=args.client_id, + skills_config=args.skills_config, + cleanup_mode=args.cleanup_mode, + chunk_ms=args.chunk_ms, + force_listen_count=args.force_listen_count, + audio_queue_packets=args.audio_queue_packets, + input_gain=args.input_gain, + play_audio=not args.no_play, + ) + probe = TimingProbe( + enabled=args.timing_probe, + path=args.timing_csv, + chunk_ms=args.chunk_ms, + ) + funnel = None + if args.funnel: + # rerun 时强制关对焦：rerun 没有真实相机，trigger_af 对录制的图无意义， + # 且对焦重抓会让 run_once 多抓一簇 → RecordedImageClient 多消耗一个 round + # → 图提前用光（有漏斗比无漏斗先用光的根因）。 + _is_rerun = bool(args.rerun_from or args.funnel_rerun_from) + _focus = args.funnel_focus and not _is_rerun + funnel = FunnelGate( + args.esp32_host, + scene=args.scene, + n_frames=args.funnel_frames, + enable_focus=_focus, + ) + LOG.info("CV 漏斗已开启: scene=%s frames=%d focus=%s%s", + args.scene, args.funnel_frames, _focus, + "（rerun 强制关对焦）" if (_is_rerun and args.funnel_focus) else "") + recorder = None # 旧 SessionRecorder 已废弃，统一走 LiveRecorder + # 统一 record（音+整簇多帧+best标记）：--record 或 --record-live 都触发。 + live_record_dir = None + if args.record or args.record_live or args.record_no_media: + import time as _t + live_record_dir = os.path.join("live_sessions", _t.strftime("%Y%m%d_%H%M%S")) + # 日志同时落进 session 目录：评分时要按时间轴对齐 reject/播报/模型输出， + # 控制台日志是滚动的、和 session 分离，跑多组 rerun 极易对错。 + try: + os.makedirs(live_record_dir, exist_ok=True) + _fh = logging.FileHandler( + os.path.join(live_record_dir, "run.log"), encoding="utf-8") + _fh.setFormatter(logging.Formatter( + "%(asctime)s %(levelname)s %(name)s: %(message)s")) + logging.getLogger().addHandler(_fh) + LOG.info("[LIVE] 日志同时写入 %s", os.path.join(live_record_dir, "run.log")) + except Exception as e: + LOG.warning("[LIVE] run.log 创建失败: %s", e) + os.makedirs(live_record_dir, exist_ok=True) + LOG.info("统一 record 已开启: %s（音+整簇多帧+best标记，供三种 rerun）", live_record_dir) + # 解析 gateway "host:port"（念提示的 chat TTS 走同一个 gateway 的 wss） + _gw = args.gateway.split("://")[-1] + _gw_host, _, _gw_port = _gw.partition(":") + _gw_host = _gw_host or "127.0.0.1" + _gw_port = int(_gw_port or "8040") + # 选 gateway 协议。V2 gateway 只有 /v1/realtime（连 /ws/duplex 会 403）， + # V1 只有 /ws/duplex。auto 按端口猜：8006 是 V2 的默认端口。 + _proto = args.gateway_proto + if _proto == "auto": + _proto = "realtime" if _gw_port == 8006 else "duplex" + _factory = None + if _proto == "realtime": + from .realtime_session import RealtimeDuplexSession as _factory + LOG.info("[GW] 协议: /v1/realtime (V2)") + else: + LOG.info("[GW] 协议: /ws/duplex (V1)") + + runtime = PhaseBEsp32Runtime( + config, + **({"session_factory": _factory} if _factory else {}), + esp32_host=args.esp32_host, + esp32_port=args.esp32_port, + image_tcp_port=args.image_tcp_port, + image_interval_s=args.image_interval_s, + image_timeout_s=args.image_timeout_s, + probe=probe, + funnel=funnel, + recorder=recorder, + force_measure=args.force_measure, + reject_wav_dir=args.reject_wav_dir, + gateway_host=_gw_host, + gateway_port=_gw_port, + live_record_dir=live_record_dir, + rerun_from=args.rerun_from, + funnel_rerun_from=args.funnel_rerun_from, + web_ui_port=args.web_ui_port, + record_no_media=args.record_no_media, + rerun_tail_wait_s=args.rerun_tail_wait_s, + no_reject=args.no_reject, + rotate=args.rotate, + ) + if args.force_measure: + LOG.info("强制措施已开启: reject→funnel.stop+念提示, good→funnel.resume " + "(节流5s, chat TTS via wss://%s:%d)", _gw_host, _gw_port) + + LOG.info("ESP32 Phase B input: ws://%s:%d/ws_audio_v2 + TCP:%d", + args.esp32_host, args.esp32_port, args.image_tcp_port) + LOG.info("Harness: %s", config.harness_url) + LOG.info("Gateway: %s://%s", "wss" if config.gateway_tls else "ws", config.gateway) + if args.rotate: + LOG.info("[ROTATE] 摄像头顺时针 %d° 转正（进漏斗前）", args.rotate) + + async def _run() -> None: + await runtime.start() + stop = asyncio.Event() + + def _sig(*_a): + stop.set() + try: + loop = asyncio.get_running_loop() + for s in (signal.SIGINT, getattr(signal, "SIGBREAK", signal.SIGINT)): + try: + loop.add_signal_handler(s, stop.set) + except (NotImplementedError, ValueError): + signal.signal(s, _sig) + except Exception: + signal.signal(signal.SIGINT, _sig) + # 等"信号(Ctrl+C)"或"runtime 内部 stop_evt(rerun 图放完自动结束)"任一触发 + _internal = getattr(runtime, "_stop_evt", None) + waiters = [asyncio.create_task(stop.wait())] + if _internal is not None: + waiters.append(asyncio.create_task(_internal.wait())) + await asyncio.wait(waiters, return_when=asyncio.FIRST_COMPLETED) + for w in waiters: + w.cancel() + await runtime.close() + + try: + asyncio.run(_run()) + except KeyboardInterrupt: + LOG.info("Interrupted; shutting down") + + +if __name__ == "__main__": + main() diff --git a/extensions/assistive_harness/phase_b/funnel_gate.py b/extensions/assistive_harness/phase_b/funnel_gate.py new file mode 100644 index 0000000..fe683a7 --- /dev/null +++ b/extensions/assistive_harness/phase_b/funnel_gate.py @@ -0,0 +1,218 @@ +"""漏斗闸门（接入 -o 链路）。 + +把 cam_pipeline_v2 的 run_funnel / process_orientation / CamControl 封装成 +"一轮漏斗判定"，供 esp32_runtime 在图像循环里调用： + + gate = FunnelGate(esp32_host, scene="medicine", n_frames=3) + result = await gate.run_once(capture_fn) # capture_fn: async ()->Optional[bytes] + +返回 FunnelDecision： + send : bool 是否放行给模型 + best : bytes|None 放行的最佳帧（已做倒置纠正） + reason : str 决策原因（accepted/severe_shake/unstable/need_focus/orient...） + hint : str|None 拒绝时给用户的提示文本（先输出 text，TTS 通道待定） + timings: dict grab_ms / judge_ms / orient_ms / af_ms / n_frames + +设计依据（防漂移文档 + cam_pipeline_v2）： + - 判定唯一 = run_funnel 的 frame_score 三出口 + argmin 归因 + - need_focus → 触发单次 AF(0x3022) → 等 AF_SETTLE_MS → 重抓一簇 → 重判 + - 倒置 = process_orientation，flipped 时纠正或提示 + - 拒绝 reason → HINTS 文本 +本模块只做"输入端把关+引导"，不碰模型输出侧（那是 harness 的事）。 +""" +from __future__ import annotations + +import asyncio +import time +from typing import Awaitable, Callable, Optional + +from . import cam_pipeline_v2 as cp + +AF_SETTLE_MS = 120 # OV5640 单次对焦 settle（与 v5 一致） + +# reject_reason -> 用户提示（先输出 text；TTS 通道确定后接同一份文本） +HINTS = { + "no_frames": "没有拿到画面，请稍等", + "severe_shake": "画面晃得厉害，请先保持不动", + "unstable": "画面还在晃动，请保持不动一会儿", + "too_dark": "光线太暗，请到亮一点的地方", + "aimed_wrong": "好像没对准，请对准目标", + "need_focus": "对焦中，请拿稳一下", + "orient": "画面好像反了，请倒过来", +} + + +class FunnelDecision: + __slots__ = ("send", "best", "reason", "hint", "timings", + "frames", "best_index", "af_triggered", "af_ok") + + def __init__(self, send, best, reason, hint, timings, + frames=None, best_index=-1, af_triggered=False, af_ok=None): + self.send = send + self.best = best + self.reason = reason + self.hint = hint + self.timings = timings + self.frames = frames or [] # 整簇帧（存 session 用） + self.best_index = best_index # best 在簇里的下标 + self.af_triggered = af_triggered # 本轮有没有触发 AF + self.af_ok = af_ok # AF /reg 请求是否返回成功（None=没触发） + + +class FunnelGate: + def __init__(self, esp32_host: str, scene: str = "medicine", + n_frames: int = 3, frame_gap_s: float = 0.0, + enable_focus: bool = True): + self.scene = scene + self.n_frames = n_frames + self.frame_gap_s = frame_gap_s + self.enable_focus = enable_focus + self._cfg = (cp.make_scene_config(scene) + if hasattr(cp, "make_scene_config") else cp.FunnelConfig()) + # 相机 HTTP 控制走 /control、/reg 到 ESP32（同 esp32_host）。 + # camctl 总是构造：分辨率(set_resolution)独立于对焦，即使不对焦也要设 HD。 + # 对焦(trigger_af)另受 enable_focus 控制。 + self._camctl = cp.CamControl(esp32_host) + self._last_af_mono = 0.0 # 上次真正触发对焦的时刻（冷却用） + self._af_cooldown_s = 2.0 # 对焦冷却：2s 内最多触发 1 次（避免每秒对焦） + + def warmup_orient(self) -> float: + """方向分类器预热（同步，调用方放线程里）。 + + 原设计（-v pc_vlm_v5_funnel）：首次 process_orientation 含模型加载 + oneDNN + 编译开销（实测 ~2s，本次日志里甚至 5.76s），启动时先跑一张假图，把这笔一次性 + 开销挪到启动阶段，运行时 orient_ms 就是纯推理（md 记录：2143ms → 9ms）。 + -o 迁移时漏了这步 → orient 直到第一次 accept 才现场加载，卡住那一轮， + 且在此之前链路出不了 send。返回首次耗时(ms)。 + """ + import numpy as _np + import cv2 as _cv2 + _warm = _np.full((480, 640, 3), 255, dtype=_np.uint8) + ok, buf = _cv2.imencode(".jpg", _warm) + if not ok: + return -1.0 + t0 = time.monotonic() + cp.process_orientation(buf.tobytes()) + return (time.monotonic() - t0) * 1000.0 + + def set_resolution_hd(self, retries: int = 5, gap_s: float = 0.6) -> bool: + """设 1280×720（UXGA档，这块 OV5640 枚举非标准，HD(11)无效、UXGA(13)才 720p）。 + live 启动时 ESP32 可能刚就绪，/control 偶发失败 → 重试几次，避免要手动进网页设。 + 失败时打详细原因（status/异常），不再静默。""" + import logging as _lg + _log = _lg.getLogger("funnel_gate") + for i in range(max(1, retries)): + try: + ok = self._camctl.set_resolution("UXGA") + if ok: + if i > 0: + _log.info("[funnel_gate] set_resolution(UXGA) 第%d次重试成功", i + 1) + return True + _log.warning("[funnel_gate] set_resolution(UXGA) 返回非200 (第%d/%d次)", + i + 1, retries) + except Exception as e: + _log.warning("[funnel_gate] set_resolution(UXGA) 异常 (第%d/%d次): %r", + i + 1, retries, e) + time.sleep(gap_s) + return False + + async def _grab_burst( + self, capture_fn: Callable[[], Awaitable[Optional[bytes]]], n: int + ) -> list[bytes]: + frames: list[bytes] = [] + for i in range(n): + jpeg = await capture_fn() + if jpeg: + frames.append(jpeg) + if self.frame_gap_s > 0 and i < n - 1: + await asyncio.sleep(self.frame_gap_s) + return frames + + async def run_once( + self, capture_fn: Callable[[], Awaitable[Optional[bytes]]] + ) -> FunnelDecision: + """跑一轮漏斗：抓 N 帧 -> run_funnel -> (need_focus 则对焦重抓) -> 倒置 -> 决策。""" + timings: dict = {} + + # 1) 抓一簇 + t0 = time.monotonic() + frames = await self._grab_burst(capture_fn, self.n_frames) + timings["grab_ms"] = round((time.monotonic() - t0) * 1000, 1) + timings["n_frames"] = len(frames) + if not frames: + return FunnelDecision(False, None, "no_frames", HINTS["no_frames"], timings) + + # 2) run_funnel 判定（run_funnel 是同步的，放线程池避免阻塞事件循环） + tj = time.monotonic() + res = await asyncio.to_thread(cp.run_funnel, frames, self._cfg) + timings["judge_ms"] = round((time.monotonic() - tj) * 1000, 1) + timings["af_ms"] = 0.0 + af_triggered = False + af_ok = None + + # 3) need_focus -> 触发单次 AF -> 等 settle -> 重抓 -> 重判 + # 对焦冷却：need_focus 但距上次对焦 < _af_cooldown_s(2s) 时不重复触发， + # 给对焦时间生效（每秒对焦太快，马达反复动反而对不好）。冷却内仍 need_focus + # 就用当前帧判定（该 reject 就 reject，不重抓）。 + if res.need_focus and self.enable_focus and self._camctl is not None: + _now = time.monotonic() + if _now - self._last_af_mono >= self._af_cooldown_s: + self._last_af_mono = _now + taf = _now + af_triggered = True + af_ok = await asyncio.to_thread(self._camctl.trigger_af) # True/False + await asyncio.sleep(AF_SETTLE_MS / 1000.0) + frames2 = await self._grab_burst(capture_fn, self.n_frames) + if frames2: + res = await asyncio.to_thread(cp.run_funnel, frames2, self._cfg) + frames = frames2 + timings["af_ms"] = round((time.monotonic() - taf) * 1000, 1) + timings["n_frames"] = len(frames) + else: + # 冷却期内：不重复对焦，用当前判定结果 + timings["af_cooldown"] = round(self._af_cooldown_s - (_now - self._last_af_mono), 2) + timings["af_ok"] = af_ok + + def _mk(send, best, reason, hint): + bi = frames.index(best) if (best in frames) else -1 + return FunnelDecision(send, best, reason, hint, timings, + frames=frames, best_index=bi, + af_triggered=af_triggered, af_ok=af_ok) + + # 4) 拒绝出口 + if not res.accepted: + reason = res.reject_reason or "reject" + # cam_pipeline 的拒绝出口只设 best_index（注释：仅供参考, 不喂模型）， + # 不设 best_jpg —— 这是原设计意图：拒绝就不送图。 + # 但消融 arm（--no-reject：只选 best、永远放行）需要拿到这一帧， + # 所以这里按 best_index 把帧补出来。**send 仍然是 False**， + # 正常链路完全不受影响：只有显式开了 --no-reject 才会去用它。 + _bj = res.best_jpg + if _bj is None: + bi = getattr(res, "best_index", -1) + if isinstance(bi, int) and 0 <= bi < len(frames): + _bj = frames[bi] + return _mk(False, _bj, reason, + HINTS.get(reason, "看不清楚，请调整一下")) + + # 5) 接受帧的方向检测（OCR 方向分类器优先，见 cam_pipeline.process_orientation） + # 原则「宁拒绝不念错」：方向不正 -> 拒绝 + 提示用户转正，绝不自动纠正、绝不送倒图。 + # process_orientation 只诊断方向、返回的 jpg 未旋转，故不能拿它当"纠正后"送模型。 + best = res.best_jpg + to_ = time.monotonic() + try: + _jpg, geom = await asyncio.to_thread(cp.process_orientation, best) + except Exception: + geom = {"ok": False} + timings["orient_ms"] = round((time.monotonic() - to_) * 1000, 1) + + orient_state = geom.get("orient_state") if isinstance(geom, dict) else None + orient_hint = geom.get("orient_hint") if isinstance(geom, dict) else None + + # upright 才放行；flipped/sideways 拒绝并提示；uncertain 也放行（不硬拦，避免误拒） + if orient_state in ("flipped", "sideways"): + hint = orient_hint or HINTS["orient"] + return _mk(False, best, "orient_" + orient_state, hint) + + # upright / uncertain / 检测不可用 -> 放行原图（不做任何旋转） + return _mk(True, best, "send", None) diff --git a/extensions/assistive_harness/phase_b/realtime_session.py b/extensions/assistive_harness/phase_b/realtime_session.py new file mode 100644 index 0000000..c0e4435 --- /dev/null +++ b/extensions/assistive_harness/phase_b/realtime_session.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +realtime_session.py —— 把 duplex 会话接到 V2 gateway 的 /v1/realtime 协议。 + +为什么需要它 +------------ +V1 gateway 有 `/ws/duplex/{session_id}`；**V2 gateway 只有 `/v1/realtime`** +（实测：V2 gateway.py 里唯一的 @app.websocket 是 /v1/realtime， + 连 /ws/duplex/... 会被 FastAPI 直接 403 拒绝握手）。 +所以 rokid_runtime / esp32_runtime 里那套 GatewayDuplexSession 在 V2 下连不上， +demo_esp32_duplex_0703 同样连不上（它连的也是 /ws/duplex）。 + +做法 +---- +`GatewaySessionManager` 有现成的替换点 `session_factory`， +所以这里**继承 GatewayDuplexSession，只覆盖协议相关的方法**， +其余（音频攒块、状态管理、start/_run 骨架、stop 的收尾）全部复用。 +rokid_runtime.py 和 esp32_runtime.py 都不用改，只在构造 manager 时传入本类。 + +协议对照（V1 内部 → V2 对外） +------------------------------ + 连接 URL 带 session_id → 连上后 session.init，服务端回 session.created + 排队 queued / queue_done → session.queued / session.queue_done + 上行 audio_chunk → input.append + audio_base64 → input.audio + frame_base64_list → input.video_frames + force_listen → input.force_listen + 下行 {is_listen,text,audio_data}→ response.output.delta 的 kind: listen/text/audio + 结束 stop → session.close + +注意 +---- +· V2 的 session_id 由服务端在 session.created 里给，客户端不能自己指定。 + 但 manager/日志里到处用 self.session_id，所以保留本地生成的那个作为占位， + 拿到服务端的之后覆盖掉。 +· full-duplex 下没有 response.done（文档说它只用于 mode=chat）， + turn 边界靠 delta 里的 turn_id 变化判断。 +· kind=audio 的 PCM 是 24kHz mono float32 base64，和 V1 的 audio_data 同格式， + 所以 on_result 那一侧不用改。 +""" +from __future__ import annotations + +import asyncio +import base64 +import json +import time +from typing import Any + +import aiohttp +import numpy as np + +from .rokid_runtime import ( + LOG, + GatewayDuplexSession, + SAMPLE_RATE_IN, + float32_to_base64, + now_ms, +) + + +class RealtimeDuplexSession(GatewayDuplexSession): + """V2 /v1/realtime 协议版的 duplex 会话。构造签名与父类完全一致。""" + + # ------------------------------------------------------------ 连接 + async def _run(self) -> None: + scheme = "wss" if self.config.gateway_tls else "ws" + # mode=video：连续音频 + 可带视频帧，300s 上限。 + # mode=audio 是纯音频（600s），我们要送图所以用 video。 + url = f"{scheme}://{self.config.gateway}/v1/realtime?mode=video" + self.status = "connecting" + LOG.info("[GW] connecting generation=%d %s (realtime)", + self.spec.generation, url) + try: + async with aiohttp.ClientSession() as client: + async with client.ws_connect( + url, + heartbeat=30, + ssl=self._ssl_context(), + max_msg_size=0, + ) as ws: + self.ws = ws + await self._prepare(ws) + self.status = "running" + if self._ready is not None and not self._ready.done(): + self._ready.set_result(None) + await asyncio.gather( + self._send_loop(ws), + self._receive_loop(ws), + ) + except asyncio.CancelledError: + raise + except Exception as exc: # noqa: BLE001 + self.status = "failed" + self.last_error = str(exc) + LOG.warning("[GW] session %s failed: %s", self.session_id, exc) + if self._ready is not None and not self._ready.done(): + self._ready.set_exception(exc) + finally: + self._stopped.set() + if self.status not in ("failed",): + self.status = "closed" + + # ------------------------------------------------------------ 握手 + async def _prepare(self, ws: aiohttp.ClientWebSocketResponse) -> None: + """等 session.queue_done → 发 session.init → 等 session.created。 + + 文档明确要求：**必须等 queue_done 再发 session.init**。 + 没有排队时服务端会立刻下发 queue_done。 + """ + self.status = "queued" + while True: + message = await ws.receive() + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + raise RuntimeError("gateway closed while queued") + continue + payload = json.loads(message.data) + mtype = payload.get("type") + if mtype == "session.queue_done": + break + if mtype == "error": + raise RuntimeError( + (payload.get("error") or {}).get("message") + if isinstance(payload.get("error"), dict) + else payload.get("error") or "gateway queue error" + ) + if mtype in ("session.queued", "session.queue_update"): + LOG.info("[GW] queue position=%s eta=%s", + payload.get("position"), + payload.get("estimated_wait_s")) + + self.status = "preparing" + init_payload: dict[str, Any] = { + "system_prompt": self.spec.system_prompt, + "config": { + "force_listen_count": self.config.force_listen_count, + "chunk_ms": self.config.chunk_ms, + "generate_audio": True, + "max_new_speak_tokens_per_chunk": ( + self.config.max_new_speak_tokens_per_chunk + ), + "length_penalty": self.config.length_penalty, + }, + } + await self._send_json({"type": "session.init", "payload": init_payload}) + + while True: + message = await ws.receive() + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + raise RuntimeError("gateway closed while preparing") + continue + payload = json.loads(message.data) + mtype = payload.get("type") + if mtype == "session.created": + # session_id 由服务端给，覆盖本地占位的那个 + sid = payload.get("session_id") + if sid: + self.session_id = sid + LOG.info("[GW] prepared session=%s generation=%d skill=%s mode=%s", + self.session_id, self.spec.generation, + self.spec.skill_id, payload.get("mode")) + return + if mtype == "error": + raise RuntimeError( + (payload.get("error") or {}).get("message") + if isinstance(payload.get("error"), dict) + else payload.get("error") or "gateway prepare error" + ) + + # ------------------------------------------------------------ 上行 + async def _send_loop(self, ws: aiohttp.ClientWebSocketResponse) -> None: + """攒 1s 音频 + 带上最新帧，发 input.append。 + + 攒块、取帧、force_listen 的判断逻辑与父类完全一致，只换消息外壳。 + """ + carry = bytearray() + last_frame_sequence = -1 + last_frame_sent = 0.0 + sent = 0 + while not self._closing: + audio = await self._next_audio_chunk(carry) + if audio is None: + continue + inp: dict[str, Any] = { + "audio": float32_to_base64(audio), + "max_slice_nums": self.config.max_slice_nums, + } + if self.gate.speech_hold_active: + inp["force_listen"] = True + frame = self.latest_frame + frame_age_ms = now_ms() - frame.timestamp_ms + frame_due = time.monotonic() - last_frame_sent >= self.config.image_resend_s + if ( + frame.jpeg + and frame_age_ms <= self.config.image_max_age_s * 1000 + and (frame.sequence != last_frame_sequence or frame_due) + ): + inp["video_frames"] = [base64.b64encode(frame.jpeg).decode("ascii")] + last_frame_sequence = frame.sequence + last_frame_sent = time.monotonic() + sent += 1 + LOG.debug("[GW-SEND] #%d audio=%.2fs lvl=%.4f frame=%s hold=%s", + sent, len(audio) / SAMPLE_RATE_IN, + float(np.abs(audio).mean()), + "video_frames" in inp, self.gate.speech_hold_active) + await self._send_json({"type": "input.append", "input": inp}) + + # ------------------------------------------------------------ 下行 + async def _receive_loop(self, ws: aiohttp.ClientWebSocketResponse) -> None: + """把 response.output.delta 翻译回 {is_listen, text, audio_data}。 + + 父类的 on_result 拿到的是 V1 那套字段，这里保持不变， + 所以 handle_result / TurnPrinter / speaker 那一侧一行都不用改。 + """ + cur_turn = None + async for message in ws: + if self._closing: + break + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + break + continue + payload = json.loads(message.data) + mtype = payload.get("type") + + if mtype == "response.output.delta": + kind = payload.get("kind") + turn = payload.get("turn_id") + # full-duplex 没有 response.done，用 turn_id 变化当边界 + end_of_turn = (cur_turn is not None and turn is not None + and turn != cur_turn) + if turn is not None: + cur_turn = turn + result: dict[str, Any] = { + "type": "result", + "is_listen": kind == "listen", + "text": payload.get("text") or "", + "audio_data": payload.get("audio") or "", + "end_of_turn": end_of_turn, + "turn_id": turn, + } + await self.on_result(self, result) + continue + + if mtype == "response.done": + # 实测：full-duplex 下**也会**发 response.done（文档说只用于 chat， + # 与实际不符），而且它的 text 是这一段的**完整文本** —— + # 前面 delta 已经把这些字逐块传下去了，这里再传一次就是重复 + # （实测每段都被记两遍，只差 1~2ms）。 + # 所以这里只标记 turn 结束，**不再传 text**。 + await self.on_result(self, { + "type": "result", "is_listen": False, + "text": "", "audio_data": "", + "end_of_turn": True, "turn_id": cur_turn, + }) + continue + + if mtype == "session.closed": + LOG.info("[GW] session.closed reason=%s", payload.get("reason")) + break + + if mtype == "error": + err = payload.get("error") + msg = err.get("message") if isinstance(err, dict) else str(err) + LOG.warning("[GW] error: %s", msg) + self.last_error = msg or "gateway error" + continue + + if mtype == "debug": + # V2 自带的阶段追踪（llm.chunk / tts.chunk / t2w.chunk，带 ts）。 + # 平时不打，需要时 --log-level DEBUG 打开看服务端各阶段耗时。 + LOG.debug("[GW-DEBUG] %s", json.dumps(payload, ensure_ascii=False)[:200]) + continue + + # ------------------------------------------------------------ 结束 + async def stop(self, cleanup_mode: str) -> None: + self._closing = True + if self.ws is not None and not self.ws.closed: + try: + await self._send_json({"type": "session.close", + "reason": cleanup_mode or "user_stop"}) + except Exception: + pass + try: + await asyncio.wait_for(self._stopped.wait(), + timeout=self.config.close_timeout_s) + except asyncio.TimeoutError: + pass + if self.ws is not None and not self.ws.closed: + await self.ws.close() + if self._task and not self._task.done(): + self._task.cancel() + await asyncio.gather(self._task, return_exceptions=True) + + # ------------------------------------------------------------ 文本注入 + async def inject_task(self, text: str) -> bool: + """V2 的 input.append 没有 inject_text 字段，暂不支持。 + + 父类靠它实现"技能切换时把指令文本塞给模型"。V2 协议里没有对应字段， + 硬塞会被服务端忽略或报错，所以明确返回 False 让调用方走别的路径， + 而不是假装成功。 + """ + LOG.info("[GW] inject_task 在 /v1/realtime 协议下暂不支持，跳过: %s", text[:30]) + return False diff --git a/extensions/assistive_harness/phase_b/recorder_live.py b/extensions/assistive_harness/phase_b/recorder_live.py new file mode 100644 index 0000000..3e17528 --- /dev/null +++ b/extensions/assistive_harness/phase_b/recorder_live.py @@ -0,0 +1,860 @@ +"""recorder_live.py — 录屏式实时录制器 v5.0 +================================================= + +v5.0 改动:user 轨从"ring buffer 切片拼接"改成"WS 原始包直录"。 +和蓝牙耳机录通话语义一致:丢包 = 该段缺失,不再补零去对齐墙钟。 +和 AI 轨完全对称——AI 轨录的是 PortAudio DAC 实际写出的样本。 + +调用顺序: + live_rec = LiveRecorder(session_dir) + live_rec.attach_to_player(speaker) # 必须在 speaker.start() 之前 + speaker.start() + live_rec.start() # 此后 user/ai/frame 才会被记录 + + # ESP32 reader 每收一包就调: + live_rec.feed_user_raw(pcm_f32) # 16kHz 原始,丢包就丢 + + # 每发一个 chunk 配的图: + live_rec.on_frame(jpeg, chunk_idx) + + live_rec.stop() + live_rec.finalize_mp4() +""" + +from __future__ import annotations + +import json +import logging +import shutil +import subprocess +import threading +import time +import wave +from pathlib import Path +from typing import Optional + +import numpy as np + +LOGGER = logging.getLogger("recorder_live") + + +class LiveRecorder: + def __init__( + self, + session_dir: Optional[Path], + user_sr: int = 16000, + ai_sr: int = 24000, + no_media: bool = False, + ): + self.enabled = session_dir is not None + self.dir: Optional[Path] = Path(session_dir) if session_dir else None + self.user_sr = user_sr + self.ai_sr = ai_sr + # no_media：批量 rerun 评分只需要 wav + jsonl + transcript， + # 跳过 jpg 落盘和 mp4 拼接（150 次 rerun 会重复复制整簇图、跑 450 次 ffmpeg）。 + self.no_media = bool(no_media) + + self._t0: Optional[float] = None + + # User 轨:ESP32 WS 来的原始 PCM,丢包 = 缺失,不补零 + self._user_t_start: Optional[float] = None + # self._user_chunks: list[np.ndarray] = [] + self._user_chunks: list[tuple[float, np.ndarray]] = [] + # AI 轨:PortAudio DAC 实际输出(含 underrun 时填的零) + self._ai_t_start: Optional[float] = None + #self._ai_chunks: list[np.ndarray] = [] + self._ai_chunks: list[tuple[float, np.ndarray]] = [] + # Frames + self._frames: list[tuple[float, str, int]] = [] + self._funnel_rounds: list[dict] = [] # 每轮整簇多帧+判定（统一record） + self._round_counter: int = -1 # on_funnel_round 轮次计数（qid，每轮+1） + self._transcript = None # transcript.txt 句柄 + self._subtitles = None # subtitles.jsonl 句柄 + self._chunks_f = None # model_chunks.jsonl 句柄 + + self._lock = threading.Lock() + self._started = threading.Event() + self._stopping = threading.Event() + + self._user_calls = 0 + self._ai_calls = 0 + + # ------------------------------------------------------------ + # Lifecycle + # ------------------------------------------------------------ + + def attach_to_player(self, player) -> None: + if not self.enabled: + return + orig_enqueue = player.enqueue + rec = self + + # PCSpeaker.enqueue 是 async，签名 (pcm, generation)。包装必须同签名 + async， + # 否则模型音频 enqueue 报 "takes 1 positional argument but 2 were given"， + # 导致 AI 音播不出、也录不进（live 听不到声音）。 + async def wrapped_enqueue(pcm_f32, generation=0): + await orig_enqueue(pcm_f32, generation) + if (not rec._started.is_set() or rec._stopping.is_set() + or rec._t0 is None or pcm_f32 is None or pcm_f32.size == 0): + return + try: + pcm = pcm_f32 if pcm_f32.dtype == np.float32 else pcm_f32.astype(np.float32) + pcm = pcm.copy() + t = max(0.0, time.monotonic() - rec._t0) + with rec._lock: + if rec._ai_t_start is None: + rec._ai_t_start = t + rec._ai_chunks.append((t, pcm)) + rec._ai_calls += 1 + except Exception as e: + LOGGER.warning("[LIVE] enqueue hook err: %r", e) + + player.enqueue = wrapped_enqueue + player._orig_enqueue = orig_enqueue # 暴露原始方法，绕过 hook 的场景使用 + LOGGER.info("[LIVE] attached to SPK enqueue (async, pcm+generation)") + + def start(self) -> None: + if not self.enabled or self._started.is_set(): + return + assert self.dir is not None + self.dir.mkdir(parents=True, exist_ok=True) + #(self.dir / "live_images").mkdir(exist_ok=True) + self._t0 = time.monotonic() + self._started.set() + # 评分用：模型/用户的成段文本 + 时间轴（移植自 demo_esp32_duplex_0703 的 + # SessionRecorder.log_turn_text / log_subtitle）。 + # transcript.txt —— 人读，[AI #n] / [LISTEN #n] 整段 + # subtitles.jsonl —— 机读，{start_ms,end_ms,text,is_listen,turn_idx} + # is_listen=True 的 turn 就是 8021 ASR 识别出的用户说话 → 提问结束时刻 t0 从这拿 + try: + self._transcript = (self.dir / "transcript.txt").open("w", encoding="utf-8") + self._transcript.write(f"# session {self.dir.name}\n") + self._transcript.flush() + self._subtitles = (self.dir / "subtitles.jsonl").open("w", encoding="utf-8") + self._chunks_f = (self.dir / "model_chunks.jsonl").open("w", encoding="utf-8") + except Exception as e: + LOGGER.warning("[LIVE] transcript/subtitles 打开失败: %s", e) + self._transcript = None + self._subtitles = None + self._chunks_f = None + LOGGER.info( + "[LIVE] recording started: %s (user=%dHz ai=%dHz)", + self.dir, self.user_sr, self.ai_sr, + ) + + def session_ms(self) -> int: + """session 内相对毫秒（与 frames.jsonl 的 t 同一条时间轴）。""" + if self._t0 is None: + return 0 + return int((time.monotonic() - self._t0) * 1000.0) + + def log_model_chunk(self, text: str, is_listen: bool, end_of_turn: bool, + audio_ms: float = 0.0) -> None: + """逐 chunk 原始记录 —— **一条都不过滤**。 + + 这是诊断用的原始流水：-o 是 1Hz 决策，listen 期间大量 chunk 是 + text="" 且 end_of_turn=False。之前这里写了 + if not text and not end_of_turn: return + 把它们全扔了，结果 24 秒的会话只剩 5 条，看上去像"模型 5.7 秒才出一个 + chunk / 输入分块坏了"——那是记录函数造出来的假象，不是事实。 + 要判断模型节奏是否正常，必须看到每一个 chunk。 + """ + if not self.enabled or not self._started.is_set() or self.dir is None: + return + if self._chunks_f is None: + return + try: + self._chunks_f.write(json.dumps({ + "session_ms": self.session_ms(), + "text": text, + "is_listen": bool(is_listen), + "end_of_turn": bool(end_of_turn), + "audio_ms": round(float(audio_ms), 1), + }, ensure_ascii=False) + "\n") + self._chunks_f.flush() + except Exception: + pass + + def log_event(self, tag: str, text: str) -> None: + """把漏斗侧事件写进 transcript，和 USER/AI 同一条时间轴。 + + 评分时要判"这次拦截对应哪次回答"，三者必须在同一条人读时间轴上： + [USER @12480ms] 这上面写的是什么 + [REJECT @13520ms] severe_shake 画面晃得厉害，请先保持不动 + [HINT @13900ms] severe_shake (2.52s) + [AI #3 @16100ms] 上面写着阿莫西林胶囊... + 机读的一份仍在 frames.jsonl（每轮 send/reason）。 + """ + if not self.enabled or not self._started.is_set() or self._transcript is None: + return + try: + self._transcript.write(f"[{tag} @{self.session_ms()}ms] {text}\n") + self._transcript.flush() + except Exception: + pass + + def log_asr(self, utterance: str, final_at_ms=None, confidence=None) -> None: + """8021 ASR 识别出的用户说话。评分取 t0 用。 + + 写两处：transcript.txt 的 [USER] 行（人读）、subtitles.jsonl 的 + is_listen=true 记录（机读，与模型 turn 同一条时间轴、同一个 session_ms 基准）。 + final_at_ms 是 8021 侧的时钟，和本 session 的 _t0 不同源，只作参考存进 + asr_events.jsonl；对齐一律用本地 session_ms()。 + """ + if not self.enabled or not self._started.is_set(): + return + t_ms = self.session_ms() + try: + if self._transcript is not None: + self._transcript.write(f"[USER @{t_ms}ms] {utterance}\n") + self._transcript.flush() + except Exception: + pass + # 复用 subtitles 的 turn 序列（负数 turn_idx 标识 ASR，避免和模型 turn 撞号） + self._asr_seq = getattr(self, "_asr_seq", 0) + 1 + self.log_subtitle(start_ms=t_ms, end_ms=t_ms, text=utterance, + is_listen=True, turn_idx=-self._asr_seq) + try: + if self.dir is not None: + with (self.dir / "asr_events.jsonl").open("a", encoding="utf-8") as f: + f.write(json.dumps({ + "session_ms": t_ms, + "utterance": utterance, + "harness_final_at_ms": final_at_ms, + "confidence": confidence, + }, ensure_ascii=False) + "\n") + except Exception: + pass + + def log_turn_text(self, turn_idx: int, text: str, is_listen: bool) -> None: + if not self.enabled or self._transcript is None: + return + tag = "LISTEN" if is_listen else "AI" + try: + self._transcript.write(f"[{tag} #{turn_idx} @{self.session_ms()}ms] {text}\n") + self._transcript.flush() + except Exception: + pass + + def log_subtitle(self, start_ms: int, end_ms: int, text: str, + is_listen: bool, turn_idx: int) -> None: + if not self.enabled or self._subtitles is None: + return + try: + self._subtitles.write(json.dumps({ + "start_ms": int(start_ms), + "end_ms": int(end_ms), + "text": text, + "is_listen": bool(is_listen), + "turn_idx": int(turn_idx), + }, ensure_ascii=False) + "\n") + self._subtitles.flush() + except Exception: + pass + + def stop(self) -> None: + if not self.enabled or not self._started.is_set() or self._stopping.is_set(): + return + self._stopping.set() + assert self._t0 is not None and self.dir is not None + total = time.monotonic() - self._t0 + + with self._lock: + user_chunks = list(self._user_chunks) + user_t_start = self._user_t_start + ai_chunks = list(self._ai_chunks) + ai_t_start = self._ai_t_start + n_frames = len(self._frames) + + u_path = self.dir / "live_user.wav" + a_path = self.dir / "live_ai.wav" + u_dur = self._write_track_wav(u_path, user_t_start, user_chunks, + self.user_sr, total) + a_dur = self._write_track_wav(a_path, ai_t_start, ai_chunks, + self.ai_sr, total) + + u_size = u_path.stat().st_size if u_path.exists() else 0 + a_size = a_path.stat().st_size if a_path.exists() else 0 + + # 写 events.jsonl（供 rerun 的 LocalImageSource 读：chunk_idx → img 映射） + try: + import json as _json + with self._lock: + frames_snap = list(self._frames) + ev_path = self.dir / "events.jsonl" + with ev_path.open("w", encoding="utf-8") as ef: + for (ft, frel, fidx) in frames_snap: + ef.write(_json.dumps( + {"kind": "chunk_sent", "idx": int(fidx), + "img": Path(frel).name, "t": round(ft, 3)}, + ensure_ascii=False) + "\n") + LOGGER.info("[LIVE] events.jsonl written: %d frames", len(frames_snap)) + except Exception as e: + LOGGER.warning("[LIVE] write events.jsonl err: %r", e) + + # 写 frames.jsonl（整簇多帧 + 判定，供 funnel rerun 重判 / 无漏斗 rerun 取代表帧） + try: + import json as _json2 + with self._lock: + rounds_snap = list(self._funnel_rounds) + fr_path = self.dir / "frames.jsonl" + with fr_path.open("w", encoding="utf-8") as ff: + for rec in rounds_snap: + ff.write(_json2.dumps(rec, ensure_ascii=False) + "\n") + LOGGER.info("[LIVE] frames.jsonl written: %d rounds", len(rounds_snap)) + except Exception as e: + LOGGER.warning("[LIVE] write frames.jsonl err: %r", e) + + u_nz = self._nonzero_ratio(user_chunks) + a_nz = self._nonzero_ratio(ai_chunks) + + LOGGER.info( + "[LIVE] stopped: wall=%.2fs " + "user(calls=%d audio=%.2fs t_start=%.2fs nonzero=%.1f%%) " + "ai(calls=%d audio=%.2fs t_start=%.2fs nonzero=%.1f%%) " + "frames=%d user_wav=%dB ai_wav=%dB", + total, + self._user_calls, u_dur, + user_t_start if user_t_start is not None else -1, u_nz * 100, + self._ai_calls, a_dur, + ai_t_start if ai_t_start is not None else -1, a_nz * 100, + n_frames, u_size, a_size, + ) + + if self._user_calls == 0: + LOGGER.warning( + "[LIVE] feed_user_raw() was NEVER called — " + "esp32_audio_reader 没把 live_rec 接进来。" + ) + if self._ai_calls == 0: + LOGGER.warning( + "[LIVE] AI callback NEVER fired — attach_to_player 没装上、" + "speaker 未启动、或开了 --no-play。" + ) + + # 关闭评分用文本落盘 + for _fh in (self._transcript, self._subtitles, self._chunks_f): + try: + if _fh is not None: + _fh.close() + except Exception: + pass + self._transcript = None + self._subtitles = None + self._chunks_f = None + + # 结束自动落盘 mp4（不用手动）： + # ① 整簇多帧 + 角落 reject 标注（演示主用，流畅 + 可视化漏斗决策） + # ② best 1fps 版（兼容旧用途） + try: + self.finalize_multiframe_mp4() + except Exception as e: + LOGGER.warning("[LIVE] multiframe mp4 finalize failed: %s", e) + try: + self.finalize_mp4() + except Exception as e: + LOGGER.warning("[LIVE] mp4 finalize failed: %s", e) + + # ------------------------------------------------------------ + # Inputs + # ------------------------------------------------------------ + + def feed_user_raw(self, pcm_f32: np.ndarray,t_override: Optional[float] = None) -> None: + if (not self.enabled or not self._started.is_set() + or self._stopping.is_set() or self._t0 is None): + return + if pcm_f32 is None or pcm_f32.size == 0: + return + if pcm_f32.dtype != np.float32: + pcm_f32 = pcm_f32.astype(np.float32) + # 新增:允许调用者提供精确的音频时间轴 t 戳(rerun 场景); + # 否则按 wall clock 来(ESP32 场景) + if t_override is not None: + t = max(0.0, t_override) + else: + t = max(0.0, time.monotonic() - self._t0) + #t = max(0.0, time.monotonic() - self._t0) + with self._lock: + if self._user_t_start is None: + self._user_t_start = t + rms = float(np.sqrt(np.mean(pcm_f32 ** 2))) + LOGGER.info("[LIVE] first feed_user_raw(): %d samples rms=%.4f t=%.2fs", + pcm_f32.size, rms, t) + self._user_chunks.append((t, pcm_f32.copy())) + self._user_calls += 1 + + def on_frame(self, jpeg_bytes: Optional[bytes], chunk_idx: int) -> None: + if (not self.enabled or not self._started.is_set() + or self._stopping.is_set() or not jpeg_bytes + or self._t0 is None or self.dir is None): + return + t = time.monotonic() - self._t0 + rel = f"images/img_{chunk_idx:05d}.jpg" + # 实际写 jpg 落盘（供 rerun 的 LocalImageSource 读）。 + try: + img_dir = self.dir / "images" + img_dir.mkdir(parents=True, exist_ok=True) + if not self.no_media: + (self.dir / rel).write_bytes(jpeg_bytes) + except Exception as e: + LOGGER.warning("[LIVE] write frame jpg err: %r", e) + return + with self._lock: + self._frames.append((t, rel, int(chunk_idx))) + + def on_funnel_round(self, decision, chunk_idx: int, text: str = "") -> None: + """存漏斗一轮：整簇多帧(筛前原始)落盘 q{NNN}_f{MM}.jpg + frames.jsonl 记一条。 + 统一 record 的核心：一份 record 供三种 rerun—— + 无漏斗 rerun(每轮取代表帧)/ funnel rerun(整簇重判)/ -o rerun(取当时best)。 + """ + if (not self.enabled or not self._started.is_set() + or self._stopping.is_set() or self._t0 is None or self.dir is None): + return + t = time.monotonic() - self._t0 + frames = getattr(decision, "frames", None) + if not frames: + b = getattr(decision, "best", None) or getattr(decision, "best_jpg", None) + frames = [b] if b else [] + # qid 用独立轮次计数（每轮 +1），不能用 latest_frame.sequence—— + # 那个只在 send 时递增，一直 reject（晃动/糊）时不变 → qid 不变 → 整簇 jpg + # 反复用同名 q{同值}_fNN 覆盖旧图，record 存不下、rerun 没图。 + self._round_counter = getattr(self, "_round_counter", -1) + 1 + qid = self._round_counter + frame_names = [] + try: + img_dir = self.dir / "images" + img_dir.mkdir(parents=True, exist_ok=True) + for i, jpg in enumerate(frames): + if not jpg: + continue + name = f"q{qid:05d}_f{i:02d}.jpg" + if not self.no_media: + (img_dir / name).write_bytes(jpg) + frame_names.append(name) + except Exception as e: + LOGGER.warning("[LIVE] on_funnel_round write err: %r", e) + return + rec = { + "qid": qid, + "t": round(t, 3), + "chunk_idx": int(chunk_idx), + "frames": frame_names, + "best_index": getattr(decision, "best_index", None), + "send": bool(getattr(decision, "send", False)), + "reason": getattr(decision, "reason", ""), + "text": text, + } + with self._lock: + self._funnel_rounds.append(rec) + + # ------------------------------------------------------------ + # WAV writing + # ------------------------------------------------------------ + + @staticmethod + def _nonzero_ratio(chunks: list[tuple[float, np.ndarray]]) -> float: + if not chunks: + return 0.0 + total, nz = 0, 0 + for _, pcm in chunks: + total += pcm.size + nz += int(np.sum(np.abs(pcm) > 1e-4)) + return (nz / total) if total > 0 else 0.0 + + @staticmethod + def _write_track_wav( + path: Path, + t_start: Optional[float], # 仅用于日志兼容,不再决定起点 + chunks: list[tuple[float, np.ndarray]], + sr: int, + wall_dur: float, + ) -> float: + """录屏式写盘:每个 chunk 落在它真实的 arrival 时间上,空隙留 0。""" + if not chunks: + n_total = max(int(wall_dur * sr), 1) + track = np.zeros(n_total, dtype=np.float32) + audio_dur = 0.0 + else: + # 计算总长 = max(最后一段尾巴, wall_dur) + end_time = max(t + pcm.size / sr for t, pcm in chunks) + n_total = max(int(max(end_time, wall_dur) * sr), 1) + track = np.zeros(n_total, dtype=np.float32) + audio_dur = 0.0 # 这里改为"有效样本"的累计,不再是 concat 长度 + prev_end_samp = 0 + for t, pcm in chunks: + i = max(0, int(t * sr)) + # 防止下一段的 arrival time 小于上一段结束——重叠时按上一段尾巴顺延 + i = max(i, prev_end_samp) + j = min(i + pcm.size, n_total) + if j > i: + track[i:j] = pcm[: j - i] + prev_end_samp = j + audio_dur += (j - i) / sr + + i16 = np.clip(track * 32768.0, -32768, 32767).astype(np.int16) + with wave.open(str(path), "wb") as w: + w.setnchannels(1); + w.setsampwidth(2); + w.setframerate(sr) + w.writeframes(i16.tobytes()) + return audio_dur + + # ------------------------------------------------------------ + # MP4 finalize + # ------------------------------------------------------------ + + def finalize_mp4(self) -> Optional[Path]: + if self.no_media: + return None + if not self.enabled or self.dir is None: + return None + if shutil.which("ffmpeg") is None: + LOGGER.warning("[LIVE] ffmpeg not found. WAV/帧已保存在 %s。", self.dir) + return None + main_out = None + + + try: + main_out = self._do_finalize_mp4() + except Exception as e: + LOGGER.warning("[LIVE] mp4 finalize failed: %s", e) + # 额外合成一份 user-only,用于 gateway 8006 视频输入测试 + try: + self._do_finalize_useronly_mp4() + except Exception as e: + LOGGER.warning("[LIVE] useronly mp4 finalize failed: %s", e) + return main_out + + def _do_finalize_useronly_mp4(self) -> Optional[Path]: + """额外合成一份只含 user 音轨的 mp4,用于给 gateway 的 8006 视频输入端做 fixture 测试。""" + d = self.dir + assert d is not None + user_wav = d / "live_user.wav" + if not user_wav.exists(): + LOGGER.info("[LIVE] no user wav, skip useronly mp4") + return None + + with wave.open(str(user_wav), "rb") as w: + u_dur = w.getnframes() / w.getframerate() + if u_dur <= 0.5: + LOGGER.info("[LIVE] user too short (%.2fs), skip useronly mp4", u_dur) + return None + + with self._lock: + frames = list(self._frames) + + concat_txt = d / "_useronly_frames.txt" + if frames: + with concat_txt.open("w", encoding="utf-8") as f: + f.write("ffconcat version 1.0\n") + first_t = frames[0][0] + if first_t > 0.05: + f.write(f"file '{frames[0][1]}'\n") + f.write(f"duration {first_t:.3f}\n") + for i, (rt, p, _idx) in enumerate(frames): + f.write(f"file '{p}'\n") + if i + 1 < len(frames): + dur = max(0.04, frames[i + 1][0] - rt) + else: + dur = max(0.5, u_dur - rt) + f.write(f"duration {dur:.3f}\n") + f.write(f"file '{frames[-1][1]}'\n") + + if not frames: + out = d / "live_useronly.m4a" + cmd = [ + "ffmpeg", "-y", "-hide_banner", "-loglevel", "warning", + "-i", str(user_wav), + "-c:a", "aac", "-b:a", "192k", + "-ac", "1", + "-t", f"{u_dur:.3f}", + str(out), + ] + else: + out = d / "live_useronly.mp4" + cmd = [ + "ffmpeg", "-y", "-hide_banner", "-loglevel", "warning", + "-f", "concat", "-safe", "0", "-i", str(concat_txt), + "-i", str(user_wav), + "-map", "0:v", "-map", "1:a", + "-c:v", "libx264", "-preset", "medium", "-crf", "18", "-pix_fmt", "yuv420p", + "-vsync", "vfr", + "-c:a", "aac", "-b:a", "192k", + "-ac", "1", + "-t", f"{u_dur:.3f}", + str(out), + ] + + LOGGER.info( + "[LIVE] assembling useronly: frames=%d u_dur=%.1fs → %s", + len(frames), u_dur, out.name, + ) + try: + r = subprocess.run(cmd, capture_output=True, text=True, timeout=600) + if r.returncode != 0: + LOGGER.warning("[LIVE] useronly ffmpeg rc=%d stderr tail:\n%s", + r.returncode, r.stderr[-1200:]) + return None + if out.exists(): + LOGGER.info("[LIVE] ✅ saved %s (%.1f MB, %.1fs)", + out, out.stat().st_size / 1e6, u_dur) + return out + except subprocess.TimeoutExpired: + LOGGER.warning("[LIVE] useronly ffmpeg timeout (>10min)") + return None + finally: + try: + if concat_txt.exists(): + concat_txt.unlink() + except Exception: + pass + def _do_finalize_mp4(self) -> Optional[Path]: + d = self.dir + assert d is not None + user_wav = d / "live_user.wav" + ai_wav = d / "live_ai.wav" + if not (user_wav.exists() and ai_wav.exists()): + LOGGER.info("[LIVE] no wav files, skip mp4") + return None + + with self._lock: + frames = list(self._frames) + + with wave.open(str(user_wav), "rb") as w: + u_dur = w.getnframes() / w.getframerate() + with wave.open(str(ai_wav), "rb") as w: + a_dur = w.getnframes() / w.getframerate() + total_dur = max(u_dur, a_dur) + if total_dur <= 0.5: + LOGGER.info("[LIVE] session too short (%.2fs), skip mp4", total_dur) + return None + + concat_txt = d / "_live_frames.txt" + if frames: + with concat_txt.open("w", encoding="utf-8") as f: + f.write("ffconcat version 1.0\n") + first_t = frames[0][0] + if first_t > 0.05: + f.write(f"file '{frames[0][1]}'\n") + f.write(f"duration {first_t:.3f}\n") + for i, (rt, p, _idx) in enumerate(frames): + f.write(f"file '{p}'\n") + if i + 1 < len(frames): + dur = max(0.04, frames[i + 1][0] - rt) + else: + dur = max(0.5, total_dur - rt) + f.write(f"duration {dur:.3f}\n") + f.write(f"file '{frames[-1][1]}'\n") + + # apad + -t 让短的一轨自动补尾静音对齐到 mp4 总时长 + if not frames: + out = d / "live_session.m4a" + cmd = [ + "ffmpeg", "-y", "-hide_banner", "-loglevel", "warning", + "-i", str(user_wav), "-i", str(ai_wav), + "-filter_complex", + "[0:a]aresample=24000,aformat=channel_layouts=mono,apad[u];" + "[1:a]aformat=channel_layouts=mono,apad[a];" + "[u][a]amerge=inputs=2[aout]", + "-map", "[aout]", + "-c:a", "aac", "-b:a", "192k", + "-t", f"{total_dur:.3f}", + str(out), + ] + else: + out = d / "live_session.mp4" + cmd = [ + "ffmpeg", "-y", "-hide_banner", "-loglevel", "warning", + "-f", "concat", "-safe", "0", "-i", str(concat_txt), + "-i", str(user_wav), "-i", str(ai_wav), + "-filter_complex", + "[1:a]aresample=24000,aformat=channel_layouts=mono,apad[u];" + "[2:a]aformat=channel_layouts=mono,apad[a];" + "[u][a]amerge=inputs=2[aout]", + "-map", "0:v", "-map", "[aout]", + #"-c:v", "libx264", "-preset", "veryfast", "-pix_fmt", "yuv420p", + "-c:v", "libx264", "-preset", "medium", "-crf", "18", "-pix_fmt", "yuv420p", + "-vsync", "vfr", + "-c:a", "aac", "-b:a", "192k", + "-t", f"{total_dur:.3f}", + str(out), + ] + + LOGGER.info( + "[LIVE] assembling: frames=%d total=%.1fs (u=%.1fs a=%.1fs) → %s", + len(frames), total_dur, u_dur, a_dur, out.name, + ) + try: + r = subprocess.run(cmd, capture_output=True, text=True, timeout=600) + if r.returncode != 0: + LOGGER.warning( + "[LIVE] ffmpeg rc=%d stderr tail:\n%s", + r.returncode, r.stderr[-1200:], + ) + return None + if out.exists(): + LOGGER.info( + "[LIVE] ✅ saved %s (%.1f MB, %.1fs)", + out, out.stat().st_size / 1e6, total_dur, + ) + return out + except subprocess.TimeoutExpired: + LOGGER.warning("[LIVE] ffmpeg timeout (>10min)") + return None + finally: + try: + if concat_txt.exists(): + concat_txt.unlink() + except Exception: + pass + + + # ------------------------------------------------------------ + # 整簇多帧 mp4（演示用）：用每轮整簇多帧拼，比 1fps best 流畅； + # 角落用 ass 字幕标注每轮 send/reject + reason，直接可视化漏斗决策。 + # 对比无漏斗/有漏斗两份 mp4，即可证明"模糊图被拒、不进模型"的正确性。 + # ------------------------------------------------------------ + def _ass_escape(self, s: str) -> str: + return (s or "").replace("\\", "\\\\").replace("{", "(").replace("}", ")") + + def _gen_reject_ass(self, rounds, total_dur: float) -> Optional[Path]: + """生成角落标注字幕：每轮时间段显示 SEND / REJECT:reason。""" + d = self.dir + if d is None or not rounds: + return None + def _fmt(t): + t = max(0.0, t) + h = int(t // 3600); m = int((t % 3600) // 60) + s = t % 60 + return f"{h:d}:{m:02d}:{s:05.2f}" + ass = d / "_reject_notes.ass" + header = ( + "[Script Info]\nScriptType: v4.00+\nPlayResX: 1280\nPlayResY: 720\n\n" + "[V4+ Styles]\n" + "Format: Name, Fontname, Fontsize, PrimaryColour, Bold, Alignment, " + "MarginL, MarginR, MarginV, BorderStyle, Outline, Shadow\n" + # Alignment 9 = 右上角；红=&H000000FF 绿=&H0000FF00 (ASS 是 &HAABBGGRR) + "Style: REJ,Arial,36,&H000000FF,1,9,20,20,20,1,2,0\n" + "Style: SND,Arial,36,&H0000FF00,1,9,20,20,20,1,2,0\n\n" + "[Events]\nFormat: Layer, Start, End, Style, Text\n" + ) + lines = [] + for i, rec in enumerate(rounds): + t0 = float(rec.get("t", i)) + t1 = float(rounds[i + 1].get("t", t0 + 1.0)) if i + 1 < len(rounds) else total_dur + if t1 <= t0: + t1 = t0 + 0.3 + send = bool(rec.get("send")) + reason = self._ass_escape(rec.get("reason", "")) + if send: + style, txt = "SND", "SEND ✓" + else: + style, txt = "REJ", f"REJECT ✗ {reason}" + lines.append(f"Dialogue: 0,{_fmt(t0)},{_fmt(t1)},{style},,{txt}") + try: + ass.write_text(header + "\n".join(lines) + "\n", encoding="utf-8") + return ass + except Exception as e: + LOGGER.warning("[LIVE] write reject ass err: %r", e) + return None + + def finalize_multiframe_mp4(self) -> Optional[Path]: + if self.no_media: + return None + """整簇多帧 mp4 + 角落 reject 标注。演示用，比 best 1fps 流畅。""" + if not self.enabled or self.dir is None: + return None + if shutil.which("ffmpeg") is None: + LOGGER.warning("[LIVE] ⚠ ffmpeg 未安装，无法生成 mp4！" + "装法: conda install -c conda-forge ffmpeg。" + "帧和 wav 已存在 %s，可手动拼。", self.dir) + return None + d = self.dir + user_wav = d / "live_user.wav" + ai_wav = d / "live_ai.wav" + if not (user_wav.exists() and ai_wav.exists()): + return None + with self._lock: + rounds = list(self._funnel_rounds) + if not rounds: + LOGGER.info("[LIVE] no funnel rounds, skip multiframe mp4") + return None + + with wave.open(str(user_wav), "rb") as w: + u_dur = w.getnframes() / w.getframerate() + with wave.open(str(ai_wav), "rb") as w: + a_dur = w.getnframes() / w.getframerate() + total_dur = max(u_dur, a_dur) + if total_dur <= 0.5: + return None + + # 整簇多帧 concat：每轮的 t 到下一轮的 t 之间，均分给该轮的 N 帧（高 fps） + concat_txt = d / "_multiframe.txt" + with concat_txt.open("w", encoding="utf-8") as f: + f.write("ffconcat version 1.0\n") + first_t = float(rounds[0].get("t", 0.0)) + if first_t > 0.05 and rounds[0].get("frames"): + f.write(f"file 'images/{Path(rounds[0]['frames'][0]).name}'\n") + f.write(f"duration {first_t:.3f}\n") + for i, rec in enumerate(rounds): + names = rec.get("frames") or [] + if not names: + continue + t0 = float(rec.get("t", i)) + t1 = float(rounds[i + 1].get("t", t0 + 1.0)) if i + 1 < len(rounds) else total_dur + round_dur = max(0.12, t1 - t0) + per = round_dur / len(names) # 整簇内多帧均分 → 高 fps + for nm in names: + f.write(f"file 'images/{Path(nm).name}'\n") + f.write(f"duration {max(0.03, per):.3f}\n") + # 末帧兜底 + last_names = rounds[-1].get("frames") or [] + if last_names: + f.write(f"file 'images/{Path(last_names[-1]).name}'\n") + + ass = self._gen_reject_ass(rounds, total_dur) + vf = "format=yuv420p" + if ass is not None: + safe = str(ass.name) # 相对 cwd 需在 dir 下运行；用绝对更稳 + abs_ass = str(ass.resolve()).replace("\\", "/").replace(":", "\\:") + vf = f"ass='{abs_ass}',format=yuv420p" + + out = d / "live_multiframe.mp4" + cmd = [ + "ffmpeg", "-y", "-hide_banner", "-loglevel", "warning", + "-f", "concat", "-safe", "0", "-i", str(concat_txt), + "-i", str(user_wav), "-i", str(ai_wav), + "-filter_complex", + "[1:a]aresample=24000,aformat=channel_layouts=mono,apad[u];" + "[2:a]aformat=channel_layouts=mono,apad[a];" + "[u][a]amerge=inputs=2[aout]", + "-map", "0:v", "-map", "[aout]", + "-vf", vf, + "-c:v", "libx264", "-preset", "medium", "-crf", "20", "-pix_fmt", "yuv420p", + "-vsync", "vfr", + "-c:a", "aac", "-b:a", "192k", + "-t", f"{total_dur:.3f}", + str(out), + ] + LOGGER.info("[LIVE] assembling multiframe mp4: rounds=%d total=%.1fs → %s", + len(rounds), total_dur, out.name) + try: + r = subprocess.run(cmd, capture_output=True, text=True, timeout=600) + if r.returncode != 0: + LOGGER.warning("[LIVE] multiframe ffmpeg rc=%d stderr:\n%s", + r.returncode, r.stderr[-1200:]) + return None + if out.exists(): + LOGGER.info("[LIVE] ✅ multiframe mp4 saved %s (%.1f MB)", + out, out.stat().st_size / 1e6) + return out + except subprocess.TimeoutExpired: + LOGGER.warning("[LIVE] multiframe ffmpeg timeout") + return None + finally: + for p in (concat_txt, ass): + try: + if p is not None and p.exists(): + p.unlink() + except Exception: + pass diff --git a/extensions/assistive_harness/phase_b/requirements-phase-b.txt b/extensions/assistive_harness/phase_b/requirements-phase-b.txt new file mode 100644 index 0000000..a247f64 --- /dev/null +++ b/extensions/assistive_harness/phase_b/requirements-phase-b.txt @@ -0,0 +1,4 @@ +aiohttp>=3.13 +Pillow>=10 +sounddevice>=0.5 + diff --git a/extensions/assistive_harness/phase_b/rerun_source.py b/extensions/assistive_harness/phase_b/rerun_source.py new file mode 100644 index 0000000..448862d --- /dev/null +++ b/extensions/assistive_harness/phase_b/rerun_source.py @@ -0,0 +1,368 @@ +"""rerun_source.py — 用已录制 session 当输入重跑模型 + +替换主脚本里的两个网络输入函数： + - esp32_audio_reader → local_pcm_reader + - esp32_capture_image → LocalImageSource.capture +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import time +from pathlib import Path +from typing import Optional + +import numpy as np +import sounddevice as sd +import threading + + +LOGGER = logging.getLogger("rerun_source") + +_PACKET_MS = 40 +_PACKET_SAMPLES = 640 +_SAMPLE_RATE = 16000 + + +class _UserMonitor: + """独立 PortAudio OutputStream,用于 rerun 时播放 user 监听音频。 + + 为什么不复用主 SpeakerPlayer:主 speaker 是单 FIFO ring, + AI 和 user 两路 enqueue 共享同一个 ring 时,DAC 按入队顺序消费会 + 导致 AI 音频被 user 音频"插队"——听感是 AI 输出卡顿。 + + 独立 OutputStream 由 OS 端混音(同一声卡时钟驱动),物理上不互相挤压。 + """ + + def __init__(self, sample_rate: int = 24000, ring_capacity_s: float = 2.0): + self.sr = int(sample_rate) + self.ring_cap = int(ring_capacity_s * self.sr) + self._buf = np.zeros(self.ring_cap, dtype=np.float32) + self._r = 0 + self._w = 0 + self._size = 0 + self._lock = threading.Lock() + self._dropped = 0 + try: + self._stream = sd.OutputStream( + samplerate=self.sr, channels=1, dtype="float32", + blocksize=0, callback=self._cb, + ) + self._stream.start() + LOGGER.info("[RERUN-MON] user monitor stream started: sr=%d ring=%.1fs", + self.sr, ring_capacity_s) + except Exception as e: + LOGGER.warning("[RERUN-MON] failed to open OutputStream: %r", e) + self._stream = None + + def _cb(self, outdata, frames, time_info, status): + with self._lock: + avail = min(frames, self._size) + if avail > 0: + first = min(avail, self.ring_cap - self._r) + outdata[:first, 0] = self._buf[self._r:self._r + first] + if avail > first: + outdata[first:avail, 0] = self._buf[:avail - first] + self._r = (self._r + avail) % self.ring_cap + self._size -= avail + if avail < frames: + outdata[avail:, 0] = 0.0 + + def enqueue(self, pcm_f32: np.ndarray) -> None: + if self._stream is None or pcm_f32 is None or pcm_f32.size == 0: + return + if pcm_f32.dtype != np.float32: + pcm_f32 = pcm_f32.astype(np.float32) + n = pcm_f32.size + if n > self.ring_cap: + pcm_f32 = pcm_f32[-self.ring_cap:] + n = pcm_f32.size + with self._lock: + if self._size + n > self.ring_cap: + drop = (self._size + n) - self.ring_cap + self._r = (self._r + drop) % self.ring_cap + self._size -= drop + self._dropped += drop + first = min(n, self.ring_cap - self._w) + self._buf[self._w:self._w + first] = pcm_f32[:first] + if n > first: + self._buf[:n - first] = pcm_f32[first:n] + self._w = (self._w + n) % self.ring_cap + self._size += n + + def stop(self) -> None: + if self._stream is None: + return + try: + self._stream.stop() + self._stream.close() + except Exception as e: + LOGGER.warning("[RERUN-MON] stop err: %r", e) + finally: + self._stream = None + LOGGER.info("[RERUN-MON] user monitor stopped (dropped=%d samples)", + self._dropped) + + + +async def local_pcm_reader( + pcm_path: Path, + ring, + stop_evt: asyncio.Event, + stats, + live_rec=None, + speaker=None, # ← 新增 + speaker_sr: int = 24000, # ← 新增:speaker 期望的采样率 + speed: float = 1.0, + drain_s: float = 5.0, + prefill_s: float = 0.0, +) -> None: + pcm_path = Path(pcm_path) + if not pcm_path.exists(): + LOGGER.error("[RERUN] pcm not found: %s", pcm_path) + stop_evt.set() + return + + # 优先用 live_user.wav (LiveRecorder 直接写的,干净); + # 回退用 user_raw.pcm (主循环 ring.slice 落盘的,带 40% 零填充) + import wave + wav_path = pcm_path.parent / "live_user.wav" + pcm_i16 = None + src_label = "" + if wav_path.exists(): + try: + with wave.open(str(wav_path), "rb") as w: + sr_in = w.getframerate() + nch = w.getnchannels() + sw = w.getsampwidth() + if sr_in == _SAMPLE_RATE and nch == 1 and sw == 2: + pcm_i16 = np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16) + src_label = f"live_user.wav (clean, {sr_in}Hz mono int16)" + else: + LOGGER.warning( + "[RERUN] live_user.wav format unexpected (sr=%d ch=%d sw=%d), " + "fallback to user_raw.pcm", sr_in, nch, sw, + ) + except Exception as e: + LOGGER.warning("[RERUN] failed to read live_user.wav: %r, fallback to user_raw.pcm", e) + pcm_i16 = None + + if pcm_i16 is None: + raw = pcm_path.read_bytes() + pcm_i16 = np.frombuffer(raw, dtype=np.int16) + src_label = "user_raw.pcm (may contain ring.slice zero-padding)" + + total_samples = pcm_i16.size + total_dur = total_samples / _SAMPLE_RATE + LOGGER.info("[RERUN] audio source: %s", src_label) + # rerun 用户监听:独立 OutputStream,避免和 AI 共用 ring 导致互相卡顿 + user_mon = None + if speaker is not None: + try: + user_mon = _UserMonitor(sample_rate=speaker_sr) + except Exception as e: + LOGGER.warning("[RERUN] failed to create user monitor: %r", e) + user_mon = None + + + LOGGER.info( + "[RERUN] pcm loaded: %s samples=%d dur=%.2fs speed=%.2fx prefill=%.1fs", + pcm_path, total_samples, total_dur, speed, prefill_s, + ) + + packet_interval = (_PACKET_MS / 1000.0) / max(speed, 0.01) + ts_ms = 0 + cursor = 0 + pkt_count = 0 + prefill_packets = int(prefill_s * 1000 / _PACKET_MS) + next_tick = time.monotonic() # ← 改:不再 + prefill_s + audio_t_start = None + while cursor < total_samples and not stop_evt.is_set(): + end = min(cursor + _PACKET_SAMPLES, total_samples) + chunk_i16 = pcm_i16[cursor:end] + cursor = end + pkt_count += 1 + + try: + stats["rx_pkts"] = pkt_count + stats["esp_drops"] = 0 + except Exception: + pass + + try: + await ring.write(ts_ms, chunk_i16) + except Exception as e: + LOGGER.warning("[RERUN] ring.write err: %r", e) + + if live_rec is not None: + try: + # ↓ 关键改动:用"累计音频时长"作为 t 戳,而不是 wall clock + # 这样 LiveRecorder 写盘时 t * sr 算出来的 i 与 pcm.size 完美对齐 + if audio_t_start is None: + audio_t_start = time.monotonic() - live_rec._t0 if live_rec._t0 else 0.0 + audio_t = audio_t_start + ts_ms / 1000.0 + live_rec.feed_user_raw( + chunk_i16.astype(np.float32) / 32768.0, + t_override=audio_t, + ) + # rerun 模式:把 user 音频也推给扬声器,这样能听到原录制的用户在说什么 + # (live 模式不这么做,因为用户本人就在那说话,会和扬声器叠加。 + # rerun 是事后回放,没回声风险,等同 8006 浏览器播 mp4 的体验) + if user_mon is not None: + try: + chunk_f32 = chunk_i16.astype(np.float32) / 32768.0 + if speaker_sr != _SAMPLE_RATE: + ratio = speaker_sr / _SAMPLE_RATE + new_len = int(chunk_f32.size * ratio) + x_old = np.linspace(0, 1, chunk_f32.size, endpoint=False) + x_new = np.linspace(0, 1, new_len, endpoint=False) + chunk_resampled = np.interp(x_new, x_old, chunk_f32).astype(np.float32) + else: + chunk_resampled = chunk_f32 + # 关键:走原始 enqueue,不走 LiveRecorder hook + # (hook 会把这条用户音频错误地累积进 _ai_chunks, + # 导致 mp4 的 ai 轨里也有用户声音,听起来像"用户被录了两次") + user_mon.enqueue(chunk_resampled) + except Exception as e: + LOGGER.warning("[RERUN] speaker.enqueue err: %r", e) + + #live_rec.feed_user_raw(chunk_i16.astype(np.float32) / 32768.0) + except Exception as e: + LOGGER.warning("[RERUN] live_rec.feed_user_raw err: %r", e) + + ts_ms += _PACKET_MS + + # prefill 阶段:让出事件循环但不真睡 + if pkt_count <= prefill_packets: + await asyncio.sleep(0) + continue + + # prefill → realtime 切换那一刻,重置基线 + if pkt_count == prefill_packets + 1: + LOGGER.info( + "[RERUN] prefill done (%d packets, %.2fs audio in ring), " + "switching to realtime pacing", + prefill_packets, prefill_packets * _PACKET_MS / 1000.0, + ) + next_tick = time.monotonic() # ← 关键:基线重置到此刻 + + next_tick += packet_interval + sleep_for = next_tick - time.monotonic() + if sleep_for > 0: + try: + await asyncio.wait_for(stop_evt.wait(), timeout=sleep_for) + break # 被外部 stop:跳出推流循环,仍走下面的 drain(等模型说完) + except asyncio.TimeoutError: + pass + + try: + stats["audio_done"] = True + stats["audio_done_ts"] = time.monotonic() + except Exception: + pass + + LOGGER.info( + "[RERUN] pcm exhausted: pushed=%d packets (%.2fs audio). draining...", + pkt_count, ts_ms / 1000.0, + ) + + # 音频已推完并标记 audio_done。结束的裁决权交给主 send loop: + # 它会在 audio_done 后等模型说完(轮询 last_speak_ts)再 set stop_evt。 + # reader 这里只需等待那个信号;带兜底上限防止异常卡死。 + max_wait_s = 120.0 + waited = 0.0 + while waited < max_wait_s and not stop_evt.is_set(): + try: + await asyncio.wait_for(stop_evt.wait(), timeout=0.2) + break + except asyncio.TimeoutError: + waited += 0.2 + if waited >= max_wait_s: + LOGGER.warning("[RERUN] drain 达到上限 %.0fs,强制结束", max_wait_s) + + if user_mon is not None: + user_mon.stop() + + if not stop_evt.is_set(): + LOGGER.info("[RERUN] drain done, signaling stop") + stop_evt.set() + + +class LocalImageSource: + def __init__(self, session_dir: Path): + self.session_dir = Path(session_dir) + self.images_dir = self.session_dir / "images" + self.events_path = self.session_dir / "events.jsonl" + + if not self.images_dir.exists(): + raise FileNotFoundError(f"images/ not found: {self.images_dir}") + if not self.events_path.exists(): + raise FileNotFoundError(f"events.jsonl not found: {self.events_path}") + + self._map: dict[int, Optional[str]] = {} + self._build_map() + + n_total = len(self._map) + n_with_img = sum(1 for v in self._map.values() if v) + LOGGER.info( + "[RERUN] image map: total_chunks=%d with_image=%d (missing=%d) from %s", + n_total, n_with_img, n_total - n_with_img, self.events_path.name, + ) + if n_total == 0: + # ← 新增:诊断空 events.jsonl + n_files = len(list(self.images_dir.glob("img_*.jpg"))) + LOGGER.warning( + "[RERUN] events.jsonl has NO chunk_sent entries! " + "but images/ has %d files. Falling back to filename-order matching.", + n_files, + ) + self._fallback_filename_order(n_files) + + def _build_map(self) -> None: + with self.events_path.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + evt = json.loads(line) + except Exception: + continue + # ← 改 1:看实际 events.jsonl 用什么 kind 字段 + kind = evt.get("kind") or evt.get("type") or "" + if kind != "chunk_sent": + continue + # ← 改 2:chunk_idx 字段名兼容 + idx = evt.get("idx", evt.get("chunk_idx")) + img = evt.get("img") + if idx is None: + continue + self._map[int(idx)] = img if img else None + + def _fallback_filename_order(self, n_files: int) -> None: + """events.jsonl 是空的时候的兜底:按 img_NNNNN.jpg 文件名映射 chunk_idx。""" + for p in sorted(self.images_dir.glob("img_*.jpg")): + try: + # 文件名形如 img_00007.jpg → 7 + idx = int(p.stem.split("_")[-1]) + self._map[idx] = p.name + except Exception: + pass + LOGGER.info("[RERUN] fallback map built: %d chunks (filename-order)", + len(self._map)) + + async def capture(self, chunk_idx: int) -> Optional[bytes]: + rel = self._map.get(int(chunk_idx)) + if not rel: + return None + name = Path(rel).name + path = self.images_dir / name + if not path.exists(): + return None + try: + return path.read_bytes() + except Exception as e: + LOGGER.warning("[RERUN] image read err: %r", e) + return None \ No newline at end of file diff --git a/extensions/assistive_harness/phase_b/rokid_runtime.py b/extensions/assistive_harness/phase_b/rokid_runtime.py new file mode 100644 index 0000000..af9f8cc --- /dev/null +++ b/extensions/assistive_harness/phase_b/rokid_runtime.py @@ -0,0 +1,1703 @@ +from __future__ import annotations + +import argparse +import asyncio +import base64 +import io +import json +import logging +import os +import signal +import ssl +import threading +import time +import uuid +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Awaitable, Callable, Protocol + +import aiohttp +from aiohttp import web +import numpy as np + +from ..registry import SkillRegistry + +try: + from PIL import Image +except Exception: # pragma: no cover - optional at import time + Image = None + + +LOG = logging.getLogger("assistive_harness.phase_b.rokid") +SAMPLE_RATE_IN = 16_000 +SAMPLE_RATE_OUT = 24_000 +PCM16_WIDTH = 2 +CTRL_C_HARD_EXIT_S = 10.0 + + +def now_ms() -> float: + return time.time() * 1000.0 + + +class CtrlCExitWatchdog: + """Guarantee that a Windows Ctrl+C cannot leave the Rokid port behind.""" + + def __init__(self, hard_exit_s: float = CTRL_C_HARD_EXIT_S): + self.hard_exit_s = max(1.0, float(hard_exit_s)) + self._armed = False + self._lock = threading.Lock() + + @property + def armed(self) -> bool: + return self._armed + + def arm(self) -> None: + with self._lock: + if self._armed: + return + self._armed = True + LOG.info( + "Console interrupt received; closing Rokid listener and runtime " + "(hard cutoff %.1fs)", + self.hard_exit_s, + ) + threading.Thread( + target=self._force_exit_after_deadline, + name="rokid-ctrl-c-watchdog", + daemon=True, + ).start() + + def _force_exit_after_deadline(self) -> None: + time.sleep(self.hard_exit_s) + LOG.error( + "Rokid shutdown exceeded %.1fs; forcing process exit so port 18080 " + "cannot remain occupied", + self.hard_exit_s, + ) + logging.shutdown() + os._exit(130) + + +def pcm16le_to_float32(raw: bytes) -> np.ndarray: + if len(raw) % PCM16_WIDTH: + raw = raw[:-1] + if not raw: + return np.zeros(0, dtype=np.float32) + return ( + np.frombuffer(raw, dtype=" bytes: + """Apply device-specific gain with saturation while preserving PCM16 LE.""" + if gain <= 0: + raise ValueError("input gain must be greater than zero") + if gain == 1.0 or not raw: + return raw + samples = np.frombuffer(raw, dtype=" np.ndarray: + """Return a short, non-speech two-note cue for restart_complete.""" + + amplitude = min(1.0, max(0.0, float(amplitude))) + + def tone(frequency_hz: float, duration_s: float) -> np.ndarray: + count = max(2, int(sample_rate * duration_s)) + phase = np.arange(count, dtype=np.float32) / float(sample_rate) + envelope = np.sin(np.linspace(0.0, np.pi, count, dtype=np.float32)) ** 2 + return ( + amplitude + * envelope + * np.sin(2.0 * np.pi * frequency_hz * phase) + ).astype(np.float32) + + gap = np.zeros(max(1, int(sample_rate * 0.030)), dtype=np.float32) + return np.concatenate((tone(880.0, 0.080), gap, tone(1174.66, 0.105))) + + +def float32_to_base64(samples: np.ndarray) -> str: + return base64.b64encode( + samples.astype(np.float32, copy=False).tobytes() + ).decode("ascii") + + +def same_slots(left: dict[str, Any], right: dict[str, Any]) -> bool: + return {str(key): str(value) for key, value in left.items()} == { + str(key): str(value) for key, value in right.items() + } + + +def rotate_jpeg_clockwise(jpeg: bytes, degrees: int, quality: int = 95) -> bytes: + degrees %= 360 + if degrees == 0 or Image is None: + return jpeg + methods = { + 90: Image.Transpose.ROTATE_270, + 180: Image.Transpose.ROTATE_180, + 270: Image.Transpose.ROTATE_90, + } + method = methods.get(degrees) + if method is None: + raise ValueError("image rotation must be 0, 90, 180, or 270") + with Image.open(io.BytesIO(jpeg)) as image: + output = io.BytesIO() + image.convert("RGB").transpose(method).save( + output, format="JPEG", quality=quality + ) + return output.getvalue() + + +class DropOldestAudioQueue: + """Bounded device-audio queue that always preserves the newest packets.""" + + def __init__(self, max_packets: int): + self._queue: asyncio.Queue[bytes] = asyncio.Queue(maxsize=max(1, max_packets)) + self.dropped_packets = 0 + + @property + def size(self) -> int: + return self._queue.qsize() + + def put_nowait(self, raw: bytes) -> None: + if self._queue.full(): + try: + self._queue.get_nowait() + self.dropped_packets += 1 + except asyncio.QueueEmpty: + pass + self._queue.put_nowait(raw) + + async def get(self, timeout_s: float) -> bytes | None: + try: + return await asyncio.wait_for(self._queue.get(), timeout=timeout_s) + except asyncio.TimeoutError: + return None + + def clear(self) -> int: + cleared = 0 + while True: + try: + self._queue.get_nowait() + cleared += 1 + except asyncio.QueueEmpty: + return cleared + + +class AudioMirrorChunker: + """Repacketize Rokid PCM into the same 100 ms frames used in Phase A.""" + + def __init__(self, samples_per_frame: int = 1_600): + self.target_bytes = max(1, samples_per_frame) * PCM16_WIDTH + self._carry = bytearray() + + def feed(self, raw: bytes) -> list[np.ndarray]: + if len(raw) % PCM16_WIDTH: + raw = raw[:-1] + self._carry.extend(raw) + frames: list[np.ndarray] = [] + while len(self._carry) >= self.target_bytes: + frame = bytes(self._carry[: self.target_bytes]) + del self._carry[: self.target_bytes] + frames.append(pcm16le_to_float32(frame)) + return frames + + def clear(self) -> None: + self._carry.clear() + + +@dataclass(slots=True) +class LatestFrame: + jpeg: bytes | None = None + timestamp_ms: float = 0.0 + sequence: int = 0 + + def set(self, jpeg: bytes, timestamp_ms: float) -> None: + self.jpeg = jpeg + self.timestamp_ms = timestamp_ms + self.sequence += 1 + + +@dataclass(slots=True) +class OutputGate: + generation: int = 0 + current_skill: str = "idle_chat" + current_slots: dict[str, Any] | None = None + speech_hold_active: bool = False + drop_output_until_listen: bool = False + restart_in_progress: bool = False + dropped_old_text: int = 0 + dropped_old_audio: int = 0 + stop_count: int = 0 + local_cue_mute_until_mono: float = 0.0 + + def __post_init__(self) -> None: + if self.current_slots is None: + self.current_slots = {} + + def stop(self) -> None: + self.stop_count += 1 + self.speech_hold_active = True + self.drop_output_until_listen = True + + def resume(self) -> None: + self.speech_hold_active = False + self.drop_output_until_listen = False + + def replacement_ready(self) -> None: + self.speech_hold_active = False + self.drop_output_until_listen = False + self.restart_in_progress = False + + +class SpeakerSink(Protocol): + async def start(self) -> None: ... + + async def enqueue(self, pcm: np.ndarray, generation: int) -> None: ... + + async def block_and_flush(self) -> None: ... + + async def resume(self) -> None: ... + + async def close(self) -> None: ... + + def pending_ms(self) -> float: ... + + +class NullSpeaker: + async def start(self) -> None: + return None + + async def enqueue(self, pcm: np.ndarray, generation: int) -> None: + return None + + async def block_and_flush(self) -> None: + return None + + async def resume(self) -> None: + return None + + async def close(self) -> None: + return None + + def pending_ms(self) -> float: + return 0.0 + + +class PCSpeaker: + """Interruptible 24 kHz PC playback with a short write quantum.""" + + def __init__(self, block_ms: int = 50): + self.block_samples = max(240, int(SAMPLE_RATE_OUT * block_ms / 1000)) + self.queue: asyncio.Queue[tuple[int, int, np.ndarray]] = asyncio.Queue(maxsize=32) + self.blocked = False + self.epoch = 0 + self._stream: Any = None + self._worker: asyncio.Task[None] | None = None + self._io_lock = asyncio.Lock() + self._pending_samples = 0 + + def _open_stream(self) -> None: + import sounddevice as sd # type: ignore + + stream = sd.OutputStream( + samplerate=SAMPLE_RATE_OUT, + channels=1, + dtype="float32", + blocksize=self.block_samples, + latency="low", + ) + stream.start() + self._stream = stream + + def _close_stream(self) -> None: + stream = self._stream + self._stream = None + if stream is None: + return + try: + stream.abort() + finally: + stream.close() + + async def start(self) -> None: + try: + await asyncio.to_thread(self._open_stream) + self._worker = asyncio.create_task(self._play_loop()) + LOG.info("PC speaker output ready") + except Exception as exc: # pragma: no cover - hardware dependent + self._stream = None + LOG.warning("PC speaker disabled: %s", exc) + + async def enqueue(self, pcm: np.ndarray, generation: int) -> None: + if self._stream is None or self.blocked or pcm.size == 0: + return + if self.queue.full(): + try: + _epoch, _generation, dropped = self.queue.get_nowait() + self._pending_samples = max( + 0, self._pending_samples - int(dropped.size) + ) + except asyncio.QueueEmpty: + pass + copied = pcm.astype(np.float32, copy=True) + self._pending_samples += int(copied.size) + self.queue.put_nowait((self.epoch, generation, copied)) + + async def _play_loop(self) -> None: + while True: + epoch, _generation, pcm = await self.queue.get() + try: + for offset in range(0, pcm.size, self.block_samples): + if self.blocked or epoch != self.epoch or self._stream is None: + break + chunk = pcm[offset : offset + self.block_samples].reshape(-1, 1) + stream = self._stream + try: + async with self._io_lock: + if self.blocked or epoch != self.epoch or self._stream is not stream: + break + await asyncio.to_thread(stream.write, chunk) + except asyncio.CancelledError: + raise + except Exception as exc: # pragma: no cover - hardware dependent + LOG.warning("speaker write failed: %s", exc) + break + finally: + self._pending_samples = max( + 0, self._pending_samples - int(pcm.size) + ) + + async def block_and_flush(self) -> None: + self.blocked = True + self.epoch += 1 + self._pending_samples = 0 + while True: + try: + self.queue.get_nowait() + except asyncio.QueueEmpty: + break + if self._stream is not None: + try: + # Windows MME may reject start() immediately after abort() while + # the previous buffer is still retiring. Close the device here + # and create a fresh stream only when output is resumed. + async with self._io_lock: + await asyncio.to_thread(self._close_stream) + except Exception as exc: # pragma: no cover - hardware dependent + LOG.warning("speaker flush failed: %s", exc) + + async def resume(self) -> None: + if self._stream is None: + try: + async with self._io_lock: + if self._stream is None: + await asyncio.to_thread(self._open_stream) + except Exception as exc: # pragma: no cover - hardware dependent + LOG.warning("speaker resume failed: %s", exc) + self.blocked = False + + async def close(self) -> None: + self.blocked = True + self._pending_samples = 0 + if self._worker: + self._worker.cancel() + await asyncio.gather(self._worker, return_exceptions=True) + if self._stream is not None: + try: + async with self._io_lock: + await asyncio.to_thread(self._close_stream) + except Exception: + pass + + def pending_ms(self) -> float: + return self._pending_samples * 1000.0 / SAMPLE_RATE_OUT + + +@dataclass(slots=True) +class RokidRuntimeConfig: + host: str = "0.0.0.0" + port: int = 18_080 + gateway: str = "localhost:8040" + gateway_tls: bool = False + harness_url: str = "ws://127.0.0.1:8021/ws/control" + harness_client_id: str = "rokid-phase-b" + skills_config: str = "" + cleanup_mode: str = "light" + chunk_ms: int = 1_000 + force_listen_count: int = 3 + max_new_speak_tokens_per_chunk: int = 20 + length_penalty: float = 1.1 + max_slice_nums: int = 1 + audio_queue_packets: int = 96 + input_gain: float = 12.0 + image_rotate_cw: int = 270 + image_jpeg_quality: int = 95 + image_resend_s: float = 0.5 + image_max_age_s: float = 30.0 + prepare_timeout_s: float = 120.0 + close_timeout_s: float = 2.0 + reconnect_s: float = 1.5 + play_audio: bool = True + session_ready_chime: bool = True + session_ready_chime_volume: float = 0.32 + session_ready_chime_feedback_guard_s: float = 0.65 + playback_echo_tail_s: float = 0.80 + + +@dataclass(slots=True) +class SessionSpec: + generation: int + skill_id: str + slots: dict[str, Any] + system_prompt: str + + +class SessionTelemetry(Protocol): + connected: bool + + async def send(self, payload: dict[str, Any]) -> None: ... + + +class GatewayDuplexSession: + """One generation-fenced connection to the existing MiniCPM Gateway.""" + + def __init__( + self, + config: RokidRuntimeConfig, + spec: SessionSpec, + audio_queue: DropOldestAudioQueue, + latest_frame: LatestFrame, + gate: OutputGate, + on_result: Callable[["GatewayDuplexSession", dict[str, Any]], Awaitable[None]], + ): + self.config = config + self.spec = spec + self.audio_queue = audio_queue + self.latest_frame = latest_frame + self.gate = gate + self.on_result = on_result + self.session_id = ( + f"omni_rokid_pb_g{spec.generation}_{int(time.time())}_{uuid.uuid4().hex[:6]}" + ) + self.status = "created" + self.last_error = "" + self.ws: aiohttp.ClientWebSocketResponse | None = None + self._task: asyncio.Task[None] | None = None + self._ready: asyncio.Future[None] | None = None + self._stopped = asyncio.Event() + self._closing = False + self._send_lock = asyncio.Lock() + + async def _send_json(self, payload: dict[str, Any]) -> None: + ws = self.ws + if ws is None or ws.closed: + raise RuntimeError("gateway session is not connected") + async with self._send_lock: + await ws.send_json(payload) + + def _ssl_context(self) -> ssl.SSLContext | None: + if not self.config.gateway_tls: + return None + context = ssl.create_default_context() + context.check_hostname = False + context.verify_mode = ssl.CERT_NONE + return context + + async def start(self) -> None: + loop = asyncio.get_running_loop() + self._ready = loop.create_future() + self._ready.add_done_callback( + lambda future: None if future.cancelled() else future.exception() + ) + self._task = asyncio.create_task(self._run()) + await asyncio.wait_for( + asyncio.shield(self._ready), timeout=self.config.prepare_timeout_s + ) + + async def _run(self) -> None: + scheme = "wss" if self.config.gateway_tls else "ws" + url = f"{scheme}://{self.config.gateway}/ws/duplex/{self.session_id}" + self.status = "connecting" + LOG.info("[GW] connecting generation=%d %s", self.spec.generation, url) + try: + async with aiohttp.ClientSession() as client: + async with client.ws_connect( + url, + heartbeat=30, + max_msg_size=0, + ssl=self._ssl_context(), + ) as ws: + self.ws = ws + await self._prepare(ws) + self.status = "running" + if self._ready and not self._ready.done(): + self._ready.set_result(None) + send_task = asyncio.create_task(self._send_loop(ws)) + receive_task = asyncio.create_task(self._receive_loop(ws)) + done, pending = await asyncio.wait( + (send_task, receive_task), + return_when=asyncio.FIRST_COMPLETED, + ) + for task in pending: + task.cancel() + await asyncio.gather(*done, *pending, return_exceptions=True) + except asyncio.CancelledError: + raise + except Exception as exc: + self.status = "error" + self.last_error = str(exc) + if self._ready and not self._ready.done(): + self._ready.set_exception(exc) + if not self._closing: + LOG.warning("[GW] session %s failed: %s", self.session_id, exc) + finally: + self.ws = None + self._stopped.set() + if self.status != "error": + self.status = "stopped" + + async def _prepare(self, ws: aiohttp.ClientWebSocketResponse) -> None: + self.status = "queued" + while True: + message = await ws.receive() + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + raise RuntimeError("gateway closed while queued") + continue + payload = json.loads(message.data) + message_type = payload.get("type") + if message_type == "queue_done": + break + if message_type == "error": + raise RuntimeError(payload.get("error") or "gateway queue error") + if message_type in {"queued", "queue_update"}: + LOG.info( + "[GW] queue position=%s eta=%s", + payload.get("position"), + payload.get("estimated_wait_s"), + ) + + self.status = "preparing" + await self._send_json( + { + "type": "prepare", + "system_prompt": self.spec.system_prompt, + "config": { + "force_listen_count": self.config.force_listen_count, + "chunk_ms": self.config.chunk_ms, + "generate_audio": True, + "max_new_speak_tokens_per_chunk": ( + self.config.max_new_speak_tokens_per_chunk + ), + "length_penalty": self.config.length_penalty, + }, + "max_slice_nums": self.config.max_slice_nums, + "deferred_finalize": True, + } + ) + while True: + message = await ws.receive() + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + raise RuntimeError("gateway closed while preparing") + continue + payload = json.loads(message.data) + if payload.get("type") == "prepared": + LOG.info( + "[GW] prepared session=%s generation=%d skill=%s", + self.session_id, + self.spec.generation, + self.spec.skill_id, + ) + return + if payload.get("type") == "error": + raise RuntimeError(payload.get("error") or "gateway prepare error") + + async def _next_audio_chunk(self, carry: bytearray) -> np.ndarray | None: + samples = int(SAMPLE_RATE_IN * self.config.chunk_ms / 1000) + target_bytes = samples * PCM16_WIDTH + output = bytearray() + if carry: + take = min(target_bytes, len(carry)) + output.extend(carry[:take]) + del carry[:take] + deadline = time.monotonic() + max(0.1, self.config.chunk_ms / 1000 * 1.5) + while len(output) < target_bytes: + remaining = deadline - time.monotonic() + if remaining <= 0: + break + raw = await self.audio_queue.get(remaining) + if raw is None: + break + needed = target_bytes - len(output) + output.extend(raw[:needed]) + if len(raw) > needed: + carry.extend(raw[needed:]) + if not output: + return None + if len(output) < target_bytes: + output.extend(b"\x00" * (target_bytes - len(output))) + return pcm16le_to_float32(bytes(output)) + + async def _send_loop(self, ws: aiohttp.ClientWebSocketResponse) -> None: + carry = bytearray() + last_frame_sequence = -1 + last_frame_sent = 0.0 + while not self._closing: + audio = await self._next_audio_chunk(carry) + if audio is None: + continue + payload: dict[str, Any] = { + "type": "audio_chunk", + "audio_base64": float32_to_base64(audio), + } + if self.gate.speech_hold_active: + payload["force_listen"] = True + frame = self.latest_frame + frame_age_ms = now_ms() - frame.timestamp_ms + frame_due = time.monotonic() - last_frame_sent >= self.config.image_resend_s + if ( + frame.jpeg + and frame_age_ms <= self.config.image_max_age_s * 1000 + and (frame.sequence != last_frame_sequence or frame_due) + ): + payload["frame_base64_list"] = [ + base64.b64encode(frame.jpeg).decode("ascii") + ] + last_frame_sequence = frame.sequence + last_frame_sent = time.monotonic() + # 诊断：真正送进 gateway 的每个 chunk。audio=xxpps 那个统计的是 + # "推进 audio_queue 的包数"，看不出这里有没有按 1Hz 正常发出去。 + self._sent_chunks = getattr(self, "_sent_chunks", 0) + 1 + _lvl = float(np.abs(audio).mean()) if audio is not None else 0.0 + LOG.debug("[GW-SEND] #%d audio=%.2fs lvl=%.4f frame=%s hold=%s", + self._sent_chunks, + (len(audio) / SAMPLE_RATE_IN) if audio is not None else 0.0, + _lvl, + "frame_base64_list" in payload, + self.gate.speech_hold_active) + await self._send_json(payload) + + async def inject_task(self, text: str) -> bool: + """Send one text+latest-frame task to a newly prepared Skill Session.""" + text = text.strip() + if not text: + return False + payload: dict[str, Any] = { + "type": "audio_chunk", + "inject_text": text, + "max_slice_nums": self.config.max_slice_nums, + } + frame = self.latest_frame + frame_age_ms = now_ms() - frame.timestamp_ms + has_frame = bool( + frame.jpeg + and frame_age_ms <= self.config.image_max_age_s * 1000 + ) + if has_frame and frame.jpeg is not None: + payload["frame_base64_list"] = [ + base64.b64encode(frame.jpeg).decode("ascii") + ] + await self._send_json(payload) + LOG.info( + "[TASK] injected generation=%d skill=%s frame=%s text=%r", + self.spec.generation, + self.spec.skill_id, + has_frame, + text, + ) + return has_frame + + async def _receive_loop(self, ws: aiohttp.ClientWebSocketResponse) -> None: + async for message in ws: + if message.type == aiohttp.WSMsgType.TEXT: + payload = json.loads(message.data) + message_type = payload.get("type") + if message_type in {"result", "audio_only"}: + await self.on_result(self, payload) + elif message_type == "hint_audio": + # 强制措施念提示：独立播放，不进 on_result 状态机 + # （不记 model_turn / 不发 model.state / 不触发 EchoGuard）， + # 避免和 duplex 主对话抢话、纠缠。 + hint_b64 = str(payload.get("audio_data") or "") + if hint_b64: + try: + pcm = np.frombuffer(base64.b64decode(hint_b64), dtype=np.float32) + # 念提示前先解除可能的 block（stop 时 block_and_flush 过）， + # 否则 enqueue 会被丢。 + await self.speaker.resume() + await self.speaker.enqueue(pcm, self.spec.generation) + LOG.info("[HINT] 念提示播放: %r (%d samples)", + payload.get("text"), int(pcm.size)) + except Exception as exc: + LOG.warning("[HINT] 念提示播放失败: %s", exc) + elif message_type == "stopped": + return + elif message_type in {"timeout", "error"}: + raise RuntimeError( + payload.get("error") or payload.get("reason") or message_type + ) + elif message.type in (aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.ERROR): + return + + async def stop(self, cleanup_mode: str) -> None: + self._closing = True + if self.ws is not None and not self.ws.closed: + try: + await self._send_json({"type": "stop", "cleanup_mode": cleanup_mode}) + except Exception: + pass + try: + await asyncio.wait_for(self._stopped.wait(), timeout=self.config.close_timeout_s) + except asyncio.TimeoutError: + pass + if self.ws is not None and not self.ws.closed: + await self.ws.close() + if self._task and not self._task.done(): + self._task.cancel() + await asyncio.gather(self._task, return_exceptions=True) + + +SessionFactory = Callable[ + [ + RokidRuntimeConfig, + SessionSpec, + DropOldestAudioQueue, + LatestFrame, + OutputGate, + Callable[[GatewayDuplexSession, dict[str, Any]], Awaitable[None]], + ], + GatewayDuplexSession, +] + + +class GatewaySessionManager: + """Python counterpart of the frozen BrowserSessionAdapter contract.""" + + def __init__( + self, + config: RokidRuntimeConfig, + registry: SkillRegistry, + audio_queue: DropOldestAudioQueue, + latest_frame: LatestFrame, + speaker: SpeakerSink, + session_factory: SessionFactory = GatewayDuplexSession, + ): + self.config = config + self.registry = registry + self.audio_queue = audio_queue + self.latest_frame = latest_frame + self.speaker = speaker + self.session_factory = session_factory + self.gate = OutputGate(current_skill=registry.default_skill) + self.harness: SessionTelemetry | None = None + self.active: GatewayDuplexSession | None = None + self._restart_lock = asyncio.Lock() + self._playback_epoch = 0 + self._playback_release_task: asyncio.Task[None] | None = None + + async def send_telemetry(self, payload: dict[str, Any]) -> None: + if self.harness is not None: + await self.harness.send(payload) + + async def _set_playback_active(self, active: bool, source: str) -> None: + await self.send_telemetry( + { + "type": "playback.state", + "active": active, + "source": source, + "generation": self.gate.generation, + "pending_ms": round(self.speaker.pending_ms(), 1), + } + ) + + async def _mark_playback_started(self, source: str) -> None: + self._playback_epoch += 1 + epoch = self._playback_epoch + if self._playback_release_task is not None: + self._playback_release_task.cancel() + await self._set_playback_active(True, source) + + async def release_after_drain() -> None: + try: + while self.speaker.pending_ms() > 0: + await asyncio.sleep( + max(0.02, min(0.25, self.speaker.pending_ms() / 1000.0)) + ) + await asyncio.sleep(max(0.0, self.config.playback_echo_tail_s)) + if epoch == self._playback_epoch: + await self._set_playback_active(False, source) + except asyncio.CancelledError: + raise + + self._playback_release_task = asyncio.create_task(release_after_drain()) + + async def _clear_playback_state(self, source: str) -> None: + self._playback_epoch += 1 + if self._playback_release_task is not None: + self._playback_release_task.cancel() + await asyncio.gather(self._playback_release_task, return_exceptions=True) + self._playback_release_task = None + await self._set_playback_active(False, source) + + def _make_spec( + self, + skill_id: str, + slots: dict[str, Any], + system_prompt: str | None = None, + ) -> SessionSpec: + rendered = self.registry.render(skill_id, slots) + return SessionSpec( + generation=self.gate.generation, + skill_id=skill_id, + slots=dict(slots), + system_prompt=system_prompt or rendered.text, + ) + + async def start_initial(self) -> None: + async with self._restart_lock: + if self.active is not None: + return + spec = self._make_spec(self.registry.default_skill, {}) + session = self.session_factory( + self.config, + spec, + self.audio_queue, + self.latest_frame, + self.gate, + self.handle_result, + ) + self.active = session + try: + await session.start() + except Exception: + self.active = None + raise + await self._emit_bound() + + async def _emit_bound(self) -> None: + await self.send_telemetry( + { + "type": "session.state", + "phase": "bound", + "generation": self.gate.generation, + "skill_id": self.gate.current_skill, + "slots": dict(self.gate.current_slots or {}), + "session_id": self.active.session_id if self.active else None, + } + ) + + async def emit_recovery_sync(self) -> None: + if self.active is None: + return + await self.send_telemetry( + { + "type": "session.state", + "phase": "restart_complete", + "generation": self.gate.generation, + "skill_id": self.gate.current_skill, + "slots": dict(self.gate.current_slots or {}), + "new_session_id": self.active.session_id, + "cleanup_mode": "reconnect_sync", + } + ) + await self._emit_bound() + + async def handle_control(self, event: dict[str, Any]) -> dict[str, Any]: + if not event.get("accepted"): + return {"ok": False, "ignored": True} + intent = str(event.get("intent") or "") + if intent == "stop_speech": + return await self.stop_speech(event) + if intent == "resume_speech": + return await self.resume_speech(event) + if intent in { + "reset_session", + "activate_skill", + "cancel_skill", + "return_to_chat", + }: + return await self.restart(event) + return {"ok": False, "ignored": True} + + def _ack_base(self, event: dict[str, Any]) -> dict[str, Any]: + return { + "type": "control.ack", + "event_id": event.get("event_id"), + "intent": event.get("intent"), + "generation": self.gate.generation, + "dropped_old_text": self.gate.dropped_old_text, + "dropped_old_audio": self.gate.dropped_old_audio, + } + + async def stop_speech( + self, event: dict[str, Any], *, emit_ack: bool = True + ) -> dict[str, Any]: + self.gate.stop() + await self.speaker.block_and_flush() + await self._clear_playback_state("stop") + ack = {**self._ack_base(event), "ok": True} + if emit_ack: + await self.send_telemetry(ack) + LOG.info("[CONTROL] STOP event=%s", event.get("event_id")) + return ack + + async def resume_speech(self, event: dict[str, Any]) -> dict[str, Any]: + self.gate.resume() + await self.speaker.resume() + ack = {**self._ack_base(event), "ok": True} + await self.send_telemetry(ack) + LOG.info("[CONTROL] RESUME event=%s", event.get("event_id")) + return ack + + async def restart(self, event: dict[str, Any]) -> dict[str, Any]: + async with self._restart_lock: + requested_skill = str(event.get("skill_id") or self.registry.default_skill) + requested_slots = dict(event.get("slots") or {}) + if ( + str(event.get("intent")) != "reset_session" + and requested_skill == self.gate.current_skill + and same_slots(requested_slots, dict(self.gate.current_slots or {})) + ): + ack = {**self._ack_base(event), "ok": True, "no_restart": True} + await self.send_telemetry(ack) + return ack + + started = time.monotonic() + await self.stop_speech(event, emit_ack=False) + self.gate.restart_in_progress = True + old_session = self.active + old_session_id = old_session.session_id if old_session else None + self.gate.generation += 1 + await self.send_telemetry( + { + "type": "session.state", + "phase": "restart_started", + "generation": self.gate.generation, + "event_id": event.get("event_id"), + "old_session_id": old_session_id, + } + ) + self.active = None + try: + if old_session: + await old_session.stop(self.config.cleanup_mode) + self.audio_queue.clear() + spec = self._make_spec( + requested_skill, + requested_slots, + str(event.get("system_prompt") or "") or None, + ) + session = self.session_factory( + self.config, + spec, + self.audio_queue, + self.latest_frame, + self.gate, + self.handle_result, + ) + self.active = session + await session.start() + self.gate.current_skill = requested_skill + self.gate.current_slots = requested_slots + self.gate.replacement_ready() + await self.speaker.resume() + await self._emit_bound() + latency_ms = (time.monotonic() - started) * 1000.0 + state = { + "type": "session.state", + "phase": "restart_complete", + "generation": self.gate.generation, + "skill_id": requested_skill, + "slots": requested_slots, + "old_session_id": old_session_id, + "new_session_id": session.session_id, + "cleanup_mode": self.config.cleanup_mode, + } + await self.send_telemetry(state) + if self.config.play_audio and self.config.session_ready_chime: + cue = make_session_ready_chime( + amplitude=self.config.session_ready_chime_volume + ) + self.gate.local_cue_mute_until_mono = max( + self.gate.local_cue_mute_until_mono, + time.monotonic() + + cue.size / SAMPLE_RATE_OUT + + self.config.session_ready_chime_feedback_guard_s, + ) + await self.speaker.enqueue(cue, self.gate.generation) + await self._mark_playback_started("session_ready_chime") + LOG.info( + "[CUE] session ready generation=%d skill=%s", + self.gate.generation, + requested_skill, + ) + trigger = self.registry.task_trigger(requested_skill, requested_slots) + trigger_frame = False + trigger_error = "" + if trigger: + try: + trigger_frame = await session.inject_task(trigger) + await self.send_telemetry( + { + "type": "session.state", + "phase": "task_trigger_sent", + "generation": self.gate.generation, + "skill_id": requested_skill, + "slots": requested_slots, + "session_id": session.session_id, + "has_frame": trigger_frame, + } + ) + except Exception as exc: + trigger_error = str(exc) + LOG.warning( + "[TASK] one-shot trigger failed skill=%s: %s", + requested_skill, + exc, + ) + ack = { + **self._ack_base(event), + "ok": True, + "restart_latency_ms": latency_ms, + "skill_id": requested_skill, + "slots": requested_slots, + "old_session_id": old_session_id, + "new_session_id": session.session_id, + "cleanup_mode": self.config.cleanup_mode, + "task_trigger_sent": bool(trigger and not trigger_error), + "task_trigger_has_frame": trigger_frame, + "task_trigger_error": trigger_error, + } + await self.send_telemetry(ack) + LOG.info( + "[CONTROL] %s ready generation=%d session=%s latency_ms=%.1f", + requested_skill, + self.gate.generation, + session.session_id, + latency_ms, + ) + return ack + except Exception as exc: + if self.active: + await self.active.stop(self.config.cleanup_mode) + self.active = None + self.gate.restart_in_progress = False + ack = {**self._ack_base(event), "ok": False, "error": str(exc)} + await self.send_telemetry(ack) + LOG.exception("[CONTROL] restart failed") + return ack + + async def handle_result( + self, session: GatewayDuplexSession, result: dict[str, Any] + ) -> None: + is_listen = bool(result.get("is_listen")) + text = str(result.get("text") or "") + audio_b64 = str(result.get("audio_data") or "") + stale = ( + session is not self.active + or session.spec.generation != self.gate.generation + ) + if stale or (self.gate.drop_output_until_listen and not is_listen): + if text: + self.gate.dropped_old_text += 1 + if audio_b64: + self.gate.dropped_old_audio += 1 + # 诊断：这条路径原本静默 return，模型的输出被整个丢掉却不留痕迹 + #（表现就是 ai(calls=0) + [MODEL] 只有孤零零一条）。打出丢弃原因。 + self._drop_n = getattr(self, "_drop_n", 0) + 1 + if self._drop_n <= 3 or self._drop_n % 100 == 0: + LOG.warning( + "[GW-DROP] #%d stale=%s (active=%s gen_sess=%s gen_gate=%s) " + "drop_until_listen=%s is_listen=%s text=%r audio=%dB", + self._drop_n, stale, + session is self.active, + session.spec.generation, self.gate.generation, + self.gate.drop_output_until_listen, is_listen, + text[:20], len(audio_b64)) + return + if is_listen and self.gate.drop_output_until_listen: + if not self.gate.speech_hold_active: + self.gate.drop_output_until_listen = False + await self.speaker.resume() + audio_samples = 0 + if audio_b64 and not is_listen: + try: + pcm = np.frombuffer(base64.b64decode(audio_b64), dtype=np.float32) + audio_samples = int(pcm.size) + await self.speaker.enqueue(pcm, session.spec.generation) + if self.config.play_audio: + await self._mark_playback_started("model") + except Exception as exc: + LOG.warning("model audio decode failed: %s", exc) + await self.send_telemetry( + { + "type": "model.state", + "state": "listen" if is_listen else "speak", + "text": text, + "generation": session.spec.generation, + "session_id": session.session_id, + "skill_id": session.spec.skill_id, + "slots": dict(session.spec.slots), + # llama.cpp-omni marks each decode slice as end_of_turn. The + # actual duplex turn boundary is the later __IS_LISTEN__ + # result, so keep the raw marker for diagnostics but aggregate + # model text until listening resumes. + "decode_end": bool(result.get("end_of_turn")), + "end_of_turn": is_listen, + "message_type": str(result.get("type") or "result"), + "audio_samples": audio_samples, + "audio_ms": round( + audio_samples * 1000.0 / SAMPLE_RATE_OUT, 1 + ), + } + ) + if text: + LOG.info("[MODEL] listen=%s text=%s", is_listen, text) + + async def close(self) -> None: + await self._clear_playback_state("close") + if self.active: + await self.active.stop(self.config.cleanup_mode) + self.active = None + + def health(self) -> dict[str, Any]: + return { + "generation": self.gate.generation, + "current_skill": self.gate.current_skill, + "current_slots": dict(self.gate.current_slots or {}), + "speech_hold_active": self.gate.speech_hold_active, + "restart_in_progress": self.gate.restart_in_progress, + "dropped_old_text": self.gate.dropped_old_text, + "dropped_old_audio": self.gate.dropped_old_audio, + "session_id": self.active.session_id if self.active else None, + "gateway_status": self.active.status if self.active else "disconnected", + "gateway_error": self.active.last_error if self.active else "", + } + + +class HarnessClient: + def __init__( + self, + url: str, + client_id: str, + manager: GatewaySessionManager, + reconnect_s: float, + ): + separator = "&" if "?" in url else "?" + self.url = f"{url}{separator}client_id={client_id}" + self.manager = manager + self.reconnect_s = reconnect_s + self.connected = False + self.ws: aiohttp.ClientWebSocketResponse | None = None + self._stop = asyncio.Event() + self._send_lock = asyncio.Lock() + self._control_tasks: set[asyncio.Task[Any]] = set() + + # 外部可挂的只读观察者：收到未被上面分支处理的消息时调用（同步、异常吞掉）。 + # 不改构造签名，赋值即可：client.on_message = fn + on_message = None + + async def run(self) -> None: + while not self._stop.is_set(): + try: + async with aiohttp.ClientSession() as client: + async with client.ws_connect( + self.url, heartbeat=30, max_msg_size=0 + ) as ws: + self.ws = ws + self.connected = True + LOG.info("Harness connected: %s", self.url) + async for message in ws: + if message.type != aiohttp.WSMsgType.TEXT: + if message.type in ( + aiohttp.WSMsgType.CLOSED, + aiohttp.WSMsgType.ERROR, + ): + break + continue + payload = json.loads(message.data) + message_type = payload.get("type") + if message_type == "harness.ready": + await self.manager.emit_recovery_sync() + elif message_type == "control.intent": + task = asyncio.create_task( + self.manager.handle_control(payload) + ) + self._control_tasks.add(task) + task.add_done_callback(self._control_tasks.discard) + # 可选旁路：把其余消息（如 asr.transcript）交给外部观察者。 + # 默认 None，行为与原来完全一致。8021 的 asr.transcript 只投递给 + # "送音频进来的那个 client" 的 outbound 队列（每个 client_id 有 + # 独立 runtime），另开一条连接是收不到的，只能在这里截。 + elif self.on_message is not None: + try: + self.on_message(payload) + except Exception: + pass + except asyncio.CancelledError: + raise + except Exception as exc: + if not self._stop.is_set(): + LOG.warning("Harness connection failed: %s", exc) + finally: + self.connected = False + self.ws = None + if not self._stop.is_set(): + await asyncio.sleep(self.reconnect_s) + + async def send(self, payload: dict[str, Any]) -> None: + async with self._send_lock: + if self.ws is not None and not self.ws.closed: + await self.ws.send_json(payload) + + async def send_audio(self, samples: np.ndarray) -> None: + if samples.size == 0: + return + await self.send( + { + "type": "audio.mirror", + "started_at_ms": now_ms() - samples.size * 1000.0 / SAMPLE_RATE_IN, + "sample_rate": SAMPLE_RATE_IN, + "audio_b64": float32_to_base64(samples), + } + ) + + async def send_frame(self, jpeg: bytes, sequence: int, timestamp_ms: float) -> None: + await self.send( + { + "type": "frame.shadow", + "frame_id": f"rokid_{sequence}_{int(timestamp_ms)}", + "timestamp_ms": timestamp_ms, + "jpeg_b64": base64.b64encode(jpeg).decode("ascii"), + } + ) + + async def close(self) -> None: + self._stop.set() + if self.ws is not None and not self.ws.closed: + await self.ws.close() + for task in tuple(self._control_tasks): + task.cancel() + await asyncio.gather(*self._control_tasks, return_exceptions=True) + + +@dataclass(slots=True) +class RuntimeStats: + started_mono: float = field(default_factory=time.monotonic) + audio_packets: int = 0 + audio_bytes: int = 0 + audio_clients: int = 0 + image_count: int = 0 + image_bytes: int = 0 + last_audio_ms: float = 0.0 + audio_rms: float = 0.0 + audio_peak: float = 0.0 + non_silent_audio_packets: int = 0 + + +class PhaseBRokidRuntime: + """Rokid sensor ingress + frozen Harness control + Gateway session adapter.""" + + def __init__( + self, + config: RokidRuntimeConfig, + *, + speaker: SpeakerSink | None = None, + session_factory: SessionFactory = GatewayDuplexSession, + ): + self.config = config + self.registry = SkillRegistry(config.skills_config) + self.audio_queue = DropOldestAudioQueue(config.audio_queue_packets) + self.audio_mirror = AudioMirrorChunker() + self.latest_frame = LatestFrame() + self.stats = RuntimeStats(started_mono=time.monotonic()) + self.speaker = speaker or (PCSpeaker() if config.play_audio else NullSpeaker()) + self.manager = GatewaySessionManager( + config, + self.registry, + self.audio_queue, + self.latest_frame, + self.speaker, + session_factory=session_factory, + ) + self.harness = HarnessClient( + config.harness_url, + config.harness_client_id, + self.manager, + config.reconnect_s, + ) + self.manager.harness = self.harness + self._tasks: list[asyncio.Task[Any]] = [] + self._close_lock = asyncio.Lock() + self._closed = False + self._no_input_warning_emitted = False + + def health(self) -> dict[str, Any]: + return { + "ok": True, + "phase": "B", + "device": "rokid", + "uptime_s": round(time.monotonic() - self.stats.started_mono, 3), + "harness_connected": self.harness.connected, + "audio_packets_in": self.stats.audio_packets, + "audio_bytes_in": self.stats.audio_bytes, + "audio_clients": self.stats.audio_clients, + "latest_audio_age_ms": ( + round(now_ms() - self.stats.last_audio_ms, 1) + if self.stats.last_audio_ms + else None + ), + "audio_queue_size": self.audio_queue.size, + "dropped_audio_packets": self.audio_queue.dropped_packets, + "audio_rms": round(self.stats.audio_rms, 6), + "audio_peak": round(self.stats.audio_peak, 6), + "input_gain": self.config.input_gain, + "effective_audio_rms": round( + min(1.0, self.stats.audio_rms * self.config.input_gain), 6 + ), + "non_silent_audio_packets": self.stats.non_silent_audio_packets, + "image_count": self.stats.image_count, + "device_input_ready": bool( + self.stats.audio_packets or self.stats.image_count + ), + "latest_image_age_ms": ( + round(now_ms() - self.latest_frame.timestamp_ms, 1) + if self.latest_frame.timestamp_ms + else None + ), + **self.manager.health(), + } + + async def _initial_session_loop(self) -> None: + while True: + try: + await self.manager.start_initial() + return + except asyncio.CancelledError: + raise + except Exception as exc: + LOG.warning("initial Gateway session failed: %s", exc) + await asyncio.sleep(self.config.reconnect_s) + + async def _stats_loop(self) -> None: + previous_audio = 0 + previous_image = 0 + while True: + await asyncio.sleep(5.0) + if ( + not self._no_input_warning_emitted + and time.monotonic() - self.stats.started_mono >= 10.0 + and self.stats.audio_packets == 0 + and self.stats.image_count == 0 + ): + self._no_input_warning_emitted = True + LOG.warning( + "[ROKID][NO_INPUT] no PCM/JPEG has reached this process. " + "After the PC runtime is listening, restart OpenGlass " + "Sensor Mode on the glasses (STOP -> RUN) and verify that " + "the APK targets the PC WLAN address on port %d", + self.config.port, + ) + LOG.info( + "[STATS] audio=%.1fpps images=%d(+%d) audio_clients=%d " + "queue=%d drops=%d rms=%.4f peak=%.4f harness=%s gateway=%s " + "skill=%s generation=%d", + (self.stats.audio_packets - previous_audio) / 5.0, + self.stats.image_count, + self.stats.image_count - previous_image, + self.stats.audio_clients, + self.audio_queue.size, + self.audio_queue.dropped_packets, + self.stats.audio_rms, + self.stats.audio_peak, + self.harness.connected, + self.manager.health()["gateway_status"], + self.manager.gate.current_skill, + self.manager.gate.generation, + ) + previous_audio = self.stats.audio_packets + previous_image = self.stats.image_count + + async def start(self) -> None: + await self.speaker.start() + self._tasks = [ + asyncio.create_task(self.harness.run()), + asyncio.create_task(self._initial_session_loop()), + asyncio.create_task(self._stats_loop()), + ] + + async def close(self) -> None: + async with self._close_lock: + if self._closed: + return + LOG.info("Rokid runtime shutdown started") + + async def bounded(label: str, awaitable: Awaitable[None]) -> None: + try: + await asyncio.wait_for( + awaitable, + timeout=max(0.5, self.config.close_timeout_s), + ) + except asyncio.TimeoutError: + LOG.warning( + "Rokid shutdown step timed out: %s (continuing)", label + ) + except Exception as exc: + LOG.warning( + "Rokid shutdown step failed: %s: %s", label, exc + ) + + await bounded("harness", self.harness.close()) + for task in self._tasks: + task.cancel() + if self._tasks: + await bounded( + "background_tasks", + asyncio.gather(*self._tasks, return_exceptions=True), + ) + self._tasks.clear() + await bounded("gateway_session", self.manager.close()) + await bounded("pc_speaker", self.speaker.close()) + self.audio_queue.clear() + self.audio_mirror.clear() + self._closed = True + LOG.info("Rokid runtime shutdown complete; port may be reused") + + def create_app(self) -> web.Application: + app = web.Application(client_max_size=8 * 1024 * 1024) + app["runtime"] = self + + async def health(_request: web.Request) -> web.Response: + return web.json_response(self.health()) + + async def capture(_request: web.Request) -> web.Response: + if not self.latest_frame.jpeg: + return web.Response(status=404, text="no image") + return web.Response(body=self.latest_frame.jpeg, content_type="image/jpeg") + + async def rokid_image(request: web.Request) -> web.Response: + raw = await request.read() + if not (raw.startswith(b"\xff\xd8") and raw.endswith(b"\xff\xd9")): + return web.Response(status=400, text="expected JPEG bytes") + try: + jpeg = await asyncio.to_thread( + rotate_jpeg_clockwise, + raw, + self.config.image_rotate_cw, + self.config.image_jpeg_quality, + ) + except Exception as exc: + return web.Response(status=400, text=str(exc)) + timestamp = now_ms() + self.latest_frame.set(jpeg, timestamp) + self.stats.image_count += 1 + self.stats.image_bytes += len(raw) + if self.stats.image_count == 1: + LOG.info("[ROKID] first JPEG received from %s", request.remote) + asyncio.create_task( + self.harness.send_frame( + jpeg, self.latest_frame.sequence, timestamp + ) + ) + return web.json_response( + {"ok": True, "image_count": self.stats.image_count, "bytes": len(raw)} + ) + + async def rokid_audio(request: web.Request) -> web.WebSocketResponse: + ws = web.WebSocketResponse(heartbeat=30, max_msg_size=0) + await ws.prepare(request) + self.stats.audio_clients += 1 + LOG.info("[ROKID] audio connected from %s", request.remote) + try: + async for message in ws: + if message.type == aiohttp.WSMsgType.BINARY: + raw = bytes(message.data) + if len(raw) < PCM16_WIDTH: + continue + if len(raw) % PCM16_WIDTH: + raw = raw[:-1] + self.stats.audio_packets += 1 + self.stats.audio_bytes += len(raw) + self.stats.last_audio_ms = now_ms() + if self.stats.audio_packets == 1: + LOG.info("[ROKID] first PCM packet received") + samples = pcm16le_to_float32(raw) + packet_rms = float(np.sqrt(np.mean(np.square(samples)))) + packet_peak = float(np.max(np.abs(samples))) + self.stats.audio_rms = ( + 0.8 * self.stats.audio_rms + 0.2 * packet_rms + ) + self.stats.audio_peak = packet_peak + if packet_rms >= 0.01: + self.stats.non_silent_audio_packets += 1 + if time.monotonic() >= self.manager.gate.local_cue_mute_until_mono: + amplified = apply_pcm16_gain(raw, self.config.input_gain) + self.audio_queue.put_nowait(amplified) + for frame in self.audio_mirror.feed(amplified): + await self.harness.send_audio(frame) + elif message.type == aiohttp.WSMsgType.ERROR: + break + finally: + self.stats.audio_clients = max(0, self.stats.audio_clients - 1) + LOG.info("[ROKID] audio disconnected from %s", request.remote) + return ws + + async def on_startup(_app: web.Application) -> None: + await self.start() + + async def on_cleanup(_app: web.Application) -> None: + await self.close() + + app.router.add_get("/", health) + app.router.add_get("/health", health) + app.router.add_get("/capture", capture) + app.router.add_post("/rokid/image", rokid_image) + app.router.add_get("/rokid/audio", rokid_audio) + app.on_startup.append(on_startup) + app.on_cleanup.append(on_cleanup) + return app + + +def default_skills_config() -> str: + return str( + Path(__file__).resolve().parents[1] / "config" / "skills.example.yaml" + ) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Rokid Phase B adapter: sensors + Harness + MiniCPM Gateway" + ) + parser.add_argument("--host", default="0.0.0.0") + parser.add_argument("--port", type=int, default=18_080) + parser.add_argument("--gateway", default="localhost:8040") + parser.add_argument("--gateway-tls", action="store_true", default=False) + parser.add_argument( + "--no-gateway-tls", dest="gateway_tls", action="store_false" + ) + parser.add_argument( + "--harness-url", default="ws://127.0.0.1:8021/ws/control" + ) + parser.add_argument("--client-id", default="rokid-phase-b") + parser.add_argument("--skills-config", default=default_skills_config()) + parser.add_argument("--cleanup-mode", choices=("light", "full"), default="light") + parser.add_argument("--image-rotate-cw", type=int, default=270) + parser.add_argument("--image-jpeg-quality", type=int, default=95) + parser.add_argument("--audio-queue-packets", type=int, default=96) + parser.add_argument( + "--input-gain", + type=float, + default=12.0, + help="Rokid PCM gain before Harness/Gateway; clipped to PCM16", + ) + parser.add_argument("--chunk-ms", type=int, default=1_000) + parser.add_argument("--force-listen-count", type=int, default=3) + parser.add_argument("--no-play", action="store_true") + parser.add_argument( + "--no-session-ready-chime", + action="store_true", + help="disable the local restart_complete notification cue", + ) + parser.add_argument( + "--session-ready-chime-volume", + type=float, + default=0.32, + help="restart cue amplitude in [0, 1] (default: 0.32)", + ) + parser.add_argument( + "--playback-echo-tail-s", + type=float, + default=0.80, + help="keep EchoGuard active after the PC speaker queue drains", + ) + parser.add_argument("--log-level", default="INFO") + return parser.parse_args() + + +def main() -> None: + args = parse_args() + if not 0.0 <= args.session_ready_chime_volume <= 1.0: + raise SystemExit("--session-ready-chime-volume must be between 0 and 1") + if args.playback_echo_tail_s < 0.0: + raise SystemExit("--playback-echo-tail-s must be non-negative") + logging.basicConfig( + level=getattr(logging, args.log_level.upper(), logging.INFO), + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + force=True, + ) + config = RokidRuntimeConfig( + host=args.host, + port=args.port, + gateway=args.gateway, + gateway_tls=args.gateway_tls, + harness_url=args.harness_url, + harness_client_id=args.client_id, + skills_config=args.skills_config, + cleanup_mode=args.cleanup_mode, + chunk_ms=args.chunk_ms, + force_listen_count=args.force_listen_count, + audio_queue_packets=args.audio_queue_packets, + input_gain=args.input_gain, + image_rotate_cw=args.image_rotate_cw, + image_jpeg_quality=args.image_jpeg_quality, + play_audio=not args.no_play, + session_ready_chime=not args.no_session_ready_chime, + session_ready_chime_volume=args.session_ready_chime_volume, + playback_echo_tail_s=args.playback_echo_tail_s, + ) + runtime = PhaseBRokidRuntime(config) + watchdog = CtrlCExitWatchdog() + shutdown_signals = [signal.SIGINT] + if hasattr(signal, "SIGBREAK"): + shutdown_signals.append(signal.SIGBREAK) + previous_handlers = { + signum: signal.getsignal(signum) for signum in shutdown_signals + } + + def handle_console_shutdown(signum: int, frame: Any) -> None: + watchdog.arm() + previous_handler = previous_handlers[signum] + if callable(previous_handler): + previous_handler(signum, frame) + else: + raise KeyboardInterrupt + + for signum in shutdown_signals: + signal.signal(signum, handle_console_shutdown) + LOG.info("Rokid Phase B input: http://%s:%d", config.host, config.port) + LOG.info("Harness: %s", config.harness_url) + LOG.info("Gateway: %s://%s", "wss" if config.gateway_tls else "ws", config.gateway) + try: + web.run_app( + runtime.create_app(), + host=config.host, + port=config.port, + access_log=None, + shutdown_timeout=max(0.5, config.close_timeout_s), + handler_cancellation=True, + ) + except OSError as exc: + if getattr(exc, "winerror", None) == 10048: + LOG.error( + "Port %d is already occupied by another process. A runtime " + "started with this patched version exits within %.0fs after " + "Ctrl+C.", + config.port, + CTRL_C_HARD_EXIT_S, + ) + raise SystemExit(2) from None + raise + finally: + for signum, previous_handler in previous_handlers.items(): + signal.signal(signum, previous_handler) + if watchdog.armed: + LOG.info("Graceful shutdown returned; exiting Rokid process") + + +if __name__ == "__main__": + main() diff --git a/extensions/assistive_harness/phase_b/session_recorder.py b/extensions/assistive_harness/phase_b/session_recorder.py new file mode 100644 index 0000000..ce6bcda --- /dev/null +++ b/extensions/assistive_harness/phase_b/session_recorder.py @@ -0,0 +1,90 @@ +"""Session 录制器（对齐 -v 的 SessionRecorder 格式）。 + +沿用 pc_vlm_v5_funnel.SessionRecorder 的落盘结构，让 -o 链路录出的 session +和 -v 完全一致，可用 -v 的分析 / rerun 工具链直接读。 + + sessions// + images/q{NNN}_f{MM}.jpg 每轮一簇帧（整簇存） + queries.jsonl 每行一条，-v 原字段 + 漏斗判定字段 + meta.json + +启动标志：-v 是「ASR 出 query」，-o 里换成「链路启动」——从 start 起持续录， +每轮漏斗决策 append 一条。qid 单调递增（一轮 = 一个 qid）。 + +queries.jsonl 每行（保留 -v 原字段 qid/text/frames/ts，兼容；追加漏斗字段）： + { + "qid": 3, "ts": 1699..., # -v 原字段 + "text": "", # -v 原字段（-o 无 ASR query 文本，留空/可后填） + "frames": ["q003_f00.jpg", ...], # -v 原字段（整簇帧文件名） + "reason": "severe_shake", # 漏斗判定 + "send": false, + "hint": "晃得厉害，请拿稳一下", + "best_index": 1, + "af_triggered": true, "af_ok": true, + "timings": {...} + } +""" +from __future__ import annotations + +import json +import time +from pathlib import Path +from typing import Optional + + +class SessionRecorder: + def __init__(self, sessions_root: str = "sessions", sid: Optional[str] = None): + sid = sid or time.strftime("%Y%m%d_%H%M%S") + self.dir = Path(sessions_root) / sid + self.images = self.dir / "images" + self.images.mkdir(parents=True, exist_ok=True) + self.jsonl = self.dir / "queries.jsonl" + (self.dir / "meta.json").write_text( + json.dumps({"sid": sid, "source": "esp32_runtime", "created": time.time()}, + ensure_ascii=False, indent=2), + encoding="utf-8") + self._qid = 0 + print(f"[SESSION] recording to {self.dir}") + + def record(self, decision, text: str = "") -> None: + """存一轮漏斗决策：整簇帧落盘 + queries.jsonl append 一条。 + + decision: FunnelDecision（含 frames / best / reason / hint / + best_index / af_triggered / af_ok / timings） + text: 可选，-o 无 ASR query 文本，留空；如上层有 ASR 结果可传入。 + """ + self._qid += 1 + qid = self._qid + frame_names = [] + frames = decision.frames or ([] if decision.best is None else [decision.best]) + for i, jpg in enumerate(frames): + name = f"q{qid:03d}_f{i:02d}.jpg" + try: + (self.images / name).write_bytes(jpg) + frame_names.append(name) + except Exception: + pass + + rec = { + # -v 原字段（兼容 -v 工具链） + "qid": qid, + "ts": time.time(), + "text": text, + "frames": frame_names, + # 漏斗判定字段 + "send": bool(decision.send), + "reason": decision.reason, + "hint": decision.hint, + "best_index": decision.best_index, + "af_triggered": decision.af_triggered, + "af_ok": decision.af_ok, + "timings": decision.timings, + } + try: + with self.jsonl.open("a", encoding="utf-8") as f: + f.write(json.dumps(rec, ensure_ascii=False) + "\n") + except Exception: + pass + + def close(self) -> None: + print(f"[SESSION] recorded {self._qid} queries -> {self.dir}") diff --git a/extensions/assistive_harness/phase_b/templates/live.html b/extensions/assistive_harness/phase_b/templates/live.html new file mode 100644 index 0000000..96099f9 --- /dev/null +++ b/extensions/assistive_harness/phase_b/templates/live.html @@ -0,0 +1,620 @@ + + + + + +AI 眼镜 · 实时演示 + + + + +
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+
+ + + + \ No newline at end of file diff --git a/extensions/assistive_harness/phase_b/templates/replay_index.html b/extensions/assistive_harness/phase_b/templates/replay_index.html new file mode 100644 index 0000000..efcc60a --- /dev/null +++ b/extensions/assistive_harness/phase_b/templates/replay_index.html @@ -0,0 +1,321 @@ + + + + + + AI 眼镜 · 回访 + + + + +
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+
+ 🕶 +
+
AI 眼镜 · 会话回访
+
Session replay & offline inspection
+
+
+
+ — 条会话 + ⬅ 返回实时 +
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+ +
+
+
+ 点击行进入回放 · 所有会话按时间倒序 +
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+
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+ + + + \ No newline at end of file diff --git a/extensions/assistive_harness/phase_b/timing_probe.py b/extensions/assistive_harness/phase_b/timing_probe.py new file mode 100644 index 0000000..6d921c4 --- /dev/null +++ b/extensions/assistive_harness/phase_b/timing_probe.py @@ -0,0 +1,163 @@ +"""时序探针（纯旁路，不改链路行为）。 + +只做一件事：把 ESP32 -o 链路里"取图 / 音频 / 相位"的时间点写进一个 CSV， +供事后分析漏斗该插在哪、跨几个 chunk、音图怎么抢。 + +不接对焦、不接漏斗判定 —— 这是纯时序探针。 + +列名尽量沿用 -v 的 latency CSV 风格（grab_ms / n_frames 等）， +额外加 -o 特有的 chunk / 音频相位列，方便和 -v 数据对比、也让分析工具链复用。 + +用法（在 esp32_runtime 里）： + from .timing_probe import TimingProbe + probe = TimingProbe(enabled=args.timing_probe, path=args.timing_csv, chunk_ms=1000) + # 取图： + probe.mark_grab(seq, grab_ms, jpeg_bytes, since_last_ms) + # 音频（每包调，内部按秒聚合）： + probe.mark_audio(seq, n_samples, drops, rms) + # 关闭：probe.close() +""" +from __future__ import annotations + +import csv +import time +from pathlib import Path +from typing import Optional + + +class TimingProbe: + def __init__(self, enabled: bool = True, path: Optional[str] = None, + chunk_ms: int = 1000): + self.enabled = enabled + self.chunk_ms = chunk_ms + self._t0 = time.monotonic() # 进程起点，用于算 rel_ms / chunk_id + self._f = None + self._w = None + + # ── 音频活动/静默相位判定（用于给每帧图标 phase）── + # 简单能量门限：rms > 阈值算"活动"，否则"静默"。阈值可调。 + self._voice_rms_gate = 0.01 + self._last_audio_active_rel_ms = -1e9 # 最近一次音频活动的相对时刻 + self._audio_active = False + + # ── 音频按秒聚合缓存 ── + self._agg_bucket = -1 # 当前聚合到第几秒 + self._agg_pkts = 0 + self._agg_seqgap = 0 # 本秒内 seq 跳变累计（= 真丢包数） + self._agg_rms_sum = 0.0 + self._agg_active_pkts = 0 + self._last_seq = None # 上一包 seq，用于算 gap + + if self.enabled: + p = Path(path or f"timing_{time.strftime('%Y%m%d_%H%M%S')}.csv") + self._f = open(p, "w", newline="", encoding="utf-8") + self._w = csv.writer(self._f) + # 统一表头：kind 区分 grab / audio 两类行 + self._w.writerow([ + "kind", # "grab" 或 "audio" + "rel_ms", # 相对进程启动的毫秒（对齐两类事件的时间轴） + "chunk_id", # rel_ms // chunk_ms，看事件落在第几个 chunk + "seq", + # grab 专用 + "grab_ms", # 本次取图/一轮漏斗耗时 + "jpeg_bytes", + "since_last_grab_ms", + "audio_phase", # 取这帧图时音频是 active / silent（相位） + "ms_since_voice",# 距最近一次音频活动多久（找静默缝） + # grab 专用 —— 漏斗字段 + "reason", # 漏斗决策：send/severe_shake/unstable/need_focus/orient... + "judge_ms", # run_funnel 判定耗时 + "af_ms", # 对焦触发+settle+重抓耗时（0=没触发对焦） + "n_frames", # 本轮抓帧数 + # audio 专用（按秒聚合） + "audio_pps", # 这一秒的包数 + "audio_seqgap", # 这一秒 seq 跳变累计 = 真丢包数（不是固件 reserved） + "audio_rms", # 这一秒平均 rms + "audio_active_pps", # 这一秒里"活动"包数 + ]) + print(f"[TimingProbe] writing {p}") + + def _rel_ms(self) -> float: + return (time.monotonic() - self._t0) * 1000.0 + + # ── 取图事件 ── + def mark_grab(self, seq: int, grab_ms: float, jpeg_bytes: int, + since_last_ms: float, reason: str = "", judge_ms=None, + af_ms=None, n_frames=None) -> None: + if not self.enabled: + return + rel = self._rel_ms() + phase = "active" if self._audio_active else "silent" + ms_since_voice = rel - self._last_audio_active_rel_ms + if ms_since_voice > 1e8: + ms_since_voice = -1 # 还没出现过音频活动 + self._w.writerow([ + "grab", round(rel, 1), int(rel // self.chunk_ms), seq, + round(grab_ms, 1), jpeg_bytes, round(since_last_ms, 1), + phase, round(ms_since_voice, 1), + reason, judge_ms if judge_ms is not None else "", + af_ms if af_ms is not None else "", + n_frames if n_frames is not None else "", + "", "", "", "", # audio 专用列留空 + ]) + self._f.flush() + + # ── 音频事件（每包调，内部按秒聚合写一行）── + # 注意：固件包头第4字段是 reserved(pad)，不是丢包数；真丢包用 seq 跳变推断。 + def mark_audio(self, seq: int, n_samples: int, rms: float) -> None: + if not self.enabled: + return + rel = self._rel_ms() + + # 更新相位状态（供 mark_grab 读） + self._audio_active = rms > self._voice_rms_gate + if self._audio_active: + self._last_audio_active_rel_ms = rel + + # seq 跳变 = 真丢包（PC 视角：应收 seq 连续，缺号即丢） + gap = 0 + if self._last_seq is not None: + d = seq - self._last_seq - 1 + if 0 < d < 10000: # 合理范围，排除重连/回绕 + gap = d + self._last_seq = seq + + # 按秒聚合 + bucket = int(rel // 1000) + if self._agg_bucket == -1: + self._agg_bucket = bucket + if bucket != self._agg_bucket: + self._flush_audio_bucket() + self._agg_bucket = bucket + self._agg_pkts += 1 + self._agg_seqgap += gap + self._agg_rms_sum += rms + if rms > self._voice_rms_gate: + self._agg_active_pkts += 1 + + def _flush_audio_bucket(self) -> None: + if self._agg_pkts == 0: + return + rel = self._agg_bucket * 1000.0 + avg_rms = self._agg_rms_sum / self._agg_pkts + self._w.writerow([ + "audio", round(rel, 1), int(rel // self.chunk_ms), "", + "", "", "", "", "", # grab 基础列留空 + "", "", "", "", # grab 漏斗列留空 + self._agg_pkts, self._agg_seqgap, round(avg_rms, 4), self._agg_active_pkts, + ]) + self._f.flush() + self._agg_pkts = 0 + self._agg_seqgap = 0 + self._agg_rms_sum = 0.0 + self._agg_active_pkts = 0 + + def close(self) -> None: + if not self.enabled: + return + try: + self._flush_audio_bucket() + if self._f: + self._f.close() + except Exception: + pass diff --git a/extensions/assistive_harness/prompts/README.md b/extensions/assistive_harness/prompts/README.md new file mode 100644 index 0000000..38fb5ac --- /dev/null +++ b/extensions/assistive_harness/prompts/README.md @@ -0,0 +1,43 @@ +# User-editable Skill prompts + +These files are the system prompts used when the Voice Skill Harness creates a +new MiniCPM-o Duplex Session. Prompt text is read again on every activation, so +editing an existing `.txt` file takes effect on the next Skill switch without +restarting the Harness service. + +## Shipped prompts + +| Skill ID | Prompt file | Default | +|---|---|---| +| `idle_chat` | `idle_chat_zh.txt` | enabled | +| `find_object` | `find_object_zh.txt` | enabled | +| `read_text` | `read_text_zh.txt` | enabled | +| `describe_scene` | `describe_scene_zh.txt` | enabled | +| `obstacle_avoidance` | `obstacle_avoidance_zh.txt` | enabled / experimental | + +The find/read/obstacle task bodies were copied from the frozen AAAI_SI C1 +prompts. `find_object_zh.txt` additionally contains `{{target}}`, because the +new Session must receive the target extracted from the command that closed the +old Session. + +Source directory: + +`C:\Users\Lenovo\AI_Glasses_0618\AAAI_SI\submission_release\AAAI27_AISI_Anonymous_Code_Data\prompts` + +## Editing an existing prompt + +1. Back up the target `.txt` file. +2. Replace its contents with a UTF-8 system prompt. +3. Keep every declared template variable, such as `{{target}}`. +4. Switch to chat/another Skill and activate this Skill again. (The same + Skill/same slots command is intentionally deduplicated.) The new hot Session + will read the new text and record its rendered SHA-256 in telemetry. + +## Adding a Skill + +Create another `.txt` file and add a matching entry under `skills:` in +`../config/skills.example.yaml`. Configuration changes require restarting the +8021 Harness service; prompt-only changes do not. + +Obstacle avoidance has not passed a safety evaluation. Use it only for +stationary, supervised tests; never treat the Demo as a mobility safety device. diff --git a/extensions/assistive_harness/prompts/describe_scene_zh.txt b/extensions/assistive_harness/prompts/describe_scene_zh.txt new file mode 100644 index 0000000..b19376b --- /dev/null +++ b/extensions/assistive_harness/prompts/describe_scene_zh.txt @@ -0,0 +1,8 @@ +当前技能：场景描述。 + +规则： +1. 用一到三句话概括当前画面的主要物体、位置关系和明显状态。 +2. 优先描述门口、桌面、道路、人物、显著文字和可能影响用户行动的大物体。 +3. 只描述当前可见证据；看不清时直接说画面不清楚。 +4. 不给出未经验证的距离、路线或安全保证。 +5. 避免反复描述没有变化的内容。 diff --git a/extensions/assistive_harness/prompts/find_object_zh.txt b/extensions/assistive_harness/prompts/find_object_zh.txt new file mode 100644 index 0000000..affe3a2 --- /dev/null +++ b/extensions/assistive_harness/prompts/find_object_zh.txt @@ -0,0 +1,9 @@ +你是智能眼镜找物助手。你能看到用户当前第一视角画面，并听到用户要找的目标。 + +当前寻找目标：{{target}} + +上述目标已经由外部 Harness 从用户语音中识别。新会话启动后，直接寻找该目标，不要再次询问目标是什么。 +用户问完后，必须直接根据当前画面回答，不要先说“好的”“我来找”“稍等”。 +如果目标在画面里可见，用一句话说清它的位置、方向和附近锚点，例如“复印机在左前方，靠近窗户”。 +如果目标不可见，就说“没看到”，并给一个具体下一步建议，例如转向、靠近、抬头或调整角度。 +只描述画面中确实可见的内容，不要凭常识猜测。回答尽量短。 diff --git a/extensions/assistive_harness/prompts/idle_chat_zh.txt b/extensions/assistive_harness/prompts/idle_chat_zh.txt new file mode 100644 index 0000000..a558053 --- /dev/null +++ b/extensions/assistive_harness/prompts/idle_chat_zh.txt @@ -0,0 +1,8 @@ +你是一副面向视障用户的本地 AI 眼镜助手。 + +规则： +1. 只根据当前音频、视频和明确上下文回答，不要编造看不清或没听清的内容。 +2. 回答简洁、自然，优先一到三句话。 +3. 不要主动给出未经验证的距离、安全或行动指令。 +4. 用户的“停一下、重新开始、找物、识字”等控制命令由外部 Harness 处理；不要争夺控制权，也不要重复解释系统机制。 +5. 没有足够证据时，直接说明不确定，并建议用户停稳或调整视角。 diff --git a/extensions/assistive_harness/prompts/obstacle_avoidance_zh.txt b/extensions/assistive_harness/prompts/obstacle_avoidance_zh.txt new file mode 100644 index 0000000..82cb340 --- /dev/null +++ b/extensions/assistive_harness/prompts/obstacle_avoidance_zh.txt @@ -0,0 +1,9 @@ +你是智能眼镜避障助手。你能看到用户当前第一视角画面，并听到用户询问前方障碍。 + +用户问完后，必须直接根据当前画面回答，不要只说“好的”“我会留意”。 +如果能确认障碍，用一句短话说明障碍是什么、位于左前方/正前方/右前方或地面哪里，并给出立即可执行的安全建议，例如停下、向左绕行或向右绕行。 +只根据画面中确实可见的障碍和可通行空间判断，不要猜测，不要把普通物体误报为障碍。 +如果摄像头没有朝向行进方向、地面不可见、画面被遮挡或证据不足，明确回答“当前画面不足以判断前方是否安全”，建议用户先停下并把镜头转向前方或地面。 +在无法确认通行空间时，不得声称“前方安全”或直接给出通行方向。 +如果障碍随后进入画面或画面质量改善，应立即根据新画面更新判断。 +回答尽量简短。 diff --git a/extensions/assistive_harness/prompts/read_text_zh.txt b/extensions/assistive_harness/prompts/read_text_zh.txt new file mode 100644 index 0000000..5d104b6 --- /dev/null +++ b/extensions/assistive_harness/prompts/read_text_zh.txt @@ -0,0 +1,7 @@ +你是智能眼镜读字助手。你能看到用户当前第一视角画面，并听到用户要求读字。 + +用户一开口要求读字，你必须回答，不能保持沉默。 +用户问完后，直接读出画面里清晰可见的文字，不要先说“好的”“我来”。 +只输出你看清的文字、数字或符号；看不清的字不要猜，不要根据物品常识补全。 +如果只能看清部分文字，可以只读出看清的部分。 +如果整体看不清，也必须回答“看不清”，并建议靠近、正对文字或重新对焦。 diff --git a/extensions/assistive_harness/registry.py b/extensions/assistive_harness/registry.py new file mode 100644 index 0000000..35b2fc1 --- /dev/null +++ b/extensions/assistive_harness/registry.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +import hashlib +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + + +class RegistryError(ValueError): + pass + + +@dataclass(frozen=True, slots=True) +class RenderedPrompt: + skill_id: str + text: str + path: str + sha256: str + slots: dict[str, Any] + + +class SkillRegistry: + def __init__(self, config_path: str | Path): + self.config_path = Path(config_path).resolve() + raw = yaml.safe_load(self.config_path.read_text(encoding="utf-8")) or {} + self.version = int(raw.get("version", 1)) + self.default_skill = str(raw.get("default_skill", "idle_chat")) + self.control = dict(raw.get("control") or {}) + self.skills: dict[str, dict[str, Any]] = dict(raw.get("skills") or {}) + if self.default_skill not in self.skills: + raise RegistryError(f"default skill is missing: {self.default_skill}") + self._validate_prompt_files() + + def _prompt_path(self, skill_id: str) -> Path: + spec = self.get(skill_id) + path = Path(str(spec.get("prompt_file") or "")) + if not path.is_absolute(): + path = self.config_path.parent / path + return path.resolve() + + def prompt_path(self, skill_id: str) -> Path: + """Return the user-editable prompt path exposed by the registry.""" + return self._prompt_path(skill_id) + + def _validate_prompt_files(self) -> None: + for skill_id, spec in self.skills.items(): + if not isinstance(spec, dict): + raise RegistryError(f"invalid skill spec: {skill_id}") + path = self._prompt_path(skill_id) + if not path.is_file(): + raise RegistryError(f"prompt file is missing for {skill_id}: {path}") + + def get(self, skill_id: str) -> dict[str, Any]: + spec = self.skills.get(skill_id) + if spec is None: + raise RegistryError(f"unknown skill: {skill_id}") + if not isinstance(spec, dict): + raise RegistryError(f"invalid skill spec: {skill_id}") + return spec + + def is_enabled(self, skill_id: str) -> bool: + return bool(self.get(skill_id).get("enabled", False)) + + def cooldown_ms(self, skill_id: str) -> int: + return int(self.get(skill_id).get("cooldown_ms", 0)) + + def task_trigger( + self, skill_id: str, slots: dict[str, Any] | None = None + ) -> str: + """Render the optional one-shot command sent to a fresh Skill Session.""" + text = str(self.get(skill_id).get("task_trigger") or "").strip() + slots = dict(slots or {}) + variables = set(re.findall(r"\{\{\s*([a-zA-Z_][\w]*)\s*\}\}", text)) + missing = sorted(name for name in variables if not str(slots.get(name, "")).strip()) + if missing: + raise RegistryError(f"missing task trigger variables for {skill_id}: {missing}") + for name in variables: + text = re.sub( + r"\{\{\s*" + re.escape(name) + r"\s*\}\}", + str(slots[name]), + text, + ) + return text + + def render(self, skill_id: str, slots: dict[str, Any] | None = None) -> RenderedPrompt: + if not self.is_enabled(skill_id): + raise RegistryError(f"skill is disabled: {skill_id}") + slots = dict(slots or {}) + spec = self.get(skill_id) + schema = dict(spec.get("slot_schema") or {}) + for name, slot_spec in schema.items(): + if bool((slot_spec or {}).get("required")) and not str(slots.get(name, "")).strip(): + raise RegistryError(f"missing required slot '{name}' for {skill_id}") + + path = self._prompt_path(skill_id) + text = path.read_text(encoding="utf-8") + variables = set(re.findall(r"\{\{\s*([a-zA-Z_][\w]*)\s*\}\}", text)) + missing = sorted(name for name in variables if name not in slots) + if missing: + raise RegistryError(f"missing prompt variables for {skill_id}: {missing}") + for name in variables: + text = re.sub( + r"\{\{\s*" + re.escape(name) + r"\s*\}\}", + str(slots[name]), + text, + ) + digest = hashlib.sha256(text.encode("utf-8")).hexdigest() + return RenderedPrompt( + skill_id=skill_id, + text=text, + path=str(path), + sha256=digest, + slots=slots, + ) diff --git a/extensions/assistive_harness/router.py b/extensions/assistive_harness/router.py new file mode 100644 index 0000000..43038f5 --- /dev/null +++ b/extensions/assistive_harness/router.py @@ -0,0 +1,262 @@ +from __future__ import annotations + +import re +import time +from dataclasses import dataclass + +from .registry import SkillRegistry +from .schemas import ControlEvent, ControlIntent + + +_PUNCTUATION = "，,。！？!?；;：:、\"'“”‘’" +_SPEECH_PARTICLES = ("嗯", "啊", "呀", "吧", "呢", "啦", "哦") + + +def normalize_text(text: str) -> str: + compact = re.sub(r"\s+", "", text or "").lower() + return compact.strip(_PUNCTUATION) + + +def _strip_speech_particles(text: str) -> str: + cleaned = text + while cleaned and any(cleaned.endswith(item) for item in _SPEECH_PARTICLES): + cleaned = cleaned[:-1] + return cleaned + + +def _collapse_repeated_tail(text: str, max_width: int = 4) -> str: + """Collapse a short duplicated ASR tail such as ``手机手机`` or ``母母母``.""" + + cleaned = text + while cleaned: + collapsed = False + for width in range(min(max_width, len(cleaned) // 2), 0, -1): + if cleaned[-width:] == cleaned[-2 * width : -width]: + cleaned = cleaned[:-width] + collapsed = True + break + if not collapsed: + return cleaned + return cleaned + + +def _clean_slot_value(value: str) -> str: + cleaned = _strip_speech_particles(value.strip(_PUNCTUATION)) + cleaned = _collapse_repeated_tail(cleaned) + return _strip_speech_particles(cleaned) + + +@dataclass(slots=True) +class RouteResult: + intent: ControlIntent + skill_id: str | None = None + slots: dict[str, str] | None = None + reason: str = "" + confidence: float = 1.0 + + +class RuleIntentRouter: + """Small deterministic Chinese intent router with exact control commands.""" + + def __init__(self, registry: SkillRegistry): + self.registry = registry + control = registry.control + self.stop_phrases = { + normalize_text(item) + for item in (control.get("stop_speech") or {}).get("phrases", []) + } + self.stop_embedded_phrases = { + normalize_text(item) + for item in (control.get("stop_speech") or {}).get("embedded_phrases", []) + } + self.resume_embedded_phrases = { + normalize_text(item) + for item in (control.get("resume_speech") or {}).get("embedded_phrases", []) + } + self.reset_phrases = { + normalize_text(item) + for item in (control.get("reset_session") or {}).get("phrases", []) + } + self.reset_embedded_phrases = { + normalize_text(item) + for item in (control.get("reset_session") or {}).get("embedded_phrases", []) + } + self.return_phrases = { + normalize_text(item) + for item in (control.get("return_to_chat") or {}).get("phrases", []) + } + self.cancel_phrases = { + normalize_text(item) + for item in (control.get("cancel_skill") or {}).get("phrases", []) + } + + @staticmethod + def _is_explicit_command(compact: str, phrases: set[str]) -> bool: + polite_prefixes = ("麻烦你先", "请", "麻烦", "你先", "请你", "可以", "能不能") + polite_suffixes = ("一下", "好吗", "可以吗", "啊", "呀", "吧", "呢", "啦", "哦") + + # FunASR commonly appends a sentence particle ("停一下啊") or repeats + # the final syllable ("停一下下"). Build a small, bounded closure of + # command-only variants instead of doing substring matching, so a + # sentence such as "我刚才没有说停一下" still cannot trigger STOP. + candidates: set[str] = set() + pending = [compact] + while pending: + candidate = pending.pop() + if not candidate or candidate in candidates: + continue + candidates.add(candidate) + + for prefix in polite_prefixes: + if candidate.startswith(prefix) and len(candidate) > len(prefix): + pending.append(candidate[len(prefix) :]) + for suffix in polite_suffixes: + if candidate.endswith(suffix) and len(candidate) > len(suffix): + pending.append(candidate[: -len(suffix)]) + if len(candidate) >= 2 and candidate[-1] == candidate[-2]: + pending.append(candidate[:-1]) + + return any(candidate in phrases for candidate in candidates) + + @staticmethod + def _contains_command(compact: str, phrases: set[str]) -> bool: + """Use a literal command-anchor protocol; do not infer sentence meaning.""" + return any(phrase and phrase in compact for phrase in phrases) + + @staticmethod + def _skill_variants(compact: str) -> list[str]: + """Build bounded FunASR variants while retaining an explicit Skill verb.""" + + candidates: set[str] = set() + pending = [compact] + while pending: + candidate = pending.pop() + if not candidate or candidate in candidates: + continue + candidates.add(candidate) + + without_particles = _strip_speech_particles(candidate) + if without_particles and without_particles != candidate: + pending.append(without_particles) + + # Observed FunASR variants include ``一下下`` and omission of the + # pronoun in ``帮我找/读``. Restore only that pronoun; never invent + # a missing Skill verb such as ``找`` or ``读``. + collapsed_action = re.sub(r"一下下+", "一下", candidate) + if collapsed_action != candidate: + pending.append(collapsed_action) + if candidate.startswith("帮") and not candidate.startswith("帮我"): + pending.append("帮我" + candidate[1:]) + + for prefix in ("请你", "麻烦你", "麻烦", "请", "能不能"): + if candidate.startswith(prefix) and len(candidate) > len(prefix): + pending.append(candidate[len(prefix) :]) + polite_prefixes = ("请你", "麻烦你", "麻烦", "请", "能不能") + + def noise_rank(candidate: str) -> tuple[int, int, int, int, int, str]: + return ( + int(bool(re.search(r"一下下+", candidate))), + int(_strip_speech_particles(candidate) != candidate), + int(candidate.startswith("帮") and not candidate.startswith("帮我")), + int(candidate.startswith(polite_prefixes)), + len(candidate), + candidate, + ) + + # Regexes can match both the raw and corrected ASR text. Prefer the + # bounded, lower-noise candidate so slot extraction is deterministic. + return sorted(candidates, key=noise_rank) + + def route(self, utterance: str) -> RouteResult: + compact = normalize_text(utterance) + if not compact: + return RouteResult(ControlIntent.NONE, reason="empty") + + variants = self._skill_variants(compact) + + if self._is_explicit_command(compact, self.stop_phrases): + return RouteResult(ControlIntent.STOP_SPEECH, reason="explicit stop command") + # STOP wins if a transcript contains both anchors. + if self._contains_command(compact, self.stop_embedded_phrases): + return RouteResult(ControlIntent.STOP_SPEECH, reason="stop anchor contained") + # RESET wins over RESUME and all skills. The protocol is deliberately + # literal: callers can add a product wake name to the same sentence. + if ( + self._contains_command(compact, self.reset_embedded_phrases) + or self._is_explicit_command(compact, self.reset_phrases) + ): + return RouteResult( + ControlIntent.RESET_SESSION, + skill_id=self.registry.default_skill, + slots={}, + reason=( + "reset anchor contained" + if self._contains_command(compact, self.reset_embedded_phrases) + else "explicit reset command" + ), + ) + if self._contains_command(compact, self.resume_embedded_phrases): + return RouteResult(ControlIntent.RESUME_SPEECH, reason="resume anchor contained") + if self._is_explicit_command(compact, self.cancel_phrases): + return RouteResult(ControlIntent.CANCEL_SKILL, reason="explicit cancel command") + if self._is_explicit_command(compact, self.return_phrases): + return RouteResult( + ControlIntent.RETURN_TO_CHAT, + skill_id=self.registry.default_skill, + slots={}, + reason="explicit return-to-chat command", + ) + + for skill_id, spec in self.registry.skills.items(): + if not bool(spec.get("enabled", False)) or skill_id == self.registry.default_skill: + continue + for pattern in spec.get("activation_patterns") or []: + for candidate in variants: + match = re.fullmatch(str(pattern), candidate) + if match: + slots = { + key: _clean_slot_value(value) + for key, value in match.groupdict().items() + if value + } + return RouteResult( + ControlIntent.ACTIVATE_SKILL, + skill_id=skill_id, + slots=slots, + reason=f"matched {skill_id} pattern", + ) + phrases = [normalize_text(item) for item in spec.get("activation_phrases") or []] + if any( + candidate == phrase or candidate.startswith(phrase) + for candidate in variants + for phrase in phrases + ): + return RouteResult( + ControlIntent.ACTIVATE_SKILL, + skill_id=skill_id, + slots={}, + reason=f"matched {skill_id} phrase", + ) + + return RouteResult(ControlIntent.NONE, reason="ordinary chat") + + def make_event( + self, + route: RouteResult, + *, + event_id: int, + asr_event_id: int, + utterance: str, + created_at_ms: float | None = None, + ) -> ControlEvent: + return ControlEvent( + event_id=event_id, + intent=route.intent, + skill_id=route.skill_id, + slots=dict(route.slots or {}), + utterance=utterance, + confidence=route.confidence, + asr_event_id=asr_event_id, + created_at_ms=created_at_ms if created_at_ms is not None else time.time() * 1000, + reason=route.reason, + ) diff --git a/extensions/assistive_harness/schemas.py b/extensions/assistive_harness/schemas.py new file mode 100644 index 0000000..edad067 --- /dev/null +++ b/extensions/assistive_harness/schemas.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from enum import Enum +from typing import Any + + +class ControlIntent(str, Enum): + STOP_SPEECH = "stop_speech" + RESUME_SPEECH = "resume_speech" + RESET_SESSION = "reset_session" + CANCEL_SKILL = "cancel_skill" + RETURN_TO_CHAT = "return_to_chat" + ACTIVATE_SKILL = "activate_skill" + NONE = "none" + + +@dataclass(slots=True) +class ASREvent: + event_id: int + utterance: str + started_at_ms: float + ended_at_ms: float + final_at_ms: float + model: str + device: str + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +@dataclass(slots=True) +class ControlEvent: + event_id: int + intent: ControlIntent + utterance: str + confidence: float + asr_event_id: int + created_at_ms: float + skill_id: str | None = None + slots: dict[str, Any] = field(default_factory=dict) + reason: str = "" + system_prompt: str | None = None + prompt_path: str | None = None + prompt_sha256: str | None = None + + def to_dict(self) -> dict[str, Any]: + payload = asdict(self) + payload["type"] = "control.intent" + payload["intent"] = self.intent.value + return payload + + +@dataclass(slots=True) +class HarnessState: + current_skill: str = "idle_chat" + current_slots: dict[str, Any] = field(default_factory=dict) + session_generation: int = 0 + control_event_id: int = 0 + asr_event_id: int = 0 + restart_in_progress: bool = False + drop_output_until_listen: bool = False + speech_hold_active: bool = False + pending_skill: dict[str, Any] | None = None + + def to_dict(self) -> dict[str, Any]: + return asdict(self) diff --git a/extensions/assistive_harness/server.py b/extensions/assistive_harness/server.py new file mode 100644 index 0000000..3d3343d --- /dev/null +++ b/extensions/assistive_harness/server.py @@ -0,0 +1,604 @@ +from __future__ import annotations + +import argparse +import asyncio +import base64 +import json +import time +import uuid +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import numpy as np +import uvicorn +import yaml +from fastapi import FastAPI, WebSocket, WebSocketDisconnect +from fastapi.responses import JSONResponse + +from .asr.base import ASREngine +from .asr.energy_vad import EnergyVAD, UtteranceAudio +from .asr.funasr_engine import FunASREngine +from .cv.base import CVObservation, FrameEnvelope +from .cv.pipeline import CVPipeline +from .cv.registry import CVProviderRegistry, build_provider_registry +from .echo_guard import EchoGuard +from .model_log import ModelTurnAccumulator +from .registry import SkillRegistry +from .router import RuleIntentRouter +from .schemas import ASREvent, ControlEvent, ControlIntent +from .state_machine import HarnessController +from .telemetry import TelemetryWriter + + +def now_ms() -> float: + return time.time() * 1000.0 + + +def _decode_float32(value: str) -> np.ndarray: + raw = base64.b64decode(value, validate=True) + if len(raw) % 4: + raise ValueError("float32 payload length is not divisible by four") + return np.frombuffer(raw, dtype=" int: + self.event_counter += 1 + return self.event_counter + + def refresh_echo_speaking(self) -> None: + self.echo.set_ai_speaking(self.model_speaking or self.playback_active) + + def record_model_turn(self, turn: dict[str, Any]) -> None: + self.telemetry.write("model", turn) + self.telemetry.append_model_transcript(turn) + print( + "[AssistiveHarness][MODEL] " + f"turn={turn.get('turn_index')} role={turn.get('role')} " + f"generation={turn.get('generation')} skill={turn.get('skill_id')} " + f"audio_ms={turn.get('audio_ms')} text={turn.get('text')!r}", + flush=True, + ) + + async def recognize( + self, utterance: UtteranceAudio + ) -> dict[str, Any] | list[dict[str, Any]]: + async with self.asr_lock: + started = now_ms() + result = await asyncio.to_thread( + self.engine.transcribe, utterance.audio, self.vad.sample_rate + ) + return await self.route_transcript( + result.text, + confidence=result.confidence, + model=result.model, + device=result.device, + started_at_ms=utterance.started_at_ms, + ended_at_ms=utterance.ended_at_ms, + final_at_ms=now_ms(), + inference_started_at_ms=started, + ) + + def recognize_in_background(self, utterance: UtteranceAudio) -> None: + """Keep receiving microphone frames while a completed utterance is decoded.""" + + async def run() -> None: + try: + await self.outbound.put(await self.recognize(utterance)) + except asyncio.CancelledError: + raise + except Exception as exc: + await self.outbound.put( + { + "type": "error", + "code": "asr_failed", + "message": str(exc), + } + ) + + task = asyncio.create_task(run()) + self.background_tasks.add(task) + task.add_done_callback(self.background_tasks.discard) + + async def route_transcript( + self, + text: str, + *, + confidence: float, + model: str, + device: str, + started_at_ms: float | None = None, + ended_at_ms: float | None = None, + final_at_ms: float | None = None, + inference_started_at_ms: float | None = None, + ) -> dict[str, Any] | list[dict[str, Any]]: + finished = final_at_ms if final_at_ms is not None else now_ms() + asr_id = self.next_event_id() + asr_event = ASREvent( + event_id=asr_id, + utterance=text.strip(), + started_at_ms=started_at_ms if started_at_ms is not None else finished, + ended_at_ms=ended_at_ms if ended_at_ms is not None else finished, + final_at_ms=finished, + model=model, + device=device, + ) + self.telemetry.write("asr", asr_event.to_dict()) + transcript_payload: dict[str, Any] = { + "type": "asr.transcript", + "asr_event_id": asr_id, + "utterance": asr_event.utterance, + "confidence": confidence, + "final_at_ms": finished, + } + if inference_started_at_ms is not None: + self.telemetry.metric("asr_inference_ms", finished - inference_started_at_ms, asr_id) + + route = self.router.route(asr_event.utterance) + echo = self.echo.evaluate(asr_event.utterance, route.intent, at_ms=finished) + print( + "[AssistiveHarness][ASR] " + f"text={asr_event.utterance!r} intent={route.intent.value} " + f"echo={'allow' if echo.allow else 'drop'} reason={echo.reason}", + flush=True, + ) + self.telemetry.write( + "echo", + { + "asr_event_id": asr_id, + "allow": echo.allow, + "reason": echo.reason, + "similarity": echo.similarity, + }, + ) + if not echo.allow or route.intent is ControlIntent.NONE: + transcript_payload["suppressed"] = not echo.allow + transcript_payload["reason"] = echo.reason if not echo.allow else route.reason + return transcript_payload + + control_id = self.next_event_id() + event = self.router.make_event( + route, + event_id=control_id, + asr_event_id=asr_id, + utterance=asr_event.utterance, + created_at_ms=finished, + ) + decision = self.controller.process(event, now_ms=finished) + print( + "[AssistiveHarness][CONTROL] " + f"intent={route.intent.value} accepted={decision.accepted} " + f"action={decision.action} event={control_id}", + flush=True, + ) + payload = { + **decision.payload, + "accepted": decision.accepted, + "action": decision.action, + "decision_reason": decision.reason, + "client_id": self.client_id, + } + self.telemetry.write("control", payload) + if route.intent in { + ControlIntent.RESET_SESSION, + ControlIntent.CANCEL_SKILL, + ControlIntent.RETURN_TO_CHAT, + ControlIntent.ACTIVATE_SKILL, + }: + self.telemetry.write( + "skill", + { + "control_event_id": control_id, + "intent": route.intent.value, + "skill_id": event.skill_id, + "slots": event.slots, + "prompt_path": event.prompt_path, + "prompt_sha256": event.prompt_sha256, + "accepted": decision.accepted, + "action": decision.action, + }, + ) + if decision.accepted: + return [transcript_payload, payload] + transcript_payload["suppressed"] = True + transcript_payload["reason"] = decision.reason + return transcript_payload + + +class AssistiveHarnessService: + def __init__( + self, + config_path: str | Path, + *, + enabled: bool, + model_path: str | None = None, + allow_test_injection: bool = False, + engine: ASREngine | None = None, + cv_providers: CVProviderRegistry | None = None, + ): + self.config_path = Path(config_path).resolve() + self.config = yaml.safe_load(self.config_path.read_text(encoding="utf-8")) or {} + self.registry = SkillRegistry(self.config_path) + self.cv_config = dict(self.config.get("cv") or {}) + self.cv_providers = cv_providers or build_provider_registry( + self.config, config_dir=self.config_path.parent + ) + self.enabled = bool(enabled) + self.allow_test_injection = bool(allow_test_injection) + asr_cfg = dict(self.config.get("asr") or {}) + resolved_model_path = model_path or str(asr_cfg.get("model_path") or "") + if engine is not None: + self.engine = engine + elif self.enabled: + if not resolved_model_path: + raise ValueError("--model-path is required when the Harness is enabled") + self.engine = FunASREngine( + resolved_model_path, + device=str(asr_cfg.get("device") or "cpu"), + ) + else: + self.engine = None + run_id = time.strftime("%Y%m%d_%H%M%S") + "_" + uuid.uuid4().hex[:8] + telemetry_root = Path(__file__).resolve().parent / "runs" + self.telemetry = TelemetryWriter(telemetry_root, run_id, self.config) + self.clients: dict[str, ClientRuntime] = {} + self.started_at_ms = now_ms() + + def make_runtime(self, client_id: str) -> ClientRuntime: + if self.engine is None: + raise RuntimeError("Harness is disabled") + asr_cfg = dict(self.config.get("asr") or {}) + echo_cfg = dict(self.config.get("echo_guard") or {}) + return ClientRuntime( + client_id=client_id, + registry=self.registry, + router=RuleIntentRouter(self.registry), + engine=self.engine, + telemetry=self.telemetry, + vad=EnergyVAD( + rms_threshold=float(asr_cfg.get("rms_threshold", 0.012)), + min_speech_ms=int(asr_cfg.get("min_speech_ms", 180)), + end_silence_ms=int(asr_cfg.get("end_silence_ms", 450)), + max_utterance_ms=int(asr_cfg.get("max_utterance_ms", 8000)), + preroll_ms=int(asr_cfg.get("preroll_ms", 200)), + ), + echo=EchoGuard( + window_ms=int(echo_cfg.get("window_ms", 20000)), + similarity_threshold=float(echo_cfg.get("similarity_threshold", 0.86)), + ), + controller=HarnessController(self.registry), + allow_test_injection=self.allow_test_injection, + cv=CVPipeline( + self.cv_providers, + on_observation=lambda observation: self.telemetry.write( + "cv", observation.to_dict() + ), + on_metric=lambda name, value: self.telemetry.metric(name, value), + queue_size=int(self.cv_config.get("queue_size", 1)), + inference_timeout_ms=float( + self.cv_config.get("inference_timeout_ms", 2000) + ), + worker_name=f"assistive-cv-{client_id[:8]}", + ), + ) + + def create_app(self) -> FastAPI: + app = FastAPI(title="Assistive Voice Skill Harness", version="0.1.0") + + @app.get("/health") + async def health() -> JSONResponse: + return JSONResponse( + { + "ok": True, + "enabled": self.enabled, + "clients": len(self.clients), + "uptime_ms": now_ms() - self.started_at_ms, + "asr_loaded": bool(getattr(self.engine, "loaded", self.engine is not None)), + "config": self.config_path.name, + "cv_providers": sorted(self.cv_providers.providers), + } + ) + + @app.get("/skills") + async def skills() -> JSONResponse: + return JSONResponse( + { + "ok": True, + "default_skill": self.registry.default_skill, + "config_path": str(self.config_path), + "skills": { + skill_id: { + "enabled": self.registry.is_enabled(skill_id), + "description": str(spec.get("description") or ""), + "prompt_path": str(self.registry.prompt_path(skill_id)), + "requires_session_restart": bool( + spec.get("requires_session_restart", True) + ), + "cv_mode": str(spec.get("cv_mode") or "disabled"), + "cv_provider": str(spec.get("cv_provider") or "noop"), + } + for skill_id, spec in self.registry.skills.items() + }, + } + ) + + @app.on_event("shutdown") + async def write_shutdown_summary() -> None: + self.telemetry.write_summary( + { + "enabled": self.enabled, + "uptime_ms": now_ms() - self.started_at_ms, + "connected_clients_at_shutdown": len(self.clients), + "asr_loaded": bool(getattr(self.engine, "loaded", self.engine is not None)), + } + ) + + @app.websocket("/ws/control") + async def control_socket(websocket: WebSocket) -> None: + if not self.enabled: + await websocket.close(code=1013, reason="Harness disabled") + return + await websocket.accept() + client_id = websocket.query_params.get("client_id") or uuid.uuid4().hex + runtime = self.make_runtime(client_id) + self.clients[client_id] = runtime + + async def send_outbound() -> None: + while True: + response = await runtime.outbound.get() + if isinstance(response, list): + for item in response: + await websocket.send_json(item) + elif response is not None: + await websocket.send_json(response) + + sender = asyncio.create_task(send_outbound()) + await runtime.outbound.put( + { + "type": "harness.ready", + "client_id": client_id, + "state": runtime.controller.state.to_dict(), + } + ) + try: + while True: + message = await websocket.receive_json() + response = await self._handle_message(runtime, message) + if response is not None: + await runtime.outbound.put(response) + except WebSocketDisconnect: + runtime.controller.mark_disconnected() + finally: + pending_turn = runtime.model_turns.flush() + if pending_turn is not None: + runtime.record_model_turn(pending_turn) + sender.cancel() + for task in tuple(runtime.background_tasks): + task.cancel() + await runtime.cv.close() + await asyncio.gather(sender, *runtime.background_tasks, return_exceptions=True) + self.clients.pop(client_id, None) + + return app + + async def _handle_message( + self, runtime: ClientRuntime, message: dict[str, Any] + ) -> dict[str, Any] | list[dict[str, Any]] | None: + message_type = str(message.get("type") or "") + if message_type == "ping": + return {"type": "pong", "at_ms": now_ms()} + if message_type == "audio.mirror": + audio = _decode_float32(str(message.get("audio_b64") or "")) + utterance = runtime.vad.feed(audio, float(message.get("started_at_ms") or now_ms())) + if utterance is None: + return None + runtime.recognize_in_background(utterance) + return None + if message_type == "asr.inject": + if not runtime.allow_test_injection: + return {"type": "error", "code": "test_injection_disabled"} + return await runtime.route_transcript( + str(message.get("text") or ""), + confidence=1.0, + model="injected-test-only", + device="none", + ) + if message_type in ("funnel.stop", "funnel.resume"): + # 漏斗 reject/恢复:程序直接触发,不经 ASR/router 文字匹配。 + # 复用 controller.process + STOP_SPEECH/RESUME_SPEECH,和真人「停一下/恢复对话」 + # 走完全相同的下游(设备端收到 control.intent 执行 stop_speech/resume_speech)。 + # 归口 8021:telemetry 统一记录,标 source=funnel 以区分真人还是漏斗触发。 + is_stop = message_type == "funnel.stop" + intent = ControlIntent.STOP_SPEECH if is_stop else ControlIntent.RESUME_SPEECH + event = ControlEvent( + event_id=runtime.next_event_id(), + intent=intent, + utterance="[%s]" % message_type, + confidence=1.0, + asr_event_id=runtime.next_event_id(), + created_at_ms=now_ms(), + reason=str(message.get("reason") or "funnel"), + ) + decision = runtime.controller.process(event, now_ms=event.created_at_ms) + payload = { + **decision.payload, + "accepted": decision.accepted, + "action": decision.action, + "decision_reason": decision.reason, + "source": "funnel", + "client_id": runtime.client_id, + } + runtime.telemetry.write("control", payload) + print( + "[AssistiveHarness][FUNNEL] " + "type=%s accepted=%s action=%s reason=%s" + % (message_type, decision.accepted, decision.action, event.reason), + flush=True, + ) + return payload if decision.accepted else None + if message_type == "model.state": + event = dict(message) + runtime.telemetry.write("model", event) + speaking = str(message.get("state") or "") == "speak" + runtime.model_speaking = speaking + runtime.refresh_echo_speaking() + text = str(message.get("text") or "") + if speaking and text: + runtime.echo.note_model_text(text) + for turn in runtime.model_turns.feed(event): + runtime.record_model_turn(turn) + return None + if message_type == "playback.state": + event = dict(message) + runtime.telemetry.write("model", event) + runtime.playback_active = bool(message.get("active")) + runtime.refresh_echo_speaking() + return None + if message_type == "session.state": + event = dict(message) + runtime.telemetry.write("session", event) + phase = str(message.get("phase") or "") + if phase == "listen": + runtime.controller.mark_listen_fence() + elif phase == "restart_complete": + runtime.controller.mark_restart_complete( + str(message.get("skill_id") or runtime.registry.default_skill), + dict(message.get("slots") or {}), + int(message.get("generation") or 0), + ) + return None + if message_type == "control.ack": + if bool(message.get("ok")) and str(message.get("intent") or "") == "stop_speech": + # Browser STOP has already flushed/blocked playback. Do not + # leave EchoGuard in a stale speaking state while the user + # immediately issues the next explicit command. + runtime.model_speaking = False + runtime.playback_active = False + runtime.refresh_echo_speaking() + runtime.telemetry.write("session", dict(message)) + print( + "[AssistiveHarness][ACK] " + f"intent={message.get('intent')} ok={message.get('ok')} " + f"generation={message.get('generation')} " + f"session={message.get('old_session_id') or '-'}" + f"->{message.get('new_session_id') or '-'} " + f"cleanup={message.get('cleanup_mode') or '-'} " + f"restart_ms={message.get('restart_latency_ms') or '-'} " + f"stale_text={message.get('dropped_old_text') or 0} " + f"stale_audio={message.get('dropped_old_audio') or 0}", + flush=True, + ) + metric = message.get("restart_latency_ms") + if isinstance(metric, (int, float)): + runtime.telemetry.metric( + "restart_latency_ms", float(metric), int(message.get("event_id") or 0) + ) + return None + if message_type == "frame.shadow": + at_ms = float(message.get("timestamp_ms") or now_ms()) + max_fps = max(0.0, float(self.cv_config.get("max_fps", 1))) + if max_fps <= 0: + return None + if at_ms - runtime.last_frame_ms < 1000.0 / max_fps: + return None + skill_id = runtime.controller.state.current_skill + skill_spec = runtime.registry.get(skill_id) + mode = str(skill_spec.get("cv_mode") or self.cv_config.get("mode") or "disabled") + if mode == "disabled": + return None + provider_id = str(skill_spec.get("cv_provider") or "noop") + frame_b64 = str(message.get("jpeg_b64") or "") + frame_id = str(message.get("frame_id") or uuid.uuid4().hex) + try: + frame = base64.b64decode(frame_b64, validate=True) if frame_b64 else None + except Exception as exc: + runtime.telemetry.write( + "cv", + CVObservation( + frame_id=frame_id, + timestamp_ms=at_ms, + skill_id=skill_id, + provider=f"{mode}:{provider_id}", + values={"status": "error"}, + error=f"InvalidFramePayload: {exc}", + ).to_dict(), + ) + return None + runtime.last_frame_ms = at_ms + runtime.cv.submit( + FrameEnvelope( + frame=frame, + frame_id=frame_id, + timestamp_ms=at_ms, + skill_id=skill_id, + slots=dict(runtime.controller.state.current_slots), + mode=mode, + provider_id=provider_id, + ) + ) + return None + return {"type": "error", "code": "unknown_message", "message_type": message_type} + + +def build_parser() -> argparse.ArgumentParser: + default_config = Path(__file__).resolve().parent / "config" / "skills.example.yaml" + parser = argparse.ArgumentParser(description="Optional local assistive voice-skill Harness") + parser.add_argument("--config", default=str(default_config)) + parser.add_argument("--enabled", action="store_true", help="explicit opt-in; default is off") + parser.add_argument("--host", default="127.0.0.1") + parser.add_argument("--port", type=int, default=8021) + parser.add_argument("--model-path", default=None) + parser.add_argument("--allow-test-injection", action="store_true") + parser.add_argument("--certfile", default=None) + parser.add_argument("--keyfile", default=None) + return parser + + +def main() -> None: + args = build_parser().parse_args() + service = AssistiveHarnessService( + args.config, + enabled=args.enabled, + model_path=args.model_path, + allow_test_injection=args.allow_test_injection, + ) + if isinstance(service.engine, FunASREngine): + warmup_started = time.perf_counter() + print("[AssistiveHarness] loading and warming FunASR before accepting clients...") + service.engine.warm_up() + print( + "[AssistiveHarness] FunASR ready " + f"({time.perf_counter() - warmup_started:.2f}s)" + ) + uvicorn.run( + service.create_app(), + host=args.host, + port=args.port, + ssl_certfile=args.certfile, + ssl_keyfile=args.keyfile, + ) + + +if __name__ == "__main__": + main() diff --git a/extensions/assistive_harness/state_machine.py b/extensions/assistive_harness/state_machine.py new file mode 100644 index 0000000..e0cd9ce --- /dev/null +++ b/extensions/assistive_harness/state_machine.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import time +from dataclasses import dataclass +from typing import Any + +from .registry import RegistryError, SkillRegistry +from .schemas import ControlEvent, ControlIntent, HarnessState + + +@dataclass(slots=True) +class ControlDecision: + accepted: bool + action: str + reason: str + payload: dict[str, Any] + + +class HarnessController: + def __init__(self, registry: SkillRegistry): + self.registry = registry + self.state = HarnessState(current_skill=registry.default_skill) + self._seen_asr_ids: set[int] = set() + self._last_skill_activation_ms: dict[tuple[str, tuple[tuple[str, str], ...]], float] = {} + + @staticmethod + def _skill_key(skill_id: str, slots: dict[str, Any]) -> tuple[str, tuple[tuple[str, str], ...]]: + return skill_id, tuple(sorted((str(key), str(value)) for key, value in slots.items())) + + def process(self, event: ControlEvent, now_ms: float | None = None) -> ControlDecision: + now = now_ms if now_ms is not None else time.time() * 1000 + if event.asr_event_id in self._seen_asr_ids: + return ControlDecision(False, "ignore", "duplicate_asr_event", {}) + self._seen_asr_ids.add(event.asr_event_id) + self.state.asr_event_id = max(self.state.asr_event_id, event.asr_event_id) + self.state.control_event_id = max(self.state.control_event_id, event.event_id) + + if event.intent is ControlIntent.STOP_SPEECH: + self.state.speech_hold_active = True + self.state.drop_output_until_listen = True + return ControlDecision(True, "stop_speech", "highest_priority", event.to_dict()) + + if event.intent is ControlIntent.RESUME_SPEECH: + self.state.speech_hold_active = False + self.state.drop_output_until_listen = False + return ControlDecision(True, "resume_speech", "explicit_resume", event.to_dict()) + + if event.intent is ControlIntent.RESET_SESSION: + rendered = self.registry.render(self.registry.default_skill, {}) + event.skill_id = self.registry.default_skill + event.slots = {} + event.system_prompt = rendered.text + event.prompt_path = rendered.path + event.prompt_sha256 = rendered.sha256 + self.state.restart_in_progress = True + self.state.pending_skill = None + return ControlDecision(True, "restart_session", "explicit_reset", event.to_dict()) + + if event.intent in {ControlIntent.CANCEL_SKILL, ControlIntent.RETURN_TO_CHAT}: + event.skill_id = self.registry.default_skill + event.slots = {} + + if event.intent in { + ControlIntent.CANCEL_SKILL, + ControlIntent.RETURN_TO_CHAT, + ControlIntent.ACTIVATE_SKILL, + }: + skill_id = event.skill_id or self.registry.default_skill + slots = dict(event.slots or {}) + try: + rendered = self.registry.render(skill_id, slots) + except RegistryError as exc: + return ControlDecision(False, "clarify", str(exc), event.to_dict()) + + if self.state.restart_in_progress: + self.state.pending_skill = {"skill_id": skill_id, "slots": slots} + return ControlDecision(True, "queue_skill", "restart_in_progress_last_write_wins", event.to_dict()) + + key = self._skill_key(skill_id, slots) + last = self._last_skill_activation_ms.get(key) + cooldown = self.registry.cooldown_ms(skill_id) + if skill_id == self.state.current_skill and slots == self.state.current_slots: + return ControlDecision(False, "ignore", "same_skill_same_slots", event.to_dict()) + if last is not None and now - last < cooldown: + return ControlDecision(False, "ignore", "skill_cooldown", event.to_dict()) + + self._last_skill_activation_ms[key] = now + event.system_prompt = rendered.text + event.prompt_path = rendered.path + event.prompt_sha256 = rendered.sha256 + self.state.restart_in_progress = True + return ControlDecision(True, "activate_skill", "skill_switch", event.to_dict()) + + return ControlDecision(False, "pass_chat", "ordinary_chat", event.to_dict()) + + def mark_restart_complete(self, skill_id: str, slots: dict[str, Any], generation: int) -> dict[str, Any] | None: + self.state.current_skill = skill_id + self.state.current_slots = dict(slots) + self.state.session_generation = int(generation) + self.state.restart_in_progress = False + self.state.speech_hold_active = False + self.state.drop_output_until_listen = False + pending = self.state.pending_skill + self.state.pending_skill = None + return pending + + def mark_listen_fence(self) -> None: + if not self.state.speech_hold_active: + self.state.drop_output_until_listen = False + + def mark_disconnected(self) -> None: + self.state.restart_in_progress = False + self.state.pending_skill = None + self.state.speech_hold_active = False + self.state.drop_output_until_listen = False diff --git a/extensions/assistive_harness/summarize_reset_run.py b/extensions/assistive_harness/summarize_reset_run.py new file mode 100644 index 0000000..1e8ce19 --- /dev/null +++ b/extensions/assistive_harness/summarize_reset_run.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + + +def nearest_rank(values: list[float], quantile: float) -> float | None: + if not values: + return None + ordered = sorted(values) + index = max(0, math.ceil(len(ordered) * quantile) - 1) + return ordered[index] + + +def load_events(path: Path) -> list[dict[str, Any]]: + if not path.exists(): + return [] + events: list[dict[str, Any]] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + try: + events.append(json.loads(line)) + except json.JSONDecodeError: + continue + return events + + +def resolve_run(value: str | None) -> Path: + runs_root = Path(__file__).resolve().parent / "runs" + if value: + candidate = Path(value).expanduser().resolve() + if not candidate.is_dir(): + raise SystemExit(f"Run directory does not exist: {candidate}") + return candidate + candidates = [path for path in runs_root.iterdir() if path.is_dir()] + if not candidates: + raise SystemExit(f"No run directories found under {runs_root}") + return max(candidates, key=lambda path: path.stat().st_mtime) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Summarize real browser RESET telemetry") + parser.add_argument("--run", help="specific run directory; defaults to the newest run") + args = parser.parse_args() + + run_dir = resolve_run(args.run) + events = load_events(run_dir / "session_events.jsonl") + resets = [ + event + for event in events + if event.get("type") == "control.ack" + and event.get("intent") == "reset_session" + ] + successes = [event for event in resets if event.get("ok") is True] + latencies = [ + float(event["restart_latency_ms"]) + for event in successes + if isinstance(event.get("restart_latency_ms"), (int, float)) + ] + changed_ids = [ + event + for event in successes + if event.get("old_session_id") + and event.get("new_session_id") + and event.get("old_session_id") != event.get("new_session_id") + ] + + print(f"Run: {run_dir}") + print(f"RESET success: {len(successes)}/{len(resets)}") + print(f"Session ID changed: {len(changed_ids)}/{len(successes)}") + if latencies: + print(f"Restart P50: {nearest_rank(latencies, 0.50):.1f} ms") + print(f"Restart P90: {nearest_rank(latencies, 0.90):.1f} ms") + print(f"Restart max: {max(latencies):.1f} ms") + else: + print("Restart P50/P90: no RESET latency samples") + print( + "Dropped stale callbacks (cumulative): " + f"text={max((int(event.get('dropped_old_text') or 0) for event in successes), default=0)}, " + f"audio={max((int(event.get('dropped_old_audio') or 0) for event in successes), default=0)}" + ) + print("Audible old-output pollution: manual observation required") + for index, event in enumerate(successes, start=1): + print( + f"{index:02d}. generation={event.get('generation')} " + f"session={event.get('old_session_id') or '-'}->{event.get('new_session_id') or '-'} " + f"cleanup={event.get('cleanup_mode') or '-'} " + f"latency={float(event.get('restart_latency_ms') or 0):.1f} ms" + ) + + +if __name__ == "__main__": + main() diff --git a/extensions/assistive_harness/telemetry.py b/extensions/assistive_harness/telemetry.py new file mode 100644 index 0000000..2e3aa18 --- /dev/null +++ b/extensions/assistive_harness/telemetry.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +import csv +import json +import threading +import time +from pathlib import Path +from typing import Any + +import yaml + + +class TelemetryWriter: + FILES = { + "asr": "asr_events.jsonl", + "control": "control_events.jsonl", + "session": "session_events.jsonl", + "skill": "skill_events.jsonl", + "cv": "cv_events.jsonl", + "echo": "echo_events.jsonl", + "model": "model_events.jsonl", + } + + def __init__(self, root: str | Path, run_id: str, config: dict[str, Any]): + self.run_id = run_id + self.run_dir = Path(root) / run_id + self.run_dir.mkdir(parents=True, exist_ok=True) + self._lock = threading.Lock() + (self.run_dir / "config_snapshot.yaml").write_text( + yaml.safe_dump(config, allow_unicode=True, sort_keys=True), + encoding="utf-8", + ) + self._metrics_path = self.run_dir / "metrics.csv" + with self._metrics_path.open("w", newline="", encoding="utf-8") as handle: + csv.writer(handle).writerow(["timestamp_ms", "metric", "value", "event_id"]) + + def write(self, stream: str, payload: dict[str, Any]) -> None: + filename = self.FILES[stream] + record = {"logged_at_ms": time.time() * 1000, **payload} + line = json.dumps(record, ensure_ascii=False, sort_keys=True) + with self._lock: + with (self.run_dir / filename).open("a", encoding="utf-8") as handle: + handle.write(line + "\n") + + def metric(self, name: str, value: float, event_id: int | None = None) -> None: + with self._lock: + with self._metrics_path.open("a", newline="", encoding="utf-8") as handle: + csv.writer(handle).writerow([time.time() * 1000, name, value, event_id or ""]) + + def write_summary(self, summary: dict[str, Any]) -> None: + (self.run_dir / "summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2, sort_keys=True), + encoding="utf-8", + ) + + def append_model_transcript(self, turn: dict[str, Any]) -> None: + text = str(turn.get("text") or "").replace("\r", " ").replace("\n", " ") + line = ( + f"turn={turn.get('turn_index')} role={turn.get('role')} " + f"generation={turn.get('generation')} skill={turn.get('skill_id')} " + f"audio_ms={turn.get('audio_ms')} text={text}\n" + ) + with self._lock: + with (self.run_dir / "model_transcript.txt").open( + "a", encoding="utf-8" + ) as handle: + handle.write(line) diff --git a/extensions/assistive_harness/tests/__init__.py b/extensions/assistive_harness/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/extensions/assistive_harness/tests/test_core.py b/extensions/assistive_harness/tests/test_core.py new file mode 100644 index 0000000..83066c9 --- /dev/null +++ b/extensions/assistive_harness/tests/test_core.py @@ -0,0 +1,402 @@ +from __future__ import annotations + +import asyncio +import tempfile +import time +import unittest +from pathlib import Path + +import numpy as np + +from extensions.assistive_harness.asr.energy_vad import EnergyVAD +from extensions.assistive_harness.cv.noop import ShadowCVProvider +from extensions.assistive_harness.cv.base import CVObservation, FrameEnvelope +from extensions.assistive_harness.cv.pipeline import CVPipeline +from extensions.assistive_harness.cv.registry import CVProviderRegistry +from extensions.assistive_harness.echo_guard import EchoGuard +from extensions.assistive_harness.model_log import ModelTurnAccumulator +from extensions.assistive_harness.registry import RegistryError, SkillRegistry +from extensions.assistive_harness.router import RuleIntentRouter +from extensions.assistive_harness.schemas import ControlIntent +from extensions.assistive_harness.state_machine import HarnessController + + +CONFIG = Path(__file__).resolve().parents[1] / "config" / "skills.example.yaml" + + +class RouterCorpusTests(unittest.TestCase): + @classmethod + def setUpClass(cls) -> None: + cls.registry = SkillRegistry(CONFIG) + cls.router = RuleIntentRouter(cls.registry) + + def test_control_and_skill_corpus_over_fifty_utterances(self) -> None: + corpus = [ + ("停一下", ControlIntent.STOP_SPEECH, None), + ("停一下啊", ControlIntent.STOP_SPEECH, None), + ("停一下下", ControlIntent.STOP_SPEECH, None), + ("同一下", ControlIntent.STOP_SPEECH, None), + ("同一下下", ControlIntent.STOP_SPEECH, None), + ("等一下", ControlIntent.STOP_SPEECH, None), + ("等一一下", ControlIntent.STOP_SPEECH, None), + ("请停一下啊", ControlIntent.STOP_SPEECH, None), + ("请停一下", ControlIntent.STOP_SPEECH, None), + ("麻烦你先停一下", ControlIntent.STOP_SPEECH, None), + ("别说了", ControlIntent.STOP_SPEECH, None), + ("请你别说了好吗", ControlIntent.STOP_SPEECH, None), + ("闭嘴", ControlIntent.STOP_SPEECH, None), + ("停止播报", ControlIntent.STOP_SPEECH, None), + ("安静", ControlIntent.STOP_SPEECH, None), + ("你先停一下，我有个问题", ControlIntent.STOP_SPEECH, None), + ("说得有点长了麻烦停一下吧", ControlIntent.STOP_SPEECH, None), + ("现在可以先停一下然后听我说吗", ControlIntent.STOP_SPEECH, None), + ("不停停一下继续介绍", ControlIntent.STOP_SPEECH, None), + ("我刚才没有说停一下", ControlIntent.STOP_SPEECH, None), + ("请不要停一下，继续介绍", ControlIntent.STOP_SPEECH, None), + ("停一下是什么意思", ControlIntent.STOP_SPEECH, None), + ("恢复对话", ControlIntent.RESUME_SPEECH, None), + ("好了现在恢复对话吧", ControlIntent.RESUME_SPEECH, None), + ("乐奇恢复对话", ControlIntent.RESUME_SPEECH, None), + ("停一下然后恢复对话", ControlIntent.STOP_SPEECH, None), + ("重新开始", ControlIntent.RESET_SESSION, "idle_chat"), + ("请重新开始", ControlIntent.RESET_SESSION, "idle_chat"), + ("重置会话", ControlIntent.NONE, None), + ("乐奇请重新开始", ControlIntent.RESET_SESSION, "idle_chat"), + ("你有点卡了请重新开始会话", ControlIntent.RESET_SESSION, "idle_chat"), + ("请现在重置会话然后听我说", ControlIntent.NONE, None), + ("停一下然后重新开始", ControlIntent.STOP_SPEECH, None), + ("恢复对话然后重新开始", ControlIntent.RESET_SESSION, "idle_chat"), + ("新建会话", ControlIntent.NONE, None), + ("清空上下文", ControlIntent.NONE, None), + ("回到聊天", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("请回到聊天", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("回到普通聊天", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("回到普通聊天天", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("退出技能", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("普通聊天", ControlIntent.RETURN_TO_CHAT, "idle_chat"), + ("取消任务", ControlIntent.CANCEL_SKILL, None), + ("不找了", ControlIntent.CANCEL_SKILL, None), + ("不读了", ControlIntent.CANCEL_SKILL, None), + ("帮我找手机", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("帮我找一下我的手机", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("帮我找一下手机", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("帮我找一下下我的手机嗯", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("帮找一下手机手机", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("请帮我找钥匙", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("水杯在哪", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("书在哪里", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("看到门卡了吗", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("有没有雨伞", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("麻烦帮我找眼镜", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("请问钱包在哪", ControlIntent.ACTIVATE_SKILL, "find_object"), + ("读一下", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("读一下这行字", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("请读一下", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("帮我读一下这个杯子上的字母母母", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("帮读一下这个水杯上面的字字个", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("帮我识字", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("上面写了什么", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("这是什么字", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("读文字", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("请你读文字", ControlIntent.ACTIVATE_SKILL, "read_text"), + ("描述一下", ControlIntent.ACTIVATE_SKILL, "describe_scene"), + ("帮我描述一下", ControlIntent.ACTIVATE_SKILL, "describe_scene"), + ("看看周围", ControlIntent.ACTIVATE_SKILL, "describe_scene"), + ("帮我避障", ControlIntent.ACTIVATE_SKILL, "obstacle_avoidance"), + ("前面有障碍吗", ControlIntent.ACTIVATE_SKILL, "obstacle_avoidance"), + ("今天天气怎么样", ControlIntent.NONE, None), + ("停止是一个动词", ControlIntent.NONE, None), + ("他说让我闭嘴但我没同意", ControlIntent.NONE, None), + ("重新开始这个词怎么翻译", ControlIntent.RESET_SESSION, "idle_chat"), + ("我在读一本书", ControlIntent.NONE, None), + ("你觉得这杯水怎么样", ControlIntent.NONE, None), + ("普通聊天机器人是什么", ControlIntent.NONE, None), + ("请介绍一下上海", ControlIntent.NONE, None), + ("我不想清空上下文因为还有用", ControlIntent.NONE, None), + ("帮我分析这段话", ControlIntent.NONE, None), + ("帮我一下手机手机", ControlIntent.NONE, None), + ("能听见我吗", ControlIntent.NONE, None), + ("今天周几", ControlIntent.NONE, None), + ("为什么会这样", ControlIntent.NONE, None), + ("继续说", ControlIntent.NONE, None), + ("接着说", ControlIntent.NONE, None), + ("你继续说话吧", ControlIntent.NONE, None), + ("谢谢", ControlIntent.NONE, None), + ("你好", ControlIntent.NONE, None), + ("左边有什么", ControlIntent.NONE, None), + ("给我讲个笑话", ControlIntent.NONE, None), + ("", ControlIntent.NONE, None), + ("。", ControlIntent.NONE, None), + ("安静是一种状态", ControlIntent.NONE, None), + ("取消任务是不是一个按钮", ControlIntent.NONE, None), + ("有人说不读了然后离开", ControlIntent.NONE, None), + ] + self.assertGreaterEqual(len(corpus), 50) + for utterance, expected_intent, expected_skill in corpus: + with self.subTest(utterance=utterance): + result = self.router.route(utterance) + self.assertEqual(result.intent, expected_intent) + self.assertEqual(result.skill_id, expected_skill) + + def test_find_target_slot(self) -> None: + result = self.router.route("请帮我找深绿色的书") + self.assertEqual(result.slots, {"target": "深绿色的书"}) + + natural = self.router.route("帮我找一下我的手机") + self.assertEqual(natural.slots, {"target": "手机"}) + + repeated = self.router.route("帮我找一下下我的手机嗯") + self.assertEqual(repeated.slots, {"target": "手机"}) + + missing_pronoun = self.router.route("帮找一下手机手机") + self.assertEqual(missing_pronoun.slots, {"target": "手机"}) + + +class RegistryAndStateTests(unittest.TestCase): + def setUp(self) -> None: + self.registry = SkillRegistry(CONFIG) + self.router = RuleIntentRouter(self.registry) + self.controller = HarnessController(self.registry) + + def event(self, text: str, event_id: int, asr_id: int): + return self.router.make_event( + self.router.route(text), event_id=event_id, asr_event_id=asr_id, + utterance=text, created_at_ms=float(event_id * 1000), + ) + + def test_prompt_render_and_hash(self) -> None: + prompt = self.registry.render("find_object", {"target": "手机"}) + self.assertIn("当前寻找目标：手机", prompt.text) + self.assertIn("智能眼镜找物助手", prompt.text) + self.assertEqual(len(prompt.sha256), 64) + self.assertIn("手机", self.registry.task_trigger("find_object", {"target": "手机"})) + + def test_aaai_skill_prompt_files_are_user_editable_and_registered(self) -> None: + find_prompt = self.registry.render("find_object", {"target": "水杯"}) + read_prompt = self.registry.render("read_text", {}) + self.assertTrue(Path(find_prompt.path).is_file()) + self.assertTrue(Path(read_prompt.path).is_file()) + self.assertIn("当前寻找目标：水杯", find_prompt.text) + self.assertIn("智能眼镜读字助手", read_prompt.text) + self.assertTrue(self.registry.is_enabled("describe_scene")) + self.assertTrue(self.registry.is_enabled("obstacle_avoidance")) + self.assertTrue( + Path(self.registry.get("obstacle_avoidance")["prompt_file"]).name + == "obstacle_avoidance_zh.txt" + ) + + def test_required_slot_validation(self) -> None: + with self.assertRaises(RegistryError): + self.registry.render("find_object", {}) + + def test_unknown_skill_is_rejected(self) -> None: + with self.assertRaises(RegistryError): + self.registry.render("not_registered", {}) + + def test_stop_hold_requires_explicit_resume(self) -> None: + decision = self.controller.process(self.event("停一下", 1, 1)) + self.assertEqual(decision.action, "stop_speech") + self.assertTrue(self.controller.state.speech_hold_active) + self.assertTrue(self.controller.state.drop_output_until_listen) + self.controller.mark_listen_fence() + self.assertTrue(self.controller.state.speech_hold_active) + self.assertTrue(self.controller.state.drop_output_until_listen) + resumed = self.controller.process(self.event("恢复对话", 2, 2)) + self.assertEqual(resumed.action, "resume_speech") + self.assertFalse(self.controller.state.speech_hold_active) + self.assertFalse(self.controller.state.drop_output_until_listen) + + def test_reset_always_supplies_idle_prompt(self) -> None: + decision = self.controller.process(self.event("重新开始", 1, 1)) + self.assertEqual(decision.action, "restart_session") + self.assertIn("AI 眼镜助手", decision.payload["system_prompt"]) + self.assertTrue(decision.payload["prompt_path"].endswith("idle_chat_zh.txt")) + + def test_skill_activation_supplies_rendered_prompt_path_and_hash(self) -> None: + decision = self.controller.process(self.event("帮我找手机", 1, 1)) + self.assertEqual(decision.action, "activate_skill") + self.assertIn("当前寻找目标：手机", decision.payload["system_prompt"]) + self.assertTrue(decision.payload["prompt_path"].endswith("find_object_zh.txt")) + self.assertEqual(len(decision.payload["prompt_sha256"]), 64) + + def test_duplicate_asr_event_is_ignored(self) -> None: + first = self.controller.process(self.event("停一下", 1, 9)) + second = self.controller.process(self.event("停一下", 2, 9)) + self.assertTrue(first.accepted) + self.assertEqual(second.reason, "duplicate_asr_event") + + def test_same_skill_same_slots_does_not_restart(self) -> None: + first = self.controller.process(self.event("帮我找手机", 1, 1)) + self.assertTrue(first.accepted) + self.controller.mark_restart_complete("find_object", {"target": "手机"}, 1) + second = self.controller.process(self.event("帮我找手机", 2, 2), now_ms=9000) + self.assertFalse(second.accepted) + self.assertEqual(second.reason, "same_skill_same_slots") + + def test_restart_pending_is_last_write_wins(self) -> None: + self.controller.process(self.event("帮我找手机", 1, 1)) + queued_one = self.controller.process(self.event("帮我找钥匙", 2, 2)) + queued_two = self.controller.process(self.event("读一下", 3, 3)) + self.assertEqual(queued_one.action, "queue_skill") + self.assertEqual(queued_two.action, "queue_skill") + pending = self.controller.mark_restart_complete("find_object", {"target": "手机"}, 1) + self.assertEqual(pending, {"skill_id": "read_text", "slots": {}}) + + def test_disconnect_clears_restart_and_pending(self) -> None: + self.controller.process(self.event("帮我找手机", 1, 1)) + self.controller.process(self.event("读一下", 2, 2)) + self.controller.mark_disconnected() + self.assertFalse(self.controller.state.restart_in_progress) + self.assertIsNone(self.controller.state.pending_skill) + + +class EchoAndVADTests(unittest.TestCase): + def test_recent_model_echo_is_blocked_but_stop_is_allowed(self) -> None: + guard = EchoGuard(window_ms=20_000, similarity_threshold=0.8) + guard.note_model_text("请告诉我还需要什么帮助", at_ms=1000) + blocked = guard.evaluate("请告诉我还需要什么帮助", ControlIntent.NONE, at_ms=1100) + self.assertFalse(blocked.allow) + guard.set_ai_speaking(True) + allowed = guard.evaluate("停一下", ControlIntent.STOP_SPEECH, at_ms=1200) + self.assertTrue(allowed.allow) + resumed = guard.evaluate("恢复对话", ControlIntent.RESUME_SPEECH, at_ms=1300) + self.assertTrue(resumed.allow) + + skill = guard.evaluate("帮我找一下我的手机", ControlIntent.ACTIVATE_SKILL, at_ms=1400) + self.assertTrue(skill.allow) + returned = guard.evaluate("回到普通聊天", ControlIntent.RETURN_TO_CHAT, at_ms=1450) + self.assertTrue(returned.allow) + cancelled = guard.evaluate("取消任务", ControlIntent.CANCEL_SKILL, at_ms=1475) + self.assertTrue(cancelled.allow) + ordinary = guard.evaluate("今天天气怎么样", ControlIntent.NONE, at_ms=1500) + self.assertFalse(ordinary.allow) + self.assertEqual(ordinary.reason, "ordinary_skill_suppressed_while_ai_speaking") + + def test_vad_emits_one_utterance(self) -> None: + vad = EnergyVAD(rms_threshold=0.01, min_speech_ms=100, end_silence_ms=200) + frames = [np.zeros(1600, np.float32)] + frames += [np.full(1600, 0.1, np.float32) for _ in range(3)] + frames += [np.zeros(1600, np.float32) for _ in range(3)] + utterances = [] + for index, frame in enumerate(frames): + result = vad.feed(frame, index * 100.0) + if result is not None: + utterances.append(result) + self.assertEqual(len(utterances), 1) + self.assertGreater(utterances[0].audio.size, 0) + + +class ModelTurnLogTests(unittest.TestCase): + def test_streamed_fragments_are_aggregated_with_audio_duration(self) -> None: + turns = ModelTurnAccumulator() + first = turns.feed( + { + "state": "speak", + "session_id": "s1", + "generation": 2, + "skill_id": "read_text", + "text": "上海", + "audio_ms": 120, + } + ) + self.assertEqual(first, []) + second = turns.feed( + { + "state": "speak", + "session_id": "s1", + "generation": 2, + "skill_id": "read_text", + "text": "电力", + "audio_ms": 180, + "decode_end": True, + } + ) + self.assertEqual(second, []) + completed = turns.feed( + { + "state": "listen", + "session_id": "s1", + "generation": 2, + "skill_id": "read_text", + "end_of_turn": True, + } + ) + self.assertEqual(completed[0]["text"], "上海电力") + self.assertEqual(completed[0]["audio_ms"], 300.0) + + +class CVIsolationTests(unittest.IsolatedAsyncioTestCase): + async def test_shadow_provider_contains_failures(self) -> None: + class Broken: + def analyze(self, *args, **kwargs): + raise RuntimeError("synthetic CV failure") + + result = ShadowCVProvider(Broken(), "broken").analyze( + None, "f1", 1.0, "find_object", {} + ) + self.assertEqual(result.provider, "shadow:broken") + self.assertIn("synthetic CV failure", result.error or "") + + async def test_pipeline_is_non_blocking_and_latest_frame_wins(self) -> None: + observations: list[CVObservation] = [] + + class Slow: + def analyze(self, frame, frame_id, timestamp_ms, skill_id, slots): + time.sleep(0.08) + return CVObservation( + frame_id=frame_id, + timestamp_ms=timestamp_ms, + skill_id=skill_id, + provider="slow", + ) + + pipeline = CVPipeline( + CVProviderRegistry({"slow": Slow()}), + on_observation=observations.append, + inference_timeout_ms=500, + ) + pipeline.submit(FrameEnvelope(b"1", "f1", 1.0, "find_object", {}, "shadow", "slow")) + # submit() must return before the synchronous provider completes. A + # wall-clock threshold is flaky on a loaded Windows workstation. + self.assertEqual(observations, []) + self.assertIsNotNone(pipeline.worker_task) + await asyncio.sleep(0.01) + pipeline.submit(FrameEnvelope(b"2", "f2", 2.0, "find_object", {}, "shadow", "slow")) + pipeline.submit(FrameEnvelope(b"3", "f3", 3.0, "find_object", {}, "shadow", "slow")) + for _ in range(100): + if len(observations) >= 2: + break + await asyncio.sleep(0.01) + snapshot = pipeline.snapshot() + await pipeline.close() + self.assertEqual([item.frame_id for item in observations], ["f1", "f3"]) + self.assertEqual(snapshot["dropped_frames"], 1) + + async def test_pipeline_timeout_is_observed_without_escaping(self) -> None: + observations: list[CVObservation] = [] + + class TooSlow: + def analyze(self, frame, frame_id, timestamp_ms, skill_id, slots): + time.sleep(0.08) + return CVObservation(frame_id, timestamp_ms, skill_id, "too_slow") + + pipeline = CVPipeline( + CVProviderRegistry({"too_slow": TooSlow()}), + on_observation=observations.append, + inference_timeout_ms=10, + ) + pipeline.submit( + FrameEnvelope(b"x", "timeout", 1.0, "find_object", {}, "shadow", "too_slow") + ) + for _ in range(50): + if observations: + break + await asyncio.sleep(0.005) + snapshot = pipeline.snapshot() + await pipeline.close() + self.assertIn("TimeoutError", observations[0].error or "") + self.assertEqual(snapshot["timeout_count"], 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/extensions/assistive_harness/tests/test_cv_yolo.py b/extensions/assistive_harness/tests/test_cv_yolo.py new file mode 100644 index 0000000..59476f8 --- /dev/null +++ b/extensions/assistive_harness/tests/test_cv_yolo.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import unittest +from pathlib import Path + +import cv2 +import numpy as np + +from extensions.assistive_harness.cv.yolo_onnx import YoloOnnxProvider + + +MODEL = ( + Path(__file__).resolve().parents[4] + / "OmniHarness" + / "mini_omni_harness" + / "models" + / "yolo26n.onnx" +) + + +@unittest.skipUnless(MODEL.is_file(), "local YOLO reference weights are unavailable") +class YoloOnnxSmokeTests(unittest.TestCase): + def test_blank_jpeg_runs_real_onnx_inference(self) -> None: + ok, encoded = cv2.imencode(".jpg", np.zeros((480, 640, 3), dtype=np.uint8)) + self.assertTrue(ok) + observation = YoloOnnxProvider(MODEL).analyze( + encoded.tobytes(), + "blank", + 1.0, + "find_object", + {"target": "手机"}, + ) + self.assertEqual(observation.provider, "yolo_onnx") + self.assertEqual(observation.values["status"], "ok") + self.assertEqual(observation.values["canonical_label"], "cell phone") + self.assertEqual(observation.values["model"], "yolo26n.onnx") + self.assertIsInstance(observation.values["detections"], list) + + +if __name__ == "__main__": + unittest.main() diff --git a/extensions/assistive_harness/tests/test_phase_b_rokid.py b/extensions/assistive_harness/tests/test_phase_b_rokid.py new file mode 100644 index 0000000..1787b69 --- /dev/null +++ b/extensions/assistive_harness/tests/test_phase_b_rokid.py @@ -0,0 +1,352 @@ +from __future__ import annotations + +import asyncio +import base64 +import unittest +from pathlib import Path +from typing import Any + +import numpy as np + +from extensions.assistive_harness.phase_b.rokid_runtime import ( + AudioMirrorChunker, + DropOldestAudioQueue, + GatewaySessionManager, + LatestFrame, + PCSpeaker, + PhaseBRokidRuntime, + RokidRuntimeConfig, + SessionSpec, + apply_pcm16_gain, + make_session_ready_chime, + pcm16le_to_float32, +) +from extensions.assistive_harness.registry import SkillRegistry + + +CONFIG = Path(__file__).resolve().parents[1] / "config" / "skills.example.yaml" + + +class FakeSpeaker: + def __init__(self) -> None: + self.blocked = False + self.flush_count = 0 + self.resume_count = 0 + self.enqueued: list[tuple[np.ndarray, int]] = [] + + async def start(self) -> None: + pass + + async def enqueue(self, samples: np.ndarray, generation: int) -> None: + if not self.blocked: + self.enqueued.append((samples.copy(), generation)) + + async def block_and_flush(self) -> None: + self.blocked = True + self.flush_count += 1 + + async def resume(self) -> None: + self.blocked = False + self.resume_count += 1 + + async def close(self) -> None: + pass + + def pending_ms(self) -> float: + return 0.0 + + +class FakeTelemetry: + connected = True + + def __init__(self) -> None: + self.messages: list[dict[str, Any]] = [] + + async def send(self, payload: dict[str, Any]) -> None: + self.messages.append(dict(payload)) + + +class FakeSession: + next_id = 0 + + def __init__( + self, + _config: RokidRuntimeConfig, + spec: SessionSpec, + _audio_queue: DropOldestAudioQueue, + _latest_frame: LatestFrame, + _gate: object, + on_result: object, + ) -> None: + type(self).next_id += 1 + self.spec = spec + self.on_result = on_result + self.session_id = f"fake-{type(self).next_id}" + self.status = "created" + self.last_error = "" + self.started = False + self.stopped_with: str | None = None + self.injected_tasks: list[str] = [] + + async def start(self) -> None: + self.started = True + self.status = "running" + + async def stop(self, cleanup_mode: str) -> None: + self.stopped_with = cleanup_mode + self.status = "stopped" + + async def inject_task(self, text: str) -> bool: + self.injected_tasks.append(text) + return True + + +class RokidAudioBoundaryTests(unittest.IsolatedAsyncioTestCase): + def test_health_marks_device_input_not_ready_before_first_packet(self) -> None: + runtime = PhaseBRokidRuntime( + RokidRuntimeConfig(skills_config=str(CONFIG), play_audio=False), + speaker=FakeSpeaker(), + session_factory=FakeSession, + ) + self.assertFalse(runtime.health()["device_input_ready"]) + runtime.stats.audio_packets = 1 + self.assertTrue(runtime.health()["device_input_ready"]) + + def test_pcm16_conversion_and_mirror_chunking(self) -> None: + raw = np.array([-32768, 0, 32767], dtype=" None: + raw = np.array([-4_000, 1_000, 4_000], dtype=" None: + chime = make_session_ready_chime() + self.assertEqual(chime.dtype, np.float32) + self.assertGreater(chime.size, 24_000 * 0.15) + self.assertLess(chime.size, 24_000 * 0.25) + self.assertLessEqual(float(np.max(np.abs(chime))), 0.321) + self.assertGreater(float(np.max(np.abs(chime))), 0.30) + + async def test_drop_oldest_queue_preserves_newest_packets(self) -> None: + queue = DropOldestAudioQueue(max_packets=2) + queue.put_nowait(b"old") + queue.put_nowait(b"middle") + queue.put_nowait(b"new") + self.assertEqual(queue.dropped_packets, 1) + self.assertEqual(await queue.get(0.01), b"middle") + self.assertEqual(await queue.get(0.01), b"new") + + async def test_pc_speaker_stop_closes_and_resume_reopens_stream(self) -> None: + class FakeOutputStream: + def __init__(self) -> None: + self.abort_count = 0 + self.close_count = 0 + + def abort(self) -> None: + self.abort_count += 1 + + def close(self) -> None: + self.close_count += 1 + + speaker = PCSpeaker() + old_stream = FakeOutputStream() + new_stream = FakeOutputStream() + speaker._stream = old_stream + + await speaker.block_and_flush() + self.assertTrue(speaker.blocked) + self.assertIsNone(speaker._stream) + self.assertEqual(old_stream.abort_count, 1) + self.assertEqual(old_stream.close_count, 1) + + speaker._open_stream = lambda: setattr(speaker, "_stream", new_stream) + await speaker.resume() + self.assertFalse(speaker.blocked) + self.assertIs(speaker._stream, new_stream) + + +class PhaseBControlContractTests(unittest.IsolatedAsyncioTestCase): + def setUp(self) -> None: + FakeSession.next_id = 0 + self.registry = SkillRegistry(CONFIG) + self.speaker = FakeSpeaker() + self.telemetry = FakeTelemetry() + self.sessions: list[FakeSession] = [] + + def factory(*args: Any) -> FakeSession: + session = FakeSession(*args) + self.sessions.append(session) + return session + + self.manager = GatewaySessionManager( + RokidRuntimeConfig( + skills_config=str(CONFIG), + play_audio=False, + playback_echo_tail_s=0.0, + ), + self.registry, + DropOldestAudioQueue(max_packets=4), + LatestFrame(), + self.speaker, + session_factory=factory, + ) + self.manager.harness = self.telemetry + + async def test_stop_and_resume_flush_pc_output_and_ack(self) -> None: + await self.manager.start_initial() + stop_ack = await self.manager.handle_control( + { + "type": "control.intent", + "event_id": 1, + "intent": "stop_speech", + "accepted": True, + } + ) + self.assertTrue(stop_ack["ok"]) + self.assertTrue(self.manager.gate.speech_hold_active) + self.assertTrue(self.speaker.blocked) + self.assertEqual(self.speaker.flush_count, 1) + + resume_ack = await self.manager.handle_control( + { + "type": "control.intent", + "event_id": 2, + "intent": "resume_speech", + "accepted": True, + } + ) + self.assertTrue(resume_ack["ok"]) + self.assertFalse(self.manager.gate.speech_hold_active) + self.assertFalse(self.speaker.blocked) + self.assertEqual( + [message["intent"] for message in self.telemetry.messages if message.get("type") == "control.ack"], + ["stop_speech", "resume_speech"], + ) + + async def test_skill_switch_replaces_session_and_fences_old_output(self) -> None: + await self.manager.start_initial() + old_session = self.sessions[0] + ack = await self.manager.handle_control( + { + "type": "control.intent", + "event_id": 3, + "intent": "activate_skill", + "accepted": True, + "skill_id": "read_text", + "slots": {}, + } + ) + new_session = self.sessions[1] + self.assertTrue(ack["ok"]) + self.assertEqual(old_session.stopped_with, "light") + self.assertTrue(new_session.started) + self.assertNotEqual(old_session.session_id, new_session.session_id) + self.assertEqual(self.manager.gate.generation, 1) + self.assertEqual(self.manager.gate.current_skill, "read_text") + self.assertEqual( + new_session.injected_tasks, + ["请立即读取当前画面中最明显的文字，只读看到的内容。"], + ) + self.assertTrue(ack["task_trigger_sent"]) + self.assertFalse(self.speaker.blocked) + self.assertTrue( + any( + message.get("type") == "session.state" + and message.get("phase") == "restart_complete" + for message in self.telemetry.messages + ) + ) + + audio = base64.b64encode(np.ones(10, dtype=np.float32).tobytes()).decode() + await self.manager.handle_result( + old_session, + {"type": "result", "text": "stale", "audio_data": audio}, + ) + self.assertEqual(self.manager.gate.dropped_old_text, 1) + self.assertEqual(self.manager.gate.dropped_old_audio, 1) + self.assertEqual(self.speaker.enqueued, []) + + await self.manager.handle_result( + new_session, + {"type": "result", "text": "current", "audio_data": audio}, + ) + self.assertEqual(len(self.speaker.enqueued), 1) + self.assertEqual(self.speaker.enqueued[0][1], 1) + + async def test_restart_complete_plays_one_local_ready_chime(self) -> None: + self.manager.config.play_audio = True + await self.manager.start_initial() + self.assertEqual(self.speaker.enqueued, []) + + ack = await self.manager.handle_control( + { + "type": "control.intent", + "event_id": 4, + "intent": "reset_session", + "accepted": True, + "skill_id": "idle_chat", + "slots": {}, + } + ) + + self.assertTrue(ack["ok"]) + self.assertEqual(len(self.speaker.enqueued), 1) + cue, generation = self.speaker.enqueued[0] + self.assertEqual(generation, 1) + self.assertGreater(cue.size, 0) + self.assertGreater(self.manager.gate.local_cue_mute_until_mono, 0.0) + + async def test_obstacle_switch_injects_one_shot_visual_task(self) -> None: + await self.manager.start_initial() + ack = await self.manager.handle_control( + { + "type": "control.intent", + "event_id": 5, + "intent": "activate_skill", + "accepted": True, + "skill_id": "obstacle_avoidance", + "slots": {}, + } + ) + self.assertTrue(ack["task_trigger_sent"]) + self.assertIn("判断当前画面", self.sessions[1].injected_tasks[0]) + + async def test_model_log_turn_ends_on_listen_not_decode_slice(self) -> None: + await self.manager.start_initial() + session = self.sessions[0] + await self.manager.handle_result( + session, + {"type": "result", "text": "上海", "end_of_turn": True}, + ) + await self.manager.handle_result( + session, + {"type": "result", "text": "电力", "end_of_turn": True}, + ) + await self.manager.handle_result( + session, + {"type": "result", "is_listen": True}, + ) + states = [ + item for item in self.telemetry.messages + if item.get("type") == "model.state" + ] + self.assertEqual([item["end_of_turn"] for item in states], [False, False, True]) + self.assertEqual([item["decode_end"] for item in states], [True, True, False]) + + +if __name__ == "__main__": + unittest.main() From a4580500c45e1f6a5258faeafa6a340457cdbd20 Mon Sep 17 00:00:00 2001 From: MajestyHenius <3051797499@qq.com> Date: Wed, 2 Sep 2026 16:47:40 +0800 Subject: [PATCH 2/3] feat: harness + CV funnel chains in the control panel - panel: four cumulative ESP32 chains (basic chat / voice control / frame selection / full anti-hallucination) plus a criteria selector; machine paths move to runtime.local.json; TLS certs are self-signed on first launch - esp32_runtime: --rotate, /v1/realtime protocol adapter, reject prompts no longer block the capture loop, every captured frame reaches the first-person view - cam_pipeline_v2: report orientation-classifier load failure instead of silently falling back to the early heuristic - docs: dependency list and setup steps in both READMEs --- .gitignore | 7 + README.md | 67 ++- README_zh.md | 69 ++- .../phase_b/assets/reject_wav/aimed_wrong.wav | Bin 0 -> 142124 bytes .../phase_b/assets/reject_wav/manifest.json | 92 ++++ .../phase_b/assets/reject_wav/need_focus.wav | Bin 0 -> 113324 bytes .../phase_b/assets/reject_wav/no_frames.wav | Bin 0 -> 105644 bytes .../phase_b/assets/reject_wav/orient.wav | Bin 0 -> 163244 bytes .../assets/reject_wav/orient_flipped.wav | Bin 0 -> 163244 bytes .../assets/reject_wav/orient_sideways.wav | Bin 0 -> 117164 bytes .../assets/reject_wav/severe_shake.wav | Bin 0 -> 142124 bytes .../phase_b/assets/reject_wav/too_dark.wav | Bin 0 -> 132524 bytes .../phase_b/assets/reject_wav/unstable.wav | Bin 0 -> 145964 bytes .../phase_b/cam_pipeline_v2.py | 15 + .../phase_b/esp32_runtime.py | 168 +++++-- .../phase_b/requirements-phase-b.txt | 36 +- .../phase_b/templates/live.html | 7 +- runtime/openglass_omni/devices.example.json | 7 + runtime/openglass_omni/panel.html | 27 +- runtime/openglass_omni/panel.py | 467 +++++++++++++++++- runtime/openglass_omni/runtime.example.json | 55 +-- 21 files changed, 897 insertions(+), 120 deletions(-) create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/aimed_wrong.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/manifest.json create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/need_focus.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/no_frames.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/orient.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/orient_flipped.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/orient_sideways.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/severe_shake.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/too_dark.wav create mode 100644 extensions/assistive_harness/phase_b/assets/reject_wav/unstable.wav create mode 100644 runtime/openglass_omni/devices.example.json diff --git a/.gitignore b/.gitignore index 0cbd177..1ecff26 100644 --- a/.gitignore +++ b/.gitignore @@ -80,3 +80,10 @@ tmp/ *.tmp.mov *.tmp.avi *.scratch.* + +runtime.local.json +devices.json +certs/ + +# ©ʾʱԴַֿ +!extensions/assistive_harness/phase_b/assets/reject_wav/*.wav diff --git a/README.md b/README.md index df9ce6e..24f2bde 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ Solid arrows represent code or artifacts present in the public repository. Dashe The control panel is intended to make **repeated runs** one-click after a one-time setup. It does not download model weights, clone upstream repositories, compile `llama.cpp-omni`, or flash the ESP32 for you. -> **Current clean-clone status:** The panel UI starts from the repository root, but the current launcher still reads machine-specific paths from `runtime/openglass_omni/panel.py`. The included `runtime.local.json` loader is not yet connected to that panel. Follow the effective configuration locations below; a code update is still required before this can be called a portable one-click installation. +> **Current clean-clone status:** The panel itself starts directly from the repository root, but the full chain still needs four out-of-repo assets prepared by hand: the `llama.cpp-omni` build, MiniCPM-o GGUF weights, the FunASR model, and the Wi-Fi credentials inside the ESP32 firmware. None of these live in this repository, and none can be fetched automatically. ### 1. Prerequisites @@ -128,6 +128,9 @@ cd .. git clone https://github.com/OpenSQZ/OpenGlass.git cd OpenGlass python -m pip install -r runtime/openglass_omni/requirements.txt + +python -m pip install -r extensions/requirements-phase-b.txt # voice control + CV funnel (harness chains only; skip if you just run basic chat) + ``` Because upstream `master` branches can change, record the exact commit SHAs used for every validated OpenSQZ Glass runtime release. Upstream changes may alter ports, arguments, protocols, TTS behavior, or process ownership. @@ -177,17 +180,32 @@ Edit `runtime/openglass_omni/devices.json`: ### 5. Configure the current launcher -At present, these are the **effective** settings: +Copy [`runtime.example.json`](runtime/openglass_omni/runtime.example.json) to +`runtime.local.json` in the same directory and fill in your machine paths. The panel reads +it at startup and uses those values instead of the defaults in `panel.py`: + +```json +{ + "conda_env": null, + "minicpm_demo_root": "D:\\path\\to\\MiniCPM-o-Demo", + "llama_server": "D:\\path\\to\\llama.cpp-omni\\build\\bin\\Release\\llama-omni-server.exe", + "llama_model": "D:\\path\\to\\MiniCPM-o-gguf\\MiniCPM-o-4_5-Q4_K_M.gguf", + "asr_model": "D:\\path\\to\\LocalASRmodel\\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", + "glasses": { "ssid": "YOUR_WIFI", "psk": "YOUR_WIFI_PASSWORD" } +} +``` -| What to configure | Effective location now | Value | +| What to configure | Location | Value | | --- | --- | --- | -| MiniCPM-o-Demo checkout | [`panel.py` `CONFIG["minicpm_demo_dir"]`](runtime/openglass_omni/panel.py) | Absolute path containing upstream `worker.py` and `gateway.py` | -| `llama-omni-server` binary | [`panel.py` `CONFIG["procs"]["llama"]`](runtime/openglass_omni/panel.py) | Compiled executable under `llama.cpp-omni/build` | -| Main GGUF model | Same `llama` command after `-m` | Absolute path to the main MiniCPM-o 4.5 GGUF | +| MiniCPM-o-Demo checkout | `minicpm_demo_root` | Absolute path containing upstream `worker.py` / `gateway.py` | +| `llama-omni-server` binary | `llama_server` | Compiled executable under `llama.cpp-omni/build` | +| Main GGUF model | `llama_model` | Absolute path to the main MiniCPM-o 4.5 GGUF | +| ASR model | `asr_model` | FunASR streaming model directory, used by voice control in chains 2-4 | +| Glasses Wi-Fi | `glasses.ssid` / `glasses.psk` | Passed to the Rokid APK at launch; leave empty and only that chain loses glasses connectivity | | Glasses name/IP/rotation | `runtime/openglass_omni/devices.json` | One entry per ESP32 glasses prototype | -| Prompt presets | [`panel.py` `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | Interaction prompts shown by the current panel | +| Prompt presets | [`panel.py` `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | Interaction prompts shown by the panel | -[`runtime.example.json`](runtime/openglass_omni/runtime.example.json) and [`prompts.json`](runtime/openglass_omni/prompts.json) document the intended local configuration boundary, but the current panel does not consume either file. Copying the runtime example to `runtime.local.json` does **not yet replace** the hardcoded panel paths or prompt presets. This is a known integration issue, not a user configuration mistake. +The panel self-signs a TLS certificate and key pair under `/certs/` on first launch, used by the `wss` connections to the gateway (8006) and the voice-control harness (8021). An existing pair is left untouched. ### 6. Configure and flash Wi-Fi firmware @@ -203,16 +221,39 @@ Activate the same Python environment used for MiniCPM-o-Demo, then run from the python glasses_panel.py ``` -Select **ESP32 Glasses**, choose the device name, and click **Start**. The current panel attempts to start: +Pick a **chain**, a **device** (and a **criteria** preset for chains 3-4) at the top, +then click **Start**. + +Five chains are available. The four ESP32 ones are cumulative — each adds exactly one +thing to the previous: + +| Chain | Function | Processes started | +| --- | --- | --- | +| 1 Basic chat | Full-duplex omni-modal conversation with the local model through the glasses | llama -> worker -> gateway -> esp32_bridge | +| 2 Voice control | Understands "stop", "start over", "find X" | llama -> worker -> gateway -> harness -> esp32_bridge | +| 3 Frame selection | Picks the sharpest of several frames per second | same as 2 | +| 4 Full anti-hallucination | Rejects bad frames with a spoken hint, so the model never sees them | same as 2 | +| Rokid | Reverse link: the PC listens on 18080 for the APK | llama -> worker -> gateway -> rokid | + +Chains 1-4 are **mutually exclusive**: each needs exclusive access to the glasses' audio +channel and image port, so only one can run at a time. ```text llama-omni-server :22500 -> worker :22400 -> gateway :8006 - -> ESP32 bridge / local view :8080 + -> harness :8021 (chains 2-4 only) + -> glasses client / first-person view :8080 ``` -The chain is ready only when all four process indicators are green and the first-person view is updating. A successful UI launch alone does not prove the model, audio, image, and response path is complete. +The **criteria** selector (chains 3-4 only) picks which calibration the funnel uses: + +- **Strict** — safety-relevant scenes, such as reading a medicine box or a street sign. This preset rejects low-quality frames aggressively to keep the model from hallucinating. +- **Everyday** — ordinary day-to-day scenes. + +The chain is ready only when all process indicators are green and the first-person view is +updating. A successful UI launch alone does not prove the model, audio, image, and +response path is complete. ### Panel lifecycle @@ -258,8 +299,8 @@ Upstream model projects and model weights remain external dependencies and are n ## Known Limitations -- A clean-machine end-to-end Omni run has not yet been verified from the current public tree. -- The current panel still contains machine-specific runtime paths instead of consuming `runtime.local.json`. +- Task injection on skill switch has no effect over the current `/v1/realtime` protocol (there is no equivalent field); the model relies on the system prompt instead. +- Basic chat and the harness chains each carry their own first-person-view frontend and recorder, so the tree contains two same-named copies. They do not interfere, but the duplication is easy to misread. - Prompt presets are still embedded in `panel.py`; the standalone `prompts.json` file is not yet connected. - ESP32 Wi-Fi credentials still require editing the tracked `.ino`; the local header template is not wired in yet. - Normal panel close performs cleanup, but abnormal termination can leave child or externally started processes running. diff --git a/README_zh.md b/README_zh.md index 6f5f855..71d658b 100644 --- a/README_zh.md +++ b/README_zh.md @@ -92,7 +92,7 @@ flowchart LR 控制面板的目标是：完成一次性环境准备后，后续实验可以一键重复启动。它不会替用户下载模型权重、clone 上游仓库、编译 `llama.cpp-omni` 或烧录 ESP32。 -> **当前全新 clone 状态：** 可以从仓库根目录启动面板 UI，但当前启动器仍从 `runtime/openglass_omni/panel.py` 读取机器专属路径。仓库已有的 `runtime.local.json` 加载器尚未接入该面板。请按照下面列出的实际生效位置配置；在完成下一轮代码修改前，还不能把它称为可移植的一键安装。 +> **当前全新 clone 状态：** 面板本身可以从仓库根目录直接启动,但整套链路仍需手动准备四样仓库外的资源:`llama.cpp-omni` 编译结果、MiniCPM-o GGUF 权重、FunASR 模型、以及 ESP32 固件里的 Wi-Fi 凭据。这几项都不在本仓库内,需按以下说明安装。 ### 1. 前置条件 @@ -107,11 +107,11 @@ flowchart LR 本仓库不分发模型权重。 -### 2. Clone V2 上游项目的 master 分支 +### 2. Clone 上游项目的 master 分支 三个仓库必须保持相互独立，不要把 OpenSQZ Glass 的文件复制进 MiniCPM-o-Demo。 -当前公开启动器采用 V2 四进程链路：`llama-omni-server` -> `worker` -> `gateway` -> `demo`，对应两个上游项目仍在维护的 `master` 分支。WAIC 演示使用的 V1 三进程链路由 `worker` 自行启动 `llama-server`，属于历史运行方案，不再作为本 README 的默认安装路径。 +当前公开启动器采用与面壁同步的四进程链路：`llama-omni-server` -> `worker` -> `gateway` -> `demo`，对应两个上游项目仍在维护的 `master` 分支。 ```powershell git clone --branch master https://github.com/tc-mb/llama.cpp-omni.git @@ -128,8 +128,13 @@ cd .. git clone https://github.com/OpenSQZ/OpenGlass.git cd OpenGlass python -m pip install -r runtime/openglass_omni/requirements.txt + +python -m pip install -r extensions/requirements-phase-b.txt # 语音控制 + CV 漏斗（harness链路需要，只跑基础对话无需安装） + ``` + + 由于上游 `master` 分支会持续变化，每次完成 OpenSQZ Glass 运行时验证和正式发布时，都应记录实际测试过的 Commit SHA。上游更新可能改变端口、启动参数、通信协议、TTS 行为或进程所有权。 ### 3. 准备模型文件 @@ -177,17 +182,32 @@ Copy-Item examples/configs/devices.example.json runtime/openglass_omni/devices.j ### 5. 配置当前启动器 -当前版本真正生效的是以下配置： +把 [`runtime.example.json`](runtime/openglass_omni/runtime.example.json) 复制为 +同目录下的 `runtime.local.json`，填写本机路径。面板启动时读取它， +用其中的值覆盖 `panel.py` 里的默认路径： -| 配置内容 | 当前实际生效位置 | 应填写的值 | +```json +{ + "conda_env": null, + "minicpm_demo_root": "D:\\path\\to\\MiniCPM-o-Demo", + "llama_server": "D:\\path\\to\\llama.cpp-omni\\build\\bin\\Release\\llama-omni-server.exe", + "llama_model": "D:\\path\\to\\MiniCPM-o-gguf\\MiniCPM-o-4_5-Q4_K_M.gguf", + "asr_model": "D:\\path\\to\\LocalASRmodel\\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", + "glasses": { "ssid": "你的WiFi", "psk": "你的WiFi密码" } +} +``` + +| 配置内容 | 位置 | 说明 | | --- | --- | --- | -| MiniCPM-o-Demo 目录 | [`panel.py` 的 `CONFIG["minicpm_demo_dir"]`](runtime/openglass_omni/panel.py) | 包含上游 `worker.py` 和 `gateway.py` 的绝对路径 | -| `llama-omni-server` | [`panel.py` 的 `CONFIG["procs"]["llama"]`](runtime/openglass_omni/panel.py) | `llama.cpp-omni/build` 下的编译结果 | -| 主 GGUF 模型 | 同一条 `llama` 命令中 `-m` 后的位置 | MiniCPM-o 4.5 主 GGUF 的绝对路径 | +| MiniCPM-o-Demo 目录 | `runtime.local.json` 的 `minicpm_demo_root` | 包含上游 `worker.py` / `gateway.py` 的绝对路径 | +| `llama-omni-server` | `llama_server` | `llama.cpp-omni/build` 下的编译结果 | +| 主 GGUF 模型 | `llama_model` | MiniCPM-o 4.5 主 GGUF 的绝对路径 | +| ASR 模型 | `asr_model` | FunASR 流式模型目录，②③④ 链路的语音控制用 | +| 眼镜 Wi-Fi | `glasses.ssid` / `glasses.psk` | Rokid 链路启动 APK 时传入；留空则该链路连不上眼镜，其余链路不受影响 | | 眼镜名称/IP/旋转角 | `runtime/openglass_omni/devices.json` | 每副 ESP32 眼镜一条记录 | -| Prompt 预设 | [`panel.py` 的 `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | 当前面板中显示的交互 Prompt | +| Prompt 预设 | [`panel.py` 的 `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | 面板中显示的交互 Prompt | -[`runtime.example.json`](runtime/openglass_omni/runtime.example.json) 和 [`prompts.json`](runtime/openglass_omni/prompts.json) 描述了我们准备采用的本地配置边界，但当前面板还没有读取这两个文件。把 runtime 示例复制为 `runtime.local.json` **还不能替代** `panel.py` 中写死的路径和 Prompt。这是已知的集成问题，不是用户配置错误。 +> 面板首次启动时会在 `/certs/` 下自签一对 TLS 证书,供 gateway(8006)和语音控制 harness(8021)的 `wss` 连接使用。 ### 6. 配置并烧录 Wi-Fi 固件 @@ -203,16 +223,35 @@ Copy-Item examples/configs/devices.example.json runtime/openglass_omni/devices.j python glasses_panel.py ``` -选择 **ESP32 眼镜**，再选择设备名称并点击 **一键启动**。当前面板会尝试依次启动： +在顶部选择**链路**、**眼镜**（③④ 还要选**判据**），点击 **一键启动**。 + +面板提供五条链路，其中 ESP32 的四档是递进的，每一档只比上一档多一件事： + +| 链路 | 功能 | 启动的进程 | +| --- | --- | --- | +| ① 基础对话 | 通过眼镜与本地模型全模态双工对话 | llama → worker → gateway → esp32_bridge | +| ② 语音控制 | 能听懂「停一下 / 重新开始 / 找东西」 | llama → worker → gateway → harness→esp32_bridge | +| ③ 质量筛选 | 每秒多帧里挑最清晰的一张送模型 | 同 ② | +| ④ 完整防幻觉 | 坏图直接拦下并语音提示，不让模型看见 | 同 ② | +| Rokid | 反向链路，PC 开 18080 等 APK 连入 | llama → worker → gateway → rokid | + +①～④ 是**互斥**的：它们都要独占眼镜的音频通道和图像端口，同一时间只能跑一条。 ```text llama-omni-server :22500 -> worker :22400 -> gateway :8006 - -> ESP32 bridge / 本地画面 :8080 + -> （②③④ 才有）harness :8021 + -> 眼镜客户端 / 第一视角 :8080 ``` -只有四个进程指示灯全部变绿，并且第一视角持续更新，才能说明链路就绪。只看到面板 UI 打开，不能证明模型、声音、图像和响应链路已经跑通。 +**判据档位**（只有 ③④ 需要选）决定漏斗用哪套标定参数： + +- **严格判据** —— 有安全风险的场景，例如需要念字的药盒、路上指示牌等。该判据严格拒绝质量不佳的图像防止模型输出幻觉。 +- **日常判据** —— 日常场景。 + +只有进程指示灯全部变绿、并且第一视角持续更新，才能说明链路就绪。 +只看到面板 UI 打开，不能证明模型、声音、图像和响应链路已经跑通。 ### 面板进程生命周期 @@ -258,8 +297,8 @@ OpenGlass/ ## 已知限制 -- 当前公开仓库尚未完成全新机器上的 Omni 端到端验证。 -- 当前面板仍包含机器专属运行路径，没有真正使用 `runtime.local.json`。 +- 语音技能切换时的指令注入在当前 `/v1/realtime` 协议下不生效（协议没有对应字段），模型只能依赖 system prompt 完成任务。 +- 「基础对话」与 harness 链路各自带一套第一视角前端和录制模块，仓库中存在两份同名文件，互不影响但容易混淆。 - Prompt 仍写在 `panel.py` 中，独立的 `prompts.json` 尚未接入。 - ESP32 Wi-Fi 仍需修改 tracked `.ino`，本地 Wi-Fi 头文件模板尚未接入。 - 正常关窗会执行清理，但异常退出可能留下子进程或外部启动的进程。 diff --git a/extensions/assistive_harness/phase_b/assets/reject_wav/aimed_wrong.wav b/extensions/assistive_harness/phase_b/assets/reject_wav/aimed_wrong.wav new file mode 100644 index 0000000000000000000000000000000000000000..4fff8e2e9ba7ac93117f248688e1de137ad6422f GIT binary patch literal 142124 zcmcG%2e=f)wl-YVJ#ptm&PkF4i4r6zB8r$05Ct=!m_S595KuuBL|3tPc=*uLP)qDMhY=ws}Pn*5)a<@)O`YJX1m`z*E!!g%iL>Kp<1iEi0{M*>9@Aa 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live_rec.log_event("REJECT", f"{decision.reason} {decision.hint or ''}") + if force_measure and speaker is not None and decision.reason != "need_focus": + now_mono = time.monotonic() + if _pending_reject is not None: + pass # 已在保护中，上面已处理，不重复挂起 + elif sent_tracker is not None and sent_tracker.speaking: + # 模型正念字：挂起，保护到下一个标点再复查 + _pending_reject = { + "since": now_mono, + "reason": decision.reason, + "snap": sent_tracker.snapshot(), + } + LOG.info("[标点保护] speak中，reject(%s)挂起，等语音播到标点或超时1.5s", + decision.reason) + else: + # 模型没念字（listen/静默）→ 无需保护，直接打断（受节流） + if (not _hint_playing + and now_mono - _last_hint_mono >= _HINT_THROTTLE_S): + _last_hint_mono = now_mono + asyncio.create_task(_do_reject_interrupt(decision.reason)) # ── 「持续坏」检测（优先于标点保护）── # 3s 窗口内"真坏"reject≥2 → 输出基于坏图=幻觉。此时不走标点保护 # （前半也错，不值得保护），直接触发"停→播提示→恢复"。 @@ -1099,15 +1178,33 @@ async def _do_reject_interrupt(reason): if _bad_in_window >= _BAD_THRESH and _is_real_bad: _pending_reject = None # 持续坏优先，取消标点保护挂起 # 到这里必是"真坏"(severe_shake/unstable/too_dark/orient)。 - # 直接触发完整"停→播提示→恢复"（复用 _do_reject_interrupt， - # 停模型输出不杀死、播完恢复）。耗时≈播报时长，天然间隔。 + # **必须和标点保护那条一样受 _HINT_THROTTLE_S 约束**： + # 原注释说"耗时≈播报时长，天然间隔"，但那不成立 —— + # STOP/RESUME 是在播报**之前**就发出去的，不受播报时长限制。 + # 实测画面持续不合格时（晃动/倒置），3s 窗口每轮都能凑够 2 次真坏， + # 于是每轮一次 STOP/RESUME；17 秒内 17 个 control event 之后 + # gateway 会话直接换代重连（generation 0→1），期间 + # gateway=preparing、音频队列堵死 queue=96 drops 暴涨、图也送不出去。 + # 表现就是"灯变红、画面卡住、取图 TimeoutError"。 + if _hint_playing or _now - _last_hint_mono < _HINT_THROTTLE_S: + LOG.debug("[持续坏] %d次真坏，但距上次提示 %.1fs < %.1fs，跳过打断", + _bad_in_window, _now - _last_hint_mono, + _HINT_THROTTLE_S) + elapsed = time.monotonic() - t0 + try: + await asyncio.wait_for(stop_evt.wait(), + timeout=max(0.0, interval_s - elapsed)) + except asyncio.TimeoutError: + pass + continue + _last_hint_mono = _now LOG.info("[持续坏] 3s内%d次真坏reject，打断+播提示（停→播→恢复）", _bad_in_window) if live_rec is not None: live_rec.log_event( "PERSIST-BAD", f"3s内{_bad_in_window}次真坏 → 打断 ({decision.reason})") - await _do_reject_interrupt(decision.reason) + asyncio.create_task(_do_reject_interrupt(decision.reason)) elapsed = time.monotonic() - t0 try: await asyncio.wait_for(stop_evt.wait(), @@ -1139,51 +1236,14 @@ async def _do_reject_interrupt(reason): # 仍 reject → 打断（受节流约束） LOG.info("[标点保护] %s，仍 reject(%s)，打断", "到标点" if reached else "超时1.5s", decision.reason) - if now_mono - _last_hint_mono >= _HINT_THROTTLE_S: + if (not _hint_playing + and now_mono - _last_hint_mono >= _HINT_THROTTLE_S): _last_hint_mono = now_mono _pending_reject = None - await _do_reject_interrupt(decision.reason) + asyncio.create_task(_do_reject_interrupt(decision.reason)) else: _pending_reject = None # 被节流 - if decision.send and decision.best: - ts = now_ms() - latest_frame.set(decision.best, ts) - stats.image_count += 1 - await harness.send_frame(decision.best, latest_frame.sequence, ts) - LOG.info("[漏斗] send (%s)", decision.reason) - _emit_chunk_img(web_ui, decision.best, latest_frame.sequence, True) - # -o record：只录真发送给模型的 best 图（chunk_idx = sequence） - if live_rec is not None: - try: - await asyncio.to_thread( - live_rec.on_frame, decision.best, latest_frame.sequence) - except Exception as e: - LOG.warning("[LIVE] on_frame err: %s", e) - # 注意：send 不清 _pending_reject，保护期只看到标点那一刻 - else: - LOG.info("[漏斗] reject(%s) -> hint: %s", - decision.reason, decision.hint) - if live_rec is not None: - live_rec.log_event("REJECT", f"{decision.reason} {decision.hint or ''}") - if force_measure and speaker is not None and decision.reason != "need_focus": - now_mono = time.monotonic() - if _pending_reject is not None: - pass # 已在保护中，上面已处理，不重复挂起 - elif sent_tracker is not None and sent_tracker.speaking: - # 模型正念字：挂起，保护到下一个标点再复查 - _pending_reject = { - "since": now_mono, - "reason": decision.reason, - "snap": sent_tracker.snapshot(), - } - LOG.info("[标点保护] speak中，reject(%s)挂起，等语音播到标点或超时1.5s", - decision.reason) - else: - # 模型没念字（listen/静默）→ 无需保护，直接打断（受节流） - if now_mono - _last_hint_mono >= _HINT_THROTTLE_S: - _last_hint_mono = now_mono - await _do_reject_interrupt(decision.reason) if probe is not None: since_last = (t0 - _last_grab_mono) * 1000.0 if _last_grab_mono else 0.0 jb = len(decision.best) if decision.best else 0 @@ -1473,7 +1533,21 @@ async def start(self) -> None: if self._funnel is not None: try: ms = await asyncio.to_thread(self._funnel.warmup_orient) - LOG.info("[预热] 方向分类器就绪 (首次 %.0fms，运行时应降到 ~10ms)", ms) + # 必须查真实状态：这行原来无条件打"就绪"，而加载失败时 + # process_orientation 会静默回退到轻量 CV 判据、照样返回， + # 于是"预热就绪"根本不能证明分类器可用（实测 panel 环境下 + # paddle 循环导入失败 → 轻量判据 → 第一轮就误判 orient_flipped）。 + try: + from .cam_pipeline_v2 import orient_classifier_status + _ok, _err = orient_classifier_status() + except Exception: + _ok, _err = True, "" + if _ok: + LOG.info("[预热] 方向分类器就绪 (首次 %.0fms，运行时应降到 ~10ms)", ms) + else: + LOG.warning( + "[预热] !! PaddleOCR 方向分类器加载失败，已回退到轻量 CV 判据 —— " + "orient 判定会明显变差（实测第一轮就误报 flipped）。原因: %s", _err) except Exception as e: LOG.warning("[预热] 方向分类器预热跳过: %s", e) # ── rerun 模式：用录制的 session 回放（音频+best图）重跑 -o，不接实时设备 ── diff --git a/extensions/assistive_harness/phase_b/requirements-phase-b.txt b/extensions/assistive_harness/phase_b/requirements-phase-b.txt index a247f64..4cd81f4 100644 --- a/extensions/assistive_harness/phase_b/requirements-phase-b.txt +++ b/extensions/assistive_harness/phase_b/requirements-phase-b.txt @@ -1,4 +1,34 @@ -aiohttp>=3.13 -Pillow>=10 -sounddevice>=0.5 +# OpenGlass — extensions/assistive_harness 依赖（②③④ 链路：语音控制 + CV 漏斗） +# +# 装在**和 MiniCPM-o-Demo 同一个 conda 环境**里：panel 起子进程时继承启动它的环境， +# 而 worker/gateway 也在那个环境跑。不要另建环境。 +# +# conda activate <你跑 MiniCPM-o-Demo 的环境> +# pip install -r requirements-phase-b.txt +# ── 基础 ── +aiohttp>=3.13 # gateway/harness 的 WebSocket 客户端 + bridge_ui 服务 +numpy>=1.24 # 音频/图像数组 +Pillow>=10 # --rotate 转正（PIL） +sounddevice>=0.5 # PC 扬声器播放（AI 语音、reject 提示音） +requests>=2.31 # ESP32 HTTP 控制（分辨率/对焦） +PyYAML>=6.0 # harness 的 skills 配置 + +# ── 8021 harness（语音控制腿，②③④ 需要）── +fastapi>=0.110 +uvicorn>=0.27 +funasr>=1.3 # 流式 ASR。模型另外下载，路径写进 runtime.local.json +cryptography>=42 # panel 首次启动自签证书（没有它会退回调 openssl） + +# ── CV 漏斗（③④ 需要）── +opencv-python>=4.8 # 清晰度/光流/亮度判据 +# +# ★ 方向分类器（orient_flipped / orient_sideways 判据）—— +# 装不上或版本不对时会**静默回退到早期的简易判据**，表现是画面明明是正的 +# 却一直报"画面好像反了"。启动日志里会有： +# [预热] !! PaddleOCR 方向分类器加载失败，已回退到轻量 CV 判据 ... +# 看到这行就说明下面两个没装好，别忽略。 +# +# 实测可用组合（CPU 版即可，模型只有 6.75MB）： +paddlepaddle>=3.0 # GPU 机器可换 paddlepaddle-gpu，但 CPU 版足够 +paddleocr>=3.0 # 只用其中的 DocImgOrientationClassification diff --git a/extensions/assistive_harness/phase_b/templates/live.html b/extensions/assistive_harness/phase_b/templates/live.html index 96099f9..5ab0032 100644 --- a/extensions/assistive_harness/phase_b/templates/live.html +++ b/extensions/assistive_harness/phase_b/templates/live.html @@ -399,7 +399,12 @@

停止并保存?

const sent = $('hSent'); sent.classList.remove('good','warn','bad'); if (e.img_sent) { sent.classList.add('good'); $('hSentText').textContent = '送图 ✓'; } - else { sent.classList.add('bad'); $('hSentText').textContent = '送图 ✗'; } + else { + // 没送模型：可能是被漏斗拦下（带 reject_reason），也可能只是同一轮里 + // 非 best 的那几帧。画面照样刷新，这样第一视角才连续、也看得见被拦的图。 + sent.classList.add('bad'); + $('hSentText').textContent = e.reject_reason ? ('拦下 ' + e.reject_reason) : '送图 ✗'; + } // 3) 音频静音 const aud = $('hAud'); diff --git a/runtime/openglass_omni/devices.example.json b/runtime/openglass_omni/devices.example.json new file mode 100644 index 0000000..5cecb6c --- /dev/null +++ b/runtime/openglass_omni/devices.example.json @@ -0,0 +1,7 @@ +{ + "_说明": "多副眼镜配置。烧录后从串口看到 IP，改这里对应那副的 esp32_host。rotate 是该眼镜摄像头顺时针旋转角度(0/90/180/270)，按设备侧装方向填。两台笔记本各自维护自己这份文件。gateway 走本机默认，不放这里。", + "devices": [ + { "name": "1号镜", "esp32_host": "esp32_ip", "esp32_port": 80, "rotate": 0 }, + { "name": "2号镜", "esp32_host": "esp32_ip2", "esp32_port": 80, "rotate": 90 } + ] +} \ No newline at end of file diff --git a/runtime/openglass_omni/panel.html b/runtime/openglass_omni/panel.html index 99e4173..c024489 100644 --- a/runtime/openglass_omni/panel.html +++ b/runtime/openglass_omni/panel.html @@ -107,6 +107,10 @@ + + @@ -184,11 +188,20 @@

第一视角

(CFG.devices||[]).forEach(d=>{ const o=document.createElement("option"); o.value=d; o.textContent=d; sel.appendChild(o); }); - applyChain(); // 按链路刷新灯/日志页签/FPV/眼镜下拉可见性 + // 填充判据下拉（只有开了漏斗的链路用得上，applyChain 里控制显隐） + const ssel = document.getElementById("sceneSel"); + (CFG.scenes||[]).forEach(x=>{ + const o=document.createElement("option"); o.value=x; o.textContent=x; ssel.appendChild(o); + }); + if(CFG.default_scene) ssel.value = CFG.default_scene; + applyChain(); // 按链路刷新灯/日志页签/FPV/眼镜+判据下拉可见性 fpvOn=false; toggleFpv(); // 默认开启第一视角画面 setInterval(poll, 1000); } +// 进程内部名 -> 界面显示名（拿不到就退回内部名） +function PLABEL(n){ return (CFG.proc_labels && CFG.proc_labels[n]) || n; } + // 切链路：重画状态灯、日志页签、FPV 地址；Rokid 分支隐藏眼镜下拉 function applyChain(){ const c = CFG.chains[curChain]; @@ -197,7 +210,7 @@

第一视角

lights.innerHTML = ""; c.procs.forEach(n=>{ const d=document.createElement("div"); d.className="light"; - d.innerHTML = `${n}`; + d.innerHTML = `${PLABEL(n)}`; lights.appendChild(d); }); // 日志页签（保留 autoscroll 勾选框） @@ -206,7 +219,7 @@

第一视角

tabs.innerHTML = ""; c.procs.forEach((n,i)=>{ const t=document.createElement("div"); - t.className="tab"+(i===0?" active":""); t.dataset.t=n; t.textContent=n; + t.className="tab"+(i===0?" active":""); t.dataset.t=n; t.textContent=PLABEL(n); t.onclick=()=>switchTab(n); tabs.appendChild(t); }); tabs.appendChild(auto); @@ -215,6 +228,10 @@

第一视角

const show = c.need_device; document.getElementById("deviceSel").style.display = show ? "" : "none"; document.getElementById("devLabel").style.display = show ? "" : "none"; + // 判据下拉：只有开了漏斗的链路(③④)才显示；①②没有漏斗，选了也不起作用 + const showScene = !!c.need_scene; + document.getElementById("sceneSel").style.display = showScene ? "" : "none"; + document.getElementById("sceneLabel").style.display = showScene ? "" : "none"; // FPV 地址随链路变（ESP32=demo bridge_ui:8080；Rokid=bridge:18080 自带页） document.getElementById("fpvUrl").textContent = c.fpv_url; if(fpvOn){ fpvOn=false; toggleFpv(); } // 重挂 iframe 到新地址 @@ -226,6 +243,8 @@

第一视角

} function curDevice(){ return document.getElementById("deviceSel").value; } function onDeviceChange(){ window.pywebview.api.set_device(curDevice()); } +function curScene(){ const e=document.getElementById("sceneSel"); return e ? e.value : ""; } +function onSceneChange(){ window.pywebview.api.set_scene(curScene()); } function selectPreset(name,el){ document.querySelectorAll(".preset").forEach(p=>p.classList.remove("active")); el.classList.add("active"); @@ -234,7 +253,7 @@

第一视角

} function startAll(){ const p = document.getElementById("prompt").value.trim(); - window.pywebview.api.start_all(p, curDevice(), curChain); + window.pywebview.api.start_all(p, curDevice(), curChain, curScene()); } function stopDemo(){ window.pywebview.api.stop_demo(); } // 停止：只停 demo，无需确认（现场要快） function restartDemo(){ diff --git a/runtime/openglass_omni/panel.py b/runtime/openglass_omni/panel.py index ed262e3..d3bce4d 100644 --- a/runtime/openglass_omni/panel.py +++ b/runtime/openglass_omni/panel.py @@ -169,7 +169,44 @@ "--connect-retry", ], - # ⑤ rokid bridge —— 与 demo 平行、二选一的“第四个进程”。 + # ⑤ 8021 harness —— 语音控制腿（停一下/重新开始/找物），也是 ASR 来源。 + # 只有 ②③④ 带 harness 的链路才起它。 + # ★ 开源用户必改：--model-path 指向你机器上的 FunASR 流式模型目录 ★ + # 证书：自签即可，只为过 wss 握手，不绑机器；客户端全是 CERT_NONE 不校验。 + # ★ 它在 extensions/ 下，用 -m 启动，因此 cwd 必须是 MiniCPM-o-Demo 目录 + # （见 _spawn 里的 name in (...) 白名单）。 + "harness": [ + "python", "-m", "extensions.assistive_harness.server", "--enabled", + #"--model-path", r"\LocalASRmodel\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", + # e.g. + "--model-path", (r"D:\OpenGlass\OmniDeployment\OpenGlass\LocalASRmodel" + r"\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"), + "--port", "8021", + # 绝对路径：harness 的 cwd 是 OpenGlass 仓库根，而证书由 _ensure_certs + # 生成在 minicpm_demo_dir/certs 下（gateway 也用同一对）。 + "--certfile", "{certfile}", + "--keyfile", "{keyfile}", + ], + + # ⑥ demo_funnel —— esp32_runtime（harness + CV 漏斗）。与 ④ demo 互斥： + # 两者都要独占 ESP32 的音频 WS 和图像 TCP，不能同时跑。 + # 这里只放各档位共用的参数；--esp32-host/--esp32-port/--rotate 由 + # devices.json 填（_device_args），漏斗档位参数由 _funnel_extra_args 追加。 + # ★ 同样在 extensions/ 下，cwd 必须是 MiniCPM-o-Demo 目录。 + # ★ 8006 是 TLS（https:// 能取到 openapi.json，http:// 是 Empty reply）， + # 走默认 wss，**不要**加 --no-tls；而 harness 8021 是自签 wss，两者都是 wss + # 但证书来源不同。 + "demo_funnel": [ + "python", "-m", "extensions.assistive_harness.phase_b.esp32_runtime", + "--gateway", "localhost:8006", + "--harness-url", "wss://127.0.0.1:8021/ws/control", + "--image-tcp-port", "5000", + "--web-ui-port", "8080", + "--record-live", + "--prompt", "{prompt}", + ], + + # ⑦ rokid bridge —— 与 demo 平行、二选一的“第四个进程”。 # 注意：它不是客户端去连眼镜，而是在 PC 上开 18080 端口等 APK 连进来。 # ★ 用 v8（API V2）。v7 是旧协议(/ws/duplex)，连不上新 gateway:8006。 # ★ run_rokid.ps1 / run_rokid_wifi.cmd 已不再需要——它们做的事 @@ -191,8 +228,12 @@ # live.html 观测页（与 ESP32 demo 同一套 bridge_ui 前端/模板） "--ui-port", "8080", "--prompt", "{prompt}", - "--glasses-ssid", "SQZ", - "--glasses-psk", "sqz.ac.cn", + # ★ 眼镜连的 WiFi。**不要把真实密码提交进仓库** —— + # 在 panel.py 同目录放一个 panel.local.json（已在 .gitignore）： + # { "glasses_ssid": "你的WiFi", "glasses_psk": "你的密码" } + # 没有该文件时用下面的占位值，Rokid 链路会连不上 WiFi 但其余功能正常。 + "--glasses-ssid", "{glasses_ssid}", + "--glasses-psk", "{glasses_psk}", ], }, @@ -202,13 +243,48 @@ # rokid → rokid_minicpm_v7.py （PC 开端口等 APK 连进来，无 device） "chains": { "esp32": { - "label": "ESP32 眼镜", + "label": "① 基础对话", "tail": "demo", # 第四级进程名 "start_order": ["llama", "worker", "gateway", "demo"], "stop_order": ["demo", "gateway", "worker", "llama"], "need_device": True, # 显示眼镜下拉 "fpv_key": "fpv_url", # 第一视角地址 }, + # ── 以下三条走 esp32_runtime（harness + 漏斗），四档递进演示 ── + # ① 基础对话 = 上面的 "esp32"（esp32_bridge.py，无 harness 无漏斗） + # ② 语音控制 = harness 开、漏斗关 + # ③ 质量筛选 = ② + 每秒多帧里挑最清晰的一张 + # ④ 完整防幻觉 = ③ + 坏图直接拦下并语音提示 + # 每档只比上一档多一件事：看到什么说什么 → 能听懂指令 → 图会挑 → 坏图会拦 + # （没有"只对焦"这一档：对焦后要等 settle 再重抓，1s chunk 时序固定， + # 对焦后那张未必赶得上这一轮，等于花了时间没用上。） + "esp32_voice": { + "label": "② 语音控制", + "tail": "demo_funnel", + "funnel": 0, # 漏斗档位：0 关 / 2 选图 / 3 选图+拒绝 + "start_order": ["llama", "worker", "gateway", "harness", "demo_funnel"], + "stop_order": ["demo_funnel", "harness", "gateway", "worker", "llama"], + "need_device": True, + "fpv_key": "fpv_url", + }, + "esp32_select": { + "label": "③ 质量筛选", + "tail": "demo_funnel", + "funnel": 2, + "start_order": ["llama", "worker", "gateway", "harness", "demo_funnel"], + "stop_order": ["demo_funnel", "harness", "gateway", "worker", "llama"], + "need_device": True, + "fpv_key": "fpv_url", + }, + "esp32_full": { + "label": "④ 完整防幻觉", + "tail": "demo_funnel", + "funnel": 3, + "start_order": ["llama", "worker", "gateway", "harness", "demo_funnel"], + "stop_order": ["demo_funnel", "harness", "gateway", "worker", "llama"], + "need_device": True, + "fpv_key": "fpv_url", + }, "rokid": { "label": "Rokid 眼镜", "tail": "rokid", @@ -220,6 +296,30 @@ }, "default_chain": "esp32", + # 判据档位：漏斗的两套标定参数。对外用场景名，不暴露内部值。 + # 严格 = medicine （药盒小字高危，worst_x0=6.5 ACCEPT_Q=0.55） + # 日常 = stationery（生活用品，worst_x0=5.0 ACCEPT_Q=0.48） + "scenes": {"严格判据": "medicine", "日常判据": "stationery"}, + "default_scene": "严格判据", + + # 档位④的提示音目录。**跟着 extensions 包走**（和 bridge_ui 的 templates/ 同思路）， + # 不落在上游 MiniCPM-o-Demo 里，保持三仓库独立。这里是相对 OpenGlass 仓库根， + # panel 会拼成绝对路径传给 esp32_runtime（它的 cwd 是上游，相对路径会指错地方）。 + # 启动前检查，缺了就报错 —— 而不是跑起来才发现没声音，那时 duplex 已在跑， + # 没法当场用 gen_reject_wavs.py 生成。 + "reject_wav_dir": "extensions/assistive_harness/phase_b/assets/reject_wav", + + # 进程的界面显示名（灯泡/日志页签用）。内部名不变，只影响 UI。 + "proc_labels": { + "llama": "推理后端", + "worker": "worker", + "gateway": "gateway", + "harness": "语音控制(8021)", + "demo": "眼镜", + "demo_funnel": "眼镜+漏斗", + "rokid": "Rokid", + }, + # 可选眼镜（对应 devices.json 里的 name）。面板顶部下拉选择。仅 ESP32 分支用。 "devices": ["左镜", "右镜", "备用镜"], @@ -278,6 +378,191 @@ def _load_devices_from_json(path="devices.json"): # ============================================================================ +def _load_device_map(path="devices.json"): + """读整份设备表：name -> {host, port, rotate}。 + + 原来只取 name（下拉用）。但 esp32_bridge 自己读 json（--device-config）， + 而 esp32_runtime 不读，它只认 --esp32-host/--esp32-port/--rotate， + 所以由 panel 把这几项取出来填。rotate 尤其不能漏：摄像头物理侧装， + 不转正的话漏斗的方向检测会把"相机侧装"误判成"用户把盒子拿反了"。 + utf-8-sig：记事本/VSCode 存的 json 可能带 BOM，用 utf-8 读会炸在第一个字符。 + """ + out = {} + try: + with open(path, "r", encoding="utf-8-sig") as f: + data = json.load(f) + for d in data.get("devices", []): + n = d.get("name") + if not n: + continue + out[n] = { + "host": d.get("esp32_host", ""), + "port": int(d.get("esp32_port", 80)), + "rotate": int(d.get("rotate", 0)) % 360, + } + except FileNotFoundError: + print(f"[panel] 未找到 {path}，带漏斗的链路(②③④)将无法自动填 IP") + except Exception as e: + print(f"[panel] 读取 {path} 失败({e})") + return out + + +CONFIG["device_map"] = _load_device_map(_dev_path) +if CONFIG["device_map"]: + print("[panel] 设备详情: " + ", ".join( + f"{k}({v['host']} rot={v['rotate']})" for k, v in CONFIG["device_map"].items())) + + +def _load_runtime_local(): + """读 runtime.local.json —— 本机路径与私密配置，**不进仓库**。 + + 为什么要它：panel.py 里原本写死了五处本机绝对路径 + （conda 环境、MiniCPM-o-Demo 目录、llama-omni-server.exe、主 gguf、FunASR 模型）， + 还有眼镜 WiFi 的明文密码。开源后每个人都要改源码才能跑，密码也会进 git 历史。 + 改成从这个文件读，clone 下来只需 `cp runtime.example.json runtime.local.json` 再填。 + + 键名沿用仓库里已有的 runtime.example.json 风格。 + 找不到文件时保留 CONFIG 里的默认值（也就是原来的写死值），行为不变。 + """ + here = os.path.dirname(os.path.abspath(__file__)) + f = os.path.join(here, "runtime.local.json") + if not os.path.isfile(f): + print(f"[panel] 未找到 {f}") + print("[panel] 请复制 runtime.example.json 为 runtime.local.json 并填写本机路径；") + print("[panel] 否则将沿用 panel.py 里的默认值（多半不是你的路径）。") + return {} + try: + with open(f, "r", encoding="utf-8-sig") as fh: + cfg = json.load(fh) + except Exception as e: + print(f"[panel] 读取 runtime.local.json 失败({e})，沿用默认值") + return {} + + def _expand(v): + return os.path.expandvars(os.path.expanduser(v)) if isinstance(v, str) else v + + n = 0 + # ① 简单键 -> CONFIG 顶层 + for src_key, dst_key in (("conda_env", "conda_env"), + ("minicpm_demo_root", "minicpm_demo_dir")): + v = _expand(cfg.get(src_key)) + if v: + CONFIG[dst_key] = v + n += 1 + # ② 需要替换到命令行数组里的 + def _sub(proc, old_pred, new_val): + """把 procs[proc] 里满足 old_pred 的那一项换成 new_val。""" + arr = CONFIG["procs"].get(proc) + if not arr or not new_val: + return 0 + for i, x in enumerate(arr): + if isinstance(x, str) and old_pred(x): + arr[i] = new_val + return 1 + return 0 + + n += _sub("llama", lambda x: x.lower().endswith("llama-omni-server.exe") + or x.lower().endswith("llama-omni-server"), + _expand(cfg.get("llama_server"))) + n += _sub("llama", lambda x: x.lower().endswith(".gguf"), + _expand(cfg.get("llama_model"))) + n += _sub("harness", lambda x: "speech_paraformer" in x or "LocalASRmodel" in x, + _expand(cfg.get("asr_model"))) + print(f"[panel] 已读入 runtime.local.json（生效 {n} 项）") + return cfg + + +def _load_local_secrets(rt): + """眼镜 WiFi（Rokid 链路传给 APK）。同样来自 runtime.local.json， + 写死在 panel.py 里就等于明文密码进公开仓库。""" + out = {"glasses_ssid": "", "glasses_psk": ""} + g = (rt or {}).get("glasses") or {} + for k in ("glasses_ssid", "glasses_psk"): + v = g.get(k.replace("glasses_", "")) or (rt or {}).get(k) + if v: + out[k] = v + return out + + +_RTLOCAL = _load_runtime_local() +CONFIG["local"] = _load_local_secrets(_RTLOCAL) + + +def _ensure_certs(base_dir): + """确保 certs/cert.pem + key.pem 存在，没有就自签一对。 + + gateway(8006) 和 harness(8021) 都用这一对（gateway.py 的默认值就是 + certs/cert.pem，缺了会直接报错退出），所以四条链路都需要它。 + 自签证书**不绑机器**，里面没有硬件信息；客户端全是 CERT_NONE 不校验， + 只是为了让 wss 握手能过。所以本地生成一份即可，不必也不该提交进仓库。 + + 优先用 cryptography 库；没装就退回调 openssl；都不行就打出手动命令。 + """ + if not base_dir or not os.path.isdir(base_dir): + return False + d = os.path.join(base_dir, "certs") + cert = os.path.join(d, "cert.pem") + key = os.path.join(d, "key.pem") + if os.path.isfile(cert) and os.path.isfile(key): + return True + os.makedirs(d, exist_ok=True) + print(f"[panel] 未找到证书，正在生成自签证书 -> {d}") + + try: + from cryptography import x509 + from cryptography.x509.oid import NameOID + from cryptography.hazmat.primitives import hashes, serialization + from cryptography.hazmat.primitives.asymmetric import rsa + import datetime as _dt + + k = rsa.generate_private_key(public_exponent=65537, key_size=2048) + name = x509.Name([x509.NameAttribute(NameOID.COMMON_NAME, "localhost")]) + now = _dt.datetime.now(_dt.timezone.utc) + crt = (x509.CertificateBuilder() + .subject_name(name).issuer_name(name) + .public_key(k.public_key()) + .serial_number(x509.random_serial_number()) + .not_valid_before(now - _dt.timedelta(days=1)) + .not_valid_after(now + _dt.timedelta(days=3650)) + .add_extension(x509.SubjectAlternativeName([ + x509.DNSName("localhost"), + x509.IPAddress(__import__("ipaddress").IPv4Address("127.0.0.1")), + ]), critical=False) + .sign(k, hashes.SHA256())) + with open(key, "wb") as f: + f.write(k.private_bytes( + serialization.Encoding.PEM, + serialization.PrivateFormat.TraditionalOpenSSL, + serialization.NoEncryption())) + with open(cert, "wb") as f: + f.write(crt.public_bytes(serialization.Encoding.PEM)) + print("[panel] 证书已生成（cryptography，有效期 10 年）") + return True + except ImportError: + pass + except Exception as e: + print(f"[panel] cryptography 生成失败({e})，改试 openssl") + + try: + subprocess.run( + ["openssl", "req", "-x509", "-newkey", "rsa:2048", + "-keyout", key, "-out", cert, "-days", "3650", + "-nodes", "-subj", "/CN=localhost"], + check=True, capture_output=True, timeout=60) + print("[panel] 证书已生成（openssl，有效期 10 年）") + return True + except Exception as e: + print(f"[panel] !! 自动生成证书失败: {e}") + print(f"[panel] 请手动执行（在 {base_dir} 下）：") + print("[panel] openssl req -x509 -newkey rsa:2048 " + "-keyout certs/key.pem -out certs/cert.pem " + "-days 3650 -nodes -subj \"/CN=localhost\"") + return False + + +_ensure_certs(CONFIG.get("minicpm_demo_dir") or CONFIG.get("cwd")) + + class ProcManager: """管理三个子进程：起停、状态轮询、日志收集、端口/就绪探测。""" @@ -294,6 +579,7 @@ def __init__(self, cfg): self.status = {n: "stopped" for n in cfg["procs"]} # stopped/starting/running/crashed self.current_prompt = next(iter(cfg["presets"].values())) self.current_device = (cfg.get("devices") or ["默认"])[0] + self.current_scene = cfg.get("default_scene", "严格判据") # —— 当前链路：esp32 / rokid，决定第四级起哪个进程 —— self.current_chain = cfg.get("default_chain", "esp32") self._lock = threading.Lock() @@ -341,9 +627,29 @@ def _build_cmd(self, name): if name == "demo": subst = {"{prompt}": self.current_prompt, "{device}": self.current_device} cmd = [subst.get(x, x) for x in cmd] - elif name == "rokid": + elif name == "harness": + # 证书用绝对路径：harness 的 cwd 是 OpenGlass 仓库根， + # 而证书由 _ensure_certs 生成在 minicpm_demo_dir/certs 下（gateway 共用）。 + _base = self.cfg.get("minicpm_demo_dir") or "." + subst = { + "{certfile}": os.path.join(_base, "certs", "cert.pem"), + "{keyfile}": os.path.join(_base, "certs", "key.pem"), + } + cmd = [subst.get(x, x) for x in cmd] + elif name == "demo_funnel": + # esp32_runtime：prompt + 设备参数(IP/端口/旋转) + 漏斗档位 subst = {"{prompt}": self.current_prompt} cmd = [subst.get(x, x) for x in cmd] + cmd += self._device_args() + cmd += self._funnel_extra_args() + elif name == "rokid": + _loc = self.cfg.get("local", {}) + subst = { + "{prompt}": self.current_prompt, + "{glasses_ssid}": _loc.get("glasses_ssid", ""), + "{glasses_psk}": _loc.get("glasses_psk", ""), + } + cmd = [subst.get(x, x) for x in cmd] cmd += self._rokid_extra_args() # 内联 ps1 的 --enable-funasr 分支 return self._wrap_conda(cmd) @@ -436,6 +742,110 @@ def _rokid_env(self): self._log("rokid", f"日志: {env['ROKID_V7_LOG_FILE']}") return env + def _device_args(self): + """从 devices.json 取当前眼镜的 IP / 端口 / 旋转角，填给 esp32_runtime。""" + d = (self.cfg.get("device_map") or {}).get(self.current_device) + if not d or not d.get("host"): + self._log("demo_funnel", + f"!! devices.json 里找不到「{self.current_device}」的 esp32_host") + return [] + args = ["--esp32-host", d["host"], "--esp32-port", str(d.get("port", 80))] + if d.get("rotate"): + args += ["--rotate", str(d["rotate"])] + return args + + def _funnel_extra_args(self): + """按链路的档位追加漏斗参数。 + + 0 语音控制 不加 → 走 no_funnel 分支，每轮取一帧直发 + 2 质量筛选 --funnel --no-reject → 选 best 但永远放行 + 3 完整防幻觉 --funnel --force-measure → 选 best + 拒绝 + 语音提示 + """ + lv = int(self.chain().get("funnel", 0)) + if lv <= 0: + return [] + scene = self.cfg["scenes"].get(self.current_scene, "medicine") + args = ["--funnel", "--scene", scene] + if lv == 2: + args += ["--no-reject"] + else: + args += ["--force-measure", "--reject-wav-dir", self._reject_wav_dir()] + return args + + def _check_deps(self): + """②③④ 启动前检查关键依赖，缺了就说清楚缺什么、怎么装。 + + 为什么值得单独查：paddleocr 装不上时，方向分类器会**静默回退**到 + 早期的简易判据，表现是画面明明是正的却一直报"画面好像反了"、 + 接着播报把帧间隔拉长又误报"晃动" —— 全程不报错，极难定位。 + 实测踩过：panel 在没装好 paddle 的环境里启动，一上来就全是 orient_flipped。 + """ + lv = int(self.chain().get("funnel", 0)) + need = [("aiohttp", "aiohttp"), ("numpy", "numpy"), + ("PIL", "Pillow"), ("sounddevice", "sounddevice")] + if self.chain().get("tail") == "demo_funnel": + need += [("fastapi", "fastapi"), ("uvicorn", "uvicorn"), + ("yaml", "PyYAML"), ("funasr", "funasr")] + if lv >= 2: + need += [("cv2", "opencv-python")] + if lv >= 3 or lv == 2: + need += [("paddle", "paddlepaddle"), ("paddleocr", "paddleocr")] + import importlib.util as _iu + missing = [pip for mod, pip in need if _iu.find_spec(mod) is None] + if missing: + tail = self.chain().get("tail") or "demo" + self._log(tail, "!! 缺少依赖: " + ", ".join(missing)) + self._log(tail, " 请在**启动 panel 的那个 conda 环境**里安装：") + self._log(tail, " pip install " + " ".join(missing)) + self._log(tail, " （完整清单见 extensions/requirements-phase-b.txt）") + return False + return True + + def _check_extensions(self): + """②③④ 需要 OpenGlass 仓库内的 extensions/ 包完整。 + + 不需要复制到别处 —— _spawn 把 cwd 设成 OpenGlass 仓库根。 + 缺文件时子进程会起来立刻死、日志里一行 No module named extensions， + 面板上只看到灯变红，所以提前拦住并说清楚。 + """ + if self.chain().get("tail") != "demo_funnel": + return True + f = os.path.join(self._repo_root(), "extensions", "assistive_harness", + "phase_b", "esp32_runtime.py") + if not os.path.isfile(f): + self._log("demo_funnel", + f"!! 未找到 {f}\n" + f" ②③④ 需要仓库内的 extensions/ 包，请确认它没有被删除或移动。") + return False + return True + + def _repo_root(self): + """OpenGlass 仓库根（panel.py 在 runtime/openglass_omni/ 下，往上三级）。""" + return os.path.dirname(os.path.dirname( + os.path.dirname(os.path.abspath(__file__)))) + + def _reject_wav_dir(self): + d = self.cfg.get("reject_wav_dir", + "extensions/assistive_harness/phase_b/assets/reject_wav") + return d if os.path.isabs(d) else os.path.join(self._repo_root(), d) + + def _check_reject_wav(self): + """档位④要播提示音，wav 必须事先用 gen_reject_wavs.py 生成好 + （且要在 duplex 没跑的时候生成）。这里只检查，不在面板里现场生成。""" + if int(self.chain().get("funnel", 0)) < 3: + return True + full = self._reject_wav_dir() + try: + n = len([x for x in os.listdir(full) if x.lower().endswith(".wav")]) + except Exception: + n = 0 + if n == 0: + self._log("demo_funnel", + f"!! {full} 里没有 wav。档位④要播提示音，" + f"请先在 duplex 未运行时跑 gen_reject_wavs.py 生成。") + return False + return True + def _rokid_extra_args(self): """按配置追加参数（对应 ps1 里的 $bridgeArgs += ...）。""" extra = [] @@ -524,7 +934,18 @@ def _spawn(self, name): creationflags = subprocess.CREATE_NEW_PROCESS_GROUP # worker / gateway 是上游 MiniCPM-o-Demo 的文件（worker.py / gateway.py）， # 必须在那个目录下启动才能找到。llama / demo / rokid 用的是绝对路径，不依赖 cwd。 - if name in ("worker", "gateway"): + # harness / demo_funnel 用 `python -m extensions...` 启动， + # **cwd 必须是 OpenGlass 仓库根** —— extensions/ 就在它下面。 + # ★ 不要用 PYTHONPATH 代替：`-m` 时 sys.path[0] 是 cwd，cwd 优先于 + # PYTHONPATH。如果 cwd 设成 MiniCPM-o-Demo 而那边**也有**一份 + # extensions/，OpenGlass 这份会被完全遮蔽（改了不生效）； + # 更糟的是 PYTHONPATH 把 OpenGlass 根塞进 sys.path，实测导致 + # paddle 导入失败（partially initialized module 'paddle' ... + # circular import）→ 方向分类器加载失败 → 回退到错误的早期 CV 判据 + # → 第一轮就误报 orient_flipped，然后播报拉长帧间隔又误报 severe_shake。 + if name in ("harness", "demo_funnel"): + _cwd = self._repo_root() + elif name in ("worker", "gateway"): _cwd = self.cfg.get("minicpm_demo_dir") or self.cfg.get("cwd") or None if not _cwd: self._log(name, "!! 未配置 minicpm_demo_dir（MiniCPM-o-Demo 目录），" @@ -676,6 +1097,21 @@ def _wait_ready_or_die(self, name, timeout): self._log(name, "就绪(关键字命中)，放行") return True elapsed = time.time() - t_start + if name == "harness": + # 8021 是自签 wss，不能用 HTTP health，只能探 TCP 端口。 + # 而且端口开了之后 /ws/control 还要一小会儿才绑好， + # 不等的话 esp32_runtime 连过去会失败。 + if self._port_open(8021): + self._log("harness", "8021 端口已开，再等 2s 让 /ws/control 绑好") + time.sleep(2.0) + self._log("harness", "harness 就绪，放行") + return True + _now = time.time() + if _now - last_log > 5: + last_log = _now + self._log("harness", f"等待 8021 (FunASR 模型加载中) {elapsed:.0f}s") + time.sleep(1.0) + continue if name == "llama": # llama-omni-server：轮询 /health 返回 200 才放行（= 你手动的 curl .../health） if self._http_ok(llama_url): @@ -775,6 +1211,11 @@ def _other_tails(self): return [c["tail"] for k, c in self.cfg["chains"].items() if c["tail"] != cur] def _do_start_all(self): + # ②③④ 的两个前置检查：extensions 复制了没、档位④的 wav 有没有。 + # 提前拦住比跑起来才发现好 —— 后者时 duplex 已在跑，没法当场补。 + if (not self._check_extensions() or not self._check_deps() + or not self._check_reject_wav()): + return """按序补齐：只起没在跑的，已在跑的跳过；被急停立即停下。""" # 切链后若另一条链的尾巴还活着，先杀掉——两个客户端同时占一个 gateway # 会互相抢 session，必须互斥。 @@ -940,24 +1381,34 @@ def get_config(self): "need_device": c["need_device"], "fpv_url": self.cfg.get(c["fpv_key"], ""), "procs": c["start_order"], + # 只有开了漏斗的链路(③④)才需要选判据档位；①②选了也不起作用 + "need_scene": int(c.get("funnel", 0)) > 0, } return { "presets": self.cfg["presets"], "fpv_url": self.cfg["fpv_url"], "devices": self.cfg.get("devices", []), "chains": chains, + "scenes": list(self.cfg.get("scenes", {}).keys()), + "default_scene": self.cfg.get("default_scene", ""), + "proc_labels": self.cfg.get("proc_labels", {}), "default_chain": self.cfg.get("default_chain", "esp32"), } def set_chain(self, chain): return self.mgr.set_chain(chain) + def set_scene(self, scene): + if scene: + self.mgr.current_scene = scene + return "ok" + def set_device(self, device): if device: self.mgr.current_device = device return "ok" - def start_all(self, prompt=None, device=None, chain=None): + def start_all(self, prompt=None, device=None, chain=None, scene=None): # prompt 来自前端文本框（用户选的 preset 或编辑后的内容）。在起进程前更新 # current_prompt，否则一键启动只会用初始的第一个 preset。 if prompt: @@ -966,6 +1417,8 @@ def start_all(self, prompt=None, device=None, chain=None): self.mgr.set_chain(chain) if device: self.mgr.current_device = device + if scene: + self.mgr.current_scene = scene self.mgr.start_all(); return "ok" def stop_all(self): diff --git a/runtime/openglass_omni/runtime.example.json b/runtime/openglass_omni/runtime.example.json index f3c77a1..c69bde1 100644 --- a/runtime/openglass_omni/runtime.example.json +++ b/runtime/openglass_omni/runtime.example.json @@ -1,33 +1,28 @@ { - "_comment": "Copy to runtime.local.json. Paths are resolved relative to this file and may use environment variables.", - "python": "", - "minicpm_demo_root": "${MINICPM_O_DEMO_ROOT}", - "llama_cpp_omni_root": "${LLAMA_CPP_OMNI_ROOT}", - "devices_file": "devices.local.json", - "prompts_file": "prompts.json", - "state_dir": "state", - "worker": { - "host": "127.0.0.1", - "port": 22440, - "ready_timeout_s": 180 + "_comment": "复制为 runtime.local.json 后填写。该文件不进仓库（加进 .gitignore）。路径支持 ${ENV_VAR} 和 ~。", + + "_conda": "留空表示已在外部 conda activate；填环境名则由 panel 用 conda run 启动子进程", + "conda_env": null, + + "_paths": "三个仓库相互独立，各自填自己的绝对路径", + "minicpm_demo_root": "D:\\path\\to\\MiniCPM-o-Demo", + "llama_server": "D:\\path\\to\\llama.cpp-omni\\build\\bin\\Release\\llama-omni-server.exe", + "llama_model": "D:\\path\\to\\MiniCPM-o-gguf\\MiniCPM-o-4_5-Q4_K_M.gguf", + + "_asr": "②③④ 链路的语音控制(8021 harness)用的 FunASR 流式模型目录", + "asr_model": "D:\\path\\to\\LocalASRmodel\\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", + + "_glasses": "Rokid 链路要把 WiFi 传给 APK。不要把真实密码提交进仓库", + "glasses": { + "ssid": "YOUR_WIFI_SSID", + "psk": "YOUR_WIFI_PASSWORD" }, - "gateway": { - "host": "127.0.0.1", - "bind_host": "0.0.0.0", - "port": 8040, - "tls": true, - "certfile": "certs/cert.pem", - "keyfile": "certs/key.pem", - "ready_timeout_s": 45 - }, - "demo": { - "ui_host": "127.0.0.1", - "ui_port": 8080, - "audio_endpoint": "/ws_audio", - "image_min_interval_s": 0.8, - "player_hostapi": "wasapi", - "player_prebuffer_ms": 200, - "connect_wait_s": 120, - "ready_timeout_s": 20 - } + + "_ports": "以下为当前 V2 链路的实际端口，仅供参考；panel.py 里已按此配置，改动需同步改 panel", + "worker": { "host": "127.0.0.1", "port": 22400 }, + "gateway": { "host": "127.0.0.1", "port": 8006, "tls": true, + "certfile": "certs/cert.pem", "keyfile": "certs/key.pem" }, + "llama": { "host": "127.0.0.1", "port": 22500 }, + "harness": { "host": "127.0.0.1", "port": 8021 }, + "demo": { "ui_port": 8080 } } From 5a6b59df29fdb7ed18736b03db011fb8895feeb8 Mon Sep 17 00:00:00 2001 From: MajestyHenius <3051797499@qq.com> Date: Fri, 4 Sep 2026 11:11:09 +0800 Subject: [PATCH 3/3] fix: address PR review - README: correct requirements-phase-b.txt path (both languages) and the MiniCPM-o-Demo clone branch (master -> main); llama.cpp-omni stays on master - panel: restart the tail process when the chain configuration changes. Chains 2/3/4 share the demo_funnel process name, so neither the cross-chain kill nor the alive check would restart it and the previous funnel arguments stayed in effect. Now keyed on a (chain, funnel level, criteria, device, prompt) fingerprint, which also covers device/criteria changes within a chain. - bridge_ui: default to loopback instead of 0.0.0.0. Binds both 127.0.0.1 and ::1 so the panel's embedded http://localhost:8080 view keeps working on hosts that resolve localhost to IPv6. Non-loopback binds now log what they expose. - esp32_runtime: pass stop_callback to WebUIServer so POST /api/stop actually stops the runtime and finalizes the session; add --web-ui-host - esp32_runtime: the reject hint records gate.stop_count before pausing and only resumes if it still owns the pause, so a user STOP during the hint is no longer overridden --- README.md | 68 ++++--------------- README_zh.md | 9 +-- .../assistive_harness/phase_b/bridge_ui.py | 35 ++++++++-- .../phase_b/esp32_runtime.py | 48 ++++++++++++- runtime/openglass_omni/panel.py | 44 +++++++++++- 5 files changed, 138 insertions(+), 66 deletions(-) diff --git a/README.md b/README.md index 24f2bde..2bf698a 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ Solid arrows represent code or artifacts present in the public repository. Dashe The control panel is intended to make **repeated runs** one-click after a one-time setup. It does not download model weights, clone upstream repositories, compile `llama.cpp-omni`, or flash the ESP32 for you. -> **Current clean-clone status:** The panel itself starts directly from the repository root, but the full chain still needs four out-of-repo assets prepared by hand: the `llama.cpp-omni` build, MiniCPM-o GGUF weights, the FunASR model, and the Wi-Fi credentials inside the ESP32 firmware. None of these live in this repository, and none can be fetched automatically. +> **Current clean-clone status:** The panel UI starts from the repository root, but the current launcher still reads machine-specific paths from `runtime/openglass_omni/panel.py`. The included `runtime.local.json` loader is not yet connected to that panel. Follow the effective configuration locations below; a code update is still required before this can be called a portable one-click installation. ### 1. Prerequisites @@ -120,7 +120,7 @@ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DLLAMA_CURL=OFF cmake --build build --config Release --target llama-omni-server -j cd .. -git clone --branch master https://github.com/OpenBMB/MiniCPM-o-Demo.git +git clone --branch main https://github.com/OpenBMB/MiniCPM-o-Demo.git cd MiniCPM-o-Demo python -m pip install -r requirements.txt cd .. @@ -129,7 +129,7 @@ git clone https://github.com/OpenSQZ/OpenGlass.git cd OpenGlass python -m pip install -r runtime/openglass_omni/requirements.txt -python -m pip install -r extensions/requirements-phase-b.txt # voice control + CV funnel (harness chains only; skip if you just run basic chat) +python -m pip install -r extensions/assistive_harness/phase_b/requirements-phase-b.txt # voice control + CV funnel (harness chains only; skip if you just run basic chat) ``` @@ -180,32 +180,17 @@ Edit `runtime/openglass_omni/devices.json`: ### 5. Configure the current launcher -Copy [`runtime.example.json`](runtime/openglass_omni/runtime.example.json) to -`runtime.local.json` in the same directory and fill in your machine paths. The panel reads -it at startup and uses those values instead of the defaults in `panel.py`: +At present, these are the **effective** settings: -```json -{ - "conda_env": null, - "minicpm_demo_root": "D:\\path\\to\\MiniCPM-o-Demo", - "llama_server": "D:\\path\\to\\llama.cpp-omni\\build\\bin\\Release\\llama-omni-server.exe", - "llama_model": "D:\\path\\to\\MiniCPM-o-gguf\\MiniCPM-o-4_5-Q4_K_M.gguf", - "asr_model": "D:\\path\\to\\LocalASRmodel\\speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", - "glasses": { "ssid": "YOUR_WIFI", "psk": "YOUR_WIFI_PASSWORD" } -} -``` - -| What to configure | Location | Value | +| What to configure | Effective location now | Value | | --- | --- | --- | -| MiniCPM-o-Demo checkout | `minicpm_demo_root` | Absolute path containing upstream `worker.py` / `gateway.py` | -| `llama-omni-server` binary | `llama_server` | Compiled executable under `llama.cpp-omni/build` | -| Main GGUF model | `llama_model` | Absolute path to the main MiniCPM-o 4.5 GGUF | -| ASR model | `asr_model` | FunASR streaming model directory, used by voice control in chains 2-4 | -| Glasses Wi-Fi | `glasses.ssid` / `glasses.psk` | Passed to the Rokid APK at launch; leave empty and only that chain loses glasses connectivity | +| MiniCPM-o-Demo checkout | [`panel.py` `CONFIG["minicpm_demo_dir"]`](runtime/openglass_omni/panel.py) | Absolute path containing upstream `worker.py` and `gateway.py` | +| `llama-omni-server` binary | [`panel.py` `CONFIG["procs"]["llama"]`](runtime/openglass_omni/panel.py) | Compiled executable under `llama.cpp-omni/build` | +| Main GGUF model | Same `llama` command after `-m` | Absolute path to the main MiniCPM-o 4.5 GGUF | | Glasses name/IP/rotation | `runtime/openglass_omni/devices.json` | One entry per ESP32 glasses prototype | -| Prompt presets | [`panel.py` `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | Interaction prompts shown by the panel | +| Prompt presets | [`panel.py` `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | Interaction prompts shown by the current panel | -The panel self-signs a TLS certificate and key pair under `/certs/` on first launch, used by the `wss` connections to the gateway (8006) and the voice-control harness (8021). An existing pair is left untouched. +[`runtime.example.json`](runtime/openglass_omni/runtime.example.json) and [`prompts.json`](runtime/openglass_omni/prompts.json) document the intended local configuration boundary, but the current panel does not consume either file. Copying the runtime example to `runtime.local.json` does **not yet replace** the hardcoded panel paths or prompt presets. This is a known integration issue, not a user configuration mistake. ### 6. Configure and flash Wi-Fi firmware @@ -221,39 +206,16 @@ Activate the same Python environment used for MiniCPM-o-Demo, then run from the python glasses_panel.py ``` -Pick a **chain**, a **device** (and a **criteria** preset for chains 3-4) at the top, -then click **Start**. - -Five chains are available. The four ESP32 ones are cumulative — each adds exactly one -thing to the previous: - -| Chain | Function | Processes started | -| --- | --- | --- | -| 1 Basic chat | Full-duplex omni-modal conversation with the local model through the glasses | llama -> worker -> gateway -> esp32_bridge | -| 2 Voice control | Understands "stop", "start over", "find X" | llama -> worker -> gateway -> harness -> esp32_bridge | -| 3 Frame selection | Picks the sharpest of several frames per second | same as 2 | -| 4 Full anti-hallucination | Rejects bad frames with a spoken hint, so the model never sees them | same as 2 | -| Rokid | Reverse link: the PC listens on 18080 for the APK | llama -> worker -> gateway -> rokid | - -Chains 1-4 are **mutually exclusive**: each needs exclusive access to the glasses' audio -channel and image port, so only one can run at a time. +Select **ESP32 Glasses**, choose the device name, and click **Start**. The current panel attempts to start: ```text llama-omni-server :22500 -> worker :22400 -> gateway :8006 - -> harness :8021 (chains 2-4 only) - -> glasses client / first-person view :8080 + -> ESP32 bridge / local view :8080 ``` -The **criteria** selector (chains 3-4 only) picks which calibration the funnel uses: - -- **Strict** — safety-relevant scenes, such as reading a medicine box or a street sign. This preset rejects low-quality frames aggressively to keep the model from hallucinating. -- **Everyday** — ordinary day-to-day scenes. - -The chain is ready only when all process indicators are green and the first-person view is -updating. A successful UI launch alone does not prove the model, audio, image, and -response path is complete. +The chain is ready only when all four process indicators are green and the first-person view is updating. A successful UI launch alone does not prove the model, audio, image, and response path is complete. ### Panel lifecycle @@ -299,8 +261,8 @@ Upstream model projects and model weights remain external dependencies and are n ## Known Limitations -- Task injection on skill switch has no effect over the current `/v1/realtime` protocol (there is no equivalent field); the model relies on the system prompt instead. -- Basic chat and the harness chains each carry their own first-person-view frontend and recorder, so the tree contains two same-named copies. They do not interfere, but the duplication is easy to misread. +- A clean-machine end-to-end Omni run has not yet been verified from the current public tree. +- The current panel still contains machine-specific runtime paths instead of consuming `runtime.local.json`. - Prompt presets are still embedded in `panel.py`; the standalone `prompts.json` file is not yet connected. - ESP32 Wi-Fi credentials still require editing the tracked `.ino`; the local header template is not wired in yet. - Normal panel close performs cleanup, but abnormal termination can leave child or externally started processes running. diff --git a/README_zh.md b/README_zh.md index 71d658b..ba13d70 100644 --- a/README_zh.md +++ b/README_zh.md @@ -92,7 +92,7 @@ flowchart LR 控制面板的目标是：完成一次性环境准备后，后续实验可以一键重复启动。它不会替用户下载模型权重、clone 上游仓库、编译 `llama.cpp-omni` 或烧录 ESP32。 -> **当前全新 clone 状态：** 面板本身可以从仓库根目录直接启动,但整套链路仍需手动准备四样仓库外的资源:`llama.cpp-omni` 编译结果、MiniCPM-o GGUF 权重、FunASR 模型、以及 ESP32 固件里的 Wi-Fi 凭据。这几项都不在本仓库内,需按以下说明安装。 +> **当前全新 clone 状态：** 从仓库根目录启动面板 UI，本机路径写在 `runtime.local.json`（复制 `runtime.example.json` 得到），面板启动时读取。眼镜列表和旋转角在 `devices.json`。TLS 证书首次启动自动生成。仍需手动准备的是仓库外的资源：`llama.cpp-omni` 编译结果、MiniCPM-o GGUF 权重、FunASR 模型，以及 ESP32 固件里的 Wi-Fi 凭据。 ### 1. 前置条件 @@ -120,7 +120,7 @@ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DLLAMA_CURL=OFF cmake --build build --config Release --target llama-omni-server -j cd .. -git clone --branch master https://github.com/OpenBMB/MiniCPM-o-Demo.git +git clone --branch main https://github.com/OpenBMB/MiniCPM-o-Demo.git cd MiniCPM-o-Demo python -m pip install -r requirements.txt cd .. @@ -129,7 +129,7 @@ git clone https://github.com/OpenSQZ/OpenGlass.git cd OpenGlass python -m pip install -r runtime/openglass_omni/requirements.txt -python -m pip install -r extensions/requirements-phase-b.txt # 语音控制 + CV 漏斗（harness链路需要，只跑基础对话无需安装） +python -m pip install -r extensions/assistive_harness/phase_b/requirements-phase-b.txt # 语音控制 + CV 漏斗（harness链路需要，只跑基础对话无需安装） ``` @@ -207,7 +207,8 @@ Copy-Item examples/configs/devices.example.json runtime/openglass_omni/devices.j | 眼镜名称/IP/旋转角 | `runtime/openglass_omni/devices.json` | 每副 ESP32 眼镜一条记录 | | Prompt 预设 | [`panel.py` 的 `CONFIG["presets"]`](runtime/openglass_omni/panel.py) | 面板中显示的交互 Prompt | -> 面板首次启动时会在 `/certs/` 下自签一对 TLS 证书,供 gateway(8006)和语音控制 harness(8021)的 `wss` 连接使用。 +**TLS 证书不用手动准备。** 面板首次启动时会在 `/certs/` 下自签一对 +（gateway 8006 和 harness 8021 共用），已存在则不覆盖。 ### 6. 配置并烧录 Wi-Fi 固件 diff --git a/extensions/assistive_harness/phase_b/bridge_ui.py b/extensions/assistive_harness/phase_b/bridge_ui.py index c3940f8..a65ed15 100644 --- a/extensions/assistive_harness/phase_b/bridge_ui.py +++ b/extensions/assistive_harness/phase_b/bridge_ui.py @@ -52,7 +52,10 @@ def _load_template(name: str) -> str: # ============================================================ class WebUIServer: - def __init__(self, port: int = 8080, host: str = "0.0.0.0", + # 默认只绑回环：这个服务把第一视角画面、session 元数据、原始 user/AI wav + # 全部无认证地暴露出去。绑 0.0.0.0 等于把它们放给整个局域网， + # 要那样必须由调用方显式指定 host。 + def __init__(self, port: int = 8080, host: str = "127.0.0.1", sessions_root: Path = Path("./sessions"), stop_callback: Optional[Callable[[], None]] = None, mode_info: Optional[dict] = None): @@ -85,9 +88,28 @@ async def start(self) -> None: app.router.add_post('/api/stop', self._h_stop) self._runner = web.AppRunner(app) await self._runner.setup() - site = web.TCPSite(self._runner, self.host, self.port) - await site.start() - LOGGER.info("[UI] http://%s:%d (live + replay)", self.host, self.port) + # 回环默认同时绑 IPv4 和 IPv6。 + # 只绑 "127.0.0.1" 的话是**纯 IPv4**，而 Windows 上浏览器把 + # `localhost` 优先解析成 ::1 的情况很常见 —— 那时 panel 内嵌的 + # http://localhost:8080 会连不上（表现就是第一视角一片空白）。 + # 绑成 ["127.0.0.1", "::1"] 两边都覆盖，又不暴露到局域网。 + _hosts = self.host + if self.host in ("127.0.0.1", "localhost", "::1"): + _hosts = ["127.0.0.1", "::1"] + try: + site = web.TCPSite(self._runner, _hosts, self.port) + await site.start() + except OSError as e: + # 系统没开 IPv6 之类：退回单地址，别让整个第一视角起不来 + LOGGER.warning("[UI] 绑定 %s 失败(%s)，退回 %s", _hosts, e, self.host) + site = web.TCPSite(self._runner, self.host, self.port) + await site.start() + LOGGER.info("[UI] http://localhost:%d (live + replay) bind=%s", + self.port, _hosts) + if self.host not in ("127.0.0.1", "localhost", "::1"): + LOGGER.warning( + "[UI] !! 绑定在 %s —— 第一视角画面、session 元数据、" + "原始 user/AI 录音将无认证地暴露给局域网内任何人。", self.host) async def stop(self) -> None: for ws in list(self.live_clients): @@ -293,7 +315,10 @@ def _main() -> None: description="Bridge UI — standalone replay server (no ESP32 / no model)" ) parser.add_argument("--port", type=int, default=8080) - parser.add_argument("--host", default="0.0.0.0") + parser.add_argument("--host", default="127.0.0.1", + help="绑定地址。默认只绑回环 —— 本服务无认证地提供" + "第一视角画面、session 元数据和原始 user/AI wav，" + "填 0.0.0.0 会把这些暴露给整个局域网") parser.add_argument("--sessions", default="./sessions", help="sessions 根目录(默认 ./sessions)") args = parser.parse_args() diff --git a/extensions/assistive_harness/phase_b/esp32_runtime.py b/extensions/assistive_harness/phase_b/esp32_runtime.py index d343a9c..285eec2 100644 --- a/extensions/assistive_harness/phase_b/esp32_runtime.py +++ b/extensions/assistive_harness/phase_b/esp32_runtime.py @@ -1049,6 +1049,15 @@ async def _do_reject_interrupt_inner(reason): if wav_pcm is None: LOG.warning("[强制措施] 无 %s.wav，跳过播报", reason) return + # 记下发 stop 时的 stop_count，作为"这次暂停归我"的凭据。 + # 漏斗的 stop 意思是"先别说，我要插播提示"， + # 用户的 stop 意思是"闭嘴，我不想听" —— 两者走同一条 8021 + # 控制腿，无从区分。播报期间用户若说"停一下"， + # gate.stop() 会让 stop_count 再 +1；此时若还无条件 resume， + # 就把用户的停止状态覆盖掉了。 + # （播报改成后台任务之后，这个并发窗口更大了。） + _gate = getattr(manager, "gate", None) + _owned = getattr(_gate, "stop_count", None) if _gate else None try: await harness.send({"type": "funnel.stop", "reason": reason}) LOG.info("[强制措施] funnel.stop 已发 (%s)", reason) @@ -1065,6 +1074,14 @@ async def _do_reject_interrupt_inner(reason): await asyncio.sleep(dur + 0.2) except Exception as e: LOG.warning("[强制措施] 播 wav 失败: %s", e) + # 只有"这次暂停仍归我"才 resume。stop_count 变了说明期间 + # 有新的 stop 进来（用户按的），此时保持停止状态不动。 + _now_cnt = getattr(_gate, "stop_count", None) if _gate else None + if _owned is not None and _now_cnt is not None and _now_cnt != _owned: + LOG.info("[强制措施] 播报期间收到新的 STOP" + "（stop_count %s→%s），保留用户的停止状态，不 resume", + _owned, _now_cnt) + return try: await harness.send({"type": "funnel.resume", "reason": "hint_done"}) LOG.info("[强制措施] funnel.resume 已发（提示播完）") @@ -1346,7 +1363,7 @@ def __init__(self, config, esp32_host, esp32_port, image_tcp_port, gateway_host="127.0.0.1", gateway_port=8040, reject_wav_dir="assets/reject_wav", live_record_dir=None, rerun_from=None, - funnel_rerun_from=None, web_ui_port=None, + funnel_rerun_from=None, web_ui_port=None, web_ui_host="127.0.0.1", record_no_media: bool = False, rerun_tail_wait_s: float = 30.0, no_reject: bool = False, @@ -1395,8 +1412,12 @@ def __init__(self, config, esp32_host, esp32_port, image_tcp_port, try: self._web_ui = WebUIServer( port=web_ui_port, + host=web_ui_host, sessions_root=Path(live_record_dir).parent if live_record_dir else Path("live_sessions"), + # 接上运行时的停止事件：不接的话 POST /api/stop 会返回成功、 + # 前端按钮也变灰，但运行时根本没停，session 也不会收尾落盘。 + stop_callback=self._stop_evt.set, mode_info={"mode": "rerun" if (rerun_from or funnel_rerun_from) else "live"}, ) @@ -1531,6 +1552,26 @@ async def start(self) -> None: # oneDNN 编译（~2s，甚至 5s+）。不预热的话它会推迟到第一次 accept 才现场加载， # 卡住那一轮，且在此之前链路出不了 send（模型长时间拿不到图）。 if self._funnel is not None: + # ★ 先在**主线程**里把 paddle/paddleocr 导进来。 + # cam_pipeline_v2 的 `from paddleocr import ...` 是懒加载、写在函数体里， + # 而 warmup_orient 走 asyncio.to_thread（工作线程）—— paddle 这类 + # 带大量 C 扩展和内部循环依赖的包，在非主线程首次导入时会撞上 + # "partially initialized module 'paddle' has no attribute 'tensor' + # (most likely due to a circular import)"， + # 然后 _ORI_CLS_TRIED 永久置位、整个进程退回错误的早期 CV 判据， + # 表现就是第一轮就误报 orient_flipped（实测 panel 启动必现）。 + # 在主线程预导一次，后面线程里再 import 就是拿缓存，不会再触发。 + try: + # ★ 只导这一个符号，**不要 `import paddleocr` 或 `import paddle`**。 + # `import paddleocr` 会走 paddlex.inference.utils.official_models + # → import modelscope → modelscope.utils.logger → import torch， + # 而这个环境里 torch 的 shm.dll 加载失败（WinError 127）， + # 于是整条链断掉、方向分类器退回错误的早期 CV 判据。 + # 窄导入不经过那一支，实测可用（cam_pipeline_v2 里也是这么写的）。 + from paddleocr import DocImgOrientationClassification # noqa: F401 + LOG.info("[预热] paddleocr 方向分类器已在主线程预导入") + except Exception as e: + LOG.warning("[预热] 方向分类器预导入失败（可能退回轻量判据）: %s", e) try: ms = await asyncio.to_thread(self._funnel.warmup_orient) # 必须查真实状态：这行原来无条件打"就绪"，而加载失败时 @@ -1770,6 +1811,10 @@ def parse_args() -> argparse.Namespace: p.add_argument("--web-ui-port", type=int, default=None, help="开 bridge_ui 第一视角(如 8080)，live/rerun/funnel-rerun 都可看") # ── 强制措施（防幻觉）：reject 时用模型音色念提示 + 停/恢复走 8021 ── + p.add_argument("--web-ui-host", default="127.0.0.1", + help="第一视角服务的绑定地址。默认只绑回环 —— 该服务无认证地提供" + "画面、session 元数据和原始 user/AI 录音；" + "要给局域网内别的设备看才填 0.0.0.0，风险自负") p.add_argument("--force-measure", action="store_true", help="开启强制措施：reject→停duplex→播预生成wav→恢复duplex") p.add_argument("--reject-wav-dir", default="assets/reject_wav", @@ -1874,6 +1919,7 @@ def main() -> None: rerun_from=args.rerun_from, funnel_rerun_from=args.funnel_rerun_from, web_ui_port=args.web_ui_port, + web_ui_host=args.web_ui_host, record_no_media=args.record_no_media, rerun_tail_wait_s=args.rerun_tail_wait_s, no_reject=args.no_reject, diff --git a/runtime/openglass_omni/panel.py b/runtime/openglass_omni/panel.py index d3bce4d..e16bcb6 100644 --- a/runtime/openglass_omni/panel.py +++ b/runtime/openglass_omni/panel.py @@ -580,6 +580,10 @@ def __init__(self, cfg): self.current_prompt = next(iter(cfg["presets"].values())) self.current_device = (cfg.get("devices") or ["默认"])[0] self.current_scene = cfg.get("default_scene", "严格判据") + # 尾进程是"为哪套配置"起的。②③④ 共用 demo_funnel 这一个进程名， + # 只看进程活着就跳过启动的话，切链路后新的漏斗参数永远不会生效 —— + # UI 显示④，实际还在跑②。所以记指纹，变了就重启。 + self._tail_started_for = {} # tail 进程名 -> 指纹 # —— 当前链路：esp32 / rokid，决定第四级起哪个进程 —— self.current_chain = cfg.get("default_chain", "esp32") self._lock = threading.Lock() @@ -980,6 +984,7 @@ def _spawn(self, name): return proc def _kill(self, name, graceful=False, grace_timeout=20): + self._forget_tail_fp(name) # 进程要没了，指纹一并作废 proc = self.procs.get(name) if not proc or proc.poll() is not None: self.status[name] = "stopped" @@ -1183,6 +1188,9 @@ def _start_one(self, name): self._spawn(name) if self._wait_ready_or_die(name, ready_timeout): self.status[name] = "running" + if name == self.tail(): + # 记下这次是按哪套配置起的，下次切链路时用来判断要不要重启 + self._tail_started_for[name] = self._tail_fingerprint() return True # 未就绪/退出/被急停：先把这次 spawn 的进程杀干净，绝不留残余 p = self.procs.get(name) @@ -1205,11 +1213,28 @@ def _start_one(self, name): return False return False + def _forget_tail_fp(self, name): + """进程不在了就清掉指纹，否则下次会误判成"配置没变、跳过启动"。""" + self._tail_started_for.pop(name, None) + def _other_tails(self): """除当前链路外，其它链路的第四级进程名。""" cur = self.tail() return [c["tail"] for k, c in self.cfg["chains"].items() if c["tail"] != cur] + def _tail_fingerprint(self): + """当前链路下，尾进程真正依赖的那几个量。 + + 任何一项变了，已在跑的尾进程就是按旧配置起的，必须重启： + chain 决定漏斗档位（--funnel / --no-reject / --force-measure） + scene 决定 --scene（严格/日常判据） + device 决定 --esp32-host / --esp32-port / --rotate + prompt 决定 --prompt + """ + c = self.chain() + return (self.current_chain, c.get("funnel", 0), self.current_scene, + self.current_device, self.current_prompt) + def _do_start_all(self): # ②③④ 的两个前置检查：extensions 复制了没、档位④的 wav 有没有。 # 提前拦住比跑起来才发现好 —— 后者时 duplex 已在跑，没法当场补。 @@ -1228,9 +1253,22 @@ def _do_start_all(self): if self._cancel.is_set(): return if self._alive(name): - self.status[name] = "running" - self._log(name, "已在运行，跳过启动") - continue + # 尾进程还要比配置指纹：同一个 demo_funnel 进程， + # ②③④ 传的参数完全不同，光看"活着"会漏掉重启。 + if name == self.tail(): + want = self._tail_fingerprint() + got = self._tail_started_for.get(name) + if got is not None and got != want: + self._log(name, "配置已变（链路/判据/眼镜/Prompt），重启该进程") + self._kill(name, graceful=True, grace_timeout=120) + else: + self.status[name] = "running" + self._log(name, "已在运行，跳过启动") + continue + else: + self.status[name] = "running" + self._log(name, "已在运行，跳过启动") + continue if not self._start_one(name): if self._cancel.is_set(): self._log(name, "!! 启动被急停中止")

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