preannoation via sdk

Makefile

@@ -71,8 +71,6 @@ check-release: ## Check if everything is ready for release
 	@echo "3. Commit: git commit -m '[LABIMP-XXXX] Prepare release vX.X.X'"
 	@echo "4. Push and create PR to main (patch) or develop (minor)"
 
-integration-test:
-	$(PYTHON) -m pytest  -v labellerr_integration_tests.py
 
 pre-commit-install:
 	pip install pre-commit

driver.py

@@ -5,7 +5,7 @@
 
 from labellerr.client import LabellerrClient
 from labellerr.core.datasets import LabellerrDataset, create_dataset
-from labellerr.core.connectors import LabellerrS3Connection
+from labellerr.core.connectors import LabellerrS3Connection, LabellerrConnection
 from labellerr.core.files import LabellerrFile
 from labellerr.core.projects import (
     LabellerrProject,
@@ -25,26 +25,6 @@
 )
 from labellerr.core.autolabel import LabellerrAutoLabel
 
-# Set logging level to DEBUG
-logging.basicConfig(level=logging.DEBUG)
-
-load_dotenv()
-
-API_KEY = os.getenv("API_KEY")
-API_SECRET = os.getenv("API_SECRET")
-CLIENT_ID = os.getenv("CLIENT_ID")
-
-if not all([API_KEY, API_SECRET, CLIENT_ID]):
-    raise ValueError(
-        "API_KEY, API_SECRET, and CLIENT_ID must be set in environment variables"
-    )
-
-# Initialize client
-client = LabellerrClient(
-    api_key=API_KEY,
-    api_secret=API_SECRET,
-    client_id=CLIENT_ID,
-)
 
 # if os.getenv("CREATE_DATASET", "").lower() == "true":
 #     from labellerr import schemas
@@ -160,25 +140,25 @@
 #     name="Amazon S3 Export Test",
 #     description="Amazon S3 Export Test",
 # )))
-project = LabellerrProject(client=client, project_id="")
-
-export = project.create_export(
-    export_config=CreateExportParams(
-        export_name="Amazon S3 Export Test",
-        export_description="Amazon S3 Export Test",
-        export_format="json",
-        statuses=["review"],
-        connection_id=os.getenv("AWS_EXPORT_CONNECTION_ID"),
-        export_destination="s3",
-        export_folder_path="",  # pattern - bucket_name/path/to/folder/ - the last slash is important
-    )
-)
+# project = LabellerrProject(client=client, project_id="")
 
-print(f"Export created: {export.report_id}")
-print(f"Current status: {export._status}")
+# export = project.create_export(
+#     export_config=CreateExportParams(
+#         export_name="Amazon S3 Export Test",
+#         export_description="Amazon S3 Export Test",
+#         export_format="json",
+#         statuses=["review"],
+#         connection_id=os.getenv("AWS_EXPORT_CONNECTION_ID"),
+#         export_destination="s3",
+#         export_folder_path="",  # pattern - bucket_name/path/to/folder/ - the last slash is important
+#     )
+# )
+
+# print(f"Export created: {export.report_id}")
+# print(f"Current status: {export._status}")
 # Uncomment to poll until completion:
-final_status = export.status()
-print(f"Final status: {final_status}")
+# final_status = export.status()
+# print(f"Final status: {final_status}")
 # print(autolabel.train(training_request=TrainingRequest(model_id="yolov11", hyperparameters=Hyperparameters(epochs=10), slice_id='', min_samples_per_class=100, job_name="Yolo V11 Training")))
 
 # dataset = LabellerrDataset(client=client, dataset_id="")
@@ -201,9 +181,9 @@
 # print(file.file_data)
 
 # project = LabellerrProject(client=client, project_id="")
-# res = project.upload_preannotations(
+# res = project.upload_preannotation(
 #     annotation_format="coco_json", annotation_file="horses_coco.json"
-# )
+# ).result()
 # print(res)
 
 # print(LabellerrProject.list_all_projects(client=client))

drivers/connectors.py

@@ -0,0 +1,73 @@
+import logging
+import os
+from dotenv import load_dotenv
+from labellerr.client import LabellerrClient
+
+# from labellerr.core.connectors import (
+#     LabellerrConnection,
+#     list_connections,
+#     delete_connection,
+#     create_connection,
+#     AWSConnectionParams,
+#     LabellerrGCSConnection,
+# )
+# from labellerr.core.schemas import (
+#     ConnectionType,
+#     ConnectorType,
+#     DatasetDataType,
+#     GCSConnectionTestParams,
+#     GCSConnectionParams,
+# )
+
+# Set logging level to DEBUG
+logging.basicConfig(level=logging.DEBUG)
+
+load_dotenv()
+
+API_KEY = os.getenv("API_KEY")
+API_SECRET = os.getenv("API_SECRET")
+CLIENT_ID = os.getenv("CLIENT_ID")
+
+if not all([API_KEY, API_SECRET, CLIENT_ID]):
+    raise ValueError(
+        "API_KEY, API_SECRET, and CLIENT_ID must be set in environment variables"
+    )
+
+# Initialize client
+client = LabellerrClient(
+    api_key=API_KEY,
+    api_secret=API_SECRET,
+    client_id=CLIENT_ID,
+)
+
+# response = LabellerrGCSConnection.test_connection(client=client, params=GCSConnectionTestParams(
+#     svc_account_json='labellerr-dev.json',
+#     path="gs://aws-labellerr-public-datasets/coco2017",
+#     connection_type=ConnectionType._IMPORT,
+#     data_type=DatasetDataType.video,
+# ))
+# print(response)
+
+# response = create_connection(client=client, connector_type=ConnectorType._GCS, params=GCSConnectionParams(
+#     svc_account_json='labellerr-dev.json',
+#     path="gs://aws-labellerr-public-datasets/coco2017",
+#     connection_type=ConnectionType._IMPORT,
+#     data_type=DatasetDataType.image, name="GCS Import Test", description="GCS Import Test"))
+# print(response)
+# connection = LabellerrConnection(client=client, connection_id='8c3dc4b4-e701-4d22-add3-28abc33e13ef')
+# response = connection.test(path="gs://aws-labellerr-public-datasets/coco2017", connection_type=ConnectionType._IMPORT, data_type=DatasetDataType.image)
+# print(response)
+# connection = create_connection(client=client, connector_type=ConnectorType._S3, params=AWSConnectionParams(
+#     aws_access_key=os.getenv("AWS_KEY"),
+#     aws_secrets_key=os.getenv("AWS_SECRET"),
+#     s3_path="s3://amazon-s3-sync-test/labellerr-processed/videos/", # this path is not part of the connection but needed to test the connection on the desired path.
+#     # This can be dynamically changed while using the connection for creating datasets.
+#     connection_type=ConnectionType._IMPORT,
+#     name="Amazon S3 Import Test",
+#     description="Amazon S3 Import Test",
+# ))
+# print(f"Connection created: {connection.connection_id}")
+# connection = LabellerrConnection(client=client, connection_id='2a30c044-57f7-42c7-8290-bab3bbac0ebc')
+# print('connection type', connection.connection_type)
+
+# print (connection.test(s3_path="s3://amazon-s3-sync-test/labellerr-processed/videos/datasets/", connection_type=ConnectionType._IMPORT))

drivers/datasets.py

@@ -0,0 +1,61 @@
+# from labellerr.core.datasets import (
+#     create_dataset_from_local,
+#     create_dataset_from_connection,
+# )
+import logging
+import os
+from dotenv import load_dotenv
+from labellerr.client import LabellerrClient
+from labellerr.core.schemas import DatasetConfig
+from labellerr.core.datasets import LabellerrDataset
+
+# from labellerr.core.connectors import (
+#     LabellerrConnection,
+#     list_connections,
+#     delete_connection,
+#     create_connection,
+#     AWSConnectionParams,
+#     LabellerrGCSConnection,
+# )
+# from labellerr.core.schemas import (
+#     ConnectionType,
+#     ConnectorType,
+#     DatasetDataType,
+#     GCSConnectionTestParams,
+#     GCSConnectionParams,
+# )
+
+# Set logging level to DEBUG
+logging.basicConfig(level=logging.DEBUG)
+
+load_dotenv()
+
+API_KEY = os.getenv("API_KEY")
+API_SECRET = os.getenv("API_SECRET")
+CLIENT_ID = os.getenv("CLIENT_ID")
+
+if not all([API_KEY, API_SECRET, CLIENT_ID]):
+    raise ValueError(
+        "API_KEY, API_SECRET, and CLIENT_ID must be set in environment variables"
+    )
+
+# Initialize client
+client = LabellerrClient(
+    api_key=API_KEY,
+    api_secret=API_SECRET,
+    client_id=CLIENT_ID,
+)
+dataset_config = DatasetConfig(
+    dataset_name="test_dataset_from_local SDK",
+    dataset_description="test dataset description",
+    data_type="image",
+)
+# dataset = create_dataset_from_local(
+#     client=client,
+#     dataset_config=dataset_config,
+#     folder_to_upload='images_single',
+# )
+dataset = LabellerrDataset(
+    client=client, dataset_id="455e3d45-55f9-436d-98c2-07a514b7894e"
+)
+print(dataset.files_count)

drivers/projects.py

@@ -0,0 +1,65 @@
+# from labellerr.core.projects import LabellerrProject
+# from labellerr.core.annotation_templates import LabellerrAnnotationTemplate
+import logging
+import os
+from dotenv import load_dotenv
+from labellerr.client import LabellerrClient
+
+# from labellerr.core.schemas.annotation_templates import CreateTemplateParams, AnnotationQuestion, QuestionType
+# from labellerr.core.annotation_templates import create_template
+# from labellerr.core.schemas.projects import CreateProjectParams, RotationConfig
+# from labellerr.core.projects import create_project
+# from labellerr.core.datasets import LabellerrDataset
+
+
+# Set logging level to DEBUG
+logging.basicConfig(level=logging.DEBUG)
+
+load_dotenv()
+
+API_KEY = os.getenv("API_KEY")
+API_SECRET = os.getenv("API_SECRET")
+CLIENT_ID = os.getenv("CLIENT_ID")
+
+if not all([API_KEY, API_SECRET, CLIENT_ID]):
+    raise ValueError(
+        "API_KEY, API_SECRET, and CLIENT_ID must be set in environment variables"
+    )
+
+# Initialize client
+client = LabellerrClient(
+    api_key=API_KEY,
+    api_secret=API_SECRET,
+    client_id=CLIENT_ID,
+)
+
+# project = LabellerrProject(client=client, project_id="rafaela_youngest_pike_23125")
+# res = project.upload_preannotation(annotation_format="coco_json", annotation_file="/Users/Ximi-Hoque/Downloads/export_to_annotate_05_15.json").result()
+# print(res)
+
+# annotation_template = LabellerrAnnotationTemplate(client=client, annotation_template_id="00016829-9051-46b1-96c6-3ec6763c342a")
+# print(annotation_template.annotation_template_data)
+
+# res = create_template(client, CreateTemplateParams(template_name="test_template_1", data_type="image",
+# questions=[
+#     AnnotationQuestion(question_number=1,
+#     question="test_question",
+#     question_id="test_question_id",
+#     question_type=QuestionType.bounding_box,
+#     required=True,
+#     color="#FF4500",
+#     )]))
+# print(res)
+
+# project = create_project(client,
+# CreateProjectParams(
+#     project_name="test_project_via_sdk",
+#     data_type="image",
+#     rotations=RotationConfig(annotation_rotation_count=1, review_rotation_count=1, client_review_rotation_count=1),
+#     use_ai=False),
+#     datasets=[LabellerrDataset(client=client, dataset_id="ca298293-7f5e-4bdd-801f-8863a5ba458b")],
+#     annotation_template=res
+# )
+# print (project.project_data)
+# project = LabellerrProject(client=client, project_id="dinnie_confidential_lynx_20766")
+# print(project.project_data)

labellerr/core/annotation_templates/__init__.py

@@ -0,0 +1,64 @@
+from .base import LabellerrAnnotationTemplate
+from ..schemas.annotation_templates import CreateTemplateParams, QuestionType, Option
+from .. import constants
+from ..client import LabellerrClient
+import uuid
+
+__all__ = [
+    "LabellerrAnnotationTemplate",
+]
+
+object_types = [
+    QuestionType.bounding_box,
+    QuestionType.polygon,
+    QuestionType.polyline,
+    QuestionType.dot,
+]
+
+
+def create_template(
+    client: LabellerrClient, params: CreateTemplateParams
+) -> LabellerrAnnotationTemplate:
+    """Create an annotation template"""
+
+    unique_id = str(uuid.uuid4())
+    for question in params.questions:
+        if question.question_type in object_types:
+            if not question.color:
+                raise ValueError(
+                    "Color is required for bounding box, polygon, polyline, and dot questions"
+                )
+            question.options = [Option(option_name=question.color)]
+        else:
+            if question.question_type != QuestionType.input and not question.options:
+                raise ValueError(
+                    "Options are required for radio, boolean, select, dropdown, stt, imc questions"
+                )
+
+    # Convert questions to the expected format
+    questions_data = []
+    for question in params.questions:
+        question_dict = question.model_dump()
+        # Convert enum to string value
+        question_dict["option_type"] = question.question_type.value
+        # Remove question_type as it's now option_type
+        question_dict.pop("question_type", None)
+        questions_data.append(question_dict)
+
+    payload = {"templateName": params.template_name, "questions": questions_data}
+    url = (
+        f"{constants.BASE_URL}/annotations/create_template?client_id={client.client_id}&data_type={params.data_type.value}"
+        f"&uuid={unique_id}"
+    )
+
+    response = client.make_request(
+        "POST",
+        url,
+        extra_headers={"content-type": "application/json"},
+        json=payload,
+        request_id=unique_id,
+    )
+    return LabellerrAnnotationTemplate(
+        client=client,
+        annotation_template_id=response.get("response", None).get("template_id"),
+    )