First class bbox mode (#412)

Moves bbox mode out of the experimental section of the SDK and makes it
generally available under the Groundlight class

Author: brandon-wada

src/groundlight/client.py

@@ -16,6 +16,7 @@
 from groundlight_openapi_client.api.user_api import UserApi
 from groundlight_openapi_client.exceptions import NotFoundException, UnauthorizedException
 from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest
+from groundlight_openapi_client.model.bounding_box_mode_configuration import BoundingBoxModeConfiguration
 from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration
 from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest
 from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest
@@ -396,9 +397,8 @@ def create_detector(  # noqa: PLR0913
 
         By default will create a binary detector but alternate modes can be created by passing in a mode argument.
 
-        Text and Bounding box detectors are in Beta, and can be created through the
-        ExperimentalApi via the :meth:`ExperimentalApi.create_text_recognition_detector` and
-        :meth:`ExperimentalApi.create_bounding_box_detector` methods.
+        Text recognition detectors are in Beta, and can be created through the
+        ExperimentalApi via the :meth:`ExperimentalApi.create_text_recognition_detector` method.
 
         **Example usage**::
 
@@ -448,6 +448,8 @@ def create_detector(  # noqa: PLR0913
         :param class_names: The name or names of the class to use for the detector. Only used for multi-class
                         and counting detectors.
         :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
+                        PrimingGroup IDs are provided by Groundlight representatives. If you would like
+                        to use a priming_group_id, please reach out to your Groundlight representative.
 
         :return: The created Detector object
         """
@@ -1605,6 +1607,8 @@ def create_counting_detector(  # noqa: PLR0913 # pylint: disable=too-many-argume
                         information like location, purpose, or related system IDs. You can retrieve this
                         metadata later by calling `get_detector()`.
         :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
+                        PrimingGroup IDs are provided by Groundlight representatives. If you would like
+                        to use a priming_group_id, please reach out to your Groundlight representative.
 
         :return: The created Detector object
         """
@@ -1720,6 +1724,8 @@ def create_multiclass_detector(  # noqa: PLR0913 # pylint: disable=too-many-argu
                         information like location, purpose, or related system IDs. You can retrieve this
                         metadata later by calling `get_detector()`.
         :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
+                        PrimingGroup IDs are provided by Groundlight representatives. If you would like
+                        to use a priming_group_id, please reach out to your Groundlight representative.
 
         :return: The created Detector object
         """
@@ -1739,3 +1745,84 @@ def create_multiclass_detector(  # noqa: PLR0913 # pylint: disable=too-many-argu
         detector_creation_input.mode_configuration = mode_config
         obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT)
         return Detector.parse_obj(obj.to_dict())
+
+    def create_bounding_box_detector(  # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals
+        self,
+        name: str,
+        query: str,
+        class_name: str,
+        *,
+        max_num_bboxes: Optional[int] = None,
+        group_name: Optional[str] = None,
+        confidence_threshold: Optional[float] = None,
+        patience_time: Optional[float] = None,
+        pipeline_config: Optional[str] = None,
+        metadata: Union[dict, str, None] = None,
+        priming_group_id: Optional[str] = None,
+    ) -> Detector:
+        """
+        Creates a bounding box detector that can detect objects in images up to a specified maximum number of bounding
+        boxes.
+
+        **Example usage**::
+
+            gl = Groundlight()
+
+            # Create a detector that counts people up to 5
+            detector = gl.create_bounding_box_detector(
+                name="people_counter",
+                query="Draw a bounding box around each person in the image",
+                class_name="person",
+                max_num_bboxes=5,
+                confidence_threshold=0.9,
+                patience_time=30.0
+            )
+
+            # Use the detector to find people in an image
+            image_query = gl.ask_ml(detector, "path/to/image.jpg")
+            print(f"Confidence: {image_query.result.confidence}")
+            print(f"Label: {image_query.result.label}")
+            print(f"Bounding boxes: {image_query.rois}")
+
+        :param name: A short, descriptive name for the detector.
+        :param query: A question about the object to detect in the image.
+        :param class_name: The class name of the object to detect.
+        :param max_num_bboxes: Maximum number of bounding boxes to detect (default: 10)
+        :param group_name: Optional name of a group to organize related detectors together.
+        :param confidence_threshold: A value that sets the minimum confidence level required for the ML model's
+                            predictions. If confidence is below this threshold, the query may be sent for human review.
+        :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a
+                            confident prediction before falling back to human review. Defaults to 30 seconds.
+        :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific
+                              prediction pipeline for this detector.
+        :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with
+                        the detector (limited to 1KB). This metadata can be used to store additional
+                        information like location, purpose, or related system IDs. You can retrieve this
+                        metadata later by calling `get_detector()`.
+        :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
+                        PrimingGroup IDs are provided by Groundlight representatives. If you would like
+                        to use a priming_group_id, please reach out to your Groundlight representative.
+
+        :return: The created Detector object
+        """
+
+        detector_creation_input = self._prep_create_detector(
+            name=name,
+            query=query,
+            group_name=group_name,
+            confidence_threshold=confidence_threshold,
+            patience_time=patience_time,
+            pipeline_config=pipeline_config,
+            metadata=metadata,
+            priming_group_id=priming_group_id,
+        )
+        detector_creation_input.mode = ModeEnum.BOUNDING_BOX
+
+        if max_num_bboxes is None:
+            mode_config = BoundingBoxModeConfiguration(class_name=class_name)
+        else:
+            mode_config = BoundingBoxModeConfiguration(max_num_bboxes=max_num_bboxes, class_name=class_name)
+
+        detector_creation_input.mode_configuration = mode_config
+        obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT)
+        return Detector.parse_obj(obj.to_dict())

src/groundlight/experimental_api.py

@@ -19,7 +19,6 @@
 from groundlight_openapi_client.api.edge_api import EdgeApi
 from groundlight_openapi_client.api.notes_api import NotesApi
 from groundlight_openapi_client.model.action_request import ActionRequest
-from groundlight_openapi_client.model.bounding_box_mode_configuration import BoundingBoxModeConfiguration
 from groundlight_openapi_client.model.channel_enum import ChannelEnum
 from groundlight_openapi_client.model.condition_request import ConditionRequest
 from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest
@@ -632,85 +631,6 @@ def update_detector_name(self, detector: Union[str, Detector], name: str) -> Non
             detector = detector.id
         self.detectors_api.update_detector(detector, patched_detector_request=PatchedDetectorRequest(name=name))
 
-    def create_bounding_box_detector(  # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals
-        self,
-        name: str,
-        query: str,
-        class_name: str,
-        *,
-        max_num_bboxes: Optional[int] = None,
-        group_name: Optional[str] = None,
-        confidence_threshold: Optional[float] = None,
-        patience_time: Optional[float] = None,
-        pipeline_config: Optional[str] = None,
-        metadata: Union[dict, str, None] = None,
-        priming_group_id: Optional[str] = None,
-    ) -> Detector:
-        """
-        Creates a bounding box detector that can detect objects in images up to a specified maximum number of bounding
-        boxes.
-
-        **Example usage**::
-
-            gl = ExperimentalApi()
-
-            # Create a detector that counts people up to 5
-            detector = gl.create_bounding_box_detector(
-                name="people_counter",
-                query="Draw a bounding box around each person in the image",
-                class_name="person",
-                max_num_bboxes=5,
-                confidence_threshold=0.9,
-                patience_time=30.0
-            )
-
-            # Use the detector to find people in an image
-            image_query = gl.ask_ml(detector, "path/to/image.jpg")
-            print(f"Confidence: {image_query.result.confidence}")
-            print(f"Label: {image_query.result.label}")
-            print(f"Bounding boxes: {image_query.rois}")
-
-        :param name: A short, descriptive name for the detector.
-        :param query: A question about the object to detect in the image.
-        :param class_name: The class name of the object to detect.
-        :param max_num_bboxes: Maximum number of bounding boxes to detect (default: 10)
-        :param group_name: Optional name of a group to organize related detectors together.
-        :param confidence_threshold: A value that sets the minimum confidence level required for the ML model's
-                            predictions. If confidence is below this threshold, the query may be sent for human review.
-        :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a
-                            confident prediction before falling back to human review. Defaults to 30 seconds.
-        :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific
-                              prediction pipeline for this detector.
-        :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with
-                        the detector (limited to 1KB). This metadata can be used to store additional
-                        information like location, purpose, or related system IDs. You can retrieve this
-                        metadata later by calling `get_detector()`.
-        :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
-
-        :return: The created Detector object
-        """
-
-        detector_creation_input = self._prep_create_detector(
-            name=name,
-            query=query,
-            group_name=group_name,
-            confidence_threshold=confidence_threshold,
-            patience_time=patience_time,
-            pipeline_config=pipeline_config,
-            metadata=metadata,
-            priming_group_id=priming_group_id,
-        )
-        detector_creation_input.mode = ModeEnum.BOUNDING_BOX
-
-        if max_num_bboxes is None:
-            mode_config = BoundingBoxModeConfiguration(class_name=class_name)
-        else:
-            mode_config = BoundingBoxModeConfiguration(max_num_bboxes=max_num_bboxes, class_name=class_name)
-
-        detector_creation_input.mode_configuration = mode_config
-        obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT)
-        return Detector.parse_obj(obj.to_dict())
-
     def create_text_recognition_detector(  # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals
         self,
         name: str,
@@ -750,6 +670,8 @@ def create_text_recognition_detector(  # noqa: PLR0913 # pylint: disable=too-man
                         information like location, purpose, or related system IDs. You can retrieve this
                         metadata later by calling `get_detector()`.
         :param priming_group_id: Optional ID of an existing PrimingGroup to associate with this detector.
+                        PrimingGroup IDs are provided by Groundlight representatives. If you would like
+                        to use a priming_group_id, please reach out to your Groundlight representative.
 
         :return: The created Detector object
         """

test/integration/test_groundlight.py

@@ -942,6 +942,9 @@ def test_delete_detector(gl: Groundlight):
 def test_create_detector_with_invalid_priming_group_id(gl: Groundlight):
     """
     Test that creating a detector with a non-existent priming_group_id returns an appropriate error.
+
+    Note: PrimingGroup IDs are provided by Groundlight representatives. If you would like to
+    use a priming_group_id, please reach out to your Groundlight representative.
     """
     name = f"Test invalid priming {datetime.utcnow()}"
     query = "Is there a dog?"