Requires class name for count detectors (#291)

Author: brandon-wada

generated/docs/CountModeConfiguration.md

@@ -4,6 +4,7 @@
 ## Properties
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
+**class_name** | **str** |  | 
 **max_count** | **int** |  | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 

generated/docs/ImageQueriesApi.md

@@ -282,6 +282,7 @@ with groundlight_openapi_client.ApiClient(configuration) as api_client:
     # Create an instance of the API class
     api_instance = image_queries_api.ImageQueriesApi(api_client)
     detector_id = "detector_id_example" # str | Choose a detector by its ID.
+    confidence_threshold = 0 # float | The confidence threshold for the image query. (optional)
     human_review = "human_review_example" # str | If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. (optional)
     image_query_id = "image_query_id_example" # str | The ID to assign to the created image query. (optional)
     inspection_id = "inspection_id_example" # str | Associate the image query with an inspection. (optional)
@@ -300,7 +301,7 @@ with groundlight_openapi_client.ApiClient(configuration) as api_client:
     # example passing only required values which don't have defaults set
     # and optional values
     try:
-        api_response = api_instance.submit_image_query(detector_id, human_review=human_review, image_query_id=image_query_id, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body)
+        api_response = api_instance.submit_image_query(detector_id, confidence_threshold=confidence_threshold, human_review=human_review, image_query_id=image_query_id, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body)
         pprint(api_response)
     except groundlight_openapi_client.ApiException as e:
         print("Exception when calling ImageQueriesApi->submit_image_query: %s\n" % e)
@@ -312,6 +313,7 @@ with groundlight_openapi_client.ApiClient(configuration) as api_client:
 Name | Type | Description  | Notes
 ------------- | ------------- | ------------- | -------------
  **detector_id** | **str**| Choose a detector by its ID. |
+ **confidence_threshold** | **float**| The confidence threshold for the image query. | [optional]
  **human_review** | **str**| If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. | [optional]
  **image_query_id** | **str**| The ID to assign to the created image query. | [optional]
  **inspection_id** | **str**| Associate the image query with an inspection. | [optional]

generated/groundlight_openapi_client/api/image_queries_api.py

@@ -170,6 +170,7 @@ def __init__(self, api_client=None):
             params_map={
                 "all": [
                     "detector_id",
+                    "confidence_threshold",
                     "human_review",
                     "image_query_id",
                     "inspection_id",
@@ -183,13 +184,21 @@ def __init__(self, api_client=None):
                 ],
                 "nullable": [],
                 "enum": [],
-                "validation": [],
+                "validation": [
+                    "confidence_threshold",
+                ],
             },
             root_map={
-                "validations": {},
+                "validations": {
+                    ("confidence_threshold",): {
+                        "inclusive_maximum": 1,
+                        "inclusive_minimum": 0,
+                    },
+                },
                 "allowed_values": {},
                 "openapi_types": {
                     "detector_id": (str,),
+                    "confidence_threshold": (float,),
                     "human_review": (str,),
                     "image_query_id": (str,),
                     "inspection_id": (str,),
@@ -200,6 +209,7 @@ def __init__(self, api_client=None):
                 },
                 "attribute_map": {
                     "detector_id": "detector_id",
+                    "confidence_threshold": "confidence_threshold",
                     "human_review": "human_review",
                     "image_query_id": "image_query_id",
                     "inspection_id": "inspection_id",
@@ -209,6 +219,7 @@ def __init__(self, api_client=None):
                 },
                 "location_map": {
                     "detector_id": "query",
+                    "confidence_threshold": "query",
                     "human_review": "query",
                     "image_query_id": "query",
                     "inspection_id": "query",
@@ -421,6 +432,7 @@ def submit_image_query(self, detector_id, **kwargs):
             detector_id (str): Choose a detector by its ID.
 
         Keyword Args:
+            confidence_threshold (float): The confidence threshold for the image query.. [optional]
             human_review (str): If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident.. [optional]
             image_query_id (str): The ID to assign to the created image query.. [optional]
             inspection_id (str): Associate the image query with an inspection.. [optional]

generated/groundlight_openapi_client/model/count_mode_configuration.py

@@ -93,6 +93,7 @@ def openapi_types():
                 and the value is attribute type.
         """
         return {
+            "class_name": (str,),  # noqa: E501
             "max_count": (int,),  # noqa: E501
         }
 
@@ -101,6 +102,7 @@ def discriminator():
         return None
 
     attribute_map = {
+        "class_name": "class_name",  # noqa: E501
         "max_count": "max_count",  # noqa: E501
     }
 
@@ -110,9 +112,12 @@ def discriminator():
 
     @classmethod
     @convert_js_args_to_python_args
-    def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
+    def _from_openapi_data(cls, class_name, *args, **kwargs):  # noqa: E501
         """CountModeConfiguration - a model defined in OpenAPI
 
+        Args:
+            class_name (str):
+
         Keyword Args:
             _check_type (bool): if True, values for parameters in openapi_types
                                 will be type checked and a TypeError will be
@@ -173,6 +178,7 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
         self._configuration = _configuration
         self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
 
+        self.class_name = class_name
         for var_name, var_value in kwargs.items():
             if (
                 var_name not in self.attribute_map
@@ -195,9 +201,12 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
     ])
 
     @convert_js_args_to_python_args
-    def __init__(self, *args, **kwargs):  # noqa: E501
+    def __init__(self, class_name, *args, **kwargs):  # noqa: E501
         """CountModeConfiguration - a model defined in OpenAPI
 
+        Args:
+            class_name (str):
+
         Keyword Args:
             _check_type (bool): if True, values for parameters in openapi_types
                                 will be type checked and a TypeError will be
@@ -256,6 +265,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
         self._configuration = _configuration
         self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
 
+        self.class_name = class_name
         for var_name, var_value in kwargs.items():
             if (
                 var_name not in self.attribute_map

generated/groundlight_openapi_client/model/counting_result.py

@@ -64,6 +64,9 @@ class CountingResult(ModelNormal):
     }
 
     validations = {
+        ("count",): {
+            "inclusive_minimum": 0,
+        },
         ("confidence",): {
             "inclusive_maximum": 1.0,
             "inclusive_minimum": 0.0,

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-12-05T21:27:07+00:00
+#   timestamp: 2024-12-07T00:51:02+00:00
 
 from __future__ import annotations
 
@@ -200,7 +200,7 @@ class BinaryClassificationResult(BaseModel):
 class CountingResult(BaseModel):
     confidence: Optional[confloat(ge=0.0, le=1.0)] = None
     source: Optional[Source] = None
-    count: int
+    count: conint(ge=0)
     greater_than_max: Optional[bool] = None
 
 
@@ -212,6 +212,7 @@ class MultiClassificationResult(BaseModel):
 
 class CountModeConfiguration(BaseModel):
     max_count: Optional[conint(ge=1, le=50)] = None
+    class_name: str
 
 
 class MultiClassModeConfiguration(BaseModel):

pyproject.toml

@@ -9,7 +9,7 @@ packages = [
     {include = "**/*.py", from = "src"},
 ]
 readme = "README.md"
-version = "0.20.0"
+version = "0.21.0"
 
 [tool.poetry.dependencies]
 # For certifi, use ">=" instead of "^" since it upgrades its "major version" every year, not really following semver

spec/public-api.yaml

@@ -347,6 +347,14 @@ paths:
             --data-binary @path/to/filename.jpeg
         ```
       parameters:
+      - in: query
+        name: confidence_threshold
+        schema:
+          type: number
+          format: float
+          minimum: 0
+          maximum: 1
+        description: The confidence threshold for the image query.
       - in: query
         name: detector_id
         schema:
@@ -1367,8 +1375,7 @@ components:
           - ALGORITHM
         count:
           type: integer
-          minimum: null
-          maximum: null
+          minimum: 0
         greater_than_max:
           type: boolean
       required:
@@ -1400,7 +1407,10 @@ components:
           type: integer
           minimum: 1
           maximum: 50
-      required: []
+        class_name:
+          type: string
+      required:
+      - class_name
     MultiClassModeConfiguration:
       type: object
       properties:
@@ -1410,8 +1420,6 @@ components:
             type: string
         num_classes:
           type: integer
-          minimum: null
-          maximum: null
       required:
       - class_names
     ChannelEnum:

src/groundlight/experimental_api.py

@@ -687,6 +687,7 @@ def create_counting_detector(  # noqa: PLR0913 # pylint: disable=too-many-argume
         self,
         name: str,
         query: str,
+        class_name: str,
         *,
         max_count: Optional[int] = None,
         group_name: Optional[str] = None,
@@ -706,6 +707,7 @@ def create_counting_detector(  # noqa: PLR0913 # pylint: disable=too-many-argume
             detector = gl.create_counting_detector(
                 name="people_counter",
                 query="How many people are in the image?",
+                class_name="person",
                 max_count=5,
                 confidence_threshold=0.9,
                 patience_time=30.0
@@ -718,6 +720,7 @@ def create_counting_detector(  # noqa: PLR0913 # pylint: disable=too-many-argume
 
         :param name: A short, descriptive name for the detector.
         :param query: A question about the count of an object in the image.
+        :param class_name: The class name of the object to count.
         :param max_count: Maximum number of objects to count (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
@@ -747,7 +750,7 @@ def create_counting_detector(  # noqa: PLR0913 # pylint: disable=too-many-argume
         # TODO: pull the BE defined default
         if max_count is None:
             max_count = 10
-        mode_config = CountModeConfiguration(max_count=max_count)
+        mode_config = CountModeConfiguration(max_count=max_count, 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())

test/unit/test_experimental.py

@@ -100,7 +100,7 @@ def test_counting_detector(gl_experimental: ExperimentalApi):
     verify that we can create and submit to a counting detector
     """
     name = f"Test {datetime.utcnow()}"
-    created_detector = gl_experimental.create_counting_detector(name, "How many dogs")
+    created_detector = gl_experimental.create_counting_detector(name, "How many dogs", "dog")
     assert created_detector is not None
     count_iq = gl_experimental.submit_image_query(created_detector, "test/assets/dog.jpeg")
     assert count_iq.result.count is not None