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Merged
brandon-wada merged 4 commits into from
Dec 9, 2024
Merged

Requires class name for count detectors #291

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e839586
Revert "Revert "require class_name for count mode detectors""
Dec 7, 2024
4dcc9a1
Turned off a dumb vscode extension that messed up the spec and appeas…
Dec 7, 2024
7aaf9cb
trailing line
Dec 7, 2024
107f42e
this is technically breaking
Dec 7, 2024
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1 change: 1 addition & 0 deletions generated/docs/CountModeConfiguration.md
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Expand Up @@ -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]

Expand Down
4 changes: 3 additions & 1 deletion generated/docs/ImageQueriesApi.md
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Expand Up @@ -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)
Expand All @@ -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)
Expand All @@ -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]
Expand Down
16 changes: 14 additions & 2 deletions generated/groundlight_openapi_client/api/image_queries_api.py
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Expand Up @@ -170,6 +170,7 @@ def __init__(self, api_client=None):
params_map={
"all": [
"detector_id",
"confidence_threshold",
"human_review",
"image_query_id",
"inspection_id",
Expand All @@ -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,),
Expand All @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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]
Expand Down
14 changes: 12 additions & 2 deletions generated/groundlight_openapi_client/model/count_mode_configuration.py
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Expand Up @@ -93,6 +93,7 @@ def openapi_types():
and the value is attribute type.
"""
return {
"class_name": (str,), # noqa: E501
"max_count": (int,), # noqa: E501
}

Expand All @@ -101,6 +102,7 @@ def discriminator():
return None

attribute_map = {
"class_name": "class_name", # noqa: E501
"max_count": "max_count", # noqa: E501
}

Expand All @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down
3 changes: 3 additions & 0 deletions generated/groundlight_openapi_client/model/counting_result.py
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Expand Up @@ -64,6 +64,9 @@ class CountingResult(ModelNormal):
}

validations = {
("count",): {
"inclusive_minimum": 0,
},
("confidence",): {
"inclusive_maximum": 1.0,
"inclusive_minimum": 0.0,
Expand Down
5 changes: 3 additions & 2 deletions generated/model.py
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@@ -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

Expand Down Expand Up @@ -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


Expand All @@ -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):
Expand Down
2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -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
Expand Down
18 changes: 13 additions & 5 deletions spec/public-api.yaml
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Expand Up @@ -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:
Expand Down Expand Up @@ -1367,8 +1375,7 @@ components:
- ALGORITHM
count:
type: integer
minimum: null
maximum: null
minimum: 0
greater_than_max:
type: boolean
required:
Expand Down Expand Up @@ -1400,7 +1407,10 @@ components:
type: integer
minimum: 1
maximum: 50
required: []
class_name:
type: string
required:
- class_name
MultiClassModeConfiguration:
type: object
properties:
Expand All @@ -1410,8 +1420,6 @@ components:
type: string
num_classes:
type: integer
minimum: null
maximum: null
required:
- class_names
ChannelEnum:
Expand Down
5 changes: 4 additions & 1 deletion src/groundlight/experimental_api.py
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Expand Up @@ -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,
Expand All @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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())
Expand Down
2 changes: 1 addition & 1 deletion test/unit/test_experimental.py
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Expand Up @@ -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
Expand Down
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