Adds sdk label support for multiclass detectors

src/groundlight/binary_labels.py

@@ -53,25 +53,3 @@ def convert_internal_label_to_display(
 
     logger.warning(f"Unrecognized internal label {label} - leaving it alone as a string.")
     return label
-
-
-def convert_display_label_to_internal(
-    context: Union[ImageQuery, Detector, str],  # pylint: disable=unused-argument
-    label: Union[Label, str],
-) -> str:
-    """Convert a label that comes from the user into the label string that we send to the server. We
-    are strict here, and only allow YES/NO.
-
-    NOTE: We accept case-insensitive label strings from the user, but we send UPPERCASE labels to
-    the server. E.g., user inputs "yes" -> the label is returned as "YES".
-    """
-    # NOTE: In the future we should validate against actually supported labels for the detector
-    if not isinstance(label, str):
-        raise ValueError(f"Expected a string label, but got {label} of type {type(label)}")
-    upper = label.upper()
-    if upper == Label.YES:
-        return DeprecatedLabel.PASS.value
-    if upper == Label.NO:
-        return DeprecatedLabel.FAIL.value
-
-    raise ValueError(f"Invalid label string '{label}'.  Must be one of '{Label.YES.value}','{Label.NO.value}'.")

src/groundlight/client.py

@@ -13,9 +13,11 @@
 from groundlight_openapi_client.api.labels_api import LabelsApi
 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.detector_creation_input_request import DetectorCreationInputRequest
 from groundlight_openapi_client.model.label_value_request import LabelValueRequest
 from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest
+from groundlight_openapi_client.model.roi_request import ROIRequest
 from model import (
     ROI,
     BinaryClassificationResult,
@@ -26,7 +28,7 @@
 )
 from urllib3.exceptions import InsecureRequestWarning
 
-from groundlight.binary_labels import Label, convert_display_label_to_internal, convert_internal_label_to_display
+from groundlight.binary_labels import Label, convert_internal_label_to_display
 from groundlight.config import API_TOKEN_MISSING_HELP_MESSAGE, API_TOKEN_VARIABLE_NAME, DISABLE_TLS_VARIABLE_NAME
 from groundlight.encodings import url_encode_dict
 from groundlight.images import ByteStreamWrapper, parse_supported_image_types
@@ -1066,16 +1068,17 @@ def _wait_for_result(
             image_query = self._fixup_image_query(image_query)
         return image_query
 
+    # pylint: disable=duplicate-code
     def add_label(
-        self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None
+        self, image_query: Union[ImageQuery, str], label: Union[Label, int, str], rois: Union[List[ROI], str, None] = None
     ):
         """
         Provide a new label (annotation) for an image query. This is used to provide ground-truth labels
         for training detectors, or to correct the results of detectors.
 
         **Example usage**::
 
-            gl = Groundlight()
+            gl = ExperimentalApi()
 
             # Using an ImageQuery object
             image_query = gl.ask_ml(detector_id, image_data)
@@ -1088,27 +1091,41 @@ def add_label(
             rois = [ROI(x=100, y=100, width=50, height=50)]
             gl.add_label(image_query, "YES", rois=rois)
 
-        :param image_query: Either an ImageQuery object (returned from methods like :meth:`ask_ml`) or an image query ID
-            string starting with "iq_".
-        :param label: The label value to assign, typically "YES" or "NO" for binary classification detectors.
-            For multi-class detectors, use one of the defined class names.
-        :param rois: Optional list of ROI objects defining regions of interest in the image.
-            Each ROI specifies a bounding box with x, y coordinates and width, height.
+        :param image_query: Either an ImageQuery object (returned from methods like
+                          `ask_ml`) or an image query ID string starting with "iq_".
+
+        :param label: The label value to assign, typically "YES" or "NO" for binary
+                     classification detectors. For multi-class detectors, use one of
+                     the defined class names.
+
+        :param rois: Optional list of ROI objects defining regions of interest in the
+                    image. Each ROI specifies a bounding box with x, y coordinates
+                    and width, height.
 
         :return: None
         """
         if isinstance(rois, str):
             raise TypeError("rois must be a list of ROI objects. CLI support is not implemented")
+        if isinstance(label, int):
+            label = str(label)
         if isinstance(image_query, ImageQuery):
             image_query_id = image_query.id
         else:
             image_query_id = str(image_query)
             # Some old imagequery id's started with "chk_"
+            # TODO: handle iqe_ for image_queries returned from edge endpoints
             if not image_query_id.startswith(("chk_", "iq_")):
                 raise ValueError(f"Invalid image query id {image_query_id}")
-        api_label = convert_display_label_to_internal(image_query_id, label)
-        rois_json = [roi.dict() for roi in rois] if rois else None
-        request_params = LabelValueRequest(label=api_label, image_query_id=image_query_id, rois=rois_json)
+        geometry_requests = [BBoxGeometryRequest(**roi.geometry.dict()) for roi in rois] if rois else None
+        roi_requests = (
+            [
+                ROIRequest(label=roi.label, score=roi.score, geometry=geometry)
+                for roi, geometry in zip(rois, geometry_requests)
+            ]
+            if rois and geometry_requests
+            else None
+        )
+        request_params = LabelValueRequest(label=label, image_query_id=image_query_id, rois=roi_requests)
         self.labels_api.create_label(request_params)
 
     def start_inspection(self) -> str:

src/groundlight/experimental_api.py

@@ -32,7 +32,7 @@
 from groundlight_openapi_client.model.verb_enum import VerbEnum
 from model import ROI, BBoxGeometry, Detector, DetectorGroup, ImageQuery, ModeEnum, PaginatedRuleList, Rule
 
-from groundlight.binary_labels import Label, convert_display_label_to_internal
+from groundlight.binary_labels import Label
 from groundlight.images import parse_supported_image_types
 from groundlight.optional_imports import Image, np
 
@@ -499,66 +499,6 @@ def create_roi(self, label: str, top_left: Tuple[float, float], bottom_right: Tu
             ),
         )
 
-    # TODO: remove duplicate method on subclass
-    # pylint: disable=duplicate-code
-    def add_label(
-        self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None
-    ):
-        """
-        Provide a new label (annotation) for an image query. This is used to provide ground-truth labels
-        for training detectors, or to correct the results of detectors.
-
-        **Example usage**::
-
-            gl = ExperimentalApi()
-
-            # Using an ImageQuery object
-            image_query = gl.ask_ml(detector_id, image_data)
-            gl.add_label(image_query, "YES")
-
-            # Using an image query ID string directly
-            gl.add_label("iq_abc123", "NO")
-
-            # With regions of interest (ROIs)
-            rois = [ROI(x=100, y=100, width=50, height=50)]
-            gl.add_label(image_query, "YES", rois=rois)
-
-        :param image_query: Either an ImageQuery object (returned from methods like
-                          `ask_ml`) or an image query ID string starting with "iq_".
-
-        :param label: The label value to assign, typically "YES" or "NO" for binary
-                     classification detectors. For multi-class detectors, use one of
-                     the defined class names.
-
-        :param rois: Optional list of ROI objects defining regions of interest in the
-                    image. Each ROI specifies a bounding box with x, y coordinates
-                    and width, height.
-
-        :return: None
-        """
-        if isinstance(rois, str):
-            raise TypeError("rois must be a list of ROI objects. CLI support is not implemented")
-        if isinstance(image_query, ImageQuery):
-            image_query_id = image_query.id
-        else:
-            image_query_id = str(image_query)
-            # Some old imagequery id's started with "chk_"
-            # TODO: handle iqe_ for image_queries returned from edge endpoints
-            if not image_query_id.startswith(("chk_", "iq_")):
-                raise ValueError(f"Invalid image query id {image_query_id}")
-        api_label = convert_display_label_to_internal(image_query_id, label)
-        geometry_requests = [BBoxGeometryRequest(**roi.geometry.dict()) for roi in rois] if rois else None
-        roi_requests = (
-            [
-                ROIRequest(label=roi.label, score=roi.score, geometry=geometry)
-                for roi, geometry in zip(rois, geometry_requests)
-            ]
-            if rois and geometry_requests
-            else None
-        )
-        request_params = LabelValueRequest(label=api_label, image_query_id=image_query_id, rois=roi_requests)
-        self.labels_api.create_label(request_params)
-
     def reset_detector(self, detector: Union[str, Detector]) -> None:
         """
         Removes all image queries and training data for the given detector. This effectively resets

test/unit/test_labels.py

@@ -0,0 +1,56 @@
+from datetime import datetime
+
+import pytest
+from groundlight import ExperimentalApi, ApiException
+
+
+def test_binary_labels(gl_experimental: ExperimentalApi):
+    name = f"Test binary labels{datetime.utcnow()}"
+    det = gl_experimental.create_detector(name, "test_query")
+    iq1 = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg")
+    gl_experimental.add_label(iq1, "YES")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "YES"
+    gl_experimental.add_label(iq1, "NO")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "NO"
+    gl_experimental.add_label(iq1, "UNCLEAR")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "UNCLEAR"
+    with pytest.raises(ApiException) as _:
+        gl_experimental.add_label(iq1, "MAYBE")
+
+def test_counting_labels(gl_experimental: ExperimentalApi):
+    name = f"Test binary labels{datetime.utcnow()}"
+    det = gl_experimental.create_counting_detector(name, "test_query")
+    iq1 = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg")
+    gl_experimental.add_label(iq1, 0)
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.count == 0
+    gl_experimental.add_label(iq1, 5)
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.count == 5
+    with pytest.raises(ApiException) as _:
+        gl_experimental.add_label(iq1, "MAYBE")
+    with pytest.raises(ApiException) as _:
+        gl_experimental.add_label(iq1, -999)
+
+def test_multiclass_labels(gl_experimental: ExperimentalApi):
+    name = f"Test binary labels{datetime.utcnow()}"
+    det = gl_experimental.create_multiclass_detector(name, "test_query", class_names=["apple", "banana", "cherry"])
+    iq1 = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg")
+    gl_experimental.add_label(iq1, "apple")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "apple"
+    gl_experimental.add_label(iq1, "banana")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "banana"
+    gl_experimental.add_label(iq1, "cherry")
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "cherry"
+    # You can submit the index of the class as well
+    gl_experimental.add_label(iq1, 2)
+    iq1 = gl_experimental.get_image_query(iq1.id)
+    assert iq1.result.label == "cherry"
+    with pytest.raises(ApiException) as _:
+        gl_experimental.add_label(iq1, "MAYBE")