fix: fix issue with NMS

pyproject.toml

@@ -74,7 +74,7 @@ lark = "~1.1.4"
 h5py = "~3.13.0"
 opencv-python-headless = "~4.12.0.88"
 toml = "~0.10.2"
-openfoodfacts = "3.3.0"
+openfoodfacts = "3.4.0"
 imagehash = "~4.3.1"
 peewee-migrate = "~1.12.2"
 diskcache = "~5.6.3"

robotoff/app/api.py

@@ -809,7 +809,7 @@ def on_get(self, req: falcon.Request, resp: falcon.Response):
             "nms_threshold", default=None
         )
         nms_eta: float | None = req.get_param_as_float("nms_eta", default=None)
-
+        nms: bool = req.get_param_as_bool("nms", default=True)
         available_object_detection_models = list(
             ObjectDetectionModel.__members__.keys()
         )
@@ -868,6 +868,7 @@ def on_get(self, req: falcon.Request, resp: falcon.Response):
                     threshold=threshold,
                     nms_threshold=nms_threshold,
                     nms_eta=nms_eta,
+                    nms=nms,
                 )
 
                 if output_image:

robotoff/prediction/object_detection/core.py

@@ -92,6 +92,7 @@ class ModelConfig(BaseModel):
         triton_model_name="price_tag_detection",
         image_size=960,
         label_names=["price-tag"],
+        default_threshold=0.25,
     ),
 }
 
@@ -110,6 +111,8 @@ def add_boxes_and_labels(image_array: np.ndarray, result: ObjectDetectionResult)
         instance_masks=None,
         use_normalized_coordinates=True,
         line_thickness=5,
+        max_boxes_to_draw=len(result.detection_boxes),
+        min_score_thresh=0.0,
     )
     image_with_boxes = Image.fromarray(image_array)
     result.boxed_image = image_with_boxes
@@ -127,6 +130,7 @@ def detect_from_image(
         threshold: float | None = None,
         nms_threshold: float | None = None,
         nms_eta: float | None = None,
+        nms: bool = True,
     ) -> ObjectDetectionResult:
         """Run an object detection model on an image.
 
@@ -144,6 +148,7 @@ def detect_from_image(
             defaults to None (0.7 will be used).
         :param nms_eta: the NMS eta parameter to use, defaults to None (1.0 will be
             used).
+        :param nms: whether to use NMS, defaults to True.
         :return: the detection result
         """
         threshold = threshold or self.config.default_threshold
@@ -158,6 +163,7 @@ def detect_from_image(
             threshold=threshold,
             nms_threshold=nms_threshold,
             nms_eta=nms_eta,
+            nms=nms,
         )
         for metric_name, duration in result.metrics.items():
             ml_metrics_logger.info(

tests/integration/insights/test_extraction.py

@@ -30,6 +30,7 @@ def detect_from_image(
         threshold: float | None = 0.5,
         nms_threshold: float | None = None,
         nms_eta: float | None = None,
+        nms: bool = True,
     ) -> ObjectDetectionResult:
         return self.raw_result