Remove unused 'strict' parameter from detection and segmentation result classes and update label handling in tilers

Author: eugene123tw

model_api/python/model_api/models/result_types/detection.py

@@ -62,7 +62,6 @@ def __str__(self) -> str:
             self.scores,
             self.labels,
             self.label_names,
-            strict=True,
         ):
             x1, y1, x2, y2 = box
             repr_str += f"{x1}, {y1}, {x2}, {y2}, {label} ({name}): {score:.3f}; "

model_api/python/model_api/models/result_types/segmentation.py

@@ -51,7 +51,6 @@ def __str__(self) -> str:
             self.labels,
             self.label_names,
             self.masks,
-            strict=True,
         ):
             x1, y1, x2, y2 = box
             repr_str += f"{x1}, {y1}, {x2}, {y2}, {label} ({name}): {score:.3f}, {(mask > 0.5).sum()}; "
@@ -123,7 +122,6 @@ def __str__(self) -> str:
             self.label_names,
             self.masks,
             self.rotated_rects,
-            strict=True,
         ):
             x1, y1, x2, y2 = box
             (cx, cy), (w, h), angle = rotated_rect

model_api/python/model_api/models/utils.py

@@ -46,7 +46,6 @@ def get_contours(seg_result: RotatedSegmentationResult | InstanceSegmentationRes
         seg_result.masks,
         seg_result.scores,
         seg_result.label_names,
-        strict=True,
     ):
         contours, _ = cv2.findContours(
             mask,

model_api/python/model_api/tilers/detection.py

@@ -112,11 +112,13 @@ def _merge_results(self, results: list[dict], shape: tuple[int, int, int]) -> De
 
         merged_vector = np.mean(feature_vectors, axis=0) if feature_vectors else np.ndarray(0)
         saliency_map = self._merge_saliency_maps(saliency_maps, shape, tiles_coords) if saliency_maps else np.ndarray(0)
+        label_names = [self.model.labels[int(label_idx)] for label_idx in detections_array[:, 0]]
 
         return DetectionResult(
             bboxes=detections_array[:, 2:].astype(np.int32),
             labels=detections_array[:, 0].astype(np.int32),
             scores=detections_array[:, 1],
+            label_names=label_names,
             saliency_map=saliency_map,
             feature_vector=merged_vector,
         )

model_api/python/model_api/tilers/instance_segmentation.py

@@ -120,16 +120,19 @@ def _merge_results(self, results, shape) -> InstanceSegmentationResult:
         saliency_map = self._merge_saliency_maps(saliency_maps, shape, tiles_coords) if saliency_maps else []
 
         labels, scores, bboxes = np.hsplit(detections_array, [1, 2])
-        resized_masks = []
-        for mask, box in zip(masks, bboxes, strict=True):
+        labels = labels.astype(np.int32)
+        resized_masks, label_names = [], []
+        for mask, box, label_idx in zip(masks, bboxes, labels, strict=True):
+            label_names.append(self.model.labels[int(label_idx)])
             resized_masks.append(_segm_postprocess(box, mask, *shape[:-1]))
 
         resized_masks = np.stack(resized_masks) if resized_masks else masks
         return InstanceSegmentationResult(
             bboxes=np.round(bboxes).astype(np.int32),
-            labels=labels.astype(np.int32).squeeze(),
+            labels=labels.squeeze(),
             scores=scores.squeeze(),
             masks=resized_masks,
+            label_names=label_names if label_names else None,
             saliency_map=saliency_map,
             feature_vector=merged_vector,
         )