Support confirmation for ULTRALYTICS YOLOv10 and YOLO11; support for ULTRALYTICS OBB models

docs/source/creators/creators_add_metadata_to_model.rst

@@ -53,6 +53,7 @@ Availeble detector types:
 - :code:`YOLO_v9`
 - :code:`YOLO_Ultralytics`
 - :code:`YOLO_Ultralytics_segmentation`
+- :code:`YOLO_Ultralytics_obb`
 
 =======
 Example

src/deepness/common/processing_parameters/detection_parameters.py

@@ -23,6 +23,7 @@ class DetectorType(enum.Enum):
     YOLO_v9 = 'YOLO_v9'
     YOLO_ULTRALYTICS = 'YOLO_Ultralytics'
     YOLO_ULTRALYTICS_SEGMENTATION = 'YOLO_Ultralytics_segmentation'
+    YOLO_ULTRALYTICS_OBB = 'YOLO_Ultralytics_obb'
 
     def get_parameters(self):
         if self == DetectorType.YOLO_v5_v7_DEFAULT:
@@ -36,7 +37,7 @@ def get_parameters(self):
                 has_inverted_output_shape=True,
                 skipped_objectness_probability=True,
             )
-        elif self == DetectorType.YOLO_ULTRALYTICS or self == DetectorType.YOLO_ULTRALYTICS_SEGMENTATION:
+        elif self == DetectorType.YOLO_ULTRALYTICS or self == DetectorType.YOLO_ULTRALYTICS_SEGMENTATION or self == DetectorType.YOLO_ULTRALYTICS_OBB:
             return DetectorTypeParameters(
                 has_inverted_output_shape=True,
                 skipped_objectness_probability=True,

src/deepness/metadata.txt

@@ -6,7 +6,7 @@
 name=Deepness: Deep Neural Remote Sensing
 qgisMinimumVersion=3.22
 description=Inference of deep neural network models (ONNX) for segmentation, detection and regression
-version=0.6.3
+version=0.6.4
 author=PUT Vision
 email=przemyslaw.aszkowski@gmail.com
 

src/deepness/processing/map_processor/map_processor_detection.py

@@ -13,6 +13,7 @@
 from deepness.processing.map_processor.utils.ckdtree import cKDTree
 from deepness.processing.models.detector import Detection, Detector
 from deepness.processing.tile_params import TileParams
+from deepness.processing.models.detector import DetectorType
 
 
 class MapProcessorDetection(MapProcessorWithModel):
@@ -49,8 +50,10 @@ def _run(self) -> MapProcessingResult:
             bounding_boxes_in_tile_batched = self._process_tile(tile_img_batched, tile_params_batched)
             all_bounding_boxes += [d for det in bounding_boxes_in_tile_batched for d in det]
 
+        with_rot = self.detection_parameters.detector_type == DetectorType.YOLO_ULTRALYTICS_OBB
+
         if len(all_bounding_boxes) > 0:
-            all_bounding_boxes_nms = self.remove_overlaping_detections(all_bounding_boxes, iou_threshold=self.detection_parameters.iou_threshold)
+            all_bounding_boxes_nms = self.remove_overlaping_detections(all_bounding_boxes, iou_threshold=self.detection_parameters.iou_threshold, with_rot=with_rot)
             all_bounding_boxes_restricted = self.limit_bounding_boxes_to_processed_area(all_bounding_boxes_nms)
         else:
             all_bounding_boxes_restricted = []
@@ -197,17 +200,20 @@ def add_to_gui():
         return add_to_gui
 
     @staticmethod
-    def remove_overlaping_detections(bounding_boxes: List[Detection], iou_threshold: float) -> List[Detection]:
+    def remove_overlaping_detections(bounding_boxes: List[Detection], iou_threshold: float, with_rot: bool = False) -> List[Detection]:
         bboxes = []
         probs = []
         for det in bounding_boxes:
-            bboxes.append(det.get_bbox_xyxy())
+            if with_rot:
+                bboxes.append(det.get_bbox_xyxy_rot())
+            else:
+                bboxes.append(det.get_bbox_xyxy())
             probs.append(det.conf)
 
         bboxes = np.array(bboxes)
         probs = np.array(probs)
 
-        pick_ids = Detector.non_max_suppression_fast(bboxes, probs, iou_threshold)
+        pick_ids = Detector.non_max_suppression_fast(boxes=bboxes, probs=probs, iou_threshold=iou_threshold, with_rot=with_rot)
 
         filtered_bounding_boxes = [x for i, x in enumerate(bounding_boxes) if i in pick_ids]
         filtered_bounding_boxes = sorted(filtered_bounding_boxes, reverse=True)