Cleanup detection (#17785)

* Fix yolov9 NMS

* Improve batched yolo NMS

* Consolidate grids and strides calculation

* Use existing variable

* Remove

* Ensure init is called

frigate/detectors/plugins/onnx.py

@@ -31,6 +31,8 @@ class ONNXDetector(DetectionApi):
     type_key = DETECTOR_KEY
 
     def __init__(self, detector_config: ONNXDetectorConfig):
+        super().__init__(detector_config)
+
         try:
             import onnxruntime as ort
 
@@ -52,31 +54,13 @@ class ONNXDetector(DetectionApi):
             path, providers=providers, provider_options=options
         )
 
-        self.h = detector_config.model.height
-        self.w = detector_config.model.width
         self.onnx_model_type = detector_config.model.model_type
         self.onnx_model_px = detector_config.model.input_pixel_format
         self.onnx_model_shape = detector_config.model.input_tensor
         path = detector_config.model.path
 
         if self.onnx_model_type == ModelTypeEnum.yolox:
-            grids = []
-            expanded_strides = []
-
-            # decode and orient predictions
-            strides = [8, 16, 32]
-            hsizes = [self.h // stride for stride in strides]
-            wsizes = [self.w // stride for stride in strides]
-
-            for hsize, wsize, stride in zip(hsizes, wsizes, strides):
-                xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-                grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-                grids.append(grid)
-                shape = grid.shape[:2]
-                expanded_strides.append(np.full((*shape, 1), stride))
-
-            self.grids = np.concatenate(grids, 1)
-            self.expanded_strides = np.concatenate(expanded_strides, 1)
+            self.calculate_grids_strides()
 
         logger.info(f"ONNX: {path} loaded")
 
@@ -86,10 +70,12 @@ class ONNXDetector(DetectionApi):
                 None,
                 {
                     "images": tensor_input,
-                    "orig_target_sizes": np.array([[self.h, self.w]], dtype=np.int64),
+                    "orig_target_sizes": np.array(
+                        [[self.height, self.width]], dtype=np.int64
+                    ),
                 },
             )
-            return post_process_dfine(tensor_output, self.w, self.h)
+            return post_process_dfine(tensor_output, self.width, self.height)
 
         model_input_name = self.model.get_inputs()[0].name
         tensor_output = self.model.run(None, {model_input_name: tensor_input})
@@ -111,17 +97,21 @@ class ONNXDetector(DetectionApi):
                 detections[i] = [
                     class_id,
                     confidence,
-                    y_min / self.h,
-                    x_min / self.w,
-                    y_max / self.h,
-                    x_max / self.w,
+                    y_min / self.height,
+                    x_min / self.width,
+                    y_max / self.height,
+                    x_max / self.width,
                 ]
             return detections
         elif self.onnx_model_type == ModelTypeEnum.yologeneric:
-            return post_process_yolo(tensor_output, self.w, self.h)
+            return post_process_yolo(tensor_output, self.width, self.height)
         elif self.onnx_model_type == ModelTypeEnum.yolox:
             return post_process_yolox(
-                tensor_output[0], self.w, self.h, self.grids, self.expanded_strides
+                tensor_output[0],
+                self.width,
+                self.height,
+                self.grids,
+                self.expanded_strides,
             )
         else:
             raise Exception(