Add OpenCV DNN option for ONNX inference (ultralytics#5136)

* Add OpenCV DNN option for ONNX inference

Usage:

```bash
python detect.py --weights yolov5s.onnx  # ONNX Runtime inference
python detect.py --weights yolov5s.onnx -dnn  # OpenCV DNN inference
```

* DNN prediction to tensor

* Update detect.py

detect.py

@@ -56,6 +56,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         hide_labels=False,  # hide labels
         hide_conf=False,  # hide confidences
         half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
         ):
     source = str(source)
     save_img = not nosave and not source.endswith('.txt')  # save inference images
@@ -72,7 +73,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     half &= device.type != 'cpu'  # half precision only supported on CUDA
 
     # Load model
-    w = weights[0] if isinstance(weights, list) else weights
+    w = str(weights[0] if isinstance(weights, list) else weights)
     classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']
     check_suffix(w, suffixes)  # check weights have acceptable suffix
     pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes)  # backend booleans
@@ -87,9 +88,13 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
             modelc = load_classifier(name='resnet50', n=2)  # initialize
             modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
     elif onnx:
-        check_requirements(('onnx', 'onnxruntime'))
-        import onnxruntime
-        session = onnxruntime.InferenceSession(w, None)
+        if dnn:
+            # check_requirements(('opencv-python>=4.5.4',))
+            net = cv2.dnn.readNetFromONNX(w)
+        else:
+            check_requirements(('onnx', 'onnxruntime'))
+            import onnxruntime
+            session = onnxruntime.InferenceSession(w, None)
     else:  # TensorFlow models
         check_requirements(('tensorflow>=2.4.1',))
         import tensorflow as tf
@@ -145,7 +150,11 @@ def wrap_frozen_graph(gd, inputs, outputs):
             visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
             pred = model(img, augment=augment, visualize=visualize)[0]
         elif onnx:
-            pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
+            if dnn:
+                net.setInput(img)
+                pred = torch.tensor(net.forward())
+            else:
+                pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
         else:  # tensorflow model (tflite, pb, saved_model)
             imn = img.permute(0, 2, 3, 1).cpu().numpy()  # image in numpy
             if pb:
@@ -281,6 +290,7 @@ def parse_opt():
     parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
     parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     print_args(FILE.stem, opt)