Add new --vid-stride inference parameter for videos (#9256)

* fps feature/skip frame added

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* predict.py updates

* Update dataloaders.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update dataloaders.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* remove unused attribute

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Cleanup

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update predict.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update detect.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update dataloaders.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Rename skip_frame to vid_stride

* cleanup

* cleanup2

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

classify/predict.py

@@ -66,6 +66,7 @@ def run(
         exist_ok=False,  # existing project/name ok, do not increment
         half=False,  # use FP16 half-precision inference
         dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
 ):
     source = str(source)
     save_img = not nosave and not source.endswith('.txt')  # save inference images
@@ -88,10 +89,10 @@ def run(
     # Dataloader
     if webcam:
         view_img = check_imshow()
-        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]))
+        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
         bs = len(dataset)  # batch_size
     else:
-        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]))
+        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
         bs = 1  # batch_size
     vid_path, vid_writer = [None] * bs, [None] * bs
 
@@ -196,6 +197,7 @@ def parse_opt():
     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     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')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     print_args(vars(opt))