fix(perception): validate decoded vegetation frames

experiments/perception/worker/lab_v1_vegetation_goose/run_vegetation_integrated_load.py

@@ -87,20 +87,12 @@ def distribution(values: list[float]) -> dict[str, float]:
     }
 
 
-def open_video(path: Path, expected_size: tuple[int, int]) -> cv2.VideoCapture:
+def open_video(path: Path) -> cv2.VideoCapture:
     if path.is_symlink() or not path.is_file():
         raise IntegratedLoadError("RAVNOVES video is unavailable")
     capture = cv2.VideoCapture(str(path))
     if not capture.isOpened():
         raise IntegratedLoadError("RAVNOVES video decoder did not open")
-    metadata = (
-        round(capture.get(cv2.CAP_PROP_FRAME_COUNT)),
-        round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
-        round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-    )
-    if metadata != (FRAME_COUNT, expected_size[0], expected_size[1]):
-        capture.release()
-        raise IntegratedLoadError("RAVNOVES video metadata changed")
     return capture
 
 
@@ -151,7 +143,7 @@ def run() -> int:
         config["ravnoves"]["expected_width"],
         config["ravnoves"]["expected_height"],
     )
-    warmup_capture = open_video(args.video, expected_size)
+    warmup_capture = open_video(args.video)
     warmup_source = decode_source(warmup_capture, expected_size)
     warmup_capture.release()
 
@@ -160,7 +152,7 @@ def run() -> int:
     warmup_tensor, _ = preprocess(warmup_source)
     warmup_latencies_ms = [infer(model, warmup_tensor)[1] for _ in range(3)]
     torch.cuda.reset_peak_memory_stats()
-    source_capture = open_video(args.video, expected_size)
+    source_capture = open_video(args.video)
     wait_for_shared_start(
         args.shared_start_ready_file,
         args.shared_start_file,