# Move to GPU if available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tensor_frame = tensor_frame.to(device) model.to(device)
# Display or save frame if needed # ...
while True: ret, frame = video_capture.read() if not ret: break # Convert to RGB and apply transform rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) tensor_frame = transform(rgb_frame) da (3).mp4
video_capture.release() This example demonstrates a basic approach to extracting features from video frames using a pre-trained ResNet50 model. You can adapt it based on your specific requirements, such as changing the model, applying different transformations, or processing the features further. # Move to GPU if available device = torch
# Get features with torch.no_grad(): features = model(tensor_frame) such as changing the model
# Move to GPU if available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tensor_frame = tensor_frame.to(device) model.to(device)
# Display or save frame if needed # ...
while True: ret, frame = video_capture.read() if not ret: break # Convert to RGB and apply transform rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) tensor_frame = transform(rgb_frame)
video_capture.release() This example demonstrates a basic approach to extracting features from video frames using a pre-trained ResNet50 model. You can adapt it based on your specific requirements, such as changing the model, applying different transformations, or processing the features further.
# Get features with torch.no_grad(): features = model(tensor_frame)