Da (3).mp4 File

# 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)

# Add batch dimension tensor_frame = tensor_frame.unsqueeze(0) da (3).mp4

# Display or save frame if needed # ...

# Process features as needed print(features.shape) # Move to GPU if available device = torch

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. such as changing the model

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)

# Get features with torch.no_grad(): features = model(tensor_frame)