Da (3).mp4 Now

# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval() # Set to evaluation mode

# Process features as needed print(features.shape)

# Transform to apply to frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) da (3).mp4

# Display or save frame if needed # ...

# Read video video_capture = cv2.VideoCapture('da (3).mp4') # Load a pre-trained model model = torchvision

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.device('cuda' if torch.cuda.is_available() else 'cpu') tensor_frame = tensor_frame.to(device) model.to(device) such as changing the model

# Add batch dimension tensor_frame = tensor_frame.unsqueeze(0)