Da (3).mp4 May 2026

# 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

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. # Process features as needed print(features

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

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

# 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)