Hmn-032-mr.mp4 -

# Prepare a transform transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])

# Define a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval()

# Do something with features...

If you're working in a field like computer vision or video analysis, "deep features" might refer to features extracted from deep learning models, such as convolutional neural networks (CNNs), that are used for various tasks including object detection, classification, or video understanding.

# Load the video video_path = "HMN-032-MR.mp4" frames = [] cap = cv2.VideoCapture(video_path) while cap.isOpened(): ret, frame = cap.read() if not ret: break # Convert to RGB and add to list frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame) HMN-032-MR.mp4

import torch import torchvision import torchvision.transforms as transforms import cv2

# Extract features features = [] with torch.no_grad(): for frame in frames: frame = transform(frame) frame = frame.unsqueeze(0) # Add batch dimension output = model(frame) features.append(output.detach().cpu().numpy()) # Prepare a transform transform = transforms

For example, if you're using PyTorch and want to extract features from a video using a pre-trained model, a basic approach might look something like this: