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.
import torch import torchvision import torchvision.transforms as transforms import cv2
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: HMN-032-MR.mp4
# Do something with features...
# Prepare a transform transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) If you're working in a field like computer
# 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)
# 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()) such as convolutional neural networks (CNNs)
# Define a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval()
