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