pip install torch torchvision opencv-python Load the video and preprocess it by resizing frames and converting them into tensors. Step 3: Choose a Deep Learning Model For feature extraction, we can use a pre-trained model like VGG16 or ResNet50. Here, we'll use VGG16 as an example. Step 4: Extract Features Below is a simplified example code snippet that demonstrates how to load a video, extract frames, and use a pre-trained VGG16 model to extract features:
import cv2 import torch import torchvision import torchvision.transforms as transforms VID-20160125.mp4
# Load video def load_video(video_path): cap = cv2.VideoCapture(video_path) frames = [] 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) cap.release() return frames pip install torch torchvision opencv-python Load the video
