# Load video and extract frames def video_to_tensor(video_path): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame - cv2.read() if not ret: break frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = transform(frame) frames.append(frame) cap.release() return torch.stack(frames)
import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import cv2 anal friend request.mp4
# Reshape for model video_tensor = video_tensor.unsqueeze(0) # Add batch dimension anal friend request.mp4
print(features.shape) The extracted features can be used for various downstream tasks such as video clustering, similarity search, classification, etc. anal friend request.mp4
# Prepare a transform for preprocessing frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])