Anal Friend Request.mp4 -

# Reshape for model video_tensor = video_tensor.unsqueeze(0) # Add batch dimension

# 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]) ])

# Load a pre-trained model model = torchvision.models.video.i3d_resnet50(pretrained=True) anal friend request.mp4

# Extract features with torch.no_grad(): features = model(video_tensor)

# 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) # Reshape for model video_tensor = video_tensor

# Assuming 'video_path' is your video file video_path = 'anal friend request.mp4' video_tensor = video_to_tensor(video_path)

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