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print(features.shape) The extracted features can be used for various downstream tasks such as video clustering, similarity search, classification, etc.
# 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]) ])
# Assuming 'video_path' is your video file video_path = 'anal friend request.mp4' video_tensor = video_to_tensor(video_path)
# 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)
# Load a pre-trained model model = torchvision.models.video.i3d_resnet50(pretrained=True)
import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import cv2
