transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])
print("Simple Features:", simple_features) print("Visual Features Shape:", len(visual_features), len(visual_features[0])) This example extracts basic metadata and uses a pre-trained ResNet50 model to extract features from each frame. Note that the complexity and specifics can vary greatly depending on your exact requirements and the type of analysis you plan to perform. vid_1158.mp4
import cv2 import numpy as np import torch from torchvision import models from torchvision.transforms import transforms transform = transforms
video_path = "vid_1158.mp4" simple_features = extract_simple_features(video_path) frames = load_video(video_path) visual_features = extract_visual_features(frames) transform = transforms.Compose([ transforms.ToTensor()
# Simple Feature Extraction def extract_simple_features(video_path): cap = cv2.VideoCapture(video_path) duration = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) cap.release() features = { "file_name": video_path, "duration": duration, "resolution": (width, height), "fps": fps } return features