# 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

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

video_path = "vid_1158.mp4" simple_features = extract_simple_features(video_path) frames = load_video(video_path) visual_features = extract_visual_features(frames) simple_features) print("Visual Features Shape:"

# Load the 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 frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame) cap.release() return frames

visual_features = [] for frame in frames: frame = transform(frame) frame = frame.unsqueeze(0).to(device) feature = model(frame) feature = feature.squeeze(0).detach().cpu().numpy() visual_features.append(feature) return visual_features vid_1158.mp4