import cv2 import numpy as np import torch from torchvision import models from torchvision.transforms import transforms
# 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 vid_1158.mp4
# 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 import cv2 import numpy as np import torch
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. simple_features) print("Visual Features Shape:"