def extract_features(video_path): # Initialize video capture cap = cv2.VideoCapture(video_path) # Metadata frame_count = 0 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) # Visual Features Example: Just count frames, similar to duration while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_count += 1 cap.release() # Duration duration = frame_count / fps print(f"Metadata:") print(f"Resolution: {width}x{height}") print(f"FPS: {fps}") print(f"Duration (seconds): {duration}") # Simple Audio Feature Extraction (Using Librosa) try: y, sr = librosa.load(video_path, sr=None) tempo, beats = librosa.beat.beat_track(y=y, sr=sr) print(f"Audio Tempo: {tempo}") except Exception as e: print(f"Failed to extract audio features: {e}")
# Example usage video_path = "CASAL TK 06.mp4" extract_features(video_path) This example provides a basic entry point into feature extraction. The actual features you choose to extract depend on your specific requirements or application, which might involve more sophisticated video analysis techniques. CASAL TK 06.mp4
import cv2 import librosa import numpy as np similar to duration while cap.isOpened(): ret
