Anal_size_queens.mp4 Apr 2026

# Example usage video_path = "Anal_Size_Queens.mp4" analyze_video_metadata(video_path) For object detection, you'll need more advanced libraries like tensorflow or torch along with specific object detection models. Here's a basic example using opencv-python with a pre-trained model for detecting faces:

def analyze_video_metadata(video_path): try: clip = VideoFileClip(video_path) print(f"Duration: {clip.duration} seconds") print(f"Resolution: {clip.w}x{clip.h}") print(f"Frame Rate: {clip.fps}") clip.close() except Exception as e: print(f"An error occurred: {e}") Anal_Size_Queens.mp4

def detect_faces(video_path): # Load the cascade face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # Open a connection to the video file cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print("Cannot open camera") exit() while True: # Capture frame-by-frame ret, frame = cap.read() if not ret: print("Can't receive frame (stream end?). Exiting ...") break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4) for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) cv2.imshow('frame', frame) # Press 'q' to exit if cv2.waitKey(1) == ord('q'): break # When everything done, release the capture cap.release() cv2.destroyAllWindows() # Example usage video_path = "Anal_Size_Queens

import cv2