Anal_size_queens.mp4 Online

import cv2

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() Anal_Size_Queens.mp4

pip install opencv-python moviepy Here's how you can extract basic metadata from a video: import cv2 def detect_faces(video_path): # Load the cascade

from moviepy.editor import VideoFileClip minNeighbors=4) for (x

# 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: