000.mp4 May 2026

cap.release()

def save_thumbnail(video_path, output_path): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print("Error opening video file") return 000.mp4

# Example usage video_path = "000.mp4" thumbnail_path = "thumbnail.jpg" save_thumbnail(video_path, thumbnail_path) For more complex features, such as video content analysis (e.g., object detection, motion detection), you would typically use more advanced techniques and possibly pre-trained models. OpenCV comes with some basic functionalities for this, but tasks like object detection often require libraries like TensorFlow or PyTorch, along with specific models like YOLO. cap.release() def save_thumbnail(video_path

frame_count = 0 while cap.isOpened(): ret, frame = cap.read() if not ret: break thumbnail_path) For more complex features

cap.release() print(f"Total frames: {frame_count}")

pip install opencv-python Here's a basic script to read a video file, extract its frames, and save them as images: