Snis-896.mp4

def extract_metadata(video_path): probe = ffmpeg.probe(video_path) video_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None) width = int(video_stream['width']) height = int(video_stream['height']) duration = float(probe['format']['duration']) return { 'width': width, 'height': height, 'duration': duration, }

while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_count += 1 sum_b += np.mean(frame[:,:,0]) sum_g += np.mean(frame[:,:,1]) sum_r += np.mean(frame[:,:,2]) cap.release() avg_b = sum_b / frame_count avg_g = sum_g / frame_count avg_r = sum_r / frame_count

import ffmpeg

To generate features from a video, you might want to extract metadata and analyze the content. Metadata includes information like the video's duration, resolution, and creation date. Content features could involve analyzing frames for color histograms, object detection, or other more complex analyses. Step 1: Install Necessary Libraries You'll need libraries like opencv-python for video processing and ffmpeg-python or moviepy for easy metadata access.

return { 'avg_color': (avg_r, avg_g, avg_b) } SNIS-896.mp4

metadata = extract_metadata("SNIS-896.mp4") print(metadata) For a basic content analysis, let's consider extracting a feature like the average color of the video:

pip install opencv-python ffmpeg-python moviepy Here's a basic example of how to extract some metadata: def extract_metadata(video_path): probe = ffmpeg

content_features = analyze_video_content("SNIS-896.mp4") print(content_features) You could combine these steps into a single function or script to generate a comprehensive set of features for your video.