Detect scene changes in videos

%pip install -qU pixeltable scenedetect opencv-python

import pixeltable as pxt

# Create a fresh directory
pxt.drop_dir('scene_demo', force=True)
pxt.create_dir('scene_demo')

# Create a video table
videos = pxt.create_table(
    'scene_demo/videos', {'video': pxt.Video, 'title': pxt.String}
)

# Insert sample videos from S3
videos.insert(
    [
        {
            'video': 's3://multimedia-commons/data/videos/mp4/ffe/ffb/ffeffbef41bbc269810b2a1a888de.mp4',
            'title': 'Sample video 1',
        }
    ]
)

# Detect scenes using content-based detection (best for hard cuts)
videos.add_computed_column(
    scenes_content=videos.video.scene_detect_content(
        threshold=27.0,  # Lower = more sensitive
        min_scene_len=15,  # Minimum frames between cuts
    )
)

# View detected scenes
videos.select(videos.title, videos.scenes_content).collect()

# Detect fade-to-black/white transitions
videos.add_computed_column(
    scenes_fade=videos.video.scene_detect_threshold(
        threshold=12.0,  # Brightness threshold for fades
        min_scene_len=15,
    )
)

# View fade-detected scenes
videos.select(videos.title, videos.scenes_fade).collect()

# Adaptive detection adjusts to video content dynamically
videos.add_computed_column(
    scenes_adaptive=videos.video.scene_detect_adaptive(
        adaptive_threshold=3.0,  # Lower = more scenes detected
        min_scene_len=15,
        fps=2.0,  # Analyze at 2 FPS for speed
    )
)

# View adaptively-detected scenes
videos.select(videos.title, videos.scenes_adaptive).collect()

{
    'start_time': 5.2,    # Scene start in seconds
    'start_pts': 156,     # Presentation timestamp
    'duration': 3.8       # Scene duration in seconds
}