Strip Photography / Slit Photography

A digital implementation of strip photography (also called slit photography) that captures a two-dimensional image as a sequence of one-dimensional images over time.

How it works: Strip photography records a moving scene over time using a camera that observes a narrow strip rather than the full field. This implementation simulates the technique by extracting the same line position from each video frame and assembling them into a composite image where:

• One axis represents space (the slit/line being observed)
• The other axis represents time (progression through video frames)

Visual effects:

• Moving objects appear as visible shapes in the final image
• Stationary objects (like background) appear as horizontal/vertical stripes
• Object width is inversely proportional to speed (faster = narrower, slower = wider)

Usage

Column Mode - Extract vertical lines (columns) from each frame:

uv run main.py test1.mkv --xcolumn 100 --output test1_column.png

Row Mode - Extract horizontal lines (rows) from each frame:

uv run main.py test1.mkv --yrow 200 --output test1_row.png

Debug Mode - Analyze changes and generate threshold graph:

uv run main.py test1.mkv --xcolumn 100 --output analysis --debug

Custom Threshold - Control compression by setting change threshold:

uv run main.py test1.mkv --xcolumn 100 --output test1_column.png --threshold 0.01

Configure

We use uv to handle pip dependencies. Install with:

uv sync

Output

• Column mode: Extracts vertical line at x-coordinate from each frame
  • Output dimensions: (source_height, total_frames, 3)
  • Width = number of frames, Height = source video height
• Row mode: Extracts horizontal line at y-coordinate from each frame
  • Output dimensions: (total_frames, source_width, 3)
  • Width = source video width, Height = number of frames

Each column/row in the output represents one frame from the input video, showing motion over time.

Advanced Features

Change Detection & Compression

The implementation includes intelligent change detection that discards frames with minimal visual changes, creating more compact outputs that focus on motion:

• Automatic filtering: Only frames with significant changes are included
• Configurable threshold: Use --threshold (0-1) to control sensitivity
• Compression stats: Shows how many frames were kept vs. skipped

Debug Mode

Use --debug to analyze your video and determine optimal threshold values:

uv run main.py video.mp4 --xcolumn 500 --output analysis --debug

This generates:

• Change graph: Visual plot of frame-to-frame changes over time
• Statistics: Mean, max, min, and standard deviation of changes
• Threshold suggestions: Recommended values with compression ratios

Threshold Selection Guide

• 0.001-0.005: High sensitivity, keeps most motion (10-30% compression)
• 0.005-0.02: Medium sensitivity, good balance (30-70% compression)
• 0.02-0.1: Low sensitivity, only major changes (70-95% compression)
• >0.1: Very low sensitivity, minimal frames (95%+ compression)

Examples with Compression

Extract with 75% compression (recommended starting point):

uv run main.py video.mp4 --xcolumn 320 --output compressed.png --threshold 0.01

Maximum compression for detecting only major scene changes:

uv run main.py video.mp4 --xcolumn 320 --output minimal.png --threshold 0.05