image-to-svglisted

# Image to SVG Reproduction Convert raster images into faithful SVG reproductions using data-driven color quantization and contour extraction. **Never hand-draw shapes from visual interpretation** — always extract geometry from the actual pixel data. ## Core Principle **Trust the data, not your imagination.** Claude's visual interpretation of images is unreliable for precise color matching, shape positioning, and spatial relationships. Every shape, color, and position must come from computational analysis of the source pixels. ## Quick Start ```bash pip install opencv-python-headless scikit-image scipy scikit-learn --break-system-packages -q apt-get install -y librsvg2-bin -qq ``` ```python import sys sys.path.insert(0, '/mnt/skills/user/image-to-svg/scripts') from pipeline import image_to_svg svg, flow = image_to_svg("source.jpg", mode="painting") with open("output.svg", "w") as f: f.write(svg) flow.summary() # timing + status per step ``` ## Mode Selection **Look at the image** and ask: "Does this have smooth gradients or hard edges?" Gradients → higher K. Hard edges → lower K. | Mode | K | Best for | Dark shape gating | |------|---|----------|-------------------| | `"graphic"` | 28 | Logos, icons, Kandinsky, flat design | Loose (keeps thin lines) | | `"illustration"` | 40 | Comics, editorial, digital art | Moderate | | `"painting"` | 56 | Renaissance, Impressionist, watercolor | Standard | | `"photo"` | 64 | Portraits, landscapes, still life | Standard (p