Palette Walk
An open-source local Python tool that uses K-means clustering to extract dominant colors from images and generate printable outdoor observation cards. The tool runs entirely locally, requires no API keys, and is designed to preserve user privacy.
Timeline (last 90 days)
Palette Walk uses Python together with scikit-learn's K-means clustering to extract dominant colors from an image, and pairs each color swatch with a fixed observation prompt.
Palette Walk is a small local prototype submitted for Hacktoberfest Open Source AI Challenge Week 1 "Touch Grass".
The prototype was built with the help of an AI coding agent, and was completed and tested on October 11, 2026.
Palette Walk's K-means algorithm fits at most three clusters on up to 4,096 sampled RGB pixels to output the dominant colors of an image.
Palette Walk does not use generative models, all observation prompts are fixed text, and the analysis is performed entirely locally without sending images to any service.
Palette Walk requires an internet connection for the initial dependency installation, after which analysis can run offline without loading any serialized models.
Cards generated by Palette Walk do not contain the source image, filename, EXIF information, or location data.
Using bilinear or bicubic interpolation during image downscaling applies a weighted average of surrounding pixels, producing intermediate colors at boundaries that do not exist in the source image and causing K-means to extract incorrect dominant colors; using nearest-neighbor sampling avoids this problem.
The demo cards provided by Palette Walk are pre-computed outputs and not a real-time in-browser image analyzer.
Palette Walk currently has five bounded tests passing, covering known colors, source color preservation, card privacy, monochrome input, transparent input, byte limits, and color labels.
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All Facts (13)
Palette Walk is a small local prototype submitted for Hacktoberfest Open Source AI Challenge Week 1 "Touch Grass".
60%UnverifiedChoosing K=3 to summarize any image into three dominant colors reduces computation and keeps cards concise; the 4,096-pixel sampling limit is an empirical trade-off between accuracy and speed.
50%UnverifiedPalette Walk uses Python together with scikit-learn's K-means clustering to extract dominant colors from an image, and pairs each color swatch with a fixed observation prompt.
50%UnverifiedPalette Walk's K-means algorithm fits at most three clusters on up to 4,096 sampled RGB pixels to output the dominant colors of an image.
50%UnverifiedPalette Walk does not use generative models, all observation prompts are fixed text, and the analysis is performed entirely locally without sending images to any service.
50%UnverifiedPalette Walk requires an internet connection for the initial dependency installation, after which analysis can run offline without loading any serialized models.
50%UnverifiedCards generated by Palette Walk do not contain the source image, filename, EXIF information, or location data.
50%UnverifiedUsing bilinear or bicubic interpolation during image downscaling applies a weighted average of surrounding pixels, producing intermediate colors at boundaries that do not exist in the source image and causing K-means to extract incorrect dominant colors; using nearest-neighbor sampling avoids this problem.
50%UnverifiedThe demo cards provided by Palette Walk are pre-computed outputs and not a real-time in-browser image analyzer.
50%UnverifiedPalette Walk currently has five bounded tests passing, covering known colors, source color preservation, card privacy, monochrome input, transparent input, byte limits, and color labels.
50%UnverifiedPalette Walk has not undergone any real outdoor user trials, and the demo uses an original geometric illustration rather than photos actually taken on a walk.
50%UnverifiedPalette Walk's tools do not recognize any objects or species, do not assess routes, weather, or outdoor safety, and the tests do not demonstrate its performance on real photographs.
50%UnverifiedThe prototype was built with the help of an AI coding agent, and was completed and tested on October 11, 2026.
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