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ncolorpalette-clusterer

2.0.1
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    • License MIT

    cluster the pixels of an image into arbitrary groups

    Package Exports

    • ncolorpalette-clusterer

    This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (ncolorpalette-clusterer) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

    Readme

    ncolorpalette-clusterer

    Group an array of pixels using k-means clustering as efficiently as possible.

    Usage

    var Clusterer = require('ncolorpalette-clusterer');
    
    // Input can be any array-like object.
    var cvs = document.querySelector('canvas')
    var ctx = cvs.getContext('2d');
    var input = ctx.getImageData(0, 0, cvs.width, cvs.height);
    
    var c = new Clusterer(input.data, {
      // listing defaults:
    
      // how many clusters to find
      clusters: 4,
    
      // input data is logically grouped by 4 indices (r,g,b,a)
      dataFactor: 4,
    
      // `false` is faster, but will lock the event loop on large images
      async: true,
    
      // what function to use when computing pixel difference, default
      // is rgba distance (which is not ideal)
      comparator: function rgbdist2(r1, g1, b1, a1, r2, g2, b2, a2) {}
    });
    
    c.solve(
      function progress(iterationCount) {},
      function complete(iterationCount) {
        var palette = [
          0, 0, 0, 255,
          63, 0, 0, 255,
          126, 0, 0, 255,
          255, 0, 0, 255
        ]
    
        // Return a new array of pixel data.
        var output = c.applyPalette(palette);
    
        // Or, update in place:
        c.applyPalette(palette, input.data);
    
        // Then do something with it:
        ctx.putImageData(input, 0, 0);
    
        // You could also manually access the clustered pixels.
        // Each cluster is an allocated-dynamic-array
        // http://npmjs.org/package/allocated-dynamic-array
        var index = c.clusters[0].get(0);
        var r = input.data[index+0];
        var g = input.data[index+1];
        var b = input.data[index+2];
        var a = input.data[index+3];
      });

    Efficiency

    The clusterer uses several primary techniques to be as efficient as possible in terms of execution speed and garbage creation:

    • A pixel is always represented as 4 uint8 integer values in contiguous TypedArrays, never as intermediate objects (like {r: 0, g: 0, b: 0, a: 255} or [0, 0, 0, 255]).
    • "Pointers" to pixels are stored in preallocated TypedArrays that simply point at the index of the r (red) value in the original input data.

    The speed of this package could be improved in a few ways, but primarily through algorithm changes, such as creating an index of unique pixel colors for large images.

    License

    MIT