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import { Histogram } from "./histogram";
import { insertIntoSortedList, PixelBox } from "./pixel-box";
import { ColorRGBA64 } from "./color-rgba-64";
function countValidBoxes(
queue: PixelBox[],
isBoxValid: ((box: PixelBox) => boolean) | null
): number {
Iif (isBoxValid === null) {
return queue.length;
}
let retVal: number = 0;
for (let i: number = 0; i < queue.length; i++) {
if (isBoxValid(queue[i])) {
retVal++;
}
}
return retVal;
}
/**
* A quantized color
* @public
*/
export interface QuantizedColor {
color: ColorRGBA64;
pixelCount: number;
colorVolume: number;
}
/**
* A quantize configuration object.
* @public
*/
export interface QuantizeConfig {
/**
* Must be in the range [1,8]. Memory use increases as 4*2^(3*significantBits). Setting significantBits to 8 requires a 64 megabyte histogram.
*/
significantBits: number;
/**
* Lowering this value increases the CPU load but includes more pixels in the calculation.
*/
pixelSkipping: number;
/**
* Desired output palette size. Actual output may vary in edge cases such as images with very few colors.
*/
targetPaletteSize: number;
/**
* For a final palette of size targetPaletteSize, we determine the first fractionByPopulation*targetPaletteSize using population as the only factor when determening sort order. For the rest of the colors the sort order is population*colorVolume. This helps highly contrasting colors in a small area to show up in some of the final output.
*/
fractionByPopulation: number;
/**
* This predicate can be used to screen out undesirable colors from the final output. EG: excluding colors with a pixelCount below a min value.
*/
isBoxValid: ((box: PixelBox) => boolean) | null;
/**
* This predicate can be used to exlude pixels from the histogram. It is passed numbers in the range [0,255] in rgba order. EG: Excluding colors too close to pure white or ones which are transparent.
*/
isHistogramPixelValid: ((pixel: number[]) => boolean) | null;
/**
* If the quantization process goes on for more iterations than maxIterations it is aborted and the current results are returned. Only likely to happen in extreme edge cases with strange input.
*/
maxIterations: number;
}
/**
* The default quantize configuration.
* @public
*/
export const defaultQuantizeConfig: QuantizeConfig = {
significantBits: 5,
pixelSkipping: 5,
targetPaletteSize: 64,
fractionByPopulation: 0.85,
isBoxValid: (box: PixelBox): boolean => {
if (box.pixelCount < 5) {
// Screen out outlier results that aren't present in the original image
return false;
}
return true;
},
isHistogramPixelValid: (pixel: number[]): boolean => {
Iif (pixel[3] < 128) {
// Ignore pixels that are too transparent
return false;
}
return true;
},
maxIterations: 1000,
};
/**
* The image stored in the source PixelBlob is reduced down to a small set of colors.
* Based on the Modified Median Cut Quantization implementation from https://github.com/DanBloomberg/leptonica/blob/master/src/colorquant2.c
*
* @public
*/
export function quantize(
source: PixelBlob,
config: QuantizeConfig = defaultQuantizeConfig
): QuantizedColor[] {
const histogram: Histogram = new Histogram(
source,
config.significantBits,
config.pixelSkipping,
config.isHistogramPixelValid
);
const initialBox: PixelBox = new PixelBox(
histogram,
histogram.minRed,
histogram.maxRed,
histogram.minGreen,
histogram.maxGreen,
histogram.minBlue,
histogram.maxBlue
);
const queue: PixelBox[] = [initialBox];
let count: number = countValidBoxes(queue, config.isBoxValid);
// For a final palette of size targetPaletteSize, we determine the first fractionByPopulation*targetPaletteSize
// using population as the only factor when determening sort order. For the rest of the colors the
// sort order is population * colorVolume. This helps highly contrasting colors in a small area to show
// up in some of the final output.
const colorsByPopulation: number = Math.floor(
config.targetPaletteSize * config.fractionByPopulation
);
const popSort: (box: PixelBox) => number = (box: PixelBox): number => {
return box.pixelCount;
};
let iterationCount: number = 0;
while (iterationCount <= config.maxIterations) {
Eif (queue.length > 0) {
const currentBox: PixelBox = queue.shift()!;
const cutBoxes: [
PixelBox | null,
PixelBox | null
] = currentBox.modifiedMedianCut();
Eif (cutBoxes[0] !== null) {
insertIntoSortedList(queue, cutBoxes[0], popSort);
}
if (cutBoxes[1] !== null) {
insertIntoSortedList(queue, cutBoxes[1], popSort);
}
}
count = countValidBoxes(queue, config.isBoxValid);
if (count >= colorsByPopulation || queue.length <= 1) {
break;
}
iterationCount++;
}
Eif (count < config.targetPaletteSize) {
const popAndVolumeSort: (box: PixelBox) => number = (box: PixelBox): number => {
return box.pixelCount * box.colorVolume;
};
queue.sort((a: PixelBox, b: PixelBox) => {
const aSort: number = popAndVolumeSort(a);
const bSort: number = popAndVolumeSort(b);
if (aSort === bSort) {
return 0;
} else if (aSort > bSort) {
return -1;
}
return 1;
});
iterationCount = 0;
while (iterationCount <= config.maxIterations) {
Eif (queue.length > 0) {
const currentBox: PixelBox = queue.shift()!;
const cutBoxes: [
PixelBox | null,
PixelBox | null
] = currentBox.modifiedMedianCut();
Eif (cutBoxes[0] !== null) {
insertIntoSortedList(queue, cutBoxes[0], popAndVolumeSort);
}
if (cutBoxes[1] !== null) {
insertIntoSortedList(queue, cutBoxes[1], popAndVolumeSort);
}
}
count = countValidBoxes(queue, config.isBoxValid);
if (count >= config.targetPaletteSize || queue.length <= 1) {
break;
}
iterationCount++;
}
}
const retVal: QuantizedColor[] = new Array(count);
let index: number = 0;
for (let i: number = 0; i < queue.length; i++) {
if (!config.isBoxValid || config.isBoxValid(queue[i])) {
retVal[index] = {
color: queue[i].averageColor,
pixelCount: queue[i].pixelCount,
colorVolume: queue[i].colorVolume,
};
index++;
}
}
return retVal;
}
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