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feat: add background correction (#449)
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‎package.json

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"ml-matrix": "^6.11.0",
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"ml-ransac": "^1.0.0",
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"ml-regression-multivariate-linear": "^2.0.4",
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"ml-regression-polynomial-2d": "^0.2.0",
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"ml-spectra-processing": "^14.3.0",
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"robust-point-in-polygon": "^1.0.3",
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"ssim.js": "^3.5.0",

‎src/operations/__tests__/__image_snapshots__/correct-background-test-ts-basic-screws-image-test-1-snap.png

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‎src/operations/__tests__/__image_snapshots__/correct-background-test-ts-basic-sudoku-image-test-1-snap.png

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‎src/operations/__tests__/correctBackground.test.ts

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import { Point } from '../../geometry';
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import { sampleBackgroundPoints } from '../../utils/sampleBackgroundPoints';
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import { correctBackground } from '../correctBackground';
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import { getMaskFromCannyEdge } from '../getMaskFromCannyEdge';
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test('basic test', () => {
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const image = testUtils.createGreyImage([
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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]);
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const mask = getMaskFromCannyEdge(image);
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const points = sampleBackgroundPoints(image, {
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mask,
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gridWidth: 3,
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gridHeight: 3,
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});
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const newImage = correctBackground(image, {
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background: points,
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order: 2,
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backgroundKind: 'dark',
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});
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const result = testUtils.createGreyImage([
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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]);
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expect(newImage).toEqual(result);
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});
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test('test with object 8x8 and manually picked points', () => {
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const image = testUtils.createGreyImage([
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 250, 250, 250, 250, 7, 8],
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[1, 2, 250, 4, 5, 250, 7, 8],
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[1, 2, 250, 4, 5, 250, 7, 8],
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[1, 2, 250, 250, 250, 250, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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]);
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const points: Point[] = [
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{ column: 0, row: 0 },
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{ column: 1, row: 6 },
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{ column: 2, row: 1 },
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{ column: 3, row: 1 },
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{ column: 4, row: 6 },
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{ column: 3, row: 7 },
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{ column: 4, row: 7 },
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{ column: 5, row: 7 },
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];
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const newImage = correctBackground(image, {
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background: points,
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backgroundKind: 'dark',
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});
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const result = testUtils.createGreyImage([
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 247, 246, 245, 244, 0, 0],
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[0, 0, 247, 0, 0, 244, 0, 0],
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[0, 0, 247, 0, 0, 244, 0, 0],
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[0, 0, 247, 246, 245, 244, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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]);
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expect(newImage).toEqual(result);
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});
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test('test with object 8x8 and sampled points', () => {
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const image = testUtils.createGreyImage([
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 250, 250, 250, 250, 7, 8],
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[1, 2, 250, 4, 5, 250, 7, 8],
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[1, 2, 250, 4, 5, 250, 7, 8],
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[1, 2, 250, 250, 250, 250, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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]);
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const mask = getMaskFromCannyEdge(image, { iterations: 0 });
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const points = sampleBackgroundPoints(image, {
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mask,
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gridWidth: 5,
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gridHeight: 5,
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});
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const newImage = correctBackground(image, {
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background: points,
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order: 3,
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backgroundKind: 'dark',
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});
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const result = testUtils.createGreyImage([
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 247, 246, 245, 244, 0, 0],
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[0, 0, 247, 0, 0, 244, 0, 0],
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[0, 0, 247, 0, 0, 244, 0, 0],
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[0, 0, 247, 246, 245, 244, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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]);
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expect(newImage).toEqual(result);
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});
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test('basic screws image test', () => {
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const image = testUtils.load('various/screws.png').grey();
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const mask = getMaskFromCannyEdge(image);
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const points = sampleBackgroundPoints(image, {
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mask,
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gridWidth: 15,
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gridHeight: 15,
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});
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const newImage = correctBackground(image, {
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background: points,
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order: 2,
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backgroundKind: 'light',
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});
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expect(newImage).toMatchImageSnapshot();
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});
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test('basic sudoku image test', () => {
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const image = testUtils.load('various/sudoku.jpg').grey();
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const mask = getMaskFromCannyEdge(image, { iterations: 0 });
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const points = sampleBackgroundPoints(image, {
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mask,
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gridWidth: 15,
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gridHeight: 15,
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});
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const newImage = correctBackground(image, { background: points });
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expect(newImage).toMatchImageSnapshot();
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});
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test('throw if insufficient number of points', () => {
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const image = testUtils.createGreyImage([
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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[1, 2, 3, 4, 5],
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]);
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const mask = getMaskFromCannyEdge(image);
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const points = sampleBackgroundPoints(image, {
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mask,
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gridWidth: 2,
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gridHeight: 2,
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});
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expect(() => {
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correctBackground(image, {
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background: points,
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order: 2,
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backgroundKind: 'dark',
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});
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}).toThrow('Insufficient number of points to create regression model.');
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});

‎src/operations/__tests__/getMaskFromCannyEdge.test.ts

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import { getMaskFromCannyEdge } from '../getMaskFromCannyEdge';
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test('basic test', () => {
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const image = testUtils.createGreyImage([
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[1, 2, 3, 4, 5],
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[1, 50, 50, 50, 5],
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[1, 50, 3, 50, 5],
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[1, 50, 50, 50, 5],
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[1, 2, 3, 4, 5],
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]);
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const mask = testUtils.createMask([
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[0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0],
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[0, 1, 1, 1, 0],
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[0, 1, 1, 1, 0],
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[0, 0, 0, 0, 0],
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]);
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const fromCannyMask = getMaskFromCannyEdge(image, { iterations: 0 });
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expect(fromCannyMask).toEqual(mask);
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});
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test('testing 10x10 with dilation', () => {
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const image = testUtils.createGreyImage([
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[40, 40, 40, 4, 5, 6, 7, 8, 9, 10],
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[40, 2, 40, 0, 5, 6, 7, 8, 9, 10],
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[40, 2, 3, 40, 5, 6, 7, 8, 9, 10],
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[40, 0, 0, 0, 5, 6, 7, 8, 9, 10],
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[1, 1, 3, 4, 5, 6, 7, 8, 9, 10],
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[1, 2, 3, 4, 5, 5, 5, 70, 70, 10],
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[1, 2, 3, 4, 5, 6, 7, 60, 9, 70],
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[1, 2, 3, 4, 5, 40, 40, 8, 9, 70],
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[1, 2, 3, 4, 5, 6, 7, 70, 9, 70],
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[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
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]);
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const mask = testUtils.createMask([
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[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
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]);
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const fromCannyMask = getMaskFromCannyEdge(image);
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expect(fromCannyMask).toEqual(mask);
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});
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test('testing 7x7 without dilation', () => {
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const image = testUtils.createGreyImage([
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[50, 50, 50, 50, 50, 50, 50],
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[50, 0, 0, 0, 0, 0, 0],
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[50, 0, 0, 0, 0, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 50, 50, 50, 50, 0, 0],
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]);
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const mask = testUtils.createMask([
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 0, 0, 0, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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]);
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const fromCannyMask = getMaskFromCannyEdge(image, { iterations: 0 });
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expect(fromCannyMask).toEqual(mask);
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});
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test('testing 7x7 with dilation', () => {
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const image = testUtils.createGreyImage([
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[50, 50, 50, 50, 50, 50, 50],
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[50, 0, 0, 0, 0, 0, 0],
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[50, 0, 0, 0, 0, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 0, 0, 0, 50, 0, 0],
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[50, 50, 50, 50, 50, 0, 0],
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]);
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const mask = testUtils.createMask([
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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]);
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const fromCannyMask = getMaskFromCannyEdge(image);
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expect(fromCannyMask).toEqual(mask);
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});

‎src/operations/correctBackground.ts

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import { PolynomialRegression2D } from 'ml-regression-polynomial-2d';
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import { Image } from '../Image';
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import { Point } from '../geometry';
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import checkProcessable from '../utils/validators/checkProcessable';
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export interface CorrectBackgroundOptions {
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/**
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* @param background - Points that are considered the background of an image.
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*/
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background: Point[];
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/**
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* @param order - Order of regression function.
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* @default `2`
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*/
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order?: number;
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/**
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* Checks if the image background is light or dark. If the background is
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* light, the output image will be inverted.
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* @default `'light'`
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*/
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backgroundKind?: 'dark' | 'light';
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}
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/**
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* Corrects background from an image for baseline correction.
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* @param image - Image to subtract background from.
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* @param options - CorrectBackgroundOptions.
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* @returns Image with corrected baseline.
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*/
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export function correctBackground(
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image: Image,
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options: CorrectBackgroundOptions,
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) {
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const { background, order = 2, backgroundKind = 'light' } = options;
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checkProcessable(image, { colorModel: ['GREY'] });
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const columns = new Array<number>();
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const rows = new Array<number>();
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const values = new Array<number>();
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for (const point of background) {
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columns.push(point.column);
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rows.push(point.row);
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values.push(image.getValueByPoint(point, 0));
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}
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const model = new PolynomialRegression2D({ x: columns, y: rows }, values, {
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order,
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});
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const points: { x: number[]; y: number[] } = { x: [], y: [] };
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for (let row = 0; row < image.height; row++) {
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for (let column = 0; column < image.width; column++) {
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points.x.push(column);
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points.y.push(row);
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}
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}
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const Y = model.predict(points);
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for (let row = 0; row < image.height; row++) {
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for (let column = 0; column < image.width; column++) {
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const value = Math.abs(
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image.getValue(column, row, 0) - Y[row * image.width + column],
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);
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image.setValue(column, row, 0, value);
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}
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}
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if (backgroundKind === 'light') {
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return image.invert();
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} else {
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return image;
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}
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}

‎src/operations/getMaskFromCannyEdge.ts

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import { Image } from '../Image';
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import { DilateOptions } from '../morphology';
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import { fromMask } from '../roi';
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/**
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* Creates a mask with ROI shapes with CannyEdge filter. Then these shapes
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* get "filled" through internalIds.
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* @param image - Image to get the mask with.
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* @param options - GetMaskFromCannyEdge options.
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* @returns Mask
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*/
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export function getMaskFromCannyEdge(image: Image, options?: DilateOptions) {
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const kernel = options?.kernel ?? [
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[1, 1, 1],
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[1, 1, 1],
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[1, 1, 1],
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];
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const iterations = options?.iterations ?? 1;
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let mask = image.cannyEdgeDetector();
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mask = mask.dilate({ iterations, kernel });
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const roiMap = fromMask(mask);
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const rois = roiMap.getRois({ kind: 'white' });
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for (const roi of rois) {
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const ids = new Set(
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roi.internalIDs.filter((value) => {
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return value < 0;
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}),
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);
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for (let i = roi.origin.row; i < roi.origin.row + roi.height; i++) {
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for (let j = roi.origin.column; j < roi.origin.column + roi.width; j++) {
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const value = roi.getMapValue(j, i);
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if (ids.has(value)) {
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mask.setBit(j, i, 1);
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}
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}
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}
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}
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return mask;
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}

‎src/operations/index.ts

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@@ -12,3 +12,4 @@ export * from './cropRectangle';
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export * from './operations.types';
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export * from './paintMaskOnImage';
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export * from './paintMaskOnMask';
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export * from './correctBackground';

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