mirror of https://github.com/vladmandic/human
30 lines
1.2 KiB
TypeScript
30 lines
1.2 KiB
TypeScript
import * as tf from '../../dist/tfjs.esm.js';
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import { Tensor } from '../tfjs/types';
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function get1dGaussianKernel(sigma: number, size: number): Tensor {
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// Generate a 1d gaussian distribution size numbers long
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const range = tf.range(Math.floor(-size / 2) + 1, Math.floor(size / 2) + 1);
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const distribution = tf.pow(tf.exp(range.div(-2.0 * (sigma * sigma))), 2);
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const normalized = distribution.div(tf.sum(distribution)) as Tensor;
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return normalized;
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}
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function get2dGaussianKernel(size: number, sigma?: number): Tensor {
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// This default is to mimic opencv2.
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sigma = sigma === undefined ? 0.3 * ((size - 1) * 0.5 - 1) + 0.8 : sigma;
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const kerne1d = get1dGaussianKernel(sigma, size);
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return tf.outerProduct(kerne1d, kerne1d);
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}
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export function getGaussianKernel(size = 5, channels = 1, sigma?: number): Tensor {
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return tf.tidy(() => {
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const kerne2d = get2dGaussianKernel(size, sigma);
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const kerne3d = channels === 3 ? tf.stack([kerne2d, kerne2d, kerne2d]) : kerne2d;
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return tf.reshape(kerne3d, [size, size, channels, 1]);
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});
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}
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export function blur(image: Tensor, kernel: Tensor, pad: number | 'valid' | 'same' = 'same'): Tensor {
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return tf.tidy(() => tf.depthwiseConv2d(image, kernel, 1, pad));
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}
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