mirror of https://github.com/vladmandic/human
88 lines
4.0 KiB
TypeScript
88 lines
4.0 KiB
TypeScript
/**
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* HandPose model implementation
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* See `handpose.ts` for entry point
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*/
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import * as tf from '../../dist/tfjs.esm.js';
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import * as util from './handposeutil';
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import * as anchors from './handposeanchors';
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import type { Tensor, GraphModel } from '../tfjs/types';
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export class HandDetector {
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model: GraphModel;
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anchors: number[][];
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anchorsTensor: Tensor;
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inputSize: number;
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inputSizeTensor: Tensor;
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doubleInputSizeTensor: Tensor;
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constructor(model) {
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this.model = model;
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this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);
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this.anchorsTensor = tf.tensor2d(this.anchors);
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this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;
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this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);
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this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);
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}
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normalizeBoxes(boxes) {
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return tf.tidy(() => {
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const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);
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const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);
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const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
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const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);
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const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
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const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
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return tf.concat2d([startPoints, endPoints], 1);
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});
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}
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normalizeLandmarks(rawPalmLandmarks, index) {
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return tf.tidy(() => {
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const landmarks = tf.add(tf.div(tf.reshape(rawPalmLandmarks, [-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
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return tf.mul(landmarks, this.inputSizeTensor);
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});
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}
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async getBoxes(input, config) {
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const t: Record<string, Tensor> = {};
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t.batched = this.model.execute(input) as Tensor;
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t.predictions = tf.squeeze(t.batched);
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t.scores = tf.tidy(() => tf.squeeze(tf.sigmoid(tf.slice(t.predictions, [0, 0], [-1, 1]))));
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const scores = await t.scores.data();
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t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);
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t.norm = this.normalizeBoxes(t.boxes);
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// box detection is flaky so we look for 3x boxes than we need results
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t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);
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const nms = await t.nms.array() as Array<number>;
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const hands: Array<{ box: Tensor, palmLandmarks: Tensor, confidence: number }> = [];
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for (const index of nms) {
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const palmBox = tf.slice(t.norm, [index, 0], [1, -1]);
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const palmLandmarks = tf.tidy(() => tf.reshape(this.normalizeLandmarks(tf.slice(t.predictions, [index, 5], [1, 14]), index), [-1, 2]));
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hands.push({ box: palmBox, palmLandmarks, confidence: scores[index] });
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}
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for (const tensor of Object.keys(t)) tf.dispose(t[tensor]); // dispose all
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return hands;
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}
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async estimateHandBounds(input, config): Promise<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }[]> {
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const inputHeight = input.shape[1];
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const inputWidth = input.shape[2];
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const image = tf.tidy(() => tf.sub(tf.div(tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]), 127.5), 1));
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const predictions = await this.getBoxes(image, config);
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tf.dispose(image);
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const hands: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number }> = [];
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if (!predictions || predictions.length === 0) return hands;
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for (const prediction of predictions) {
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const boxes = await prediction.box.data();
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const startPoint = boxes.slice(0, 2);
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const endPoint = boxes.slice(2, 4);
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const palmLandmarks = await prediction.palmLandmarks.array();
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tf.dispose(prediction.box);
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tf.dispose(prediction.palmLandmarks);
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hands.push(util.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
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}
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return hands;
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}
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}
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