mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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Double_V
0a22979c35
* add application include paddle.js web demo and xcx * cp PR #5 * add readme * fix comments and link * fix xcx readme * fix Task 1 * fix bugs * refine readme * delete ocrxcx readme * refine readme * fix bugs * delete old readme * 200px to 300px * revert 200px to 300px Co-authored-by: Jason <jiangjiajun@baidu.com>
579 lines
18 KiB
JavaScript
579 lines
18 KiB
JavaScript
/* global wx, Page */
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import * as paddlejs from '@paddlejs/paddlejs-core';
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import '@paddlejs/paddlejs-backend-webgl';
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import clipper from 'js-clipper';
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import { divide, enableBoundaryChecking, plus } from 'number-precision';
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import { recDecode } from 'recPostprocess.js';
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// eslint-disable-next-line no-undef
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const plugin = requirePlugin('paddlejs-plugin');
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const Polygon = require('d3-polygon');
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global.wasm_url = 'pages/index/wasm/opencv_js.wasm.br';
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const CV = require('./wasm/opencv.js');
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plugin.register(paddlejs, wx);
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let DETSHAPE = 960;
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let RECWIDTH = 320;
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const RECHEIGHT = 32;
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// 声明后续图像变换要用到的canvas；此时未绑定
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let canvas_det;
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let canvas_rec;
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let my_canvas;
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let my_canvas_ctx;
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const imgList = [
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'https://paddlejs.bj.bcebos.com/xcx/ocr.png'
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];
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// eslint-disable-next-line max-lines-per-function
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const outputBox = (res) => {
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const thresh = 0.3;
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const box_thresh = 0.5;
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const max_candidates = 1000;
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const min_size = 3;
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const width = 960;
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const height = 960;
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const pred = res;
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const segmentation = [];
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pred.forEach(item => {
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segmentation.push(item > thresh ? 255 : 0);
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});
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function get_mini_boxes(contour) {
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// 生成最小外接矩形
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const bounding_box = CV.minAreaRect(contour);
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const points = [];
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const mat = new CV.Mat();
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// 获取矩形的四个顶点坐标
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CV.boxPoints(bounding_box, mat);
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for (let i = 0; i < mat.data32F.length; i += 2) {
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const arr = [];
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arr[0] = mat.data32F[i];
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arr[1] = mat.data32F[i + 1];
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points.push(arr);
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}
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function sortNumber(a, b) {
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return a[0] - b[0];
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}
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points.sort(sortNumber);
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let index_1 = 0;
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let index_2 = 1;
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let index_3 = 2;
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let index_4 = 3;
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if (points[1][1] > points[0][1]) {
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index_1 = 0;
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index_4 = 1;
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}
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else {
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index_1 = 1;
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index_4 = 0;
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}
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if (points[3][1] > points[2][1]) {
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index_2 = 2;
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index_3 = 3;
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}
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else {
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index_2 = 3;
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index_3 = 2;
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}
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const box = [
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points[index_1],
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points[index_2],
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points[index_3],
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points[index_4]
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];
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const side = Math.min(bounding_box.size.height, bounding_box.size.width);
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mat.delete();
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return {
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points: box,
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side
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};
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}
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function box_score_fast(bitmap, _box) {
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const h = height;
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const w = width;
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const box = JSON.parse(JSON.stringify(_box));
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const x = [];
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const y = [];
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box.forEach(item => {
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x.push(item[0]);
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y.push(item[1]);
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});
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// clip这个函数将将数组中的元素限制在a_min, a_max之间，大于a_max的就使得它等于 a_max，小于a_min,的就使得它等于a_min。
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const xmin = clip(Math.floor(Math.min(...x)), 0, w - 1);
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const xmax = clip(Math.ceil(Math.max(...x)), 0, w - 1);
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const ymin = clip(Math.floor(Math.min(...y)), 0, h - 1);
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const ymax = clip(Math.ceil(Math.max(...y)), 0, h - 1);
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// eslint-disable-next-line new-cap
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const mask = new CV.Mat.zeros(ymax - ymin + 1, xmax - xmin + 1, CV.CV_8UC1);
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box.forEach(item => {
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item[0] = Math.max(item[0] - xmin, 0);
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item[1] = Math.max(item[1] - ymin, 0);
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});
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const npts = 4;
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const point_data = new Uint8Array(box.flat());
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const points = CV.matFromArray(npts, 1, CV.CV_32SC2, point_data);
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const pts = new CV.MatVector();
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pts.push_back(points);
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const color = new CV.Scalar(255);
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// 多个多边形填充
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CV.fillPoly(mask, pts, color, 1);
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const sliceArr = [];
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for (let i = ymin; i < ymax + 1; i++) {
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sliceArr.push(...bitmap.slice(960 * i + xmin, 960 * i + xmax + 1));
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}
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const mean = num_mean(sliceArr, mask.data);
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mask.delete();
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points.delete();
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pts.delete();
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return mean;
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}
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function clip(data, min, max) {
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return data < min ? min : data > max ? max : data;
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}
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function unclip(box) {
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const unclip_ratio = 1.6;
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const area = Math.abs(Polygon.polygonArea(box));
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const length = Polygon.polygonLength(box);
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const distance = area * unclip_ratio / length;
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const tmpArr = [];
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box.forEach(item => {
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const obj = {
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X: 0,
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Y: 0
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};
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obj.X = item[0];
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obj.Y = item[1];
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tmpArr.push(obj);
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});
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const offset = new clipper.ClipperOffset();
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offset.AddPath(tmpArr, clipper.JoinType.jtRound, clipper.EndType.etClosedPolygon);
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const expanded = [];
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offset.Execute(expanded, distance);
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let expandedArr = [];
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expanded[0] && expanded[0].forEach(item => {
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expandedArr.push([item.X, item.Y]);
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});
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expandedArr = [].concat(...expandedArr);
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return expandedArr;
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}
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function num_mean(data, mask) {
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let sum = 0;
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let length = 0;
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for (let i = 0; i < data.length; i++) {
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if (mask[i]) {
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sum = plus(sum, data[i]);
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length++;
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}
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}
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return divide(sum, length);
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}
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// eslint-disable-next-line new-cap
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const src = new CV.matFromArray(960, 960, CV.CV_8UC1, segmentation);
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const contours = new CV.MatVector();
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const hierarchy = new CV.Mat();
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// 获取轮廓
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CV.findContours(src, contours, hierarchy, CV.RETR_LIST, CV.CHAIN_APPROX_SIMPLE);
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const num_contours = Math.min(contours.size(), max_candidates);
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const boxes = [];
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const scores = [];
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const arr = [];
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for (let i = 0; i < num_contours; i++) {
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const contour = contours.get(i);
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let {
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points,
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side
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} = get_mini_boxes(contour);
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if (side < min_size) {
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continue;
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}
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const score = box_score_fast(pred, points);
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if (box_thresh > score) {
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continue;
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}
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let box = unclip(points);
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// eslint-disable-next-line new-cap
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const boxMap = new CV.matFromArray(box.length / 2, 1, CV.CV_32SC2, box);
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const resultObj = get_mini_boxes(boxMap);
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box = resultObj.points;
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side = resultObj.side;
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if (side < min_size + 2) {
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continue;
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}
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box.forEach(item => {
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item[0] = clip(Math.round(item[0]), 0, 960);
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item[1] = clip(Math.round(item[1]), 0, 960);
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});
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boxes.push(box);
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scores.push(score);
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arr.push(i);
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boxMap.delete();
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}
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src.delete();
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contours.delete();
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hierarchy.delete();
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return {
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boxes,
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scores
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};
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};
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const sorted_boxes = (box) => {
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function sortNumber(a, b) {
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return a[0][1] - b[0][1];
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}
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const boxes = box.sort(sortNumber);
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const num_boxes = boxes.length;
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for (let i = 0; i < num_boxes - 1; i++) {
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if (Math.abs(boxes[i + 1][0][1] - boxes[i][0][1]) < 10
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&& boxes[i + 1][0][0] < boxes[i][0][0]) {
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const tmp = boxes[i];
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boxes[i] = boxes[i + 1];
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boxes[i + 1] = tmp;
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}
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}
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return boxes;
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}
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function flatten(arr) {
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return arr.toString().split(',').map(item => +item);
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}
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function int(num) {
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return num > 0 ? Math.floor(num) : Math.ceil(num);
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}
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function clip(data, min, max) {
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return data < min ? min : data > max ? max : data;
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}
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function get_rotate_crop_image(img, points) {
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const img_crop_width = int(Math.max(
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linalg_norm(points[0], points[1]),
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linalg_norm(points[2], points[3])
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));
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const img_crop_height = int(Math.max(
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linalg_norm(points[0], points[3]),
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linalg_norm(points[1], points[2])
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));
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const pts_std = [
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[0, 0],
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[img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]
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];
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const srcTri = CV.matFromArray(4, 1, CV.CV_32FC2, flatten(points));
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const dstTri = CV.matFromArray(4, 1, CV.CV_32FC2, flatten(pts_std));
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// 获取到目标矩阵
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const M = CV.getPerspectiveTransform(srcTri, dstTri);
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const src = CV.imread(img);
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const dst = new CV.Mat();
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const dsize = new CV.Size(img_crop_width, img_crop_height);
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// 透视转换
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CV.warpPerspective(src, dst, M, dsize, CV.INTER_CUBIC, CV.BORDER_REPLICATE, new CV.Scalar());
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const dst_img_height = dst.rows;
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const dst_img_width = dst.cols;
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let dst_rot;
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// 图像旋转
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if (dst_img_height / dst_img_width >= 1.5) {
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dst_rot = new CV.Mat();
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const dsize_rot = new CV.Size(dst.rows, dst.cols);
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const center = new CV.Point(dst.cols / 2, dst.cols / 2);
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const M = CV.getRotationMatrix2D(center, 90, 1);
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CV.warpAffine(dst, dst_rot, M, dsize_rot, CV.INTER_CUBIC, CV.BORDER_REPLICATE, new CV.Scalar());
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}
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const dst_resize = new CV.Mat();
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const dsize_resize = new CV.Size(0, 0);
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let scale;
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if (dst_rot) {
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scale = RECHEIGHT / dst_rot.rows;
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CV.resize(dst_rot, dst_resize, dsize_resize, scale, scale, CV.INTER_AREA);
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dst_rot.delete();
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}
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else {
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scale = RECHEIGHT / dst_img_height;
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CV.resize(dst, dst_resize, dsize_resize, scale, scale, CV.INTER_AREA);
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}
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canvas_det.width = dst_resize.cols;
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canvas_det.height = dst_resize.rows;
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canvas_det.getContext('2d').clearRect(0, 0, canvas_det.width, canvas_det.height);
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CV.imshow(canvas_det, dst_resize);
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src.delete();
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dst.delete();
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dst_resize.delete();
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srcTri.delete();
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dstTri.delete();
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}
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function linalg_norm(x, y) {
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return Math.sqrt(Math.pow(x[0] - y[0], 2) + Math.pow(x[1] - y[1], 2));
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}
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function resize_norm_img_splice(
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image,
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origin_width,
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origin_height,
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index = 0
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) {
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canvas_rec.width = RECWIDTH;
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canvas_rec.height = RECHEIGHT;
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const ctx = canvas_rec.getContext('2d');
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ctx.fillStyle = '#fff';
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ctx.clearRect(0, 0, canvas_rec.width, canvas_rec.height);
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// ctx.drawImage(image, -index * RECWIDTH, 0, origin_width, origin_height);
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ctx.putImageData(image, -index * RECWIDTH, 0);
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}
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// 声明检测和识别Runner；未初始化
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let detectRunner;
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let recRunner;
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Page({
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data: {
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photo_src:'',
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imgList: imgList,
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imgInfo: {},
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result: '',
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select_mode: false,
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loaded: false
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},
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switch_choose(){
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this.setData({
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select_mode: true
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})
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},
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switch_example(){
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this.setData({
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select_mode: false
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})
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},
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chose_photo:function(evt){
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let _this = this
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wx.chooseImage({
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count: 1,
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sizeType: ['original', 'compressed'],
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sourceType: ['album', 'camera'],
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success(res) {
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console.log(res.tempFilePaths) //一个数组，每个元素都是“http://...”图片地址
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_this.setData({
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photo_src: res.tempFilePaths[0]
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})
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}
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})
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},
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reselect:function(evt){
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let _this = this
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wx.chooseImage({
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count: 1,
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sizeType: ['original', 'compressed'],
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sourceType: ['album', 'camera'],
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success(res) {
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_this.setData({
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photo_src: res.tempFilePaths[0]
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})
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}
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})
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},
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photo_preview:function(evt){
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let _this = this;
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let imgs = [];
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imgs.push(_this.data.photo_src);
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wx.previewImage({
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urls:imgs
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})
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},
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predect_choose_img() {
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console.log(this.data.photo_src)
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this.getImageInfo(this.data.photo_src);
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},
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onLoad() {
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enableBoundaryChecking(false);
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// 绑定canvas；该操作是异步，因此最好加延迟保证后续使用时已完成绑定
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wx.createSelectorQuery()
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.select('#canvas_det')
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.fields({ node: true, size: true })
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.exec(async(res) => {
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canvas_det = res[0].node;
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});
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wx.createSelectorQuery()
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.select('#canvas_rec')
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.fields({ node: true, size: true })
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.exec(async(res) => {
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canvas_rec = res[0].node;
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});
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wx.createSelectorQuery()
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.select('#myCanvas')
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.fields({ node: true, size: true })
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.exec((res) => {
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my_canvas = res[0].node;
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my_canvas_ctx = my_canvas.getContext('2d');
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});
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const me = this;
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// 初始化Runner
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detectRunner = new paddlejs.Runner({
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modelPath: 'https://paddleocr.bj.bcebos.com/PaddleJS/PP-OCRv3/ch/ch_PP-OCRv3_det_infer_js_960/model.json',
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mean: [0.485, 0.456, 0.406],
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std: [0.229, 0.224, 0.225],
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bgr: true,
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webglFeedProcess: true
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});
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recRunner = new paddlejs.Runner({
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modelPath: 'https://paddleocr.bj.bcebos.com/PaddleJS/PP-OCRv3/ch/ch_PP-OCRv3_rec_infer_js/model.json',
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fill: '#000',
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mean: [0.5, 0.5, 0.5],
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std: [0.5, 0.5, 0.5],
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bgr: true,
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webglFeedProcess: true
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});
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// 等待模型数据全部加载完成
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Promise.all([detectRunner.init(), recRunner.init()]).then(_ => {
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me.setData({
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loaded: true
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});
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});
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},
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selectImage(event) {
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const imgPath = this.data.imgList[event.target.dataset.index];
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this.getImageInfo(imgPath);
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},
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getImageInfo(imgPath) {
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const me = this;
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wx.getImageInfo({
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src: imgPath,
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success: (imgInfo) => {
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const {
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path,
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width,
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height
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} = imgInfo;
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const canvasPath = imgPath.includes('http') ? path : imgPath;
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let sw = 960;
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let sh = 960;
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let x = 0;
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let y = 0;
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if (height / width >= 1) {
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sw = Math.round(sh * width / height);
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x = Math.floor((960 - sw) / 2);
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}
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else {
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sh = Math.round(sw * height / width);
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y = Math.floor((960 - sh) / 2);
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}
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my_canvas.width = sw;
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my_canvas.height = sh;
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// 微信上canvas输入图片
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const image = my_canvas.createImage();
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image.src = canvasPath;
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image.onload = () => {
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my_canvas_ctx.clearRect(0, 0, my_canvas.width, my_canvas.height);
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my_canvas_ctx.drawImage(image, x, y, sw, sh);
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const imageData = my_canvas_ctx.getImageData(0, 0, sw, sh);
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|
// 开始识别
|
|
me.recognize({
|
|
data: imageData.data,
|
|
width: 960,
|
|
height: 960
|
|
}, {canvasPath, sw, sh, x, y});
|
|
}
|
|
}
|
|
});
|
|
},
|
|
|
|
async recognize(res, img) {
|
|
const me = this;
|
|
// 文本框选坐标点
|
|
let points;
|
|
await detectRunner.predict(res, function (detectRes) {
|
|
points = outputBox(detectRes);
|
|
});
|
|
|
|
// 绘制文本框
|
|
me.drawCanvasPoints(img, points.boxes);
|
|
|
|
// 排序，使得最后结果输出尽量按照从上到下的顺序
|
|
const boxes = sorted_boxes(points.boxes);
|
|
|
|
const text_list = [];
|
|
|
|
for (let i = 0; i < boxes.length; i++) {
|
|
const tmp_box = JSON.parse(JSON.stringify(boxes[i]));
|
|
// 获取tmp_box对应图片到canvas_det
|
|
get_rotate_crop_image(res, tmp_box);
|
|
// 这里是计算要识别文字的图片片段是否大于识别模型要求的输入宽度；超过了的话会分成多次识别，再拼接结果
|
|
const width_num = Math.ceil(canvas_det.width / RECWIDTH);
|
|
|
|
let text_list_tmp = '';
|
|
for (let j = 0; j < width_num; j++) {
|
|
// 根据原图的宽度进行裁剪拼接，超出指定宽度会被截断；然后再次识别，最后拼接起来
|
|
resize_norm_img_splice(canvas_det.getContext('2d').getImageData(0, 0, canvas_det.width, canvas_det.height), canvas_det.width, canvas_det.height, j);
|
|
|
|
const imgData = canvas_rec.getContext('2d').getImageData(0, 0, canvas_rec.width, canvas_rec.height);
|
|
|
|
await recRunner.predict(imgData, function(output){
|
|
// 将输出向量转化为idx再传化为对应字符
|
|
const text = recDecode(output);
|
|
text_list_tmp = text_list_tmp.concat(text.text);
|
|
});
|
|
}
|
|
text_list.push(text_list_tmp);
|
|
}
|
|
me.setData({
|
|
result: JSON.stringify(boxes) + JSON.stringify(text_list)
|
|
});
|
|
},
|
|
|
|
drawCanvasPoints(img, points) {
|
|
// 设置线条
|
|
my_canvas_ctx.strokeStyle = 'blue';
|
|
my_canvas_ctx.lineWidth = 5;
|
|
|
|
// 先绘制图片
|
|
const image = my_canvas.createImage();
|
|
image.src = img.canvasPath;
|
|
image.onload = () => {
|
|
my_canvas_ctx.clearRect(0, 0, my_canvas_ctx.width, my_canvas_ctx.height);
|
|
my_canvas_ctx.drawImage(image, img.x, img.y, img.sw, img.sh);
|
|
// 绘制线框
|
|
points.length && points.forEach(point => {
|
|
my_canvas_ctx.beginPath();
|
|
// 设置路径起点坐标
|
|
my_canvas_ctx.moveTo(point[0][0], point[0][1]);
|
|
my_canvas_ctx.lineTo(point[1][0], point[1][1]);
|
|
my_canvas_ctx.lineTo(point[2][0], point[2][1]);
|
|
my_canvas_ctx.lineTo(point[3][0], point[3][1]);
|
|
my_canvas_ctx.lineTo(point[0][0], point[0][1]);
|
|
my_canvas_ctx.stroke();
|
|
my_canvas_ctx.closePath();
|
|
});
|
|
}
|
|
|
|
}
|
|
});
|