52 lines
No EOL
2.8 KiB
JavaScript
52 lines
No EOL
2.8 KiB
JavaScript
import * as tslib_1 from "tslib";
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import * as tf from '@tensorflow/tfjs-core';
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import { createCanvas, createCanvasFromMedia, env, getContext2dOrThrow, } from 'tfjs-image-recognition-base';
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import { normalize } from './normalize';
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export function extractImagePatches(img, boxes, _a) {
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var width = _a.width, height = _a.height;
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return tslib_1.__awaiter(this, void 0, void 0, function () {
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var imgCtx, bitmaps, imagePatchesDatas;
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var _this = this;
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return tslib_1.__generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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imgCtx = getContext2dOrThrow(img);
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return [4 /*yield*/, Promise.all(boxes.map(function (box) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
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var _a, y, ey, x, ex, fromX, fromY, imgData;
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return tslib_1.__generator(this, function (_b) {
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_a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
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fromX = x - 1;
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fromY = y - 1;
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imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
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return [2 /*return*/, env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)];
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});
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}); }))];
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case 1:
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bitmaps = _b.sent();
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imagePatchesDatas = [];
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bitmaps.forEach(function (bmp) {
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var patch = createCanvas({ width: width, height: height });
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var patchCtx = getContext2dOrThrow(patch);
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patchCtx.drawImage(bmp, 0, 0, width, height);
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var data = patchCtx.getImageData(0, 0, width, height).data;
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var currData = [];
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// RGBA -> BGR
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for (var i = 0; i < data.length; i += 4) {
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currData.push(data[i + 2]);
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currData.push(data[i + 1]);
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currData.push(data[i]);
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}
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imagePatchesDatas.push(currData);
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});
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return [2 /*return*/, imagePatchesDatas.map(function (data) {
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var t = tf.tidy(function () {
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var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
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return normalize(imagePatchTensor);
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});
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return t;
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})];
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}
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});
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});
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}
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//# sourceMappingURL=extractImagePatches.js.map
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