@tensorflow/tfjs-node
Version:
This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
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JavaScript
"use strict";
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (g && (g = 0, op[0] && (_ = 0)), _) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var tfjs_1 = require("@tensorflow/tfjs");
// tslint:disable-next-line: no-imports-from-dist
var jasmine_util_1 = require("@tensorflow/tfjs-core/dist/jasmine_util");
var fs = require("fs");
var util_1 = require("util");
var image_1 = require("./image");
var tf = require("./index");
var readFile = (0, util_1.promisify)(fs.readFile);
describe('decode images', function () {
it('decode png', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_test.png')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodePng(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode png 1 channels', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_test.png')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array, 1);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 1]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [130, 50, 59, 124]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode png 3 channels', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_test.png')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode png 4 channels', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_4_channel_test.png')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array, 4);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 4]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [
238, 101, 0, 255, 50, 50, 50, 255, 100, 50, 0, 255, 200, 100, 50, 255
]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode bmp', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_bmp_test.bmp')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeBmp(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode bmp through decodeImage', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_bmp_test.bmp')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode jpg', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeJpeg(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode jpeg node bindings do not leak', function () { return __awaiter(void 0, void 0, void 0, function () {
var uint8array, i, imageTensor, beforeMem, i, imageTensor, afterMem;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _a.sent();
// Warm up the node bindings
for (i = 0; i < 10000; i++) {
imageTensor = tf.node.decodeJpeg(uint8array);
imageTensor.dispose();
}
beforeMem = process.memoryUsage().rss;
for (i = 0; i < 100000; i++) {
imageTensor = tf.node.decodeJpeg(uint8array);
imageTensor.dispose();
}
afterMem = process.memoryUsage().rss;
// Due to GC fluctuations, There has to be a large 1Mb margain for this
// test, but if decodeJpeg leaks more than 10 bytes per run, it will be
// detected.
expect(afterMem).toBeLessThan(beforeMem + 1e6 /* 1Mb */);
return [2 /*return*/];
}
});
}); });
it('decode jpg 1 channel', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array, 1);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 1]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [130, 47, 56, 121]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode jpg 3 channels', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array, 3);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode jpg with 0 channels, use the number of channels in the ' +
'JPEG-encoded image', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode jpg with downscale', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_jpeg_test.jpeg')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeJpeg(uint8array, 0, 2);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([1, 1, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [147, 75, 25]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode gif', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/gif_test.gif')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [
238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50,
200, 100, 50, 34, 68, 102, 170, 0, 102, 255, 255, 255
]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('decode gif with no expandAnimation', function () { return __awaiter(void 0, void 0, void 0, function () {
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/gif_test.gif')];
case 1:
uint8array = _c.sent();
imageTensor = tf.node.decodeImage(uint8array, 3, 'int32', false);
expect(imageTensor.dtype).toBe('int32');
expect(imageTensor.shape).toEqual([2, 2, 3]);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 2:
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors + 1);
return [2 /*return*/];
}
});
}); });
it('throw error if request non int32 dtype', function () { return __awaiter(void 0, void 0, void 0, function () {
var uint8array;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_test.png')];
case 1:
uint8array = _a.sent();
expect(function () { return tf.node.decodeImage(uint8array, 0, 'uint8'); }).toThrowError('decodeImage could only return Tensor of type `int32` for now.');
return [2 /*return*/];
}
});
}); });
it('throw error if decode invalid image type', function () { return __awaiter(void 0, void 0, void 0, function () {
var uint8array;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, getUint8ArrayFromImage('package.json')];
case 1:
uint8array = _a.sent();
expect(function () { return tf.node.decodeImage(uint8array); }).toThrowError('Expected image (BMP, JPEG, PNG, or GIF), ' +
'but got unsupported image type');
return [2 /*return*/];
}
});
}); });
it('throw error if backend is not tensorflow', function () { return __awaiter(void 0, void 0, void 0, function () {
var testBackend, uint8array_1;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
testBackend = new jasmine_util_1.TestKernelBackend();
(0, tfjs_1.registerBackend)('fake', function () { return testBackend; });
(0, tfjs_1.setBackend)('fake');
_a.label = 1;
case 1:
_a.trys.push([1, , 3, 4]);
return [4 /*yield*/, getUint8ArrayFromImage('test_objects/images/image_png_test.png')];
case 2:
uint8array_1 = _a.sent();
expect(function () { return tf.node.decodeImage(uint8array_1); }).toThrowError('Expect the current backend to be "tensorflow", but got "fake"');
return [3 /*break*/, 4];
case 3:
(0, tfjs_1.setBackend)('tensorflow');
return [7 /*endfinally*/];
case 4: return [2 /*return*/];
}
});
}); });
});
describe('encode images', function () {
it('encodeJpeg', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, beforeNumTensors, beforeNumTFTensors, jpegEncodedData;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]), [2, 2, 3]);
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodeJpeg(imageTensor)];
case 1:
jpegEncodedData = _a.sent();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect((0, image_1.getImageType)(jpegEncodedData)).toEqual(image_1.ImageType.JPEG);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
it('encodeJpeg grayscale', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, beforeNumTensors, beforeNumTFTensors, jpegEncodedData;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 0, 47, 0]), [2, 2, 1]);
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodeJpeg(imageTensor, 'grayscale')];
case 1:
jpegEncodedData = _a.sent();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect((0, image_1.getImageType)(jpegEncodedData)).toEqual(image_1.ImageType.JPEG);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
it('encodeJpeg with parameters', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, format, quality, progressive, optimizeSize, chromaDownsampling, densityUnit, xDensity, yDensity, beforeNumTensors, beforeNumTFTensors, jpegEncodedData;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]), [2, 2, 3]);
format = 'rgb';
quality = 50;
progressive = true;
optimizeSize = true;
chromaDownsampling = false;
densityUnit = 'cm';
xDensity = 500;
yDensity = 500;
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodeJpeg(imageTensor, format, quality, progressive, optimizeSize, chromaDownsampling, densityUnit, xDensity, yDensity)];
case 1:
jpegEncodedData = _a.sent();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
expect((0, image_1.getImageType)(jpegEncodedData)).toEqual(image_1.ImageType.JPEG);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
it('encodePng', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, beforeNumTensors, beforeNumTFTensors, pngEncodedData, pngDecodedTensor, pngDecodedData, _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]), [2, 2, 3]);
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodePng(imageTensor)];
case 1:
pngEncodedData = _c.sent();
return [4 /*yield*/, tf.node.decodePng(pngEncodedData)];
case 2:
pngDecodedTensor = _c.sent();
return [4 /*yield*/, pngDecodedTensor.data()];
case 3:
pngDecodedData = _c.sent();
pngDecodedTensor.dispose();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
expect((0, image_1.getImageType)(pngEncodedData)).toEqual(image_1.ImageType.PNG);
_b = (_a = tfjs_1.test_util).expectArraysEqual;
return [4 /*yield*/, imageTensor.data()];
case 4:
_b.apply(_a, [_c.sent(), pngDecodedData]);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
it('encodePng grayscale', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, beforeNumTensors, beforeNumTFTensors, pngEncodedData;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 0, 47, 0]), [2, 2, 1]);
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodePng(imageTensor)];
case 1:
pngEncodedData = _a.sent();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
expect((0, image_1.getImageType)(pngEncodedData)).toEqual(image_1.ImageType.PNG);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
it('encodePng with parameters', function () { return __awaiter(void 0, void 0, void 0, function () {
var imageTensor, compression, beforeNumTensors, beforeNumTFTensors, pngEncodedData;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
imageTensor = tf.tensor3d(new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]), [2, 2, 3]);
compression = 4;
beforeNumTensors = (0, tfjs_1.memory)().numTensors;
beforeNumTFTensors = tf.backend().getNumOfTFTensors();
return [4 /*yield*/, tf.node.encodePng(imageTensor, compression)];
case 1:
pngEncodedData = _a.sent();
expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors);
expect(tf.backend().getNumOfTFTensors())
.toBe(beforeNumTFTensors);
expect((0, image_1.getImageType)(pngEncodedData)).toEqual(image_1.ImageType.PNG);
imageTensor.dispose();
return [2 /*return*/];
}
});
}); });
});
function getUint8ArrayFromImage(path) {
return __awaiter(this, void 0, void 0, function () {
var image, buf;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, readFile(path)];
case 1:
image = _a.sent();
buf = Buffer.from(image);
return [2 /*return*/, new Uint8Array(buf)];
}
});
});
}