"use strict";
|
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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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; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
|
}
|
op = body.call(thisArg, _);
|
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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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.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* 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,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var tfjs_1 = require("@tensorflow/tfjs");
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// tslint:disable-next-line: no-imports-from-dist
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var jasmine_util_1 = require("@tensorflow/tfjs-core/dist/jasmine_util");
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var fs = require("fs");
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var util_1 = require("util");
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var image_1 = require("./image");
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var tf = require("./index");
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var readFile = (0, util_1.promisify)(fs.readFile);
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describe('decode images', function () {
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it('decode png', function () { return __awaiter(void 0, void 0, void 0, function () {
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var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
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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')];
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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()];
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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*/];
|
}
|
});
|
}); });
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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')];
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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()];
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case 2:
|
_b.apply(_a, [_c.sent(), [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]]);
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expect((0, tfjs_1.memory)().numTensors).toBe(beforeNumTensors + 1);
|
expect(tf.backend().getNumOfTFTensors())
|
.toBe(beforeNumTFTensors + 1);
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return [2 /*return*/];
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}
|
});
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}); });
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it('decode png 4 channels', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
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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()];
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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*/];
|
}
|
});
|
}); });
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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*/];
|
}
|
});
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}); });
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it('decode bmp through decodeImage', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var beforeNumTensors, beforeNumTFTensors, uint8array, imageTensor, _a, _b;
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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')];
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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;
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return [4 /*yield*/, imageTensor.data()];
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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)];
|
}
|
});
|
});
|
}
|
