@tensorflow/tfjs-node
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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
;
/**
* @license
* Copyright 2019 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 __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.");
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switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
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default:
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
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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 });
exports.getImageType = exports.encodePng = exports.encodeJpeg = exports.decodeImage = exports.decodeGif = exports.decodeBmp = exports.decodePng = exports.decodeJpeg = exports.ImageType = void 0;
var tfjs_1 = require("@tensorflow/tfjs");
var nodejs_kernel_backend_1 = require("./nodejs_kernel_backend");
var ImageType;
(function (ImageType) {
ImageType["JPEG"] = "jpeg";
ImageType["PNG"] = "png";
ImageType["GIF"] = "gif";
ImageType["BMP"] = "BMP";
})(ImageType = exports.ImageType || (exports.ImageType = {}));
/**
* Decode a JPEG-encoded image to a 3D Tensor of dtype `int32`.
*
* @param contents The JPEG-encoded image in an Uint8Array.
* @param channels An optional int. Defaults to 0. Accepted values are
* 0: use the number of channels in the JPEG-encoded image.
* 1: output a grayscale image.
* 3: output an RGB image.
* @param ratio An optional int. Defaults to 1. Downscaling ratio. It is used
* when image is type Jpeg.
* @param fancyUpscaling An optional bool. Defaults to True. If true use a
* slower but nicer upscaling of the chroma planes. It is used when image is
* type Jpeg.
* @param tryRecoverTruncated An optional bool. Defaults to False. If true try
* to recover an image from truncated input. It is used when image is type
* Jpeg.
* @param acceptableFraction An optional float. Defaults to 1. The minimum
* required fraction of lines before a truncated input is accepted. It is
* used when image is type Jpeg.
* @param dctMethod An optional string. Defaults to "". string specifying a hint
* about the algorithm used for decompression. Defaults to "" which maps to
* a system-specific default. Currently valid values are ["INTEGER_FAST",
* "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg
* library changes to a version that does not have that specific option.) It
* is used when image is type Jpeg.
* @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3].
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function decodeJpeg(contents, channels, ratio, fancyUpscaling, tryRecoverTruncated, acceptableFraction, dctMethod) {
if (channels === void 0) { channels = 0; }
if (ratio === void 0) { ratio = 1; }
if (fancyUpscaling === void 0) { fancyUpscaling = true; }
if (tryRecoverTruncated === void 0) { tryRecoverTruncated = false; }
if (acceptableFraction === void 0) { acceptableFraction = 1; }
if (dctMethod === void 0) { dctMethod = ''; }
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
return (0, tfjs_1.tidy)(function () {
return (0, nodejs_kernel_backend_1.nodeBackend)()
.decodeJpeg(contents, channels, ratio, fancyUpscaling, tryRecoverTruncated, acceptableFraction, dctMethod)
.toInt();
});
}
exports.decodeJpeg = decodeJpeg;
/**
* Decode a PNG-encoded image to a 3D Tensor of dtype `int32`.
*
* @param contents The PNG-encoded image in an Uint8Array.
* @param channels An optional int. Defaults to 0. Accepted values are
* 0: use the number of channels in the PNG-encoded image.
* 1: output a grayscale image.
* 3: output an RGB image.
* 4: output an RGBA image.
* @param dtype The data type of the result. Only `int32` is supported at this
* time.
* @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3/4].
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function decodePng(contents, channels, dtype) {
if (channels === void 0) { channels = 0; }
if (dtype === void 0) { dtype = 'int32'; }
tfjs_1.util.assert(dtype === 'int32', function () { return 'decodeImage could only return Tensor of type `int32` for now.'; });
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
return (0, tfjs_1.tidy)(function () {
return (0, nodejs_kernel_backend_1.nodeBackend)().decodePng(contents, channels).toInt();
});
}
exports.decodePng = decodePng;
/**
* Decode the first frame of a BMP-encoded image to a 3D Tensor of dtype
* `int32`.
*
* @param contents The BMP-encoded image in an Uint8Array.
* @param channels An optional int. Defaults to 0. Accepted values are
* 0: use the number of channels in the BMP-encoded image.
* 3: output an RGB image.
* 4: output an RGBA image.
* @returns A 3D Tensor of dtype `int32` with shape [height, width, 3/4].
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function decodeBmp(contents, channels) {
if (channels === void 0) { channels = 0; }
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
return (0, tfjs_1.tidy)(function () {
return (0, nodejs_kernel_backend_1.nodeBackend)().decodeBmp(contents, channels).toInt();
});
}
exports.decodeBmp = decodeBmp;
/**
* Decode the frame(s) of a GIF-encoded image to a 4D Tensor of dtype `int32`.
*
* @param contents The GIF-encoded image in an Uint8Array.
* @returns A 4D Tensor of dtype `int32` with shape [num_frames, height, width,
* 3]. RGB channel order.
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function decodeGif(contents) {
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
return (0, tfjs_1.tidy)(function () {
return (0, nodejs_kernel_backend_1.nodeBackend)().decodeGif(contents).toInt();
});
}
exports.decodeGif = decodeGif;
/**
* Given the encoded bytes of an image, it returns a 3D or 4D tensor of the
* decoded image. Supports BMP, GIF, JPEG and PNG formats.
*
* @param content The encoded image in an Uint8Array.
* @param channels An optional int. Defaults to 0, use the number of channels in
* the image. Number of color channels for the decoded image. It is used
* when image is type Png, Bmp, or Jpeg.
* @param dtype The data type of the result. Only `int32` is supported at this
* time.
* @param expandAnimations A boolean which controls the shape of the returned
* op's output. If True, the returned op will produce a 3-D tensor for PNG,
* JPEG, and BMP files; and a 4-D tensor for all GIFs, whether animated or
* not. If, False, the returned op will produce a 3-D tensor for all file
* types and will truncate animated GIFs to the first frame.
* @returns A Tensor with dtype `int32` and a 3- or 4-dimensional shape,
* depending on the file type. For gif file the returned Tensor shape is
* [num_frames, height, width, 3], and for jpeg/png/bmp the returned Tensor
* shape is [height, width, channels]
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function decodeImage(content, channels, dtype, expandAnimations) {
if (channels === void 0) { channels = 0; }
if (dtype === void 0) { dtype = 'int32'; }
if (expandAnimations === void 0) { expandAnimations = true; }
tfjs_1.util.assert(dtype === 'int32', function () { return 'decodeImage could only return Tensor of type `int32` for now.'; });
var imageType = getImageType(content);
// The return tensor has dtype uint8, which is not supported in
// TensorFlow.js, casting it to int32 which is the default dtype for image
// tensor. If the image is BMP, JPEG or PNG type, expanding the tensors
// shape so it becomes Tensor4D, which is the default tensor shape for image
// ([batch,imageHeight,imageWidth, depth]).
switch (imageType) {
case ImageType.JPEG:
return decodeJpeg(content, channels);
case ImageType.PNG:
return decodePng(content, channels);
case ImageType.GIF:
// If not to expand animations, take first frame of the gif and return
// as a 3D tensor.
return (0, tfjs_1.tidy)(function () {
var img = decodeGif(content);
return expandAnimations ? img : img.slice(0, 1).squeeze([0]);
});
case ImageType.BMP:
return decodeBmp(content, channels);
default:
return null;
}
}
exports.decodeImage = decodeImage;
/**
* Encodes an image tensor to JPEG.
*
* @param image A 3-D uint8 Tensor of shape [height, width, channels].
* @param format An optional string from: "", "grayscale", "rgb".
* Defaults to "". Per pixel image format.
* - '': Use a default format based on the number of channels in the image.
* - grayscale: Output a grayscale JPEG image. The channels dimension of
* image must be 1.
* - rgb: Output an RGB JPEG image. The channels dimension of image must
* be 3.
* @param quality An optional int. Defaults to 95. Quality of the compression
* from 0 to 100 (higher is better and slower).
* @param progressive An optional bool. Defaults to False. If True, create a
* JPEG that loads progressively (coarse to fine).
* @param optimizeSize An optional bool. Defaults to False. If True, spend
* CPU/RAM to reduce size with no quality change.
* @param chromaDownsampling An optional bool. Defaults to True.
* See http://en.wikipedia.org/wiki/Chroma_subsampling.
* @param densityUnit An optional string from: "in", "cm". Defaults to "in".
* Unit used to specify x_density and y_density: pixels per inch ('in') or
* centimeter ('cm').
* @param xDensity An optional int. Defaults to 300. Horizontal pixels per
* density unit.
* @param yDensity An optional int. Defaults to 300. Vertical pixels per
* density unit.
* @param xmpMetadata An optional string. Defaults to "". If not empty, embed
* this XMP metadata in the image header.
* @returns The JPEG encoded data as an Uint8Array.
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function encodeJpeg(image, format, quality, progressive, optimizeSize, chromaDownsampling, densityUnit, xDensity, yDensity, xmpMetadata) {
if (format === void 0) { format = ''; }
if (quality === void 0) { quality = 95; }
if (progressive === void 0) { progressive = false; }
if (optimizeSize === void 0) { optimizeSize = false; }
if (chromaDownsampling === void 0) { chromaDownsampling = true; }
if (densityUnit === void 0) { densityUnit = 'in'; }
if (xDensity === void 0) { xDensity = 300; }
if (yDensity === void 0) { yDensity = 300; }
if (xmpMetadata === void 0) { xmpMetadata = ''; }
return __awaiter(this, void 0, void 0, function () {
var backendEncodeImage;
return __generator(this, function (_a) {
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
backendEncodeImage = function (imageData) {
return (0, nodejs_kernel_backend_1.nodeBackend)().encodeJpeg(imageData, image.shape, format, quality, progressive, optimizeSize, chromaDownsampling, densityUnit, xDensity, yDensity, xmpMetadata);
};
return [2 /*return*/, encodeImage(image, backendEncodeImage)];
});
});
}
exports.encodeJpeg = encodeJpeg;
/**
* Encodes an image tensor to PNG.
*
* @param image A 3-D uint8 Tensor of shape [height, width, channels].
* @param compression An optional int. Defaults to 1. Compression level.
* @returns The PNG encoded data as an Uint8Array.
*
* @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
*/
function encodePng(image, compression) {
if (compression === void 0) { compression = 1; }
return __awaiter(this, void 0, void 0, function () {
var backendEncodeImage;
return __generator(this, function (_a) {
(0, nodejs_kernel_backend_1.ensureTensorflowBackend)();
backendEncodeImage = function (imageData) {
return (0, nodejs_kernel_backend_1.nodeBackend)().encodePng(imageData, image.shape, compression);
};
return [2 /*return*/, encodeImage(image, backendEncodeImage)];
});
});
}
exports.encodePng = encodePng;
function encodeImage(image, backendEncodeImage) {
return __awaiter(this, void 0, void 0, function () {
var encodedDataTensor, _a, _b, encodedPngData;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
_a = backendEncodeImage;
_b = Uint8Array.bind;
return [4 /*yield*/, image.data()];
case 1:
encodedDataTensor = _a.apply(void 0, [new (_b.apply(Uint8Array, [void 0, _c.sent()]))()]);
// tslint:disable-next-line:no-any
return [4 /*yield*/, encodedDataTensor.data()];
case 2:
encodedPngData = (
// tslint:disable-next-line:no-any
_c.sent())[0];
encodedDataTensor.dispose();
return [2 /*return*/, encodedPngData];
}
});
});
}
/**
* Helper function to get image type based on starting bytes of the image file.
*/
function getImageType(content) {
// Classify the contents of a file based on starting bytes (aka magic number:
// https://en.wikipedia.org/wiki/Magic_number_(programming)#Magic_numbers_in_files)
// This aligns with TensorFlow Core code:
// https://github.com/tensorflow/tensorflow/blob/4213d5c1bd921f8d5b7b2dc4bbf1eea78d0b5258/tensorflow/core/kernels/decode_image_op.cc#L44
if (content.length > 3 && content[0] === 255 && content[1] === 216 &&
content[2] === 255) {
// JPEG byte chunk starts with `ff d8 ff`
return ImageType.JPEG;
}
else if (content.length > 4 && content[0] === 71 && content[1] === 73 &&
content[2] === 70 && content[3] === 56) {
// GIF byte chunk starts with `47 49 46 38`
return ImageType.GIF;
}
else if (content.length > 8 && content[0] === 137 && content[1] === 80 &&
content[2] === 78 && content[3] === 71 && content[4] === 13 &&
content[5] === 10 && content[6] === 26 && content[7] === 10) {
// PNG byte chunk starts with `\211 P N G \r \n \032 \n (89 50 4E 47 0D 0A
// 1A 0A)`
return ImageType.PNG;
}
else if (content.length > 3 && content[0] === 66 && content[1] === 77) {
// BMP byte chunk starts with `42 4d`
return ImageType.BMP;
}
else {
throw new Error('Expected image (BMP, JPEG, PNG, or GIF), but got unsupported ' +
'image type');
}
}
exports.getImageType = getImageType;