@tensorflow/tfjs-core/dist/ops/browser.d.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/browser" />
import { Tensor2D, Tensor3D } from '../tensor';
import { DrawOptions, PixelData, TensorLike } from '../types';
/**
 * Creates a `tf.Tensor` from an image.
 *
 * ```js
 * const image = new ImageData(1, 1);
 * image.data[0] = 100;
 * image.data[1] = 150;
 * image.data[2] = 200;
 * image.data[3] = 255;
 *
 * tf.browser.fromPixels(image).print();
 * ```
 *
 * @param pixels The input image to construct the tensor from. The
 * supported image types are all 4-channel. You can also pass in an image
 * object with following attributes:
 * `{data: Uint8Array; width: number; height: number}`
 * @param numChannels The number of channels of the output tensor. A
 * numChannels value less than 4 allows you to ignore channels. Defaults to
 * 3 (ignores alpha channel of input image).
 *
 * @returns A Tensor3D with the shape `[height, width, numChannels]`.
 *
 * Note: fromPixels can be lossy in some cases, same image may result in
 * slightly different tensor values, if rendered by different rendering
 * engines. This means that results from different browsers, or even same
 * browser with CPU and GPU rendering engines can be different. See discussion
 * in details:
 * https://github.com/tensorflow/tfjs/issues/5482
 *
 * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
 */
declare function fromPixels_(pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | ImageBitmap, numChannels?: number): Tensor3D;
/**
 * Creates a `tf.Tensor` from an image in async way.
 *
 * ```js
 * const image = new ImageData(1, 1);
 * image.data[0] = 100;
 * image.data[1] = 150;
 * image.data[2] = 200;
 * image.data[3] = 255;
 *
 * (await tf.browser.fromPixelsAsync(image)).print();
 * ```
 * This API is the async version of fromPixels. The API will first
 * check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to
 * imageBitmap if the flag is set to true.
 *
 * @param pixels The input image to construct the tensor from. The
 * supported image types are all 4-channel. You can also pass in an image
 * object with following attributes:
 * `{data: Uint8Array; width: number; height: number}`
 * @param numChannels The number of channels of the output tensor. A
 * numChannels value less than 4 allows you to ignore channels. Defaults to
 * 3 (ignores alpha channel of input image).
 *
 * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
 */
export declare function fromPixelsAsync(pixels: PixelData | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | ImageBitmap, numChannels?: number): Promise<Tensor3D>;
/**
 * Draws a `tf.Tensor` of pixel values to a byte array or optionally a
 * canvas.
 *
 * When the dtype of the input is 'float32', we assume values in the range
 * [0-1]. Otherwise, when input is 'int32', we assume values in the range
 * [0-255].
 *
 * Returns a promise that resolves when the canvas has been drawn to.
 *
 * @param img A rank-2 tensor with shape `[height, width]`, or a rank-3 tensor
 * of shape `[height, width, numChannels]`. If rank-2, draws grayscale. If
 * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws
 * grayscale. When depth of 3, we draw with the first three components of
 * the depth dimension corresponding to r, g, b and alpha = 1. When depth of
 * 4, all four components of the depth dimension correspond to r, g, b, a.
 * @param canvas The canvas to draw to.
 *
 * @doc {heading: 'Browser', namespace: 'browser'}
 */
export declare function toPixels(img: Tensor2D | Tensor3D | TensorLike, canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray>;
/**
 * Draws a `tf.Tensor` to a canvas.
 *
 * When the dtype of the input is 'float32', we assume values in the range
 * [0-1]. Otherwise, when input is 'int32', we assume values in the range
 * [0-255].
 *
 * @param image The tensor to draw on the canvas. Must match one of
 * these shapes:
 *   - Rank-2 with shape `[height, width`]: Drawn as grayscale.
 *   - Rank-3 with shape `[height, width, 1]`: Drawn as grayscale.
 *   - Rank-3 with shape `[height, width, 3]`: Drawn as RGB with alpha set in
 *     `imageOptions` (defaults to 1, which is opaque).
 *   - Rank-3 with shape `[height, width, 4]`: Drawn as RGBA.
 * @param canvas The canvas to draw to.
 * @param options The configuration arguments for image to be drawn and the
 *     canvas to draw to.
 *
 * @doc {heading: 'Browser', namespace: 'browser'}
 */
export declare function draw(image: Tensor2D | Tensor3D | TensorLike, canvas: HTMLCanvasElement, options?: DrawOptions): void;
export declare const fromPixels: typeof fromPixels_;
export {};