Tensorflow.js tf.browser.fromPixelsAsync()函数
Tensorflow.js是谷歌开发的一个开源库,用于在浏览器或节点环境中运行机器学习模型和深度学习神经网络。
tf.browser.fromPixelsAsync()函数用于以异步方式创建指定图像的像素值的张量。
句法:
tf.browser.fromPixelsAsync (pixels, numChannels)
参数:此函数接受两个参数,如下所示。
- 像素:将构建张量的输入图像的像素。支持的图像类型都是 4 通道。
- numchannels:输出Tensor的通道数。默认值为 3,上限为 4。
返回值:此函数返回指定图像的像素值的创建张量。
示例 1:
Javascript
// Creating a image from some specified
// pixels values
const image = new ImageData(2, 2);
image.data[0] = 5;
image.data[1] = 10;
image.data[2] = 15;
image.data[3] = 20;
// Calling the .fromPixelsAsync() function
// over the above image as its parameter
// without using numChannels value so
// it print only 3 pixels value as
// the default value of numchannels
// parameter is 3
(await tf.browser.fromPixelsAsync(image)).print();
Javascript
// Creating a image from some specified
// pixels values
const image = new ImageData(1, 1);
image.data[0] = 5;
image.data[1] = 10;
image.data[2] = 15;
image.data[3] = 20;
// Calling the .fromPixelsAsync() function
// over the above image as its parameter
// along with 4 value for numChannels parameter
(await tf.browser.fromPixelsAsync(image, 4)).print();
输出:
Tensor
[[[5, 10, 15],
[0, 0 , 0 ]],
[[0, 0 , 0 ],
[0, 0 , 0 ]]]
示例 2:
Javascript
// Creating a image from some specified
// pixels values
const image = new ImageData(1, 1);
image.data[0] = 5;
image.data[1] = 10;
image.data[2] = 15;
image.data[3] = 20;
// Calling the .fromPixelsAsync() function
// over the above image as its parameter
// along with 4 value for numChannels parameter
(await tf.browser.fromPixelsAsync(image, 4)).print();
输出:
Tensor
[ [[5, 10, 15, 20],]]
参考: https://js.tensorflow.org/api/latest/#browser.fromPixelsAsync