Tensorflow.js is an open-source library developed by Google for running machine learning models and deep learning neural networks in the browser or node environment.
The tf.browser.fromPixelsAsync() function is used to create a Tensor of pixel values of a specified image in an async way.
Syntax:
tf.browser.fromPixelsAsync (pixels, numChannels)
Parameters: This function accepts two parameters which are illustrated below.
- pixels: It is the pixels of the input image from which the Tensor is going to be constructed. The supported image types are all 4-channel.
- numchannels: It is the number of channels of the output Tensor. It’s default value is 3 and the upper limit is up to 4.
Return Value: This function returns the created Tensor of pixels values of the specified image.
Example 1:
Javascript
// Creating a image from some specified// pixels valuesconst 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(); |
Output:
Tensor
[[[5, 10, 15],
[0, 0 , 0 ]],
[[0, 0 , 0 ],
[0, 0 , 0 ]]]
Example 2:
Javascript
// Creating a image from some specified// pixels valuesconst 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(); |
Output:
Tensor
[ [[5, 10, 15, 20],]]
Reference:https://js.tensorflow.org/api/latest/#browser.fromPixelsAsync
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