Tensorflow.js tf.image.nonMaxSuppressionAsync() Function

Last Updated : 30 Jul, 2021

Tensorflow.js is an open-source library developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.

The .image.nonMaxSuppressionAsync() function is used to execute the non maximum suppression of the limiting boxes on the basis of iou i.e. intersection over union. Moreover, its the async interpretation of nonMaxSuppression() method.

Syntax:

tf.image.nonMaxSuppressionAsync(boxes, 
    scores, maxOutputSize, iouThreshold?, scoreThreshold?)

Parameters:

boxes: The stated 2d tensor, which is of configuration [numBoxes, 4]. And every access is [y1, x1, y2, x2], allowing that (y1, x1) and (y2, x2) are the edges of the restricting box. It can be of type tf.Tensor2D, TypedArray, or Array.
scores: The stated 1d tensor, provided that the box scores is of configuration [numBoxes]. It is of type tf.Tensor2D, TypedArray, or Array.
maxOutputSize: It is the maximum count of the stated boxes that is to be picked. It is of type number.
iouThreshold: It is the stated float signifying the threshold in order to decide if the stated boxes intersect too much with reference to the IOU. It should be in the midst of [0, 1]. The by default value is 0.5 i.e. 50 percent of the box intersects. It is optional and is of type number.
scoreThreshold: It is the stated threshold in order to decide at which time boxes are to be removed on the basis of the stated score. The by default value is -inf, i.e. every single score is allowed. It is optional and is of type number.

Return Value: It returns Promise of tf.Tensor1D.

Example 1: In this example, we will be going to use a 2d tensor, scores, and maxOutputSize parameters.

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Calling image.nonMaxSuppressionAsync() method 
const output = await tf.image.nonMaxSuppressionAsync( 
    tf.tensor2d([1, 2, 3, 4, 2, 4, 6, 7],  
    [2, 4]), [1, 1], 4); 
  
// Printing output 
output.print();

Output:

Tensor
    [0, 1]

Example 2: In this example, we will be going to use an array of floats, iouThreshold, as well as scoreThreshold.

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Defining an array of floats 
const arr = [[11.1, 2.3, 7.3, 6.4], [3, 6]] 
  
// Calling image.nonMaxSuppressionAsync() method 
const res = await tf.image.nonMaxSuppressionAsync( 
    arr, [2.1, 0], 100, 0.5, 1); 
  
// Printing output 
res.print();