Otsu's Thresholding
======================
Description:
Otsu's thresholding is a technique to automatically determine the optimal threshold value for image thresholding. It's a widely used method in image processing and computer vision.
Example:
Consider an image with a binary object on a white background. We can use Otsu's thresholding to automatically determine the optimal threshold value.
Constraints:
* The input image is a 2D array of pixel intensities.
* The output is the optimal threshold value.
* The output is a scalar value.
Goal:
Implement a function to perform Otsu's thresholding using NumPy.
Test Cases
Test Case 1
Input:
[[10, 20, 30], [40, 50, 60]]Expected:
50Test Case 2
Input:
[[100, 200, 300], [400, 500, 600]]Expected:
300+ 3 hidden test cases