Adaptive Thresholding
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Description:
Adaptive thresholding is a technique to automatically determine the optimal threshold value for image thresholding based on the local neighborhood of each pixel. 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 adaptive thresholding to automatically determine the optimal threshold value for each pixel.
Constraints:
* The input image is a 2D array of pixel intensities.
* The output image is a binary array where pixels with intensity above the threshold are set to 1 and below the threshold are set to 0.
* The output image has the same shape as the input image.
Goal:
Implement a function to perform adaptive thresholding using NumPy.
Test Cases
Test Case 1
Input:
[[10, 20, 30], [40, 50, 60]]Expected:
[[0, 0, 0], [0, 0, 0]]Test Case 2
Input:
[[100, 200, 300], [400, 500, 600]]Expected:
[[1, 1, 1], [1, 1, 1]]+ 3 hidden test cases