How to identify the pixels with the highest activation from four or more grayscale images and save a-CodePudding

I have four attention maps, each of dimension 217 x 217 and in the form of array of float64. I need to perform an operation using these attention maps and save the final attention map where only the pixels with the highest activations are present. A custom function is needed in this regard. Attached are the heatmaps:

The starter code is here:

import cv2
import numpy as np
import os
from PIL import Image

im1 = np.array(Image.open("heatmap1.png").convert('L'))
im2 = np.array(Image.open("heatmap2.png").convert('L'))
im3 = np.array(Image.open("heatmap3.png").convert('L'))
im4 = np.array(Image.open("heatmap4.png").convert('L'))

#compute to save only the pixels with the highest activation

#save final heatmap as a PNG file
cv2.imwrite("final_heatmap.png", bitwise_AND)

CodePudding user response：

To compute the pixelwise maximum between two images, use np.maximum(im1, im2). The bitwise logical AND gives the maximum only for Boolean values, but does not generalize to gray-scale values.

To find the pixelwise maximum across four images, use a pyramidal scheme with pairwise operators:

out1 = np.maximum(im1, im2)
out2 = np.maximum(im3, im4)
out = np.maximum(out1, out2)

You can do the above a bit more efficiently if you are willing to overwrite inputs:

np.maximum(im1, im2, out=im1)
np.maximum(im3, im4, out=im3)
np.maximum(im1, im3, out=im1)  # im1 now has the result

CodePudding user response：

you can use the numpy's built-in function

logical_and
bitwise_or
bitwise_xor
binary_repr

an example usage is like following

np.bitwise_and(arr1, arr2)