Efficient way to split image into overlapping patches, do math on the patches, then merge the image back together by taking the max of at every pixel

Basically trying to just do what the tital says and I have a working function, but it is running much to slow. The input image to this function will always have the same shape and dimension (2d).

Here's my working but slow code

def z_score_detect():      data = currentData - bkgData      tileY =12      tileX =12      h,w = data.shape        DETECTED = np.zeros(data.shape).astype(np.float32)      for curY in range(tileY//2, h-tileY//2,tileY//2):          for curX in range(tileX//2, w-tileX//2,tileX//2):              # Get one of the patches of data              S = data[curY-tileY//2:curY+tileY//2,curX-tileX//2:curX+tileX//2]              # comput stdev, median              rms_bot_75 = rms(np.sort(S,axis=None)[:int((tileX*tileY)*.75)])              std_dev = np.std(S)              if std_dev != 0:                  # IF SIGMA NOT ZERO                  S = (S-rms_bot_75)/std_dev                  # Done with processing this patch so update our output image              else:                  continue              DETECTED[curY-tileY//2:curY+tileY//2,curX-tileX//2:curX+tileX//2] = np.maximum(S,DETECTED[curY-tileY//2:curY+tileY//2,curX-tileX//2:curX+tileX//2])                                return DETECTED  

https://stackoverflow.com/questions/65604927/efficient-way-to-split-image-into-overlapping-patches-do-math-on-the-patches-t January 07, 2021 at 08:00AM