blur_transformation.py

#create a copy of my grey image and just get first layer (because all layers are same)
copyImg = greyImage.copy()[:,:,0]

#get number of rows, columns in our image
numRows,numCols = copyImg.shape

#define how big we want our blurring box to be, the bigger the blurrier
boxSize = 31

#get half the size of the box 
halfBoxSize = boxSize/2

#we can only blur pixels where the box can fit around them (i.e. the edges wont get blurred)
startRow = halfBoxSize
startCol = halfBoxSize

#loop over all the valid pixels in the image
for row in range(startRow, numRows-halfBoxSize):
    for col in range(startCol, numCols-halfBoxSize):
        
        #create the local box around a given pixel
        localPixels = greyImage[row-halfBoxSize:row+halfBoxSize+1, col-halfBoxSize:col+halfBoxSize+1][:,:,0]
        
        #take the mean of the pixels in the local box
        blurredValue = np.mean(localPixels)
        
        #set the new value at that pixel to be that blurred value
        copyImg[row,col] = blurredValue

#reshape the flat image to give it a third dimension of 1
copyImg = copyImg.reshape([numRows, numCols, 1])

#stack 3 copies of the transformed image together to create the blurred image
blurredImage = np.concatenate([copyImg]*3, axis=2)