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import numpy as np#Load NumPy library for easier numerical computations
from PIL import Image
from PIL import ImageDraw
import random
import pdb

def normalize(input_im):
    base = input_im.min()
    roof = input_im.max()
    diff = roof - base
    scale = diff/255

    input_im = input_im - base
    output = input_im/scale

    return output


# Load image with PIL, convert to grayscale and convert to numpy array
def np_from_img(fname):
    im = Image.open(fname)
    return np.asarray(im.convert('L'), dtype=np.int32)


# save numpy array as PIL and then save image as file
def save_as_img(ar, fname):
    Image.fromarray(normalize(ar).round().astype(np.uint8)).save(fname)


#Normalize the numpy array to 8 bit image consistency
def norm(ar):
    return 255.*np.absolute(ar)/np.max(ar)



#Rotate kernel for convolution by 180 degree.(else its correlation)
def conv_transform(kernel):

    kernel_cp = np.copy(kernel)
    if kernel.ndim == 1:
       nCol = kernel.shape[0]
       for i in range(nCol):
            kernel_cp[i] = kernel[kernel.shape[0]-i-1]
    else:
       nRow = kernel.shape[0]
       nCol = kernel.shape[1]
       for i in range(nRow):
            for j in range(nCol):
                kernel_cp[i][j] = kernel[kernel.shape[0]-i-1][kernel.shape[1]-j-1]

    return kernel_cp...
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