NormEqua.py

import matplotlib.pyplot as plt
import numpy as np
import cv2
from PIL import Image


# ----------------------------- Normalization function -------------------------
def normalize(img):
    lmin = float(img.min())
    lmax = float(img.max())
    return np.floor((img-lmin)/(lmax-lmin)*225.0)

# ----------------------------- Equalization function --------------------------
def equalize(img_filename,save_filename):
        #load file as pillow Image 
        img = Image.open(img_filename)

        # convert to grayscale
        imgray = img.convert(mode='L')

        #convert to NumPy array
        img_array = np.asarray(imgray)

        """
        STEP 1: Normalized cumulative histogram
        """
        #flatten image array and calculate histogram via binning
        histogram_array = np.bincount(img_array.flatten(), minlength=256)

        #normalize
        num_pixels = np.sum(histogram_array)
        histogram_array = histogram_array/num_pixels

        #normalized cumulative histogram
        chistogram_array = np.cumsum(histogram_array)

        """
        STEP 2: Pixel mapping lookup table
        """
        transform_map = np.floor(255 * chistogram_array).astype(np.uint8)

        """
        STEP 3: Transformation
        """
        # flatten image array into 1D list
        img_list = list(img_array.flatten())

        # transform pixel values to equalize
        eq_img_list = [transform_map[p] for p in img_list]

        # reshape and write back into img_array
        eq_img_array = np.reshape(np.asarray(eq_img_list), img_array.shape)


        ######################################
        # WRITE EQUALIZED IMAGE TO FILE
        ######################################
        #convert NumPy array to pillow Image and write to file
        eq_img = Image.fromarray(eq_img_array, mode='L')
        eq_img.save(save_filename)