from matplotlib import pyplot as plt
import numpy as np
import cv2
%matplotlib inline

img = cv2.imread('images/low_contrast.jpg', 0)

plt.imshow(img, cmap='gray')

def make_histogram(image, bins=256):
    
    # array with size of bins, set to zeros
    histogram = np.zeros(bins)
    
    # loop through pixels and sum up counts of pixels
    for pixel in image:
        histogram[pixel] += 1
    
    # return our final result
    return histogram

def cumsum(values):
    result = [values[0]]
    for i in values[1:]:
        result.append(result[-1] + i)
    return result

def normalize(entries):
    
    numerator = (entries - np.min(entries)) * 255
    denorminator = np.max(entries) - np.min(entries)

    # re-normalize the cdf
    result = numerator / denorminator
    result.astype('uint8') # convert float into int

    return result

def equalizeHist(img):
    
    flatten_img = img.flatten() # convert array into 1D
    
    cumulativeSum = cumsum(make_histogram(flatten_img))
    
    cumulativeSum_norm = normalize(cumulativeSum)
    
    img_new_his = cumulativeSum_norm[flatten_img]
    
    # convert array back to original shape
    img_new = np.reshape(img_new_his, img.shape)
    
    return img_new, cumulativeSum_norm

def drawImage(orignal, result, hist):

    fig, axes = plt.subplots(2, 2, figsize=(10, 7))
    axes[0, 0].imshow(orignal, cmap='gray')
    axes[0, 0].set_title('Result before equalization')
    
    axes[1, 0].hist(orignal.flatten(), 256, [0,256])
    
    
    axes[0, 1].imshow(result, cmap='gray')
    axes[0, 1].set_title('Result after equalization')
    
    # Here you need to calculate hist of resulted image
    axes[1, 1].hist(result.ravel(),256,[0,256]);
    
    fig.savefig('Result.png')

result, normalized_cumsum = equalizeHist(img)

drawImage(img, result, normalized_cumsum)