INDEX
01 step — Equalizing Manually
02 step — And by using the OpenCV function

%matplotlib inline

from IPython.display import display, Math, Latex

import numpy as np

import matplotlib.pyplot as plt

from PIL import Image

img = Image.open('DATA/einstein.jpg')
plt.imshow(img)

<matplotlib.image.AxesImage at 0x1d0b37d2250>

img = np.asanyarray(img)

img.shape

(2354, 2560, 3)

import cv2

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img.shape

(2354, 2560)

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

<matplotlib.image.AxesImage at 0x1d0b415e100>

img.max()

255

img.min()

0

img.shape

(2354, 2560)

flat = img.flatten()

# 1 row 2354 x 2560 = 6.026.240

flat.shape

(6026240,)

plt.hist(flat, bins=50)

# formula for creating the histogram

display(Math(r'P_x(j) = \sum_{i=0}^{j} P_x(i)'))

# create our own histogram function

def get_histogram(image, bins):

  # 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

hist = get_histogram(flat, 256)

plt.plot(hist)

[<matplotlib.lines.Line2D at 0x1d0b4e4da90>]

# create our cumulative sum function

def cumsum(a):

  a = iter(a)

  b = [next(a)]

  for i in a:

    b.append(b[-1] + i)
   
    return np.array(b)

# execute the fn

cs = cumsum(hist)

# display the result

plt.plot(cs)

[<matplotlib.lines.Line2D at 0x1d0b4eafb20>]

# formula to calculate cumulation sum

display(Math(r's_k = \sum_{j=0}^{k} {\frac{n_j}{N}}'))

# re-normalize cumsum values to be between 0-255

# numerator & denomenator

nj = (cs - cs.min()) * 255

N = cs.max() - cs.min()

# re-normalize the cdf

cs = nj / N

plt.plot(cs)

[<matplotlib.lines.Line2D at 0x1d0b4f0b7c0>]

# cast it back to uint8 since we can't use floating point values in images

cs = cs.astype('uint8')

plt.plot(cs)

[<matplotlib.lines.Line2D at 0x1d0b5f8bd60>]

# get the value from cumulative sum for every index in flat, and set that as img_new

img_new = cs[flat]

# we see a much more evenly distributed histogram

plt.hist(img_new, bins=50)

# get the value from cumulative sum for every index in flat, and set that as img_new

img_new = cs[flat]

# we see a much more evenly distributed histogram

plt.hist(img_new, bins=50)

# put array back into original shape since we flattened it

img_new = np.reshape(img_new, img.shape)

img_new

array([[233, 231, 228, ..., 216, 216, 215],
       [233, 230, 228, ..., 215, 215, 214],
       [233, 231, 229, ..., 213, 213, 212],
       ...,
       [115, 107,  96, ..., 180, 187, 194],
       [111, 103,  93, ..., 187, 189, 192],
       [111, 103,  93, ..., 187, 189, 192]], dtype=uint8)

# set up side-by-side image display

fig = plt.figure()

fig.set_figheight(15)

fig.set_figwidth(15)

fig.add_subplot(1,2,1)

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

# display the new image

fig.add_subplot(1,2,2)

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

plt.show(block=True)

# Reading image via OpenCV and Equalize it right away!

img = cv2.imread('DATA/einstein.jpg',0)

equ = cv2.equalizeHist(img)

fig = plt.figure()

fig.set_figheight(15)

fig.set_figwidth(15)

fig.add_subplot(1,2,1)

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

# display the Equalized (equ) image

fig.add_subplot(1,2,2)

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

plt.show(block=True)

print("That´s it! Thank you once again!\nI hope will be helpful.")

That´s it! Thank you once again!
I hope will be helpful.