In [1]:
# to run in google colab
import sys
if "google.colab" in sys.modules:
def download_from_web(url):
import requests
response = requests.get(url)
if response.status_code == 200:
with open(url.split("/")[-1], "wb") as file:
file.write(response.content)
else:
raise Exception(f"Failed to download the image. Status code: {response.status_code}")
download_from_web(
"https://github.com/YoniChechik/AI_is_Math/raw/master/c_02a_basic_image_processing/Unequalized_Hawkes_Bay_NZ.jpg"
)
In [2]:
import cv2
import matplotlib.pyplot as plt
import numpy as np
figsize = (10, 10)
First, read the image as grayscale
In [3]:
# read as grayscale
I = cv2.imread("Unequalized_Hawkes_Bay_NZ.jpg", 0)
plt.figure(figsize=figsize)
plt.imshow(I, cmap="gray", vmin=0, vmax=255)
plt.title("Original image")
plt.show()
Let's start by calculating and showing the original histogram
In [4]:
bins_edges_min_max = [0, 256]
num_bins = 256
bin_count, bins_edges = np.histogram(I, num_bins, bins_edges_min_max)
bins_start = bins_edges[:-1]
In [5]:
def draw_hist(x_axis, input):
fig, ax = plt.subplots(figsize=figsize)
# why not using plt.hist? because we want to plot also some derivations of this hist, so this is easier
plt.bar(x_axis, input, width=input.shape[0] / (x_axis[-1] - x_axis[0] + 1))
return fig, ax
draw_hist(bins_start, bin_count)
plt.title("Original histogram")
plt.show()
Normalize the histogram to gat a discrete PDF
In [6]:
pdf = bin_count / np.sum(bin_count)
draw_hist(bins_start, pdf)
plt.title("Original PDF")
plt.show()
Get the CDF by calculating the cumulative sum of the pdf data
In [7]:
cdf = np.cumsum(pdf)
plt.figure(figsize=figsize)
plt.plot(cdf)
plt.title("Original CDF")
plt.show()
In [8]:
fig, ax = draw_hist(bins_start, pdf)
ax.plot(cdf * np.max(pdf), "r")
plt.title("Original PDF+ const*CDF to show the connection between the two")
plt.show()
The final step is to un-normalize the CDF to become the equalization function
In [9]:
f_eq = np.round(cdf * 255).astype(int)
f_eq
Out[9]:
Use the equalization function to get the equalized image
In [10]:
I_eq = f_eq[I]
plt.figure(figsize=figsize)
plt.imshow(I_eq, cmap="gray", vmin=0, vmax=255)
plt.title("equalized image")
plt.show()
Plot the equalized histogram, PDF and CDF
In [11]:
bin_count, bins_edges = np.histogram(I_eq, num_bins, bins_edges_min_max)
bins_start = bins_edges[:-1]
draw_hist(bins_start, bin_count)
plt.title("equalized histogram")
plt.show()
In [12]:
pdf = bin_count / np.sum(bin_count)
cdf = np.cumsum(pdf)
fig, ax = draw_hist(bins_start, pdf)
ax.plot(cdf * np.max(pdf), "r")
plt.title("equalized PDF and const*CDF")
plt.show()
In [13]:
I_eq_cv2 = cv2.equalizeHist(I)
plt.figure(figsize=figsize)
plt.imshow(I_eq_cv2, cmap="gray", vmin=0, vmax=255)
plt.title("cv2.equalizeHist() result")
plt.show()
