[PYTHON] Grayscale and brightness smoothing

Execution environment

Google Colaboratory

Preparing to load images with Google Colaboratory

python

from google.colab import files
from google.colab import drive
drive.mount('/content/drive')

Loading the required libraries

python

import cv2 #opencv
import matplotlib.pyplot as plt 
%matplotlib inline
img = plt.imread("/content/drive/My Drive/Colab Notebooks/img/Lenna.bmp")
#↑ plt from this article.I decided to read it with imread.

Various conversions

python

plt.figure(figsize=(9, 6), dpi=100,
           facecolor='w', linewidth=0, edgecolor='w')

#Original image
plt.subplot(3,3,1)
plt.imshow(img)
plt.subplot(3,3,4)
color = ('b','g','r')
for i,col in enumerate(color):
    histr = cv2.calcHist([img],[i],None,[256],[0,256])
    plt.plot(histr,color = col)
    plt.xlim([0,256])

#grayscale
plt.subplot(3,3,2)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) 
plt.imshow(gray)
plt.subplot(3,3,5)
plt.hist(gray.ravel(),256,[0,256])

#Brightness smoothing
plt.subplot(3,3,3)
dst = cv2.equalizeHist(gray)
plt.imshow(dst)
plt.subplot(3,3,6)
plt.hist(dst.ravel(),256,[0,256])

plt.show()

result

From the left Original / Grayscale / Brightness smoothing

grammar grayscale

python

cv2.cvtColor(src, cv2.COLOR_RGB2GRAY) 

Brightness smoothing

python

cv2.equalizeHist(src)

By smoothing the brightness, the histogram spreads evenly, It's easier to understand the light and dark. It seems better to do this to detect the feature.