histogram-of-an-images's Introduction

Histogram-of-an-images

Aim

To obtain a histogram for finding the frequency of pixels in an Image with pixel values ranging from 0 to 255. Also write the code using OpenCV to perform histogram equalization.

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1:

Read the gray and color image using imread()

Step2:

Print the image using imshow().

Step3:

Use calcHist() function to mark the image in graph frequency for gray and color image.

step4:

Use calcHist() function to mark the image in graph frequency for gray and color image.

Step5:

The Histogram of gray scale image and color image is shown.

Program:

Developed By: M Gautham
Register Number: 212221230027

import cv2
import matplotlib.pyplot as plt

gray_image=cv2.imread('cat.jpg')
hist=cv2.calcHist([gray_image],[0],None,[256],[0,256])
plt.imshow(gray_image)
plt.show()
plt.figure()
plt.title("histogram")
plt.xlabel('grayscale value')
plt.ylabel('pixel count')
plt.stem(hist)
plt.show()

color_image=cv2.imread('leo.jpg')
hist1=cv2.calcHist([color_image],[1],None,[250],[0,250])
plt.imshow(color_image)
plt.show()
plt.figure()
plt.title("histogram of color image :Green channel")
plt.xlabel('intensity value')
plt.ylabel('pixel count')
plt.stem(hist1)
plt.show()

gray_image = cv2.imread('cat.jpg',0)
equ=cv2.equalizeHist(gray_image)
cv2.imshow('Gray image',gray_image)
cv2.imshow('Equalized Image',equ)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

Input Grayscale Image and Color Image:

Histogram of Grayscale Image and any channel of Color Image:

Grey scale:

Color Image:

Histogram Equalization of Grayscale Image:

Result:

Thus the histogram for finding the frequency of pixels in an image with pixel values ranging from 0 to 255 is obtained. Also,histogram equalization is done for the gray scale image using OpenCV.

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