COLOR_CONVERSIONS_OF-IMAGE

AIM

To write a python program using OpenCV to do the following image manipulations.

i) Read, display, and write an image.

ii) Access the rows and columns in an image.

iii) Cut and paste a small portion of the image.

iv)To perform the color conversion between RGB, BGR, HSV, and YCbCr color models.

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1:

Choose an image and save it as a filename.jpg ,

Step2:

Use imread(filename, flags) to read the file.

Step3:

Use imshow(window_name, image) to display the image.

Step4:

Use imwrite(filename, image) to write the image.

Step5:

End the program and close the output image windows.

Step6:

Convert BGR and RGB to HSV and GRAY

Step7:

Convert HSV to RGB and BGR

Step8:

Convert RGB and BGR to YCrCb

Step9:

Split and Merge RGB Image

Step10:

Split and merge HSV Image

Program:

Developed By:S.THIRISHA

Register Number: 212222230160

Output:

i) Read and display the image

    import cv2
    image=cv2.imread('dog.png',1)
    image=cv2.resize(image,(300,300))
    cv2.imshow('3SHA',image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

ii)Write the image

    import cv2
    image=cv2.imread('dog.png',0)
    cv2.imwrite('demos.jpg',image)

iii)Shape of the Image

    import cv2
    image=cv2.imread('dop.png',1)
    print(image.shape)

iv)Access rows and columns

import random
    import cv2
    image=cv2.imread('dog.png',1)
    image=cv2.resize(image,(500,500))
    for i in range (250,500):
      for j in range(image.shape[1]):
          image[i][j]=[random.randint(0,255),
                       random.randint(0,255),
                       random.randint(0,255)] 
    cv2.imshow('part image',image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

v)Cut and paste portion of image

  import cv2
  image=cv2.imread('dog.png',1)
  image=cv2.resize(image,(300,300))
  tag =image[150:200,110:160]
  image[110:160,150:200] = tag
  cv2.imshow('image1',image)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

vi) BGR and RGB to HSV and GRAY

import cv2
img = cv2.imread('dog.png',1)
img = cv2.resize(img,(200,200))
cv2.imshow('Original Image',img)

hsv1 = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
cv2.imshow('BGR2HSV',hsv1)

hsv2 = cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
cv2.imshow('RGB2HSV',hsv2)

gray1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('BGR2GRAY',gray1)

gray2 = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
cv2.imshow('RGB2GRAY',gray2)

cv2.waitKey(0)
cv2.destroyAllWindows()

vii) HSV to RGB and BGR

import cv2
img = cv2.imread('dog.png')
img = cv2.resize(img,(200,200))

img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
cv2.imshow('Original HSV Image',img)

RGB = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
cv2.imshow('2HSV2BGR',RGB)

BGR = cv2.cvtColor(img,cv2.COLOR_HSV2BGR)
cv2.imshow('HSV2RGB',BGR)

cv2.waitKey(0)
cv2.destroyAllWindows()

viii) RGB and BGR to YCrCb

import cv2
img = cv2.imread('dog.png')
img = cv2.resize(img,(200,200))
cv2.imshow('Original RGB Image',img)

YCrCb1 = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
cv2.imshow('RGB-2-YCrCb',YCrCb1)

YCrCb2 = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
cv2.imshow('BGR-2-YCrCb',YCrCb2)

cv2.waitKey(0)
cv2.destroyAllWindows()

ix) Split and merge RGB Image

import cv2
img = cv2.imread('dog.png',1)
img = cv2.resize(img,(200,200))

R = img[:,:,2]
G = img[:,:,1]
B = img[:,:,0]

cv2.imshow('R-Channel',R)
cv2.imshow('G-Channel',G)
cv2.imshow('B-Channel',B)

merged = cv2.merge((B,G,R))
cv2.imshow('Merged RGB image',merged)

cv2.waitKey(0)
cv2.destroyAllWindows()

x) Split and merge HSV Image

import cv2
img = cv2.imread("dog.png",1)
img = cv2.resize(img,(200,200))
img=cv2.cvtColor(img,cv2.COLOR_RGB2HSV)

H,S,V=cv2.split(img)

cv2.imshow('Hue',H)
cv2.imshow('Saturation',S)
cv2.imshow('Value',V)

merged = cv2.merge((H,S,V))
cv2.imshow('Merged',merged)

cv2.waitKey(0)
cv2.destroyAllWindows()

Result:

Thus the images are read, displayed, and written ,and color conversion was performed between RGB, HSV and YCbCr color models successfully using the python program.

thirisha-s / color_conversions_of-image Goto Github PK