IMAGE-TRANSFORMATIONS

Aim

To perform image transformation such as Translation, Scaling, Shearing, Reflection, Rotation and Cropping using OpenCV and Python.

Software Required:

Anaconda - Python 3.7

Algorithm:

Step1:

Import the required packages.

Step2:

Load the image file in the program.

Step3:

Use the techniques for Translation, Scaling, Shearing, Reflection, Rotation and Cropping using OpenCV and Python.

Step4:

Display the modified image output.

Step5:

End the program.

Program:

Developed By: JOEL P
Register Number: 212222230057

i)Image Translation
import numpy as np
import cv2
import matplotlib.pyplot as plt
input_image = cv2.imread( "car.jpg")
input_image = cv2.cvtColor(input_image,cv2.COLOR_BGR2RGB)
plt.axis( 'off')
plt.imshow(input_image)
plt.show()
rows,cols,dim =input_image.shape
M =np.float32([[1,0,100],
               [0,1,100],
               [0,0,1]])
translated_image =cv2.warpPerspective(input_image, M, (cols, rows))
plt.axis('off')
plt.imshow(translated_image)
plt.show()

ii) Image Scaling

import numpy as np
import cv2
import matplotlib.pyplot as plt
in_img=cv2.imread("timesquare.jpg")
in_img=cv2.cvtColor(in_img,cv2.COLOR_BGR2RGB)
rows,cols,dim=in_img.shape
M=np.float32([[1.5,0 ,0],
              [0,1.8,0],
              [0,0,1]])
scaled_img=cv2.warpPerspective(in_img, M,(cols,rows))
plt.axis('off')
plt.imshow(scaled_img)
plt.show()

iii)Image shearing

import numpy as np
import cv2
import matplotlib.pyplot as plt
in_img=cv2.imread("sphere.jpg")
in_img=cv2.cvtColor(in_img,cv2.COLOR_BGR2RGB)
rows,cols,dim=in_img.shape
M_x=np.float32([[1,0.5,0],
                [0,1 ,0],
                [0,0 ,1]])
M_y=np.float32([[1,  0,0],
                [0.5,1,0],
                [0,  0,1]])
sheared_img_x=cv2.warpPerspective(in_img,M_x,(int(cols),int(rows)))
sheared_img_y=cv2.warpPerspective(in_img,M_y,(int(cols),int(rows)))
plt.axis('off')
plt.imshow(sheared_img_x)
plt.show()
plt.axis('off')
plt.imshow(sheared_img_y)
plt.show()

iv)Image Reflection

import numpy as np
import cv2
import matplotlib.pyplot as plt
in_img=cv2.imread("statue.jpg")
in_img=cv2.cvtColor(in_img,cv2.COLOR_BGR2RGB)
rows,cols,dim=in_img.shape
M_x=np.float32([[1,  0,0  ],
                [0,-1,rows],
                [0,0,1  ]])
M_y=np.float32([[-1,0,cols],
                [ 0,1,0  ],
                [ 0,0,1  ]])
reflect_x=cv2.warpPerspective(in_img,M_x,(int(cols),int(rows)))
reflect_y=cv2.warpPerspective(in_img,M_y,(int(cols),int(rows)))
plt.axis('off')
plt.imshow(reflect_x)
plt.show()
plt.axis('off')
plt.imshow(reflect_y)
plt.show()

v)Image Rotation

import numpy as np
import cv2
import matplotlib.pyplot as plt
in_img=cv2.imread("photograph.jpg")
in_img=cv2.cvtColor(in_img,cv2.COLOR_BGR2RGB)
rows,cols,dim=in_img.shape
angle=np.radians(10)
M=np.float32([[np.cos(angle),-(np.sin(angle)),0],
              [np.sin(angle),np.cos(angle),0],
              [0,0,1]])
rotated_img=cv2.warpPerspective(in_img,M,(int(cols),int(rows)))
plt.axis('off')
plt.imshow(rotated_img)
plt.show()

vi)Image Cropping

import numpy as np
import cv2
import matplotlib.pyplot as plt
in_img = cv2.imread("beach.jpg")
in_img = cv2.cvtColor(in_img,cv2.COLOR_BGR2RGB)
plt.imshow(in_img)
plt.show()
cropped_img=in_img[50:200 ,50:500]
plt.imshow(cropped_img)
plt.show()

Output:

ORINGINAL IMAGE:

i)Image Translation

ii) Image Scaling

iii)Image shearing

iv)Image Reflection

v)Image Rotation

vi)Image Cropping

Result:

Thus the different image transformations such as Translation, Scaling, Shearing, Reflection, Rotation and Cropping are done using OpenCV and python programming.

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