Image Transformations using OpenCV in Python

In this tutorial, we are going to learn Image Transformation using the OpenCV module in Python.

What is Image Transformation?

Image Transformation involves the transformation of image data in order to retrieve information from the image or preprocess the image for further usage. In this tutorial we are going to implement the following image transformation:

• Image Translation
• Reflection 
• Rotation
• Scaling
• Cropping
• Shearing in x-axis
• Shearing in y-axis

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.  By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as NumPy, Python is capable of processing the OpenCV array structure for analysis.

Image Translation

In computer vision or image processing, image translation is the rectilinear shift of an image from one location to another, so the shifting of an object is called translation. In other words,  translation is the shifting of an object’s location.

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
M = np.float32([[1, 0, 100], [0, 1, 50]])
dst = cv.warpAffine(img, M, (cols, rows))
cv.imshow('img', dst)
cv.waitKey(0)
cv.destroyAllWindows()

In the above code, we have imported NumPy and OpenCV module then read the image by using imread() function, and then translation takes place with the warpAffine() method which is defined as follows:

In the first argument, we passed the image, in the second argument it takes a matrix as a parameter in the matrix we give x = 100, which means we are telling the function to shift the image 70 units on the right side and y= 50, which means we are telling the function to shift the image 50 units downwards.  In the third argument, where we mentioned the cols and rows, we told the function to do not to crop the image from both the x and y sides.

dst = cv.warpAffine(img,M,(cols,rows))

Output:

Image Reflection

Image reflection is used to flip the image vertically or horizontally. For reflection along the x-axis, we set the value of Sy to -1, Sx to 1, and vice-versa for the y-axis reflection.

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
M = np.float32([[1,  0, 0],
                [0, -1, rows],
                [0,  0, 1]])
reflected_img = cv.warpPerspective(img, M,
                                   (int(cols),
                                    int(rows)))
cv.imshow('img', reflected_img)
cv.imwrite('reflection_out.jpg', reflected_img)
cv.waitKey(0)
cv.destroyAllWindows()

To flip the image horizontally:

M = np.float32([[1, 0, 0], [0, -1, rows],[0,  0, 1]])

To flip the image vertically:

M = np.float32([[-1, 0, cols], [0, 1, 0], [0, 0, 1]])

Output:

Image Rotation

Image rotation is a common image processing routine with applications in matching, alignment, and other image-based algorithms, in image rotation the image is rotated by a definite angle. It is used extensively in data augmentation, especially when it comes to image classification.

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
M = np.float32([[1,  0, 0], [0, -1, rows], [0,  0, 1]])
img_rotation = cv.warpAffine(img,
                             cv.getRotationMatrix2D((cols/2, rows/2),
                                                    30, 0.6),
                             (cols, rows))
cv.imshow('img', img_rotation)
cv.imwrite('rotation_out.jpg', img_rotation)
cv.waitKey(0)
cv.destroyAllWindows()

We have used the get rotation matrix function to define the parameter required in the warpAffine function to tell the function to make a matrix that can give a required rotation angle( here it is 30 degrees) with shrinkage of the image by 40%.   

img_rotation = cv.warpAffine(img,
                             cv.getRotationMatrix2D((cols/2, rows/2), 30, 0.6),
                             (cols, rows))

Output:

Image Scaling 

Image scaling is a process used to resize a digital image. We perform two things in the image scaling either we enlarge the image or we shrink the image, OpenCV has a built-in function cv2.resize() for image scaling.

Shrinking an image:

img_shrinked = cv2.resize(image, (350, 300),
                          interpolation = cv2.INTER_AREA) 

Note:  Here 350 and 300 are the height and width of the shrunk image respectively

Enlarging Image:

img_enlarged = cv2.resize(img_shrinked, None,
                          fx=1.5, fy=1.5,
                          interpolation=cv2.INTER_CUBIC)

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
img_shrinked = cv.resize(img, (250, 200),
                         interpolation=cv.INTER_AREA)
cv.imshow('img', img_shrinked)
img_enlarged = cv.resize(img_shrinked, None,
                         fx=1.5, fy=1.5,
                         interpolation=cv.INTER_CUBIC)
cv.imshow('img', img_enlarged)
cv.waitKey(0)
cv.destroyAllWindows()

Output:

Image Cropping

Cropping is the removal of unwanted outer areas from an image.

cropped_img = img[100:300, 100:300]

OpenCV loads the image as a NumPy array, we can crop the image simply by indexing the array, in our case, we choose to get 200 pixels from 100 to 300 on both axes.

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
cropped_img = img[100:300, 100:300]
cv.imwrite('cropped_out.jpg', cropped_img)
cv.waitKey(0)
cv.destroyAllWindows()

Output:

Image Shearing in X-Axis

While the shearing image is on the x-axis, the boundaries of the image that are parallel to the x-axis keep their location, and the edges parallel to the y-axis change their place depending on the shearing factor.

M = np.float32([[1, 0.5, 0], [0, 1, 0], [0, 0, 1]])
sheared_img = cv.warpPerspective(img, M,
                                 (int(cols*1.5),
                                 int(rows*1.5)))

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
M = np.float32([[1, 0.5, 0], [0, 1, 0], [0, 0, 1]])
sheared_img = cv.warpPerspective(img, M, (int(cols*1.5), int(rows*1.5)))
cv.imshow('img', sheared_img)
cv.waitKey(0)
cv.destroyAllWindows()

Output:

Image Shearing in Y-Axis

When shearing is done in the y-axis direction, the boundaries of the image that are parallel to the y-axis keep their location, and the edges parallel to the x-axis change their place depending on the shearing factor.

M = np.float32([[1, 0, 0], [0.5, 1, 0], [0, 0, 1]])
sheared_img = cv.warpPerspective(img, M,
                                 (int(cols*1.5),
                                 int(rows*1.5)))

Python3

import numpy as np
import cv2 as cv
img = cv.imread('girlImage.jpg', 0)
rows, cols = img.shape
M = np.float32([[1,   0, 0], [0.5, 1, 0], [0,   0, 1]])
sheared_img = cv.warpPerspective(img, M, (int(cols*1.5), int(rows*1.5)))
cv.imshow('sheared_y-axis_out.jpg', sheared_img)
cv.waitKey(0)
cv.destroyAllWindows()

Output: