In this chapter, we will learn how to resize and rotate an image with the help of OpenCVPython.

Resize an Image

It is possible to scale up or down an image with the use of cv2.resize() function.

The resize() function is used as follows −

resize(src, dsize, dst, fx, fy, interpolation)

In general, interpolation is a process of estimating values between known data points.

When graphical data contains a gap, but data is available on either side of the gap or at a few specific points within the gap. Interpolation allows us to estimate the values within the gap.

In the above resize() function, interpolation flags determine the type of interpolation used for calculating size of destination image.

Types of Interpolation

The types of interpolation are as follows −

• INTER_NEAREST − A nearest-neighbor interpolation.

• INTER_LINEAR − A bilinear interpolation (used by default)

• INTER_AREA − Resampling using pixel area relation. It is a preferred method for image decimation but when the image is zoomed, it is similar to the INTER_NEAREST method.

• INTER_CUBIC − A bicubic interpolation over 4×4 pixel neighborhood

• INTER_LANCZOS4 − A Lanczos interpolation over 8×8 pixel neighborhood

Preferable interpolation methods are cv2.INTER_AREA for shrinking and cv2.INTER_CUBIC (slow) & cv2.INTER_LINEAR for zooming.

Example

Following code resizes the ‘messi.jpg’ image to half its original height and width.

import numpy as np
import cv2
img = cv2.imread(''messi.JPG'',1)
height, width = img.shape[:2]
res = cv2.resize(img,(int(width/2), int(height/2)), interpolation =
cv2.INTER_AREA)

cv2.imshow(''image'',res)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

Rotate an image

OpenCV uses affine transformation functions for operations on images such as translation and rotation. The affine transformation is a transformation that can be expressed in the form of a matrix multiplication (linear transformation) followed by a vector addition (translation).

The cv2 module provides two functions cv2.warpAffine and cv2.warpPerspective, with which you can have all kinds of transformations. cv2.warpAffine takes a 2×3 transformation matrix while cv2.warpPerspective takes a 3×3 transformation matrix as input.

To find this transformation matrix for rotation, OpenCV provides a function, cv2.getRotationMatrix2D, which is as follows −

getRotationMatrix2D(center, angle, scale)

We then apply the warpAffine function to the matrix returned by getRotationMatrix2D() function to obtain rotated image.

Following program rotates the original image by 90 degrees without changing the dimensions −

Example

import numpy as np
import cv2
img = cv2.imread(''OpenCV_Logo.png'',1)
h, w = img.shape[:2]

center = (w / 2, h / 2)
mat = cv2.getRotationMatrix2D(center, 90, 1)
rotimg = cv2.warpAffine(img, mat, (h, w))
cv2.imshow(''original'',img)
cv2.imshow(''rotated'', rotimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

Original Image

Rotated Image