Python OpenCV – Affine Transformation

21 Apr, 2020
OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. When it integrated with various libraries, such as Numpuy, Python is capable of processing the OpenCV array structure for analysis.
Note: For more information, refer to OpenCV Python Tutorial
In Affine transformation, all parallel lines in the original image will still be parallel in the output image. To find the transformation matrix, we need three points from input image and their corresponding locations in the output image. Then cv2.getAffineTransform will create a 2×3 matrix which is to be passed to cv2.warpAffine.
cv2.getAffineTransform method:
Syntax: cv2.getPerspectiveTransform(src, dst)
Parameters:src: Coordinates of quadrangle vertices in the source image.dst: Coordinates of the corresponding quadrangle vertices in the destination image.
cv2.warpAffine method:
Syntax: cv2.warpAffine(src, M, dsize, dst, flags, borderMode, borderValue)
Parameters:src: input image.dst: output image that has the size dsize and the same type as src.M: transformation matrix.dsize: size of the output image.flags: combination of interpolation methods (see resize() ) and the optional flagWARP_INVERSE_MAP that means that M is the inverse transformation (dst->src).borderMode: pixel extrapolation method; when borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the “outliers” in the source image are not modified by the function.borderValue: value used in case of a constant border; by default, it is 0.
Example 1:
import cv2import numpy as npfrom matplotlib import pyplot as plt    img = cv2.imread('food.jpeg')rows, cols, ch = img.shape  pts1 = np.float32([[50, 50],                   [200, 50],                    [50, 200]])  pts2 = np.float32([[10, 100],                   [200, 50],                    [100, 250]])  M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(img, M, (cols, rows))  plt.subplot(121)plt.imshow(img)plt.title('Input')  plt.subplot(122)plt.imshow(dst)plt.title('Output')  plt.show()  # Displaying the imagewhile(1):          cv2.imshow('image', img)    if cv2.waitKey(20) & 0xFF == 27:        break          cv2.destroyAllWindows()
Output:
Example 2:
import cv2import numpy as npfrom matplotlib import pyplot as plt    img = cv2.imread('food.jpeg')rows, cols, ch = img.shape  pts1 = np.float32([[50, 50],                    [200, 50],                   [50, 200]])  pts2 = np.float32([[10, 100],                   [200, 50],                    [100, 250]])  M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(img, M, (cols, rows))  plt.subplot(121)plt.imshow(img)plt.title('Input')  plt.subplot(122)plt.imshow(dst)plt.title('Output')plt.show()  # Displaying the imagewhile(1):          cv2.imshow('image', img)    if cv2.waitKey(20) & 0xFF == 27:        break          cv2.destroyAllWindows()
Output:
Image-Processing
Python-OpenCV
Python
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