7. Perspective Transformation

7.1. Perspective Transformation

Perspective transformation is also called projection transformation. The affine transformation we often talk about is a special case of perspective transformation. The purpose of perspective transformation is to transform objects that are straight lines in reality into straight lines in the image. Perspective transformation can map a rectangle to an arbitrary quadrilateral.

cv2. warpPerspective(src, M, dsize[,flag, [,borderMode[,borderValue]]])

src: source image

M: 3X3 transformation matrix

dsize: output image size.

flags: interpolation method, default INTER_LINEAR (bilinear interpolation), when WARP_INVERSE_MAP, it means that M is an inverse transformation, which can achieve an inverse transformation from the target dst to src.

borderMode: border type. The default is BORDER_CONSTANT. When the value is BORDER_TRANSPARENT, the values in the target image are not changed, and these values correspond to the outliers in the original image.

borderValue: Boundary value, default is 0.

Like affine transformation, OpenCV still provides a function cv2.getPerspectiveTransform() to provide the transformation matrix above.

The function is as follows:

cv2.getPerspectiveTransform(matSrc, matDst)

matSrc: Four vertex coordinates of the input image.

matDst: Four vertex coordinates of the output image.

7.2. Actual effect display

Code path:

/home/pi/DOGZILLA_Lite_class/4.Open Source CV/B.Geometric_Transformations/07_Perspective_Transformation.ipynb

​x
import cv2
import numpy as np
import matplotlib.pyplot as plt
​
img = cv2.imread('yahboom.jpg',1)
​
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#src 4->dst 4 (左上角，左下角，右上角，右下角 Top left, bottom left, top right, bottom right)
matSrc = np.float32([[200,100],[200,400],[600,100],[width-1,height-1]])
matDst = np.float32([[200,200],[200,300],[500,200],[500,400]])
#组合
matAffine = cv2.getPerspectiveTransform(matSrc,matDst)# mat 1 src 2 dst
dst = cv2.warpPerspective(img,matAffine,(width,height))
img_bgr2rgb = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
plt.imshow(img_bgr2rgb)
plt.show()