■ Similarity Transform(相似变换)
– Similarity Transform(相似变换) = Rotation(旋转) + Translation(平移) + Scale(放缩)
● 性质:Right angle is still right angle (保角性)
代码:
import cv2
import matplotlib.pyplot as plt
import numpy as np
img = cv2.imread('lenna.jpg', 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# print(img.shape)
# 得到相似变换的矩阵 # center:旋转中心 angle:旋转角度 scale:缩放比例
M = cv2.getRotationMatrix2D(center = (img.shape[0]/2,img.shape[1]/2),
angle = 30,
scale = 0.5)
# 原图像按照相似矩阵进行相似变换 三个参数:原图像,相似矩阵,画布面积
img_rotate = cv2.warpAffine(img, M, (img.shape[0], img.shape[1]))
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(img_rotate)
plt.show()
原图和相似变换后的图像:
■ Affine Transform(仿射变换)
● 性质:Parallel lines are still parallel lines(不再具有保角性,具有保平行性)
● 三个非共线的点对(6 parameters)确定一个仿射变换。
代码:
import cv2
import matplotlib.pyplot as plt
import numpy as np
# 3 Src(原始) Points + 3 Dst(目标) Points
# cols:列/长 rows:行/宽
img = cv2.imread('lenna.jpg', 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# print(img.shape)
cols = img.shape[0]
rows = img.shape[1]
pt1 = np.float32([[0,0], [cols, 0], [0, rows]])
pt2 = np.float32([[cols*0.3, rows*0.3], [cols*0.8, rows*0.2], [cols*0.1, rows*0.9]])
# [[0,0], [cols, 0], [0, rows]] --> [[cols*0.3, rows*0.3], [cols*0.8, rows*0.2], [cols*0.1, rows*0.9]]
M = cv2.getAffineTransform(pt1, pt2) # 仿射变换矩阵
dst = cv2.warpAffine(img, M, (cols, rows))
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(dst)
plt.show()
原图和相似变换后的图像:
■ Perspective Transform(透视/投影变换)
● 性质:Lines are still lines(不保角,不保平行,保直线性)
● 四个非共线的点对(8 parameters)确定一个透视变换。
代码:
import cv2
import matplotlib.pyplot as plt
import numpy as np
img = cv2.imread('lenna.jpg', 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
width = img.shape[1]
height = img.shape[0]
pts1 = np.float32([[0,0], [width,0], [0,height], [width,height]])
pts2 = np.float32([[width*0.1,height*0.1], [width*0.9, width*0.1], [height*0.2,height*0.8], [width*0.7,height*0.7]])
M_warp = cv2.getPerspectiveTransform(pts1, pts2) # 单应性矩阵
img_warp = cv2.warpPerspective(img, M_warp, (width, height))
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(img_warp)
plt.show()
原图和透视变换后的图像: