计算机视觉：6.2~6.5 图像的基本变换与仿射变换

# 图像的翻转
import cv2
import numpy as np

# 读取图片
doge = cv2.imread('./doge.jpg')

new_doge1 = cv2.flip(doge, flipCode=0)
new_doge2 = cv2.flip(doge, flipCode=1)
new_doge3 = cv2.flip(doge, flipCode=-1)

cv2.imshow('flip', np.hstack((doge, new_doge1, new_doge2, new_doge3)))
cv2.waitKey(0)
cv2.destroyAllWindows()

# 图像的翻转
import cv2
import numpy as np

doge = cv2.imread('./doge.jpg')
new_doge = dog[::-1, ::-1]

cv2.imshow('ndarray_flip', np.hstack((doge, new_doge)))
cv2.waitKey(0)
cv2.destroyAllWindows()

# 图像的旋转
import cv2
import numpy as np

# 读取图片
dog = cv2.imread('./dog.jpg')

new_dog1 = cv2.rotate(dog, rotateCode=cv2.ROTATE_90_CLOCKWISE)
new_dog2 = cv2.rotate(dog, rotateCode=cv2.ROTATE_180)
new_dog3 = cv2.rotate(dog, rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE)

cv2.imshow('flip', np.hstack((dog, new_dog1, new_dog2, new_dog3)))
cv2.waitKey(0)
cv2.destroyAllWindows()

# 仿射变换之平移
import cv2
import numpy as np

# 导入图片
dog = cv2.imread('./dog.jpg')

h, w, ch = dog.shape

# x轴正向偏移50，y轴正向偏移50
M = np.float32([[1, 0, 50], [0, 1, 50]])

# 注意OpenCV中先宽度后高度
new = cv2.warpAffine(dog, M, (w, h))

cv2.imshow('Affine', new)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 除了平移，仿射矩阵还可以完成图像的旋转
# 旋转同样需要进行矩阵计算，为了方便计算旋转矩阵
# 使用getRotationMatrix2D方法可以获得想要的旋转矩阵
import cv2
import numpy as np

# 导入图片
dog = cv2.imread('./dog.jpg')

h, w, ch = dog.shape

# 获取变换矩阵，逆时针45度转动并缩放至0.7倍
M = cv2.getRotationMatrix2D((w/2, h/2), 45, 0.7)

new = cv2.warpAffine(dog, M, (w, h))

cv2.imshow('Affine', np.hstack((dog, new)))
cv2.waitKey(0)
cv2.destroyAllWindows()

# 通过三点的起止位置来获得变换矩阵
import cv2
import numpy as np

dog = cv2.imread('./dog.jpg')
h, w, ch = dog.shape

# 顺时针旋转90度后的起止三个点的坐标
src = np.float32([[0,0], [100,0], [0,100]])
dst = np.float32([[100,0], [100,100], [0,0]])
# 需要原始图片的三点坐标，和变换之后三个对应的坐标
M = cv2.getAffineTransform(src, dst)
new = cv2.warpAffine(dog, M, (w, h))
cv2.imshow('Affine', np.hstack((dog, new)))
cv2.waitKey(0)
cv2.destroyAllWindows()

# 通过三点的起止位置来获得变换矩阵
import cv2
import numpy as np

img = cv2.imread('./book.jpg')

print(img.shape)
# 获取变换矩阵
# src是原图中对象的最小外接矩形的4个顶点坐标
src = np.float32([[0, 540], [2000, 540], [2500, 1986], [0, 1986]])
dst = np.float32([[0, 0], [2000, 0], [2100, 1446], [0, 1446]])

M = cv2.getPerspectiveTransform(src, dst)

# 透视变换
new = cv2.warpPerspective(img, M, (1600, 1446))
cv2.imshow('Affine_raw', img)
cv2.imshow('Affine_new', new)
cv2.waitKey(0)
cv2.destroyAllWindows()