文本挖掘 day5：文本挖掘与贝叶斯网络方法识别化学品安全风险因素

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt


img = cv.imread("../SampleImages/pomeranian.png", cv.IMREAD_COLOR)
rows,cols = img.shape[:2]
print(img.shape[:2])


#使用getAffineTransform来获得仿射变换的矩阵M
#cv.getAffineTransform(pts 1,pts 2)
#pts 1: 原图中的3个点坐标
#pts 2：经过放射变换后的3个点坐标
#参考资料：https://blog.csdn.net/weixin_45335726/article/details/122531876
pts1 = np.float32([[50,50],[200,50],[50,200]])
pts2 = np.float32([[100,100],[200,50],[100,250]])
M = cv.getAffineTransform(pts1,pts2)
img_affine_transform = cv.warpAffine(img, M, (cols,rows))


#显示图像
fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(10,8),dpi=100)
axes[0].imshow(img[:,:,::-1])
axes[0].set_title("Original")
axes[1].imshow(img_affine_transform[:,:,::-1])
axes[1].set_title("Affine Transformed")