【OpenCV基础 1】几何变换、形态学处理、阈值分割、区域提取和脱敏处理

目录

一、图像几何变化

1、对图片进行放大、缩小、水平放大和垂直放大

2、旋转、缩放、控制画布大小

二、图像形态学处理

1、梯度运算

2、闭运算

3、礼帽运算

4、黑帽运算

三、图像阈值分割

1、二值化处理

2、反二值化处理

3、截断阈值处理

4、超阈值零处理

5、低阈值零处理

6、自适应阈值处理

7、Otsu处理

四、图像处理基础

1、感兴趣区域的提取

2、人脸部分进行脱敏处理

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一、图像几何变化

我们以lena图片作为素材

1、对图片进行放大、缩小、水平放大和垂直放大

import cv2
import numpy as np'''1、将lena_color.jpg 放大到600*600
2、将lena_color.jgp 缩小到50*50
3、将lena_color.jgp 在水平方向放大到2位，垂直方向放大到1.5倍
4、将以上所有图像进行显示。'''img = cv2.imread('project_demo/class_picture/lena_color.jpg')
img1 = cv2.resize(img, (600, 600))
img2 = cv2.resize(img, (50, 50))
img3 = cv2.resize(img, (0, 0), fx=2, fy=1.5)
cv2.imshow('img1', img1)
cv2.imshow('img2', img2)
cv2.imshow('img3', img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

2、旋转、缩放、控制画布大小

'''
将lena_color.jpga、以图像中心为旋转中心，顺时针旋转60度，b、图像缩小为原来的0.4，c、画布大小为原始图像大小。
再将图像进行平移至原始图像的(0,0)->变换后图像的(50,25)
'''
from tkinter import W
import cv2
import numpy as np
# 读取图像
img = cv2.imread('project_demo/class_picture/lena_color.jpg')
# 图像旋转
h, w = img.shape[:2]    # 获取图像的宽高
M = cv2.getRotationMatrix2D((w / 2, h / 2), -60, scale=0.4) # 获取旋转矩阵
rotated1 = cv2.warpAffine(img, M, (w, h))   # 进行旋转
cv2.imshow('rotated1', rotated1)
# 图像平移
src = np.float32([[0, 0], [0, w-1], [h-1, 0]])  # 获取原始图像的三个点
dst = np.float32([[50, 25], [50, w + 25], [h + 50, 25]])    # 获取变换后的三个点
M2 = cv2.getAffineTransform(src, dst)   # 获取仿射变换矩阵
rotated2 = cv2.warpAffine(rotated1, M2, (w, h)) # 进行仿射变换
cv2.imshow('rotated2', rotated2)cv2.waitKey(0)
cv2.destroyAllWindows()

效果图： 

二、图像形态学处理

1、梯度运算

import cv2
import numpy as np# 形态学梯度运算
image = cv2.imread('project_demo/class_picture/gradient.bmp')
kernel = np.ones((3, 3), np.uint8)
gradient_image = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
cv2.imshow('Original Image', image)
cv2.imshow('Gradient Image', gradient_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

2、闭运算

image = cv2.imread('project_demo/class_picture/gradient.bmp')
kernel = np.ones((3, 3), np.uint8)
closing_image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
cv2.imshow('Original Image', image)
cv2.imshow('Closing Image', closing_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

3、礼帽运算

image = cv2.imread('project_demo/class_picture/lena.bmp')
image2 = cv2.imread('project_demo/class_picture/gradient.bmp')
kernel = np.ones((3, 3), np.uint8)
tophat_image = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel)
tophat_image2 = cv2.morphologyEx(image2, cv2.MORPH_TOPHAT, kernel)
cv2.imshow('Original Image', image)
cv2.imshow('Top Hat Image', tophat_image)
cv2.imshow('Original Image2', image2)
cv2.imshow('Top Hat Image2', tophat_image2)
cv2.waitKey(0)
cv2.destroyAllWindows()

4、黑帽运算

image = cv2.imread('project_demo/class_picture/lena.bmp')
image2 = cv2.imread('project_demo/class_picture/gradient.bmp')
kernel = np.ones((3, 3), np.uint8)
blackhat_image = cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, kernel)
blackhat_image2 = cv2.morphologyEx(image2, cv2.MORPH_BLACKHAT, kernel)
cv2.imshow('Original Image', image)
cv2.imshow('Black Hat Image', blackhat_image)
cv2.imshow('Original Image2', image2)
cv2.imshow('Black Hat Image2', blackhat_image2)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

三、图像阈值分割

1、二值化处理

import numpy as npimg = cv2.imread('project_demo/class_picture/lena_gray.jpg')
# 二进制阈值化，设阈值127
retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

2、反二值化处理

# 反二进制阈值化，设阈值127
retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

3、截断阈值处理

retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

4、超阈值零处理

# 超阈值化零处理，设阈值127
retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

5、低阈值零处理

# 低阈值零处理，设阈值127
retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

6、自适应阈值处理

# 自适应阈值处理
dst = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

7、Otsu处理

# Otsu处理
retval, dst = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

四、图像处理基础

1、感兴趣区域的提取

步骤：

        ① 读取一张图像，如果成功读取，则显示该图像，如果读取失败，则显示“The file is not exist”

        ② 读取图像

        ③ 对兴趣区域提取

'''
对兴趣区域提取
1.读取一张图像，如果成功读取，则显示该图像，如果读取失败，则显示“The file is not exist”
2.读取图像
3.对兴趣区域提取
'''
import cv2
import matplotlib.pyplot as plt
def show_plt():iamge_path = 'project_demo/class_picture/kongfu_panda.jpg'image = plt.imread(iamge_path)plt.imshow(image)plt.axis('off')plt.show()
def main():img = cv2.imread('project_demo/class_picture/kongfu_panda.jpg')if img is None:print('The file is not exist')else:# 提取兴趣区域img1 = img[70:325, 48:221]img2 = img[83:471, 377:592]img3 = img[259:481, 621:737]cv2.imshow('img1', img1)cv2.imshow('img2', img2)cv2.imshow('img3', img3)cv2.waitKey(0)cv2.destroyAllWindows()
if __name__ == '__main__':show_plt()main()

2、人脸部分进行脱敏处理

'''
对海报的人脸部分进行脱敏处理'''
import cv2
import matplotlib.pyplot as plt
import numpy as npdef show_plt():iamge_path = 'project_demo/class_picture/kongfu_panda.jpg'image = plt.imread(iamge_path)plt.imshow(image)plt.axis('off')plt.show()
def main():img = cv2.imread('project_demo/class_picture/police_story.png')cv2.namedWindow('image', cv2.WINDOW_NORMAL)cv2.resizeWindow('image', 500, 500)if img is None:print('The file is not exist')else:face = np.random.randint(0, 255, (600, 445, 3))img[50:650, 364:809, :] = facecv2.imshow('image', img)cv2.waitKey(0)cv2.destroyAllWindows()
if __name__ == '__main__':#show_pltmain()