2. OpenCV-Python——阈值、平滑处理、形态学操作

一、阈值

ret, dst = cv2.threshold(src, thresh, maxval, type)

• src： 输入图，只能输入单通道图像，通常来说为灰度图

• dst： 输出图

• ret：  返回阈值的数值

• thresh： 阈值

• maxval： 当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值

• type：二值化操作的类型，包含以下5种类型： cv2.THRESH_BINARY；cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV

• cv2.THRESH_BINARY 超过阈值部分取maxval（最大值），否则取0

• cv2.THRESH_BINARY_INV THRESH_BINARY的反转

• cv2.THRESH_TRUNC 大于阈值部分设为阈值，否则不变

• cv2.THRESH_TOZERO 大于阈值部分不改变，否则设为0

• cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转

# *******************阈值**********************开始
import cv2
import matplotlib.pyplot as plt

img = cv2.imread('cat.jpg')                        # 读取图像
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    # 灰度化

ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in range(6):
    plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()
# *******************阈值**********************结束

二、平滑处理

# *******************平滑处理**********************开始
import cv2
import numpy as np
# import matplotlib.pyplot as plt

img = cv2.imread('lenaNoise.png')

# cv2.imshow('img', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

# 均值滤波
# 简单的平均卷积操作
blur = cv2.blur(img, (3, 3))  # 3*3的卷积核

# cv2.imshow('blur', blur)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

# 方框滤波
# 基本和均值一样，可以选择归一化，不做归一化容易越界溢出
box = cv2.boxFilter(img,-1,(3,3), normalize=True)  # -1表示在颜色通道上保持一致

# cv2.imshow('box', box)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

# 高斯滤波
# 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的
aussian = cv2.GaussianBlur(img, (5, 5), 1)

# cv2.imshow('aussian', aussian)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

# 中值滤波
# 相当于用中值代替
median = cv2.medianBlur(img, 5)  # 中值滤波

# cv2.imshow('median', median)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

# 展示所有的滤波结果
res = np.hstack((blur,aussian,median))  # vstack为垂直方向
#print (res)
cv2.imshow('median vs average', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************平滑处理**********************结束

三、形态学操作

1、腐蚀操作

# *******************形态学-腐蚀**********************开始
import cv2
import numpy as np

img = cv2.imread('dige.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1) # irerations迭代次数

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************形态学-腐蚀**********************结束

2、膨胀操作

# *******************形态学-膨胀**********************开始
import cv2
import numpy as np

img = cv2.imread('dige.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 先腐蚀
kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
# 后膨胀
kernel = np.ones((3,3),np.uint8)
dige_dilate = cv2.dilate(erosion,kernel,iterations = 1)

cv2.imshow('dilate', dige_dilate)
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************形态学-膨胀**********************结束

3、开运算与闭运算

# *******************形态学-开运算与闭运算**********************开始
import cv2
import numpy as np

# 开：先腐蚀，再膨胀
img = cv2.imread('dige.png')

kernel = np.ones((5,5),np.uint8)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

cv2.imshow('opening', opening)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 闭：先膨胀，再腐蚀
img = cv2.imread('dige.png')

kernel = np.ones((5,5),np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

cv2.imshow('closing', closing)
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************形态学-开运算与闭运算**********************结束

4、梯度运算

# *******************形态学-梯度运算**********************开始
import cv2
import numpy as np

# 梯度=膨胀-腐蚀
pie = cv2.imread('pie.png')
kernel = np.ones((7,7),np.uint8)
#---------------------------------------------------------
dilate = cv2.dilate(pie,kernel,iterations = 5)
erosion = cv2.erode(pie,kernel,iterations = 5)

res = np.hstack((dilate,erosion)) # 显示分别膨胀和腐蚀的结果
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

#---------------------------------------------------------
gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel) # 梯度运算
cv2.imshow('gradient', gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************形态学-梯度运算**********************结束

5、礼帽与黑帽

• 礼帽 = 原始输入-开运算结果
• 黑帽 = 闭运算-原始输入

# *******************形态学-礼帽与黑帽**********************开始
import cv2
import numpy as np

#-------------------------------------------------------
#礼帽
img = cv2.imread('dige.png')
cv2.imshow('image',img)
kernel = np.ones((7,7),np.uint8)
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
cv2.imshow('tophat', tophat)
cv2.waitKey(0)
cv2.destroyAllWindows()

#-------------------------------------------------------
#黑帽
img = cv2.imread('dige.png')
kernel = np.ones((7,7),np.uint8)
blackhat  = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT, kernel)
cv2.imshow('blackhat ', blackhat )
cv2.waitKey(0)
cv2.destroyAllWindows()
# *******************形态学-礼帽与黑帽**********************结束