opencv提供函数



#形态学处理腐蚀
import sys,cv2,math
import numpy as np
import matplotlib.pyplot as plt
def calcGrayHist(image):
rows,cols = image.shape
grayHist = np.zeros([256],np.uint64)
for r in range(rows):
for c in range(cols):
grayHist[image[r][c]] +=1#把图像灰度值作为索引
return(grayHist)
def otsu(image):
rows,cols = image.shape
#灰度直方图
grayHist = calcGrayHist(image)
#归一化灰度直方图
uniformGrayHist = grayHist/float(rows*cols)
#计算零阶累积矩和一阶累积矩
zeroCumuMoment = np.zeros([256],np.float32)
oneCumuMoment = np.zeros([256],np.float32)
for k in range(256):
if k==0:
zeroCumuMoment[k] = uniformGrayHist[0]
oneCumuMoment[k] = (k)uniformGrayHist[0]
else:
zeroCumuMoment[k] = zeroCumuMoment[k-1]+uniformGrayHist[k]
oneCumuMoment[k] = oneCumuMoment[k-1]+kuniformGrayHist[k]
#计算类间方差
variance = np.zeros([256],np.float32)
for k in range(255):
if zeroCumuMoment[k] == 0 or zeroCumuMoment[k] ==1:
variance[k] = 0
else:
variance[k]=math.pow(oneCumuMoment[255]zeroCumuMoment[k]-
oneCumuMoment[k],2)/(zeroCumuMoment[k](1.0-zeroCumuMoment[k]))
threshLoc = np.where(variance[0:255]==np.max(variance[0:255]))
thresh = threshLoc[0][0]
#
threshold = np.copy(image)
threshold[threshold>thresh]=255
threshold[threshold<=thresh]=0
return(thresh,threshold)
if name == ‘main’:
src = cv2.imread(‘E:/sy2/6/img7.jpg’,cv2.IMREAD_GRAYSCALE)
t,I = otsu(src)
#创建矩形结构元
s=cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
#腐蚀图像，迭代次数默认1
r = cv2.erode(I,s)
#边界提取
e = I- r
cv2.imshow(‘I’,I)
cv2.imshow(‘r’,r)
cv2.imshow(‘e’,e)
cv2.waitKey(0)
cv2.destroyAllWindows()