熵算法阈值

#熵算法阈值
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 threshEntroy(image):
    rows,cols = image.shape
    #灰度直方图
    grayHist = calcGrayHist(image)
    #归一化灰度直方图
    normGrayHist = grayHist/float(rows*cols)
    #计算累加直方图
    zeroCumuMoment = np.zeros([256],np.float32)
    for k in range(256):
        if k==0:
            zeroCumuMoment[k] = normGrayHist[k]
        else:
            zeroCumuMoment[k] = zeroCumuMoment[k-1] + normGrayHist[k]
    #计算各个灰度级的熵
    entropy = np.zeros([256],np.float32)
    for k in range(256):
        if k==0:
            if normGrayHist[k] ==0:
                entropy[k]==0
            else:
                entropy[k] = -normGrayHist[k]*math.log10(normGrayHist[k])
        else:
            if normGrayHist[k] ==0:
                entropy[k]=entropy[k-1]
            else:
                entropy[k]=entropy[k-1]-normGrayHist[k]*math.log10(normGrayHist[k])
    #找阈值
    fT = np.zeros([256],np.float32)
    ft1,ft2 = 0.0,0.0
    totalEntroy = entropy[255]
    for k in range(255):
        #找最大值
        maxFront = np.max(normGrayHist[0:k+1])
        maxBack = np.max(normGrayHist[k+1:256])
        if (maxFront==0 or zeroCumuMoment[k]==0 or maxFront==1 or zeroCumuMoment[k]==1 or totalEntroy==0):
            ft1 = 0
        else:
            ft1=entropy[k]/totalEntroy*(math.log10(zeroCumuMoment[k])/math.log10(maxFront))
        if (maxBack==0 or 1-zeroCumuMoment[k]==0 or maxBack==1 or 1-zeroCumuMoment[k]==1 ):
            ft2 = 0
        else:
            if totalEntroy==0:
                ft2 = (math.log10(1-zeroCumuMoment[k])/math.log10(maxBack))
            else:
                ft2=(1-entropy[k]/totalEntroy)*(math.log10(1-zeroCumuMoment[k])/math.log10(maxBack))
        fT[k] = ft1+ft2
    #找最大值的索引，作为得到的阈值
    threshLoc = np.where(fT==np.max(fT))
    thresh = threshLoc[0][0]
    #阈值处理
    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)   
    re,ra = threshEntroy(src)
    print(re)
    print(ra)
    cv2.imshow('ra',ra)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    

结果

95
[[  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ... 255 255 255]
 ...
 [  0   0   0 ...   0 255 255]
 [  0   0   0 ...   0   0 255]
 [255   0   0 ...   0   0   0]]