opencv阈值分割之直方图（分割）技术法和OTSU

import cv2 as cv import numpy as np from matplotlib import pyplot as plt #计算灰度直方图 def calcGrayHist(grayimage): #灰度图像矩阵的高，宽 rows, cols = grayimage.shape print(grayimage.shape) #存储灰度直方图 grayHist = np.zeros([256],np.uint64) for r in range(rows): for c in range(cols): grayHist[grayimage[r][c]] += 1 return grayHist #OTSU自动阈值分割 def OTSU(image): if len(image.shape) == 2: gray = image else: gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) rows,cols = gray.shape #1.计算灰度直方图 grayHist = calcGrayHist(gray) #2.灰度直方图归一化 uniformGrayHist = grayHist/float(rows*cols) #3.计算零阶累计矩何一阶累计矩 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] + k*uniformGrayHist[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])) #找到阈值、 threshLoc = np.where(variance[0:255] == np.max(variance[0:255])) thresh = threshLoc[0][0] #阈值处理 threshold = np.copy(gray) threshold[threshold > thresh] = 255 threshold[threshold <= thresh] = 0 return threshold, thresh #阈值分割：直方图技术法 def threshTwoPeaks(image): if len(image.shape) == 2: gray = image else: gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) print(666666) #计算灰度直方图 histogram = calcGrayHist(gray) #寻找灰度直方图的最大峰值对应的灰度值 maxLoc = np.where(histogram==np.max(histogram)) firstPeak = maxLoc[0][0] #寻找灰度直方图的第二个峰值对应的灰度值 measureDists = np.zeros([256],np.float32) for k in range(256): measureDists[k] = pow(k-firstPeak,2)*histogram[k] maxLoc2 = np.where(measureDists==np.max(measureDists)) secondPeak = maxLoc2[0][0] #找到两个峰值之间的最小值对应的灰度值，作为阈值 thresh = 0 if firstPeak > secondPeak:#第一个峰值再第二个峰值的右侧 temp = histogram[int(secondPeak):int(firstPeak)] minloc = np.where(temp == np.min(temp)) thresh = secondPeak + minloc[0][0] + 1 else:#第一个峰值再第二个峰值的左侧 temp = histogram[int(firstPeak):int(secondPeak)] minloc = np.where(temp == np.min(temp)) thresh =firstPeak + minloc[0][0] + 1 #找到阈值之后进行阈值处理，得到二值图 threshImage_out = gray.copy() #大于阈值的都设置为255 threshImage_out[threshImage_out > thresh] = 255 threshImage_out[threshImage_out <= thresh] = 0 return thresh, threshImage_out def threshTwoPeaks(image): if len(image.shape) == 2: gray = image else: gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) print(666666) #计算灰度直方图 histogram = calcGrayHist(gray) #寻找灰度直方图的最大峰值对应的灰度值 maxLoc = np.where(histogram==np.max(histogram)) firstPeak = maxLoc[0][0] #寻找灰度直方图的第二个峰值对应的灰度值 measureDists = np.zeros([256],np.float32) for k in range(256): measureDists[k] = pow(k-firstPeak,2)*histogram[k] maxLoc2 = np.where(measureDists==np.max(measureDists)) secondPeak = maxLoc2[0][0] #找到两个峰值之间的最小值对应的灰度值，作为阈值 thresh = 0 if firstPeak > secondPeak:#第一个峰值再第二个峰值的右侧 temp = histogram[int(secondPeak):int(firstPeak)] minloc = np.where(temp == np.min(temp)) thresh = secondPeak + minloc[0][0] + 1 else:#第一个峰值再第二个峰值的左侧 temp = histogram[int(firstPeak):int(secondPeak)] minloc = np.where(temp == np.min(temp)) thresh =firstPeak + minloc[0][0] + 1 #找到阈值之后进行阈值处理，得到二值图 threshImage_out = gray.copy() #大于阈值的都设置为255 threshImage_out[threshImage_out > thresh] = 255 #小于阈值的都设置为0 threshImage_out[threshImage_out <= thresh] = 0 return thresh, threshImage_out if __name__ == "__main__": img = cv.imread('./123.png') kkk,kkkk = threshTwoPeaks(img) print(kkk) cv.imshow('66',kkkk) cv.waitKey(0)

结果如下： 原图： 自适应阈值分割后的二值图： 可见，直方图阈值分割计数法能够较为有效的将背景何前景区分开来，比较完整的分割出图片中的目标物体。值得一提的是，对于任何一张图像，它的直方图中如果存在较为明显的双峰，用直方图分割技术法可以达到很好的效果，否则，达到的效果会很不理想.