第六节：阈值分割

一： 全阈值分割

实例代码：

image = cv2.imread('img.jpg', cv2.IMREAD_GRAYSCALE)

the = 100 # 设置阈值为100

maxval = 255

dst, img = cv2.threshold(image, the, maxval, cv2.THRESH_BINARY)

cv2.imshow('hand_thresh', img)

cv2.waitKey(0)

cv2.destroyAllWindows()

给出你的阈值 ，然后告诉你的最大阈值是多少 。。。也就是你二值图中一个阈值为0，另外一个阈值可以指定为多少。。这里指定为255

看一下输出结果。。

二：局部阈值分割

局部阈值分割的核心是计算阈值矩阵。。比较常用的是后面提到的自适应阈值算法。。我们等会后面讲实现。。

三：直方图技术法

代码实现：

import numpy as np

import cv2

def calcGrayHist(image):

'''

统计像素值

:param image:

:return:

'''

# 灰度图像的高，宽

rows, cols = image.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[image[r][c]] += 1

return grayHist

def threshTwoPeaks(image):

# 计算灰度直方图

histogram = calcGrayHist(image)

# 找到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:

temp = histogram[int(secondPeak): int(firstPeak)]

minLoc = np.where(temp == np.min(temp))

thresh = secondPeak + minLoc[0][0] + 1

else:

temp = histogram[int(firstPeak): int(secondPeak)]

minLoc = np.where(temp == np.min(temp))

thresh = firstPeak + minLoc[0][0] + 1

# 找到阈值，我们进行处理

img = image.copy()

img[img > thresh] = 255

img[img <= thresh] = 0

cv2.imshow('deal_image', img)

cv2.waitKey(0)

cv2.destroyAllWindows()

if __name__ == '__main__':

image = cv2.imread('img.jpg', cv2.IMREAD_GRAYSCALE)

threshTwoPeaks(image)

输出结果：

四：熵算法

代码实现：

import numpy as np

import cv2

import math

def calcGrayHist(image):

'''

统计像素值

:param image:

:return:

'''

# 灰度图像的高，宽

rows, cols = image.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[image[r][c]] += 1

return grayHist

def threshEntroy(image):

rows, cols = image.shape

# 求灰度直方图

grayHist = calcGrayHist(image)

# 归一化灰度直方图，即概率直方图

normGrayHist = grayHist / float(rows*cols)

# 第一步:计算累加直方图，也称零阶累积矩

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]

# 第二步: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])

# 第三步:找阈值

fT = np.zeros([256], np.float32)

ft1, ft2 = 0.0, 0.0

totalEntropy = 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 totalEntropy == 0):

ft1 = 0

else:

ft1 = entropy[k] / totalEntropy*(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 totalEntropy == 0:

ft2 = (math.log10(1-zeroCumuMoment[k]) / math.log10(maxBack))

else:

ft2 = (1-entropy[k]/totalEntropy)*(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 threshold

if __name__ == '__main__':

image = cv2.imread('img5.jpg', cv2.IMREAD_GRAYSCALE)

img = threshEntroy(image)

cv2.imshow('origin', image)

cv2.imshow('deal_image', img)

cv2.waitKey(0)

cv2.destroyAllWindows()

输出结果：

五：Otsu算法

这里就不具体实现了。。我们调用opencv给的API

image = cv2.imread('img.jpg', cv2.IMREAD_GRAYSCALE)

maxval = 255

otsuThe = 0

otsuThe, dst_Otsu = cv2.threshold(image, otsuThe, maxval, cv2.THRESH_OTSU)

cv2.imshow('Otsu', dst_Otsu)

cv2.waitKey(0)

cv2.destroyAllWindows()

输出结果：

六：自适应阈值算法

代码实现：

import cv2

import numpy as np

def adaptiveThresh(I, winSize, ratio=0.15):

# 第一步:对图像矩阵进行均值平滑

I_mean = cv2.boxFilter(I, cv2.CV_32FC1, winSize)

# 第二步:原图像矩阵与平滑结果做差

out = I - (1.0 - ratio) * I_mean

# 第三步:当差值大于或等于0时，输出值为255；反之，输出值为0

out[out >= 0] = 255

out[out < 0] = 0

out = out.astype(np.uint8)

return out

if __name__ == '__main__':

image = cv2.imread('img7.jpg', cv2.IMREAD_GRAYSCALE)

img = adaptiveThresh(image, (5, 5))

cv2.imshow('origin', image)

cv2.imshow('deal_image', img)

cv2.waitKey(0)

cv2.destroyAllWindows()

结果展示：

未完待续。。。。。。

下节你将能够学到一些形态学处理(腐蚀，膨胀等等)