图像二值化

理论

图像的二值化,就是将图像上的像素点的灰度值设置为0或255,也就是将整个图像呈现出明显的只有黑和白的视觉效果。

一幅图像包括目标物体、背景还有噪声,要想从多值的数字图像中直接提取出目标物体,常用的方法就是设定一个阈值T,用T将图像的数据分成两部分:大于T的像素群和小于T的像素群。这是研究灰度变换的最特殊的方法,称为图像的二值化(Binarization)。

常见的二值化方法有三种,分别是固定阈值法、平均值法、自适应阈值法和直方图法。

固定阈值法就是设定一个固定阈值K,小于等于K的像素值设为0(黑色),大于K的像素值设为255(白色)。

平均值法计算像素的平均值K,然后扫描图像的每个像素值,小于等于K像素值设为0(黑色),大于K的像素值设为255(白色)。

自适应阈值法对平均值法进行改进,规定一个区域大小,求区域平均值作为阈值K,然后区域中的像素值与K进行比较。

直方图方法主要是发现图像的两个最高的峰,然后阈值K取值在两个峰之间的峰谷最低处。图像的直方图用来表征该图像像素值的分布情况。用一定数目的小区间(bin)来指定表征像素值的范围,每个小区间会得到落入该小区间表示范围的像素数目。

更多内容参考图像的二值化之python+opencvopencv python图像二值化

实践

固定阈值法

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import cv2
from matplotlib import pyplot as plt
img=cv2.imread('../image/test.jpg')
GrayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh1=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY)
ret,thresh2=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3=cv2.threshold(GrayImage,127,255,cv2.THRESH_TRUNC)
ret,thresh4=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO)
ret,thresh5=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO_INV)
titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [GrayImage, 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()

如果报错没有matplotlib,那么先执行pip install matplotlib进行安装。

retval,dst = cv.threshold(src, thresh, maxval, type[, dst] )参数解释:

  • src:原图像,原图像应该是灰度图。
  • thresh:用来对像素值进行分类的阈值。
  • maxval:大于阈值置为maxval。
  • type:不同的阈值方法。

平均阈值法

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import cv2
import numpy as np
from matplotlib import pyplot as plt
img=cv2.imread('../image/test.jpg')
GrayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
k=np.mean(GrayImage)
ret,thresh1=cv2.threshold(GrayImage,k,255,cv2.THRESH_BINARY)
ret,thresh2=cv2.threshold(GrayImage,k,255,cv2.THRESH_BINARY_INV)
ret,thresh3=cv2.threshold(GrayImage,k,255,cv2.THRESH_TRUNC)
ret,thresh4=cv2.threshold(GrayImage,k,255,cv2.THRESH_TOZERO)
ret,thresh5=cv2.threshold(GrayImage,k,255,cv2.THRESH_TOZERO_INV)
titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [GrayImage, 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()

自适应阈值法

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import cv2
from matplotlib import pyplot as plt
img=cv2.imread('../image/test.jpg')
GrayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY)

th2 = cv2.adaptiveThreshold(GrayImage,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,3,4)
th3 = cv2.adaptiveThreshold(GrayImage,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,3,4)
titles = ['Gray Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [GrayImage, th1, th2, th3]
for i in range(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()

dst = cv.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst] )参数解释:

  • src:指原图像,原图像应该是灰度图。
  • maxValue:大于阈值置为maxValue。
  • adaptiveMethod:要使用的自适应阈值算法。
  • thresholdType:阈值类型必须是THRESH_BINARY或THRESH_BINARY_INV。
  • blockSize:用于计算像素的阈值的像素邻域的大小:3,5,7等。
  • C:从平均值或加权平均值中减去常数。通常情况下,它是正数,但也可能为零或负数。

直方图法

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import cv2
import numpy as np
from matplotlib import pyplot as plt
img=cv2.imread('../image/test.jpg')
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# global thresholding
ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# plot all the images and their histograms
images = [img, 0, th1,
img, 0, th2,
blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
'Original Noisy Image','Histogram',"Otsu's Thresholding",
'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]

for i in range(3):
plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])

plt.show()

后记

至此,实现了常用的四种图像二值化算法。根据不同的需要,选择不同的算法。比如对于这幅图,如果要最佳的二值化显示效果,那么平均值法最好;如果要提取轮廓,那么自适应阈值法最好;如果要获取兔斯基,那么直方图法最好。

书签

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