数学之路-python计算实战(8)-机器视觉-图像二值化

二值化

hreshold

Applies a fixed-level threshold to each array element.

C++: double threshold(InputArray src, OutputArray dst, double thresh, doublemaxval, int type)

Python: cv2.threshold(src, thresh, maxval, type[, dst]) → retval, dst

C: double cvThreshold(const CvArr* src, CvArr* dst, double threshold, doublemax_value, int threshold_type)

Parameters:	src – input array (single-channel, 8-bit or 32-bit floating point). dst – output array of the same size and type as src. thresh – threshold value. maxval – maximum value to use with the THRESH_BINARY andTHRESH_BINARY_INV thresholding types. type – thresholding type (see the details below).

The function applies fixed-level thresholding to a single-channel array. The function is typically used to get a bi-level (binary) image out of a grayscale image (compare() could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type :

• THRESH_BINARY

  

• THRESH_BINARY_INV

  

• THRESH_TRUNC

  

• THRESH_TOZERO

  

• THRESH_TOZERO_INV

  

Also, the special value THRESH_OTSU may be combined with one of the above values. In this case, the function determines the optimal threshold value using the Otsu’s algorithm and uses it instead of the specified thresh . The function returns the computed threshold value. Currently, the Otsu’s method is implemented only for 8-bit images.

import cv2

fn="test3.jpg"
myimg=cv2.imread(fn)
img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)



retval, newimg=cv2.threshold(img,40,255,cv2.THRESH_BINARY)
cv2.imshow(‘preview‘,newimg)
cv2.waitKey()
cv2.destroyAllWindows()

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自适应二值化

adaptiveThreshold函数可以二值化，也可以提取边缘：

Python: cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) → dst

C: void cvAdaptiveThreshold(const CvArr* src, CvArr* dst, double max_value, intadaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, intthreshold_type=CV_THRESH_BINARY, int block_size=3, double param1=5 )

src – Source 8-bit single-channel image. dst – Destination image of the same size and the same type as src . maxValue – Non-zero value assigned to the pixels for which the condition is satisfied. See the details below. adaptiveMethod – Adaptive thresholding algorithm to use,ADAPTIVE_THRESH_MEAN_C orADAPTIVE_THRESH_GAUSSIAN_C . See the details below. thresholdType – Thresholding type that must be eitherTHRESH_BINARY or THRESH_BINARY_INV . blockSize – Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. C – Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well. block_size参数决定局部阈值的block的大小，block很小时，如block_size=3 or 5 or 7时，表现为边缘提取函数。当把block_size设为比较大的值时，如block_size=21、51等，便是二值化 下面是提取边缘 import cv2 fn="test3.jpg" myimg=cv2.imread(fn) img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY) newimg=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,5,2) cv2.imshow(‘preview‘,newimg) cv2.waitKey() cv2.destroyAllWindows() 二值化如下： import cv2 fn="test3.jpg" myimg=cv2.imread(fn) img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY) newimg=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,51,2) cv2.imshow(‘preview‘,newimg) cv2.waitKey() cv2.destroyAllWindows()

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数学之路-python计算实战(8)-机器视觉-图像二值化

原文：http://blog.csdn.net/myhaspl/article/details/37699509