数学思想方法-python计算战(8)-机器视觉-二值化
2015-09-15 12:03
951 查看
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, dstC: 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). |
---|
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()
本博客全部内容是原创,假设转载请注明来源
http://blog.csdn.net/myhaspl/
[b][/b]
[b]自适应二值化[/b]
[b]adaptiveThreshold函数能够二值化,也能够提取边缘:[/b]
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參数决定局部阈值的block的大小。block非常小时。如block_size=3 or 5 or 7时,表现为边缘提取函数。当把block_size设为比較大的值时,如block_size=21、51等,便是二值化 以下是提取边缘 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() 二值化例如以下: 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() |
相关文章推荐
- Python+OpenCV学习(13)---meanshift目标跟踪
- Python一个有意思的地方:reduce、map、filter
- 使用python将ppm格式转换成jpg
- python程序运行时间
- 不错的python中级电子书
- alembic 使用
- 转:python命令行解析工具Argparse
- python核心编程第5章课后题答案
- python 文件获取绝对路径
- ipython notebook 浏览器中编写数学公式和现实
- python decimal和fractions模块
- python中的gil是什么?
- python 一些有用的库
- python 插入排序
- python3.X爬虫-图片获取
- python random模块
- python or not python
- 使用python中的matplotlib进行绘图分析数据
- Python学习笔记(三)
- python之xpath爬虫