大津法python

http://blog.csdn.net/u012771236/article/details/44975831

import numpy as np  def OTSU_enhance(img_gray, th_begin=0, th_end=256, th_step=1):      assert img_gray.ndim == 2, "must input a gary_img"      max_g = 0      suitable_th = 0      for threshold in xrange(th_begin, th_end, th_step):          bin_img = img_gray > threshold          bin_img_inv = img_gray <= threshold          fore_pix = np.sum(bin_img)          back_pix = np.sum(bin_img_inv)          if 0 == fore_pix:              break          if 0 == back_pix:              continue          w0 = float(fore_pix) / img_gray.size          u0 = float(np.sum(img_gray * bin_img)) / fore_pix          w1 = float(back_pix) / img_gray.size          u1 = float(np.sum(img_gray * bin_img_inv)) / back_pix          # intra-class variance          g = w0 * w1 * (u0 - u1) * (u0 - u1)          if g > max_g:              max_g = g              suitable_th = threshold      return suitable_th  

#include "opencv2\opencv.hpp"  #include <iostream>  using namespace std;using namespace cv;int main() {    Mat frame = imread("11.png");    imshow("src", frame);    cvtColor(frame, frame, COLOR_BGR2GRAY);//图像灰度化      threshold(frame, frame, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);//大津法      imshow("大津法", frame);    waitKey(0);    return 0;}