Opencv轮廓计数(学习)

本文详细介绍了使用OpenCV进行图像处理的过程,包括读取图片、灰度转换、二值化、形态学操作、距离变换、阈值化二值分割、连通区域分析等步骤,并通过代码示例展示了具体实现。

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#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>

using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;

Mat img1, img2, img3, img4, img5, img6, img_result, img_gray1, img_gray2, img_gray3, img_canny1, img_binary1, img_binary2, img_dist1, img_dist2, kernel_1, kernel_2, img_laplance, img_sharp;

char win1[] = "window1";
char win2[] = "window2";
char win3[] = "window3";
char win4[] = "window4";
char win5[] = "window5";
char win6[] = "window6";
char win7[] = "window7";

int thread_value = 10;
int max_value = 255;
RNG rng1(12345);
RNG rng2(1235);

double harris_min = 0;
double harris_max = 0;

int Demo_SURF();
void Demo_1(int, void*);
void Demo_2(int, void*);

int Demo_SURF()
{
  namedWindow(win1, CV_WINDOW_AUTOSIZE);
  //namedWindow(win2, CV_WINDOW_AUTOSIZE);
  //namedWindow(win3, CV_WINDOW_AUTOSIZE);

  img1 = imread("D://images//71.png");
  //img1 = imread("D://images//77.jpg");
  if (img1.empty())
  {
    cout << "could not load image..." << endl;
    return 0;
  }
  imshow(win1, img1);

  //转灰度
  cvtColor(img1, img_gray1, COLOR_BGR2GRAY);
  //二值化
  threshold(img_gray1, img_binary1, 0, 255, THRESH_BINARY | THRESH_TRIANGLE);
  imshow(win2,img_binary1);

  //形态学操作
  kernel_1 = getStructuringElement(MORPH_RECT, Size(5, 5), Point(-1, -1));
  dilate(img_binary1, img_binary2, kernel_1, Point(-1, -1),1);
  imshow(win3, img_binary2);

  
  //背景变换
  bitwise_not(img_binary2,img_binary2);
  //距离变换
  distanceTransform(img_binary2, img2, CV_DIST_L2, 3);
  //归一化(0-1)
  normalize(img2, img2, 0, 1.0, NORM_MINMAX);
  imshow(win4, img2);

  //阈值化二值分割
  threshold(img2, img2, 0.6, 1.0, THRESH_BINARY);
  img2.convertTo(img3, CV_8U);
  adaptiveThreshold(img3,img3,255,ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,87,0.0);
  kernel_2 = getStructuringElement(MORPH_RECT, Size(5, 5), Point(-1, -1));
  dilate(img3, img3, kernel_2, Point(-1, -1), 4);
  //normalize(img3, img3, 0, 255, NORM_MINMAX);
  imshow(win5,img3);

  //连通区域
  vector<vector<Point>> vec_contours;
  findContours(img3, vec_contours, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

  img4 = Mat::zeros(img1.size(),CV_8UC3);

  //绘制轮廓
  for (size_t k=0;k<vec_contours.size();k++)
  {
    Scalar color_1 = Scalar(rng1.uniform(0,255),rng1.uniform(0,255),rng1.uniform(0,255));
    drawContours(img4, vec_contours, static_cast<int>(k), color_1, 2, 8,Mat());
  }
  cout << "size:"<<vec_contours.size() << endl;
  imshow(win6,img4);

  return 0;
}

void Demo_1(int, void*)
{
  
}

void Demo_2(int, void*)
{
  
}

int main()
{
  Demo_SURF();

  waitKey(0);
  return 0;
}

 

 

 

转载于:https://www.cnblogs.com/herd/p/9780824.html

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