/
pxsnsr.cpp
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/
pxsnsr.cpp
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/*利用兴趣区红蓝像素总合的比值除去噪声*/
/*输入的参数依次为，连通域，红色通道阈值分割结果，蓝色通道阈值分割结果*/
# pragma warning (disable:4819)
#include "objectDetect.h"
void pxsnsr(vector<vector<Point>> &contours, Mat &Seg_img_red, Mat &Seg_img_blue)
{
Rect *r = new Rect[contours.size()]; // 定义外接矩形数组
Mat obj_rec_thr = Mat::zeros(Seg_img_red.size(), CV_8UC3);
for (unsigned int i = 0; i < contours.size(); i++)
{
r[i] = boundingRect(Mat(contours[i]));// boundingRect获取这个外接矩形
}
/// 绘出轮廓及其凸包
Mat Seg_img_blue_hull(Seg_img_blue.size(), CV_8U, Scalar(0));
vector<vector<Point> >hull(contours.size());
for (unsigned int i = 0; i< contours.size(); i++)
{
convexHull(Mat(contours[i]), hull[i], false);
fillConvexPoly(Seg_img_blue_hull, &hull[i][0], hull[i].size(), Scalar(1));
}
erode(Seg_img_blue_hull, Seg_img_blue_hull, Mat(), Point(-1, -1), dilate_size); // 对图像进行之前的膨胀恢复
Seg_img_blue_hull.convertTo(Seg_img_blue_hull, CV_32F);
imshow("Seg_img_blue _hull", Seg_img_blue_hull);
double Evaluation = 0; // 对潜在目标的打分值初始化
double Evaluation_max = 0; // 对潜在目标的打分最大值初始化
int index_best = -1; // 最优轮廓标记 <注意此处初始值应为-1>
int index = 0; // 轮廓标记
cout << contours.size() << endl;
vector<vector<Point>>::const_iterator itContours = contours.begin();
//for (; itContours != contours.end(); ++itContours)
while (itContours != contours.end())
{
Mat imageROI_red = Seg_img_red(cv::Rect(r[index].x, r[index].y, r[index].width, r[index].height));
Mat imageROI_blue = Seg_img_blue_hull(cv::Rect(r[index].x, r[index].y, r[index].width, r[index].height));
Mat imageROI_red_blue = imageROI_red + imageROI_blue;
threshold(imageROI_red_blue, imageROI_red_blue, 1, 1, THRESH_BINARY); // 进行阈值分割（大于阈值1等于2时候取 1）
Scalar s1 = sum(imageROI_red_blue);
Scalar s2 = sum(imageROI_blue);
double sum_red = s1.val[0]; // 获取图像块包含在蓝色中的红色像素数量
double sum_blue = s2.val[0]; // 获取图像块中蓝色像素数量
double pixel_sum_rate = rate(sum_red, sum_blue); // 计算图像块中红蓝像素的比例
cout << "sum_red:" << sum_red << "\t" << "," << "sum_blue:" << sum_blue << "\t";
cout << "pixel_sum_rate:" << pixel_sum_rate << endl;
// 将红蓝像素比太低的连通域轮廓删除
if ((pixel_sum_rate < threshold_value_pixel_rate) || (sum_red < 12)) // 如当前轮廓不含一定的红色像素
{
itContours = contours.erase(itContours); // 删除当前轮廓 <特别注意：删除当前轮廓后迭代器自动指向后一个>
index++;
continue;
}
else // 如当前轮廓含一定的红色像素
{
int aaa = contours.size(); // 用于设置条件断点
Evaluation = sum_red; // 目标轮廓简单评价值定义
if (Evaluation > Evaluation_max)
{
Evaluation_max = Evaluation;
index_best++;
cout << "index_best - " << index_best << endl;
}
index++;
itContours++; // 继续检索下一个轮廓 <此处必须要代码指定迭代器转向后一个轮廓>
}
}
int ttt = contours.size(); // 用于设置条件断点
// 如果仍然有大于一个潜在目标则选取最优的一个
if (ttt > 1)
{
int index = 0;
vector<vector<Point>>::const_iterator itContours_2 = contours.begin();
while(itContours_2 != contours.end())
{
if (index != index_best)
itContours_2 = contours.erase(itContours_2);
else
++itContours_2;
index++;
}
}
delete[] r;
}