Question

我试图计算平均值＆amp; Std偏差仅为红色轮廓。我怀疑Vec3b的红色Hue值的HSV像素存储在0-10和165-179之间。

这是我的代码：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <vector>
#include <cmath>


using namespace cv;
using namespace std;

int main(int argc, char** argv) {


// Mat Declarations
// Mat img = imread("white.jpg");
// Mat src = imread("Rainbro.png");
Mat src = imread("multi.jpg");
// Mat src = imread("DarkRed.png");
Mat Hist;
Mat HSV;
Mat Edges;
Mat Grey;

vector<vector<Vec3b>> hueMEAN;
vector<vector<Point>> contours;

// Variables
int edgeThreshold = 1;
int const max_lowThreshold = 100;
int ratio = 3;
int kernel_size = 3;
int lowThreshold = 0;

// Windows
namedWindow("img", WINDOW_NORMAL);
namedWindow("HSV", WINDOW_AUTOSIZE);
namedWindow("Edges", WINDOW_AUTOSIZE);
namedWindow("contours", WINDOW_AUTOSIZE);

// Color Transforms
cvtColor(src, HSV, CV_BGR2HSV);
cvtColor(src, Grey, CV_BGR2GRAY);
// Perform Hist Equalization to help equalize Red hues so they stand out for 
// better Edge Detection

equalizeHist(Grey, Grey);


// Image Transforms
blur(Grey, Edges, Size(3, 3));
Canny(Edges, Edges, max_lowThreshold, lowThreshold * ratio, kernel_size);
findContours(Edges, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);

//Rainbro MAT
//Mat drawing = Mat::zeros(432, 700, CV_8UC1);

//Multi MAT
Mat drawing = Mat::zeros(630, 1200, CV_8UC1);

//Red variation Mat
//Mat drawing = Mat::zeros(600, 900, CV_8UC1);

vector <vector<Point>> ContourPoints;

/* This code for loops through all contours and assigns the value of the y coordinate as a parameter
for the row pointer in the HSV mat. The value vec3b pointer pointing to the pixel in the mat is accessed
and stored for any Hue value that is between 0-10 and 165-179 as Red only contours.*/

for (int i = 0; i < contours.size(); i++) {
    vector<Vec3b> vf;
    vector<Point> points;
    bool isContourRed = false;

    for (int j = 0; j < contours[i].size(); j++) {
        //Row Y-Coordinate of Mat from Y-Coordinate of Contour
        int MatRow = int(contours[i][j].y);
        //Row X-Coordinate of Mat from X-Coordinate of Contour
        int MatCol = int(contours[i][j].x);

        Vec3b *HsvRow = HSV.ptr <Vec3b>(MatRow);

        int h = int(HsvRow[int(MatCol)][0]);
        int s = int(HsvRow[int(MatCol)][1]);
        int v = int(HsvRow[int(MatCol)][2]);

        cout << "Coordinate: ";
        cout << contours[i][j].x;
        cout << ",";
        cout << contours[i][j].y << endl;
        cout << "Hue: " << h << endl;

        // Get contours that are only in the red spectrum Hue 0-10, 165-179
        if ((h <= 10 || h >= 165 && h <= 180) && ((s > 0) && (v > 0))) {
            cout << "Coordinate: ";
            cout << contours[i][j].x;
            cout << ",";
            cout << contours[i][j].y << endl;
            cout << "Hue: " << h << endl;

            vf.push_back(Vec3b(h, s, v));
            points.push_back(contours[i][j]);
            isContourRed = true;
        }

    }
    if (isContourRed == true) {
        hueMEAN.push_back(vf);
        ContourPoints.push_back(points);
    }
}

drawContours(drawing, ContourPoints, -1, Scalar(255, 255, 255), 2, 8);

// Calculate Mean and STD for each Contour
cout << "contour Means & STD of Vec3b:" << endl;
for (int i = 0; i < hueMEAN.size(); i++) {

    Scalar meanTemp = mean(hueMEAN.at(i));
    Scalar sdTemp;
    cout << i << ": " << endl;
    cout << meanTemp << endl;
    cout << " " << endl;
    meanStdDev(hueMEAN.at(i), meanTemp, sdTemp);
    cout << sdTemp << endl;
    cout << " " << endl;
}
cout << "Actual Contours: " << contours.size() << endl;
cout << "# Contours: " << hueMEAN.size() << endl;

imshow("img", src);
imshow("HSV", HSV);
imshow("Edges", Edges);
imshow("contours", drawing);
waitKey(0);

return 0;
}

我在这个特殊情况下遇到过一个问题：

右侧是原始图像，左侧显示HSV垫，边缘检测，箭头指向过滤后绘制的轮廓垫。

以下是源图片：

过滤完成后，我只计算均值和STD。

我觉得0-10和165-179的范围不正确。任何建议或进一步改进都会有很大帮助。

感谢。

Answer 1

快速测试显示范围是正确的。没有所有轮廓提取的东西，如果我只使用0-10和165-179范围过滤颜色，我会在输入图像的中低范围内得到两个红色框。

您看到的轮廓伪影实际上可能来自JPEG工件（如果您在白色和红色框之间放大，您可以看到由于JPEG压缩而逐渐且不锐利），而且你只是在Hue频道中进行阈值处理。在低饱和度时，您不想要的许多灰色颜色实际上都适合您的色调阈值。解决方案是过滤S和V通道中的像素值。

在您的代码中，这意味着将行if ((h <= 10 || h >= 165 && h <= 180) && ((s > 0) && (v > 0))) {更改为if ((h <= 10 || h >= 165 && h <= 180) && ((s > 50) && (v > 50))) {

值50正在处理特定的样本图像，但当然正确的值取决于您的输入图像。

仅使用具有HSV范围的像素过滤仅红色轮廓像素

1 个答案: