使用OpenCV进行多颜色对象检测

Question

我试图检测两个白色墙壁上的红色墙壁和白色方块的图像中的位置,红色顶部和白色"柱子":

我的方法是进行阈值处理以找到我现在可以从输出中轻松检测到的红色墙壁:

现在我的问题是检测白色方块的位置,但考虑到白色墙壁,这更难.如果我基于白色的阈值,我仍然在白色方柱之间保留不需要的白色墙壁.

任何帮助将不胜感激.

Answer 1

一种方法在于进行阈值与输入图像cv::inRange():

cv::Mat image = cv::imread(argv[1]);
if (image.empty())
{
    std::cout << "!!! Failed imread()" << std::endl;
    return -1;
}

cv::Mat red_image;
cv::inRange(image, cv::Scalar(40, 0, 180), cv::Scalar(135, 110, 255), red_image);
//cv::imwrite("out1.png", red_image);

产出:

我们可以使用它cv::findContours来检索阈值图像的轮廓,以便能够为它们创建边界框,这是一种描述的技术:

std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours( red_image, 
                  contours, 
                  hierarchy, 
                  CV_RETR_TREE, 
                  CV_CHAIN_APPROX_SIMPLE, 
                  cv::Point(0, 0) );

std::vector<std::vector<cv::Point> > contours_poly( contours.size() );
std::vector<cv::Rect> boundRect( contours.size() );
for( int i = 0; i < contours.size(); i++ )
    { 
        cv::approxPolyDP( cv::Mat(contours[i]), contours_poly[i], 3, true );
        boundRect[i] = cv::boundingRect( cv::Mat(contours_poly[i]) );
    }   


// Debug purposes: draw bonding rects
//cv::Mat tmp = cv::Mat::zeros( red_image.size(), CV_8UC3 );
//for( int i = 0; i< contours.size(); i++ )
//  rectangle( tmp, boundRect[i].tl(), boundRect[i].br(), cv::Scalar(0, 255, 0), 2, 8, 0 );
//cv::imwrite("out2.png", tmp);  

输出:

上图中显示的所有矩形都作为cv::Rect对象存储在boundRect矢量中.每个矩形由2个相对的cv::Point对象组成,因此我们迭代此向量以创建cv::Point仅由对象组成的新向量:

// Two opposite cv::Point can be used to draw a rectangle.
// Iterate on the cv::Rect vector and retrieve all cv::Point 
// and store them in a cv::Point vector.
std::vector<cv::Point> rect_points; 
for( int i = 0; i < contours.size(); i++ )
{
    rect_points.push_back(boundRect[i].tl());
    rect_points.push_back(boundRect[i].br());
}

//cv::Mat drawing = cv::Mat::zeros( red_image.size(), CV_8UC3 );
cv::Mat drawing = image.clone();

找到白色方块的逻辑是:假设彼此相距25x25的2个像素定义了一个白色方块:

// Draw a rectangle when 2 points are less than 25x25 pixels of 
// distance from each other
for( int i = 0; i < rect_points.size(); i++ )
{
    for( int j = 0; j < rect_points.size(); j++ )
    {
        if (i == j) 
            continue;

        int x_distance = (rect_points[i].x - rect_points[j].x);
        if (x_distance < 0) 
            x_distance *= -1;

        int y_distance = (rect_points[i].y - rect_points[j].y);
        if (y_distance < 0) 
            y_distance *= -1;

        if ( (x_distance < 25) && (y_distance < 25) )
        {
            std::cout << "Drawing rectangle " << i << " from " 
                      << rect_points[i] << " to " << rect_points[j] 
                      << " distance: " << x_distance << "x" << y_distance << std::endl;

            cv::rectangle( drawing, 
                           rect_points[i], 
                           rect_points[j], 
                           cv::Scalar(255, 50, 0), 
                           2 );
            break;
        }
    }

}

    //cv::imwrite("out3.png", drawing);
cv::imshow("white rectangles", drawing);    
cv::waitKey();

输出:

这个算法非常原始,并且错过了底部的2个白色方块,因为它们下面没有红色的墙,只有它们在它们之上.

所以我把它留给你来改进这种方法:)

祝好运.