边缘提取建议 OpenCV

Question

我正在寻找建议来改进我的算法以搜索下图中的零件

到目前为止我有以下

GaussianBlur(canny, canny, Size(5, 5), 2, 2);
Canny(canny, canny, 100, 200, 5);
HoughCircles(canny, Part_Centroids, CV_HOUGH_GRADIENT, 2, 30, 100, 50, 50, 60);

我的边缘检测输出看起来像这样

我正在使用 HoughCircle 来尝试找到零件。 不过，我并没有取得巨大的成功，因为 HoughCircle 看起来非常挑剔，并且经常返回一个并不是真正与零件最匹配的圆。

有关改进此搜索算法的任何建议

编辑：

我已经尝试了下面评论中的建议。标准化做了一些改进，但在霍夫圆改变所需的设置但不改变稳定性之前删除精明。

我认为现在我需要做一些像霍夫圆这样阈值非常开放的事情，然后找到一种对结果进行评分的方法。是否有任何好的方法来对霍夫圆的结果进行评分或将结果与精明的匹配百分比输出相关联

Answer 1

我想我会发布我的解决方案，因为有人可能会发现我学到的经验教训很有价值。

我首先拍摄几帧并对它们进行平均。 这解决了我遇到的一些噪音问题，同时保留了强边缘。 接下来我做了一个基本的过滤器和精明的边缘来提取一个像样的边缘图。

    Scalar cannyThreshold = mean(filter);
    // Canny Edge Detection
    Canny(filter, canny, cannyThreshold[0]*(2/3), cannyThreshold[0]*(1+(1/3)), 3);

接下来，我使用与直径增加的模板的互相关，并存储得分超过阈值的匹配

    // Iterate through diameter ranges
    for (int r = 40; r < 70; r++)
    {
        Mat _mask, _template(Size((r * 2) + 4, (r * 2) + 4), CV_8U);
        _template = Scalar(0, 0, 0);
        _mask = _template.clone();
        _mask = Scalar(0, 0, 0);
        circle(_template, Point(r + 4, r + 4), r, Scalar(255, 255, 255), 2, CV_AA);
        circle(_template, Point(r + 4, r + 4), r / 3.592, Scalar(255, 255, 255), 2, CV_AA);
        circle(_mask, Point(r + 4, r + 4), r + 4, Scalar(255, 255, 255), -1);

        Mat res_32f(canny.rows, canny.cols, CV_32FC1);
        matchTemplate(canny, _template, res_32f, CV_TM_CCORR_NORMED, _mask);
        Mat resize(canny.rows, canny.cols, CV_32FC1);
        resize = Scalar(0, 0, 0);
        res_32f.copyTo(resize(Rect((resize.cols - res_32f.cols) / 2, (resize.rows - res_32f.rows) / 2, res_32f.cols, res_32f.rows)));
        // Strore Well Scoring Results
        double minVal, maxVal;
        double threshold = .25;
        do
        {
            Point minLoc, maxLoc;
            minMaxLoc(resize, &minVal, &maxVal, &minLoc, &maxLoc);
            if (maxVal > threshold)
            {
                matches.push_back(CircleScore(maxLoc.x, maxLoc.y, r, maxVal,1));
                circle(resize, maxLoc, 30, Scalar(0, 0, 0), -1);
            }

        } while (maxVal > threshold);
    }

我筛选出每个区域中最佳匹配的圆圈

// Sort Matches For Best Match
    for (size_t i = 0; i < matches.size(); i++)
    {
        size_t j = i + 1;
        while (j < matches.size())
        {
            if (norm(Point2f(matches[i].X, matches[i].Y) - Point2f(matches[j].X, matches[j].Y)) - abs(matches[i].Radius - matches[j].Radius) < 15)
            {
                if (matches[j].Score > matches[i].Score)
                {
                    matches[i] = matches[j];
                }
                matches[j] = matches[matches.size() - 1];
                matches.pop_back();
                j = i + 1;
            }
            else j++;
        }
    }

接下来是棘手的一步。 我想看看哪个部分可能位于顶部。 我通过检查每组比半径总和更接近的零件来做到这一点，然后查看重叠区域中的边缘是否比其他零件更匹配。任何被覆盖的圆在重叠区域都应该有小的强边缘。

    // Layer Sort On Intersection
    for (size_t i = 0; i < matches.size(); i++)
    {
        size_t j = i + 1;
        while (j < matches.size())
        {
            double distance = norm(Point2f(matches[i].X, matches[i].Y) - Point2f(matches[j].X, matches[j].Y));
            // Potential Overlapping Part
            if (distance < ((matches[i].Radius+matches[j].Radius) - 10))
            {
                int score_i = 0, score_j = 0;
                Mat intersect_a(canny.rows, canny.cols, CV_8UC1);
                Mat intersect_b(canny.rows, canny.cols, CV_8UC1);
                intersect_a = Scalar(0, 0, 0);
                intersect_b = Scalar(0, 0, 0);
                circle(intersect_a, Point(cvRound(matches[i].X), cvRound(matches[i].Y)), cvRound(matches[i].Radius) +4, Scalar(255, 255, 255), -1);
                circle(intersect_a, Point(cvRound(matches[i].X), cvRound(matches[i].Y)), cvRound(matches[i].Radius / 3.592-4), Scalar(0, 0, 0), -1);
                circle(intersect_b, Point(cvRound(matches[j].X), cvRound(matches[j].Y)), cvRound(matches[j].Radius) + 4, Scalar(255, 255, 255), -1);
                circle(intersect_b, Point(cvRound(matches[j].X), cvRound(matches[j].Y)), cvRound(matches[j].Radius / 3.592-4), Scalar(0, 0, 0), -1);
                bitwise_and(intersect_a, intersect_b, intersect_a);
                double a, h;
                a = (matches[i].Radius*matches[i].Radius - matches[j].Radius*matches[j].Radius + distance*distance) / (2 * distance);
                h = sqrt(matches[i].Radius*matches[i].Radius - a*a);
                Point2f p0((matches[j].X - matches[i].X)*(a / distance) + matches[i].X, (matches[j].Y - matches[i].Y)*(a / distance) + matches[i].Y);
                circle(intersect_a, Point2f(p0.x + h*(matches[j].Y - matches[i].Y) / distance, p0.y - h*(matches[j].X - matches[i].X) / distance), 6, Scalar(0, 0, 0), -1);
                circle(intersect_a, Point2f(p0.x - h*(matches[j].Y - matches[i].Y) / distance, p0.y + h*(matches[j].X - matches[i].X) / distance), 6, Scalar(0, 0, 0), -1);
                bitwise_and(intersect_a, canny, intersect_a);
                intersect_b = Scalar(0, 0, 0);
                circle(intersect_b, Point(cvRound(matches[i].X), cvRound(matches[i].Y)), cvRound(matches[i].Radius), Scalar(255, 255, 255), 6);
                bitwise_and(intersect_a, intersect_b, intersect_b);
                score_i = countNonZero(intersect_b);
                intersect_b = Scalar(0, 0, 0);
                circle(intersect_b, Point(cvRound(matches[j].X), cvRound(matches[j].Y)), cvRound(matches[j].Radius), Scalar(255, 255, 255), 6);
                bitwise_and(intersect_a, intersect_b, intersect_b);
                score_j = countNonZero(intersect_b);
                if (score_i < score_j)matches[i].Layer = matches[j].Layer + 1;
                if (score_j < score_i)matches[j].Layer = matches[i].Layer + 1;
            }
            j++;
        }
    }

之后很容易提取最好的部分来挑选（我也与深度数据相关

蓝色圆圈是零件，绿色圆圈是最高的堆栈，红色圆圈是位于其他零件下方的零件。

我希望这可以帮助解决类似问题的其他人