Question

我需要一种单独分割每个箭头的方法。我尝试使用OpenCv findContours，但它破坏了它或将其添加到多个形状和箭头中，以共享形状的边界。我尝试了OpenCV连接的组件，但是几乎在某些图形中的该箭头都将其连接了起来。麻烦的是，边界几乎与箭头具有相同的颜色。在这类图片中，每个箭头包含不同的颜色。对这个问题有任何看法。

这是一个示例图。我必须处理像这样的更困难的图。

Answer 1

对于您的示例，这可能很简单。图片（png）有4个通道，第4个通道是透明蒙版。它只能与透明通道一起使用，并且可以过滤带有瞬间的箭头：

cv::Mat img = cv::imread("voXFs.png", cv::IMREAD_UNCHANGED);
std::cout << "imsize = " << img.size() << ", chans = " << img.channels() << std::endl;
cv::imshow("img", img);

std::vector<cv::Mat> chans;
cv::split(img, chans);

cv::imshow("transp", chans.back());

cv::Mat mask;
cv::threshold(chans.back(), mask, 50, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);

std::vector<std::vector<cv::Point> > contours;
cv::findContours(mask, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

cv::Mat draw;
cv::cvtColor(mask, draw, cv::COLOR_GRAY2BGR);

for (size_t i = 0; i < contours.size(); ++i)
{
    double area = cv::contourArea(contours[i]);
    double len = cv::arcLength(contours[i], false);
    double k = len / area;

    if (area > 10 && len > 60 && k > 2)
    {
        std::cout << "area = " << area << ", len = " << len << ", k = " << k << std::endl;
        cv::drawContours(draw, contours, i, cv::Scalar(255, 0, 0), 1);
    }
}

cv::imshow("mask", mask);
cv::imshow("draw", draw);
cv::waitKey(0);

但要获得更可靠的结果：

从图像（tesseract或cv :: text :: ERFilter）中查找和删除文本区域。
腐蚀蒙版，通过轮廓找到所有形状，绘制并扩张它们。按位和掩码和结果的运算。
结束！

Answer 2

好，处理新图片。 1.将箭头（和形状）二值化：

cv::Mat imgCl = cv::imread("62uoU.jpg", cv::IMREAD_COLOR);
cv::Mat img;
cv::cvtColor(imgCl, img, cv::COLOR_BGR2GRAY);

cv::Mat mask1;
cv::threshold(img, mask1, 30, 255, cv::THRESH_BINARY_INV);

cv::Mat mask2;
cv::threshold(img, mask2, 120, 255, cv::THRESH_BINARY_INV);

cv::Mat diff;
cv::absdiff(mask1, mask2, diff);

cv::imshow("diff1", diff);

结果1：

删除矩形形状：

cv :: Rect objRect（0，0，diff.cols，diff.rows）; cv :: Size minSize（objRect.width / 100，objRect.height / 100）;

cv::Mat bin = cv::Mat(diff, objRect).clone();

for (;;)
{
    cv::Rect cutRect;
    if (!PosRefinement(bin, cutRect, 0.9f, minSize))
    {
        break;
    }
    cv::rectangle(bin, cutRect, cv::Scalar(0, 0, 0), cv::FILLED);
    cv::rectangle(diff, cutRect, cv::Scalar(0, 0, 0), cv::FILLED);

    objRect.x += cutRect.x;
    objRect.y += cutRect.y;
    objRect.width = cutRect.width;
    objRect.height = cutRect.height;
}

cv::imshow("diff", diff);

结果2：

查找行：

std::vector<cv::Vec4i> linesP;
cv::HoughLinesP(diff, linesP, 1, CV_PI / 180, 20, 10, 5);
for (size_t i = 0; i < linesP.size(); i++)
{
    cv::Vec4i l = linesP[i];
    cv::line(imgCl, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar(0, 0, 255), 3, cv::LINE_AA);
}
cv::imshow("img", imgCl);

结果3：

黑色箭头成立。它可以改进此解决方案：从图像（tesseract或cv :: text :: ERFilter）中查找和删除文本区域。并添加一些形态，以绘制带有霍夫线的箭头提示。

P.S。实用功能：

bool PosRefinement(
    cv::Mat bin,
    cv::Rect& cutRect,
    double kThreshold,
    cv::Size minSize
    )
{
    const double areaThreshold = 100;
    const int radius = 5;
    const int maxIters = 100;

    std::vector<std::vector<cv::Point>> contours;
    std::vector<cv::Vec4i> hierarchy;
    cv::findContours(bin, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE, cv::Point());

    size_t bestCont = contours.size();
    double maxArea = 0;
    for (size_t i = 0; i < contours.size(); i++)
    {
        double area = cv::contourArea(contours[i]);
        if (area > maxArea)
        {
            maxArea = area;
            bestCont = i;
        }
    }
    if (maxArea < areaThreshold)
    {
        return false;
    }

    cv::Moments m = cv::moments(contours[bestCont]);
    cv::Point mc(cvRound(m.m10 / m.m00), cvRound(m.m01 / m.m00));

    cv::Rect currRect(mc.x - radius / 2, mc.y - radius / 2, radius, radius);

    auto Clamp = [](int v, int hi) -> bool
    {
        if (v < 0)
        {
            v = 0;
            return true;
        }
        else if (hi && v > hi - 1)
        {
            v = hi - 1;
            return true;
        }
        return false;
    };
    auto RectClamp = [&](cv::Rect& r, int w, int h) -> bool
    {
        return Clamp(r.x, w) || Clamp(r.x + r.width, w) || Clamp(r.y, h) || Clamp(r.y + r.height, h);
    };

    int stepL = radius / 2;
    int stepR = radius / 2;
    int stepT = radius / 2;
    int stepB = radius / 2;

    double k = 0;

    struct State
    {
        double k = 0;
        int stepL = 0;
        int stepR = 0;
        int stepT = 0;
        int stepB = 0;
        cv::Rect currRect;

        State() = default;
        State(double k_, int stepL_, int stepR_, int stepT_, int stepB_, cv::Rect currRect_)
            :
              k(k_),
              stepL(stepL_),
              stepR(stepR_),
              stepT(stepT_),
              stepB(stepB_),
              currRect(currRect_)
        {
        }
        bool operator==(const State& st) const
        {
            return (st.k == k) && (st.stepL == stepL) && (st.stepR == stepR) && (st.stepT == stepT) && (st.stepB == stepB) && (st.currRect == currRect);
        }
    };
    const size_t statesCount = 2;
    State prevStates[statesCount];
    size_t stateInd = 0;

    for (int it = 0; it < maxIters; ++it)
    {
        cv::Rect rleft(currRect.x - stepL, currRect.y, currRect.width + stepL, currRect.height);
        cv::Rect rright(currRect.x, currRect.y, currRect.width + stepR, currRect.height);
        cv::Rect rtop(currRect.x, currRect.y - stepT, currRect.width, currRect.height + stepT);
        cv::Rect rbottom(currRect.x, currRect.y, currRect.width, currRect.height + stepB);

        double kleft = 0;
        double kright = 0;
        double ktop = 0;
        double kbottom = 0;

        if (!RectClamp(rleft, bin.cols, bin.rows))
        {
            cv::Rect rstep(currRect.x - stepL, currRect.y, stepL, currRect.height);
            if (cv::sum(bin(rstep))[0] / (255.0 * rstep.area()) > kThreshold / 2)
            {
                kleft = cv::sum(bin(rleft))[0] / (255.0 * rleft.area());
            }
        }
        if (!RectClamp(rright, bin.cols, bin.rows))
        {
            cv::Rect rstep(currRect.x + currRect.width, currRect.y, stepR, currRect.height);
            if (cv::sum(bin(rstep))[0] / (255.0 * rstep.area()) > kThreshold / 2)
            {
                kright = cv::sum(bin(rright))[0] / (255.0 * rright.area());
            }
        }
        if (!RectClamp(rtop, bin.cols, bin.rows))
        {
            cv::Rect rstep(currRect.x, currRect.y - stepT, currRect.width, stepT);
            if (cv::sum(bin(rstep))[0] / (255.0 * rstep.area()) > kThreshold / 2)
            {
                ktop = cv::sum(bin(rtop))[0] / (255.0 * rtop.area());
            }
        }
        if (!RectClamp(rbottom, bin.cols, bin.rows))
        {
            cv::Rect rstep(currRect.x, currRect.y + currRect.height, currRect.width, stepB);
            if (cv::sum(bin(rstep))[0] / (255.0 * rstep.area()) > kThreshold / 2)
            {
                kbottom = cv::sum(bin(rbottom))[0] / (255.0 * rbottom.area());
            }
        }

        bool wasEnlargeX = false;
        if (kleft > kThreshold)
        {
            currRect.x -= stepL;
            currRect.width += stepL;
            wasEnlargeX = true;

            if (kleft > k)
            {
                ++stepL;
            }
        }
        else
        {
            if (stepL > 1)
            {
                --stepL;
            }
            currRect.x += 1;
            currRect.width -= 1;
        }
        if (kright > kThreshold)
        {
            currRect.width += stepR;
            wasEnlargeX = true;

            if (kright > k)
            {
                ++stepR;
            }
        }
        else
        {
            if (stepR > 1)
            {
                --stepR;
            }
            currRect.width -= 1;
        }

        bool wasEnlargeY = false;
        if (ktop > kThreshold)
        {
            currRect.y -= stepT;
            currRect.height += stepT;
            wasEnlargeY = true;

            if (ktop > k)
            {
                ++stepT;
            }
        }
        else
        {
            if (stepT > 1)
            {
                --stepT;
            }
            currRect.y += 1;
            currRect.height -= 1;
        }
        if (kbottom > kThreshold)
        {
            currRect.height += stepB;
            wasEnlargeY = true;

            if (kbottom > k)
            {
                ++stepB;
            }
        }
        else
        {
            if (stepB > 1)
            {
                --stepB;
            }
            currRect.height -= 1;
        }

        k = cv::sum(bin(currRect))[0] / (255.0 * currRect.area());

        State currState(k, stepL, stepR, stepT, stepB, currRect);

        bool repState = false;
        for (size_t i = 0; i < statesCount; ++i)
        {
            if (prevStates[i] == currState)
            {
                repState = true;
                break;
            }
        }
        if (repState)
        {
            break;
        }
        else
        {
            prevStates[stateInd] = currState;
            stateInd = (stateInd + 1 < statesCount) ? (stateInd + 1) : 0;
        }

        if (k < kThreshold && (stepL + stepR + stepT + stepB == 4) && !wasEnlargeX && !wasEnlargeY)
        {
            break;
        }
    }

    cutRect.x = std::max(0, currRect.x - 1);
    cutRect.width = currRect.width + 2;
    cutRect.y = std::max(0, currRect.y - 1);
    cutRect.height = currRect.height + 2;

    return (cutRect.width >= minSize.width) && (cutRect.height >= minSize.height);
}

图像中的箭头分割

2 个答案: