c++ - OpenCV:如何检测特定颜色的线条？

我正在一个小的OpenCV项目中，通过从手机相机中检测某种颜色的线条。

总之想:

将输入图像转换为某种颜色的图像(例如，特定上下限范围内的红色)

对所生成的图像应用霍夫线变换，以便仅检测该特定颜色的线

将检测到的线条叠加在原始图像上

这些是我想使用的功能，但我不能安静地确定如何填充丢失的位。

- (void)processImage:(Mat&)image;
{
cv::Mat orig_image = image.clone();

cv::Mat red_image = ??

// Apply houghes transformation to detect lines between a minimum length and a maximum length (I was thinking of using the CV_HOUGH_PROBABILISTIC method..)
// Comment.. see below..

vector<Vec2f> lines;

HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );

for( size_t i = 0; i < lines.size(); i++ )
{

}

任何帮助将不胜感激。

最佳答案

您可以使用HSV色彩空间提取色调信息。

如果有任何问题，这里是一些带有注释的代码:

int main(int argc, char* argv[])
{
    cv::Mat input = cv::imread("C:/StackOverflow/Input/coloredLines.png");

    // convert to HSV color space
    cv::Mat hsvImage;
    cv::cvtColor(input, hsvImage, CV_BGR2HSV);

    // split the channels
    std::vector<cv::Mat> hsvChannels;
    cv::split(hsvImage, hsvChannels);

    // hue channels tells you the color tone, if saturation and value aren't too low.

    // red color is a special case, because the hue space is circular and red is exactly at the beginning/end of the circle.
    // in literature, hue space goes from 0 to 360 degrees, but OpenCV rescales the range to 0 up to 180, because 360 does not fit in a single byte. Alternatively there is another mode where 0..360 is rescaled to 0..255 but this isn't as common.
    int hueValue = 0; // red color
    int hueRange = 15; // how much difference from the desired color we want to include to the result If you increase this value, for example a red color would detect some orange values, too.

    int minSaturation = 50; // I'm not sure which value is good here...
    int minValue = 50; // not sure whether 50 is a good min value here...

    cv::Mat hueImage = hsvChannels[0]; // [hue, saturation, value]

    // is the color within the lower hue range?
    cv::Mat hueMask;
    cv::inRange(hueImage, hueValue - hueRange, hueValue + hueRange, hueMask);

    // if the desired color is near the border of the hue space, check the other side too:
    // TODO: this won't work if "hueValue + hueRange > 180" - maybe use two different if-cases instead... with int lowerHueValue = hueValue - 180
    if (hueValue - hueRange < 0 || hueValue + hueRange > 180)
    {
        cv::Mat hueMaskUpper;
        int upperHueValue = hueValue + 180; // in reality this would be + 360 instead
        cv::inRange(hueImage, upperHueValue - hueRange, upperHueValue + hueRange, hueMaskUpper);

        // add this mask to the other one
        hueMask = hueMask | hueMaskUpper;
    }

    // now we have to filter out all the pixels where saturation and value do not fit the limits:
    cv::Mat saturationMask = hsvChannels[1] > minSaturation;
    cv::Mat valueMask = hsvChannels[2] > minValue;

    hueMask = (hueMask & saturationMask) & valueMask;

    cv::imshow("desired color", hueMask);

    // now perform the line detection
    std::vector<cv::Vec4i> lines;
    cv::HoughLinesP(hueMask, lines, 1, CV_PI / 360, 50, 50, 10);

    // draw the result as big green lines:
    for (unsigned int i = 0; i < lines.size(); ++i)
    {
        cv::line(input, cv::Point(lines[i][0], lines[i][1]), cv::Point(lines[i][2], lines[i][3]), cv::Scalar(0, 255, 0), 5);
    }


    cv::imwrite("C:/StackOverflow/Output/coloredLines_mask.png", hueMask);
    cv::imwrite("C:/StackOverflow/Output/coloredLines_detection.png", input);

    cv::imshow("input", input);
    cv::waitKey(0);
    return 0;
}

使用此输入图像:



将提取此“红色”颜色(调整hueValue和hueRange以检测不同的颜色):



和HoughLinesP从 mask 中检测到这些行(应类似地与HoughLines一起使用):



这也是另一组带有非线的图像...







关于您的不同问题:

有两个函数HoughLines和HoughLinesP。 HoughLines不会提取线长，但是您可以在后期处理中通过再次检查边缘蒙版的哪些像素(HoughLines输入)对应于提取的线来计算它。

参数:

图片-边缘图片(应该清楚吗？)
lines-由 Angular 和位置给出的线，无长度或sth。他们被解释成无限长
rho-累加器分辨率。线条较大时，它应该越大，越健壮，但是提取出的线条的位置/ Angular 准确性越差
阈值-越大，误报越少，但是您可能会漏掉一些行
theta- Angular 分辨率:可以检测到的线越小(取决于方向)越多。如果线条的方向不适合 Angular 步长，则可能无法检测到线条。例如，如果您的CV_PI/180将以1°分辨率进行检测，则如果您的行的方向为0.5°(例如33.5°)，则可能会错过它。

我不太确定所有参数，也许您需要阅读有关霍夫线检测的文献，或者其他人可以在此处添加一些提示。

如果改用cv::HoughLinesP，则会检测到具有起点和终点的线段，这更易于解释，您可以根据cv::norm(cv::Point(lines[i][0], lines[i][1]) - cv::Point(lines[i][2], lines[i][3]))计算线长

关于c++ - OpenCV:如何检测特定颜色的线条？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/35866411/