基于颜色的OpenCV Edge / Border检测

[英]OpenCV Edge/Border detection based on color

I'm fairly new to OpenCV, and very excited to learn more. I've been toying with the idea of outlining edges, shapes.


I've come across this code (running on an iOS device), which uses Canny. I'd like to be able to render this in color, and circle each shape. Can someone point me in the right direction?



IplImage *grayImage = cvCreateImage(cvGetSize(iplImage), IPL_DEPTH_8U, 1);
cvCvtColor(iplImage, grayImage, CV_BGRA2GRAY);

IplImage* img_blur = cvCreateImage( cvGetSize( grayImage ), grayImage->depth, 1);
cvSmooth(grayImage, img_blur, CV_BLUR, 3, 0, 0, 0);

IplImage* img_canny = cvCreateImage( cvGetSize( img_blur ), img_blur->depth, 1);
cvCanny( img_blur, img_canny, 10, 100, 3 );

cvNot(img_canny, img_canny);

And example might be these burger patties. OpenCV would detect the patty, and outline it.enter image description here

例如可能是这些汉堡肉饼。 OpenCV会检测到patty,并概述它。

Original Image:

enter image description here

1 个解决方案



Color information is often handled by conversion to HSV color space which handles "color" directly instead of dividing color into R/G/B components which makes it easier to handle same colors with different brightness etc.

颜色信息通常通过转换为HSV颜色空间来处理,该颜色空间直接处理“颜色”而不是将颜色分成R / G / B组件,这使得更容易处理具有不同亮度的相同颜色等。

if you convert your image to HSV you'll get this:


cv::Mat hsv;

std::vector<cv::Mat> channels;
cv::split(hsv, channels);

cv::Mat H = channels[0];
cv::Mat S = channels[1];
cv::Mat V = channels[2];

Hue channel:

enter image description here

Saturation channel:

enter image description here

Value channel:

enter image description here

typically, the hue channel is the first one to look at if you are interested in segmenting "color" (e.g. all red objects). One problem is, that hue is a circular/angular value which means that the highest values are very similar to the lowest values, which results in the bright artifacts at the border of the patties. To overcome this for a particular value, you can shift the whole hue space. If shifted by 50° you'll get something like this instead:


cv::Mat shiftedH = H.clone();
int shift = 25; // in openCV hue values go from 0 to 180 (so have to be doubled to get to 0 .. 360) because of byte range from 0 to 255
for(int j=0; j<shiftedH.rows; ++j)
    for(int i=0; i<shiftedH.cols; ++i)
        shiftedH.at<unsigned char>(j,i) = (shiftedH.at<unsigned char>(j,i) + shift)%180;

enter image description here

now you can use a simple canny edge detection to find edges in the hue channel:


cv::Mat cannyH;
cv::Canny(shiftedH, cannyH, 100, 50);

enter image description here

You can see that the regions are a little bigger than the real patties, that might be because of the tiny reflections on the ground around the patties, but I'm not sure about that. Maybe it's just because of jpeg compression artifacts ;)


If you instead use the saturation channel to extract edges, you'll end up with something like this:


cv::Mat cannyS;
cv::Canny(S, cannyS, 200, 100);

enter image description here

where the contours aren't completely closed. Maybe you can combine hue and saturation within preprocessing to extract edges in the hue channel but only where saturation is high enough.


At this stage you have edges. Regard that edges aren't contours yet. If you directly extract contours from edges they might not be closed/separated etc:


// extract contours of the canny image:
std::vector<std::vector<cv::Point> > contoursH;
std::vector<cv::Vec4i> hierarchyH;
cv::findContours(cannyH,contoursH, hierarchyH, CV_RETR_TREE , CV_CHAIN_APPROX_SIMPLE);

// draw the contours to a copy of the input image:
cv::Mat outputH = input.clone();
for( int i = 0; i< contoursH.size(); i++ )
   cv::drawContours( outputH, contoursH, i, cv::Scalar(0,0,255), 2, 8, hierarchyH, 0);

enter image description here

you can remove those small contours by checking cv::contourArea(contoursH[i]) > someThreshold before drawing. But you see the two patties on the left to be connected? Here comes the hardest part... use some heuristics to "improve" your result.

您可以通过在绘制之前检查cv :: contourArea(contoursH [i])> someThreshold来删除这些小轮廓。但是你看到左边的两个肉饼要连接?这是最难的部分......使用一些启发式方法来“改善”你的结果。

cv::dilate(cannyH, cannyH, cv::Mat());
cv::dilate(cannyH, cannyH, cv::Mat());
cv::dilate(cannyH, cannyH, cv::Mat());

Dilation before contour extraction will "close" the gaps between different objects but increase the object size too.

enter image description here

if you extract contours from that it will look like this:


enter image description here

If you instead choose only the "inner" contours it is exactly what you like:


cv::Mat outputH = input.clone();
for( int i = 0; i< contoursH.size(); i++ )
    if(cv::contourArea(contoursH[i]) < 20) continue; // ignore contours that are too small to be a patty
    if(hierarchyH[i][3] < 0) continue;  // ignore "outer" contours

    cv::drawContours( outputH, contoursH, i, cv::Scalar(0,0,255), 2, 8, hierarchyH, 0);

enter image description here

mind that the dilation and inner contour stuff is a little fuzzy, so it might not work for different images and if the initial edges are placed better around the object border it might 1. not be necessary to do the dilate and inner contour thing and 2. if it is still necessary, the dilate will make the object smaller in this scenario (which luckily is great for the given sample image.).


EDIT: Some important information about HSV: The hue channel will give every pixel a color of the spectrum, even if the saturation is very low ( = gray/white) or if the color is very low (value) so often it is desired to threshold the saturation and value channels to find some specific color! This might be much easier and much more stavle to handle than the dilation I've used in my code.




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