C ++
我想要做的是测量眼镜框的厚度。 我有想法测量框架轮廓的厚度（可能是更好的方法？）。 到目前为止，我已经概述了眼镜的框架，但是线条不符合的地方存在差距。 我想过使用HoughLinesP，但我不确定这是否是我需要的。
到目前为止，我已经执行了以下步骤：
这些是结果：
这是我迄今为止的代码：
//convert to grayscale
cv::Mat grayscaleImg;
cv::cvtColor( img, grayscaleImg, CV_BGR2GRAY );
//create ROI
cv::Mat eyeAreaROI(grayscaleImg, centreEyesRect);
cv::imshow("roi", eyeAreaROI);
//blur
cv::Mat blurredROI;
cv::blur(eyeAreaROI, blurredROI, Size(3,3));
cv::imshow("blurred", blurredROI);
//dilate thin lines
cv::Mat dilated_dst;
int dilate_elem = 0;
int dilate_size = 1;
int dilate_type = MORPH_RECT;
cv::Mat element = getStructuringElement(dilate_type,
cv::Size(2*dilate_size + 1, 2*dilate_size+1),
cv::Point(dilate_size, dilate_size));
cv::dilate(blurredROI, dilated_dst, element);
cv::imshow("dilate", dilated_dst);
//edge detection
int lowThreshold = 100;
int ratio = 3;
int kernel_size = 3;
cv::Canny(dilated_dst, dilated_dst, lowThreshold, lowThreshold*ratio, kernel_size);
//create matrix of the same type and size as ROI
Mat dst;
dst.create(eyeAreaROI.size(), dilated_dst.type());
dst = Scalar::all(0);
dilated_dst.copyTo(dst, dilated_dst);
cv::imshow("edges", dst);
//join the lines and fill in
vector<Vec4i> hierarchy;
vector<vector<Point>> contours;
cv::findContours(dilated_dst, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
cv::imshow("contours", dilated_dst);
我不完全确定接下来的步骤是什么，或者如上所述，如果我应该使用HoughLinesP以及如何实现它。 很感谢任何形式的帮助！
我认为有两个主要问题。
分割眼镜框架
找到分割帧的厚度
现在我将发布一种方法来分割您的示例图像的眼镜。 也许这种方法也适用于不同的图像，但您可能需要调整参数，或者您可以使用主要想法。
主要想法是：首先，找到图像中最大的轮廓，应该是眼镜。 其次，找出以前发现的最大轮廓中的两个最大轮廓，这应该是框架内的眼镜！
我使用这个图像作为输入（这应该是你模糊但不扩张的图像）：
// this functions finds the biggest X contours. Probably there are faster ways, but it should work...
std::vector<std::vector<cv::Point>> findBiggestContours(std::vector<std::vector<cv::Point>> contours, int amount)
{
std::vector<std::vector<cv::Point>> sortedContours;
if(amount <= 0) amount = contours.size();
if(amount > contours.size()) amount = contours.size();
for(int chosen = 0; chosen < amount; )
{
double biggestContourArea = 0;
int biggestContourID = -1;
for(unsigned int i=0; i<contours.size() && contours.size(); ++i)
{
double tmpArea = cv::contourArea(contours[i]);
if(tmpArea > biggestContourArea)
{
biggestContourArea = tmpArea;
biggestContourID = i;
}
}
if(biggestContourID >= 0)
{
//std::cout << "found area: " << biggestContourArea << std::endl;
// found biggest contour
// add contour to sorted contours vector:
sortedContours.push_back(contours[biggestContourID]);
chosen++;
// remove biggest contour from original vector:
contours[biggestContourID] = contours.back();
contours.pop_back();
}
else
{
// should never happen except for broken contours with size 0?!?
return sortedContours;
}
}
return sortedContours;
}
int main()
{
cv::Mat input = cv::imread("../Data/glass2.png", CV_LOAD_IMAGE_GRAYSCALE);
cv::Mat inputColors = cv::imread("../Data/glass2.png"); // used for displaying later
cv::imshow("input", input);
//edge detection
int lowThreshold = 100;
int ratio = 3;
int kernel_size = 3;
cv::Mat canny;
cv::Canny(input, canny, lowThreshold, lowThreshold*ratio, kernel_size);
cv::imshow("canny", canny);
// close gaps with "close operator"
cv::Mat mask = canny.clone();
cv::dilate(mask,mask,cv::Mat());
cv::dilate(mask,mask,cv::Mat());
cv::dilate(mask,mask,cv::Mat());
cv::erode(mask,mask,cv::Mat());
cv::erode(mask,mask,cv::Mat());
cv::erode(mask,mask,cv::Mat());
cv::imshow("closed mask",mask);
// extract outermost contour
std::vector<cv::Vec4i> hierarchy;
std::vector<std::vector<cv::Point>> contours;
//cv::findContours(mask, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
cv::findContours(mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// find biggest contour which should be the outer contour of the frame
std::vector<std::vector<cv::Point>> biggestContour;
biggestContour = findBiggestContours(contours,1); // find the one biggest contour
if(biggestContour.size() < 1)
{
std::cout << "Error: no outer frame of glasses found" << std::endl;
return 1;
}
// draw contour on an empty image
cv::Mat outerFrame = cv::Mat::zeros(mask.rows, mask.cols, CV_8UC1);
cv::drawContours(outerFrame,biggestContour,0,cv::Scalar(255),-1);
cv::imshow("outer frame border", outerFrame);
// now find the glasses which should be the outer contours within the frame. therefore erode the outer border ;)
cv::Mat glassesMask = outerFrame.clone();
cv::erode(glassesMask,glassesMask, cv::Mat());
cv::imshow("eroded outer",glassesMask);
// after erosion if we dilate, it's an Open-Operator which can be used to clean the image.
cv::Mat cleanedOuter;
cv::dilate(glassesMask,cleanedOuter, cv::Mat());
cv::imshow("cleaned outer",cleanedOuter);
// use the outer frame mask as a mask for copying canny edges. The result should be the inner edges inside the frame only
cv::Mat glassesInner;
canny.copyTo(glassesInner, glassesMask);
// there is small gap in the contour which unfortunately cant be closed with a closing operator...
cv::dilate(glassesInner, glassesInner, cv::Mat());
//cv::erode(glassesInner, glassesInner, cv::Mat());
// this part was cheated... in fact we would like to erode directly after dilation to not modify the thickness but just close small gaps.
cv::imshow("innerCanny", glassesInner);
// extract contours from within the frame
std::vector<cv::Vec4i> hierarchyInner;
std::vector<std::vector<cv::Point>> contoursInner;
//cv::findContours(glassesInner, contoursInner, hierarchyInner, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
cv::findContours(glassesInner, contoursInner, hierarchyInner, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// find the two biggest contours which should be the glasses within the frame
std::vector<std::vector<cv::Point>> biggestInnerContours;
biggestInnerContours = findBiggestContours(contoursInner,2); // find the one biggest contour
if(biggestInnerContours.size() < 1)
{
std::cout << "Error: no inner frames of glasses found" << std::endl;
return 1;
}
// draw the 2 biggest contours which should be the inner glasses
cv::Mat innerGlasses = cv::Mat::zeros(mask.rows, mask.cols, CV_8UC1);
for(unsigned int i=0; i<biggestInnerContours.size(); ++i)
cv::drawContours(innerGlasses,biggestInnerContours,i,cv::Scalar(255),-1);
cv::imshow("inner frame border", innerGlasses);
// since we dilated earlier and didnt erode quite afterwards, we have to erode here... this is a bit of cheating :-(
cv::erode(innerGlasses,innerGlasses,cv::Mat() );
// remove the inner glasses from the frame mask
cv::Mat fullGlassesMask = cleanedOuter - innerGlasses;
cv::imshow("complete glasses mask", fullGlassesMask);
// color code the result to get an impression of segmentation quality
cv::Mat outputColors1 = inputColors.clone();
cv::Mat outputColors2 = inputColors.clone();
for(int y=0; y<fullGlassesMask.rows; ++y)
for(int x=0; x<fullGlassesMask.cols; ++x)
{
if(!fullGlassesMask.at<unsigned char>(y,x))
outputColors1.at<cv::Vec3b>(y,x)[1] = 255;
else
outputColors2.at<cv::Vec3b>(y,x)[1] = 255;
}
cv::imshow("output", outputColors1);
/*
cv::imwrite("../Data/Output/face_colored.png", outputColors1);
cv::imwrite("../Data/Output/glasses_colored.png", outputColors2);
cv::imwrite("../Data/Output/glasses_fullMask.png", fullGlassesMask);
*/
cv::waitKey(-1);
return 0;
}
我得到这个分割结果：
原始图像中的叠加层会给您一种质量的印象：
和反向：
代码中有一些棘手的部分，它尚未整理。 我希望这是可以理解的。
下一步将是计算分段帧的厚度。 我的建议是计算反转蒙版的距离变换。 从这里，您将需要计算脊线检测或者将面具镂空以找到脊线。 之后使用岭距的中值。
无论如何，我希望这篇文章能够帮助你一点，尽管这还不是解决方案。
根据照明，框架颜色等，这可能会或可能不会工作，但如何简单的颜色检测来分离框架？ 框架颜色通常会比人体皮肤黑暗得多。 你会得到一个二进制图像（只是黑色和白色），并通过计算黑色像素的数量（面积），你可以得到帧的区域。
另一种可能的方法是通过调整/扩大/侵蚀/两者来获得更好的边缘检测，直到获得更好的轮廓。 您还需要区分镜头轮廓，然后应用cvContourArea。
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