我正在尝试编写一种方法,该方法将在 HSV 空间中为放置在屏幕中心的对象找到适当的阈值。这些值用于对象跟踪算法。我已经用手工编码的阈值测试了那段代码,它运行良好。该方法背后的想法是,它应该计算每个通道的直方图,然后返回每个通道的第 5 个和第 95 个百分位数以用作阈值。(credit: How to find RGB/HSV color parameters for color tracking? ) 传入的图像是要跟踪的物体的图片(在整个过程开始之前由用户设置。下面是代码
std::vector<cv::Scalar> HSV_Threshold_Determiner::Get_Threshold_Values(const cv::Mat& image)
{
cv::Mat inputImage;
cv::cvtColor(image, inputImage, CV_BGR2HSV);
std::vector<cv::Mat> bgrPlanes;
cv::split(inputImage, bgrPlanes);
cv::Mat hHist, sHist, vHist;
int hMax = 180, svMax = 256;
float hRanges[] = { 0, (float)hMax };
const float* hRange = { hRanges };
float svRanges[] = { 0, (float)svMax };
const float* svRange = { svRanges };
//float sRanges[] = { 0, 256 };
cv::calcHist(&bgrPlanes[0], 1, 0, cv::Mat(), hHist, 1, &hMax, &hRange);
cv::calcHist(&bgrPlanes[1], 1, 0, cv::Mat(), sHist, 1, &svMax, &svRange);
cv::calcHist(&bgrPlanes[2], 1, 0, cv::Mat(), vHist, 1, &svMax, &svRange);
int totalEntries = image.cols * image.rows;
int fiveCutoff = (int)(totalEntries * .05);
int ninetyFiveCutoff = (int)(totalEntries * .95);
float hTotal = 0, sTotal = 0, vTotal = 0;
bool hMinFound = false, hMaxFound = false, sMinFound = false, sMaxFound = false,
vMinFound = false, vMaxFound = false;
cv::Scalar hThresholds;
cv::Scalar sThresholds;
cv::Scalar vThresholds;
for(int i = 0; i < vHist.rows; ++i)
{
if(i < hHist.rows)
{
hTotal += hHist.at<float>(i, 0);
if(hTotal >= fiveCutoff && !hMinFound)
{
hThresholds.val[0] = i;
hMinFound = true;
}
else if(hTotal>= ninetyFiveCutoff && !hMaxFound)
{
hThresholds.val[1] = i;
hMaxFound = true;
}
}
sTotal += sHist.at<float>(i, 0);
vTotal += vHist.at<float>(i, 0);
if(sTotal >= fiveCutoff && !sMinFound)
{
sThresholds.val[0] = i;
sMinFound = true;
}
else if(sTotal >= ninetyFiveCutoff && !sMaxFound)
{
sThresholds.val[1] = i;
sMaxFound = true;
}
if(vTotal >= fiveCutoff && !vMinFound)
{
vThresholds.val[0] = i;
vMinFound = true;
}
else if(vTotal >= ninetyFiveCutoff && !vMaxFound)
{
vThresholds.val[1] = i;
vMaxFound = true;
}
if(vMaxFound && sMaxFound && hMaxFound)
{
break;
}
}
std::vector<cv::Scalar> returnVect;
returnVect.push_back(hThresholds);
returnVect.push_back(sThresholds);
returnVect.push_back(vThresholds);
return returnVect;
}
我想要做的是总结每个桶中的条目数,直到我得到一个大于或等于总数的百分之五和百分之九十五的数字。不幸的是,如果我手动进行阈值处理,我得到的数字永远不会接近我得到的数字。