public void ProcessImage(Image <Gray, byte> grayFrame)
{
if (equalizeHist)
{
grayFrame._EqualizeHist();//autocontrast
}
//smoothed
Image <Gray, byte> smoothedGrayFrame = grayFrame.PyrDown();
smoothedGrayFrame = smoothedGrayFrame.PyrUp();
//canny
Image <Gray, byte> cannyFrame = null;
if (noiseFilter)
{
cannyFrame = smoothedGrayFrame.Canny(new Gray(cannyThreshold), new Gray(cannyThreshold));
}
//smoothing
if (blur)
{
grayFrame = smoothedGrayFrame;
}
//binarize
CvInvoke.cvAdaptiveThreshold(grayFrame, grayFrame, 255, Emgu.CV.CvEnum.ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY, adaptiveThresholdBlockSize + adaptiveThresholdBlockSize % 2 + 1, adaptiveThresholdParameter);
//
grayFrame._Not();
//
if (addCanny)
{
if (cannyFrame != null)
{
grayFrame._Or(cannyFrame);
}
}
//
this.binarizedFrame = grayFrame;
//dilate canny contours for filtering
if (cannyFrame != null)
{
cannyFrame = cannyFrame.Dilate(3);
}
//find contours
var sourceContours = grayFrame.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_NONE, Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_LIST);
//filter contours
contours = FilterContours(sourceContours, cannyFrame, grayFrame.Width, grayFrame.Height);
//find templates
lock (foundTemplates)
foundTemplates.Clear();
samples.Clear();
lock (templates)
Parallel.ForEach <Contour <Point> >(contours, (contour) =>
{
var arr = contour.ToArray();
Template sample = new Template(arr, contour.Area, samples.templateSize);
lock (samples)
samples.Add(sample);
if (!onlyFindContours)
{
FoundTemplateDesc desc = finder.FindTemplate(templates, sample);
if (desc != null)
{
lock (foundTemplates)
foundTemplates.Add(desc);
}
}
}
);
//
FilterByIntersection(ref foundTemplates);
}
public void ProcessImage(Image <Gray, byte> grayFrame)
{
if (equalizeHist)
{
grayFrame._EqualizeHist();//autocontrast
}
//高斯平滑
Image <Gray, byte> smoothedGrayFrame = grayFrame.PyrDown();
smoothedGrayFrame = smoothedGrayFrame.PyrUp();
//canny
Image <Gray, byte> cannyFrame = null;
if (noiseFilter)
{
cannyFrame = smoothedGrayFrame.Canny(cannyThreshold, cannyThreshold);
}
//smoothing
if (blur)
{
grayFrame = smoothedGrayFrame;
}
//局部自适应阈值二值化，阈值本身作为了一个变量，检测更有效
//CvInvoke.cvAdaptiveThreshold(grayFrame, grayFrame, 255, Emgu.CV.CvEnum.ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY, adaptiveThresholdBlockSize + adaptiveThresholdBlockSize % 2 + 1, adaptiveThresholdParameter);
CvInvoke.AdaptiveThreshold(grayFrame, grayFrame, 255, Emgu.CV.CvEnum.AdaptiveThresholdType.MeanC, Emgu.CV.CvEnum.ThresholdType.Binary, adaptiveThresholdBlockSize + adaptiveThresholdBlockSize % 2 + 1, adaptiveThresholdParameter);
//
grayFrame._Not();
//
if (addCanny)
{
if (cannyFrame != null)
{
grayFrame._Or(cannyFrame); //试验了一下，这样轮廓会更加明显
}
}
//
this.binarizedFrame = grayFrame;
//dilate canny contours for filtering
//膨胀操作，使白色区域的像素增加一圈，看起来轮廓更细了
if (cannyFrame != null)
{
cannyFrame = cannyFrame.Dilate(3);
}
//find contours
VectorOfVectorOfPoint sourceContours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(grayFrame, sourceContours, null, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
//var sourceContours = grayFrame.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_NONE, Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_LIST);
//filter contours
contours = FilterContours(sourceContours, cannyFrame, grayFrame.Width, grayFrame.Height);
//find templates
lock (foundTemplates)
foundTemplates.Clear();
samples.Clear();
lock (templates) //多线程，异步执行
Parallel.ForEach <VectorOfPoint>(contours, (contour) =>
{
var arr = contour.ToArray();
Template sample = new Template(arr, CvInvoke.ContourArea(contour), samples.templateSize);
lock (samples)
samples.Add(sample);
if (!onlyFindContours)
{
FoundTemplateDesc desc = finder.FindTemplate(templates, sample);
if (desc != null)
{
lock (foundTemplates)
foundTemplates.Add(desc);
}
}
}
);
//
FilterByIntersection(ref foundTemplates);
}
