如何避免MinAreaRect检测到的直肠旋转? [英] How to avoid rotation of rects, detected by MinAreaRect?
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问题描述
我试图检测Windows窗体上的文本字段,但CvInvoke.MinAreaRect(contour)
返回以-7.29419661
角度旋转的矩形.
I am trying to detect text fields on Windows Form but CvInvoke.MinAreaRect(contour)
returns rectangle, rotated by -7.29419661
Angle.
我的代码是
Image<Bgr, Byte> a =
new Image<Bgr, byte>(@"d:/Art/documents/Projects/InputFieldsDetector/Images/Form345_1.PNG");
imageBox1.Image = a;
UMat grayed = new UMat();
CvInvoke.CvtColor(a, grayed, ColorConversion.Bgr2Gray);
imageBox2.Image = grayed;
UMat canny = new UMat();
CvInvoke.Canny(grayed, canny, 50, 200, 3);
imageBox3.Image = canny;
VectorOfVectorOfPoint cnts = new VectorOfVectorOfPoint();
UMat hierarchy = new UMat();
CvInvoke.FindContours(canny, cnts, null, RetrType.Tree, ChainApproxMethod.ChainApproxSimple);
Image<Bgr, Byte> justCountor = a.Clone();
List<string> sizes = new List<string>();
int count = cnts.Size;
for (int i = 0; i < count; i++)
{
VectorOfPoint contour = cnts[i];
var area = CvInvoke.ContourArea(contour);
//if (area > 10000 && area < 15000)
if (area > 200 && area < 300)
{
sizes.Add(area.ToString());
Point[] pts = contour.ToArray();
var forDraw = CvInvoke.MinAreaRect(contour);
// forDraw.Angle = 0;
//forDraw.Center.Y += 10;
justCountor.Draw(forDraw, new Bgr(Color.DarkOrange), 2);
}
}
imageBox4.Image = justCountor;
List<double> result = sizes.Select(x => double.Parse(x)).ToList();
result.Sort();
sizes = result.Select(x => x.ToString()).ToList();
File.WriteAllLines("c:/temp/qqq.txt", sizes);
原始图片是:
如果我取消评论
forDraw.Angle = 0;
forDraw.Center.Y += 10;
检测到的矩形的大小类似于字段的大小...
sizes of detected rects are similar to sizes of fields...
请告诉我,为什么退回的矩形旋转,如何解决?
Tell me, please, why returned rects are rotated and how to fix that?
推荐答案
您可以在Canny输出中看到,该算法将阴影解释为边界.最简单的修复方法是使用接近框背景白色的阈值对图像进行预过滤.
You can see in the Canny output that the algorithm is interpreting the shadows as borders. The easiest way to fix that is prefilter the image with a threshold with a high value near to the white of the box background.
Image<Bgr, Byte> a =
new Image<Bgr, byte>(@"d:/Art/documents/Projects/InputFieldsDetector/Images/Form345_1.PNG");
imageBox1.Image = a;
UMat grayed = new UMat();
CvInvoke.CvtColor(a, grayed, ColorConversion.Bgr2Gray);
imageBox2.Image = grayed;
UMat thresholded = new UMat();
CvInvoke.Threshold(grayed, thresholded, 128, 255, ThresholdType.Binary);
imageBox5.Image = thresholded;
UMat canny = new UMat();
CvInvoke.Canny(thresholded, canny, 50, 200, 3);
imageBox3.Image = canny;
VectorOfVectorOfPoint cnts = new VectorOfVectorOfPoint();
UMat hierarchy = new UMat();
CvInvoke.FindContours(canny, cnts, null, RetrType.Tree, ChainApproxMethod.ChainApproxSimple);
Image<Bgr, Byte> justCountor = a.Clone();
List<string> sizes = new List<string>();
int count = cnts.Size;
for (int i = 0; i < count; i++)
{
VectorOfPoint contour = cnts[i];
var area = CvInvoke.ContourArea(contour);
if (area > 200 && area < 300)
{
sizes.Add(area.ToString());
Point[] pts = contour.ToArray();
var forDraw = CvInvoke.MinAreaRect(contour);
// forDraw.Angle = 0;
//forDraw.Center.Y += 10;
if (forDraw.Angle==0)
justCountor.Draw(forDraw, new Bgr(Color.DarkOrange), 2);
}
}
imageBox4.Image = justCountor;
List<double> result = sizes.Select(x => double.Parse(x)).ToList();
result.Sort();
sizes = result.Select(x => x.ToString()).ToList();
File.WriteAllLines("c:/temp/qqq.txt", sizes);
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