private Point matchTemple(Mat src, Mat temple, TemplateMatchingType type, Mat dest)
{
Mat srcCopy = new Mat();
Point center = new Point();
Point matchLoc = new Point();
double minVal = new double(); double maxVal = 0;
Point minLoc = new Point(); Point maxLoc = new Point();
CvInvoke.CvtColor(src, srcCopy, ColorConversion.Bgr2Gray);
Mat MatchResult = new Mat(srcCopy.Rows - temple.Rows + 1, srcCopy.Cols - temple.Cols + 1, DepthType.Cv32F, 1);
CvInvoke.MatchTemplate(srcCopy, temple, MatchResult, type);
CvInvoke.MinMaxLoc(MatchResult, ref minVal, ref maxVal, ref minLoc, ref maxLoc);
if (type == TemplateMatchingType.SqdiffNormed ||
type == TemplateMatchingType.Sqdiff)
{
matchLoc = minLoc;
}
else
{
matchLoc = maxLoc;
}
Rectangle MatchRect = new Rectangle(matchLoc, temple.Size);
CvInvoke.Rectangle(dest, MatchRect, new Bgr(Color.Red).MCvScalar);
center.X = matchLoc.X + temple.Width / 2;
center.Y = matchLoc.Y + temple.Height / 2;
imageBox9.Image = temple;
//imageBox10.Image = dest;
return(center);
}
/// <summary>
/// A class that uses OpenCV and WindowsInput to automate Windows using computer vision
/// </summary>
/// <param name="matchingAccuracy">specifies the matching accuracy used on the result of OpenCV MatchTemplate (0-1). 0.75 seems adequate</param>
public Automator(double matchingAccuracy)
{
screenMaxWidth = Screen.PrimaryScreen.Bounds.Width;
screenMaxHeight = Screen.PrimaryScreen.Bounds.Height;
inputSimulator = new InputSimulator();
mouse = inputSimulator.Mouse;
keyboard = inputSimulator.Keyboard;
this.matchingAccuracy = matchingAccuracy;
matchingType = TemplateMatchingType.CcoeffNormed;
}
private void EmguCV_MatchTemplateViewer_Load(object sender, EventArgs e)
{
Image <Bgr, byte> imageToShow = _TemplateImg.Copy();
if (_MatchAlgoritmh == "CCOEFF_NORMALIZED")
{
method = TemplateMatchingType.CcoeffNormed;
}
if (_MatchAlgoritmh == "CCORR_NORMALIZED")
{
method = TemplateMatchingType.CcorrNormed;
}
if (_MatchAlgoritmh == "SQDIFF_NORMALIZED")
{
method = TemplateMatchingType.SqdiffNormed;
}
using (Image <Gray, float> result = _SearchImage.MatchTemplate(_TemplateImg, method))
{
double[] minValues, maxValues;
Point[] minLocations, maxLocations;
result.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);
_ConfidenceScore = maxValues[0];
lblFindMatchScore.Text = "Find Match Score: " + _ConfidenceScore;
if (maxValues[0] > _MatchTemplateScore)
{
Rectangle match = new Rectangle(maxLocations[0], _TemplateImg.Size);
imageToShow.Draw(match, new Bgr(0, 255, (double)byte.MaxValue), 3);
pbSearchImage.Image = _SearchImage.Bitmap;
pbTemplate.Image = imageToShow.Bitmap;
lblFindMatchScore.Text = "Match Score: " + maxValues[0].ToString();
Bitmap bmp = new Bitmap(this.Width, this.Height);
DrawToBitmap(bmp, new Rectangle(0, 0, bmp.Width, bmp.Height));
_ImageEvidence = new Image <Bgr, byte>(bmp);
}
}
if (!_Debug)
{
Timer.Start();
}
this.Refresh();
}
private bool MatchPatternInPicture(Bitmap picture, Bitmap pattern, TemplateMatchingType matchingType)
{
Image <Gray, float> screenImage = new Image <Gray, float>(picture);
Image <Gray, float> patterImage = new Image <Gray, float>(pattern);
using (Image <Gray, float> result = screenImage.MatchTemplate(patterImage, matchingType))
{
double[] minValues, maxValues;
Point[] minLocations, maxLocations;
result.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);
if (Thresholds[matchingType] < maxValues[0])
{
return(true);
}
}
return(false);
}
static void Main(string[] args)
{
string patchImagePath = @"";
string fullImagePath = @"";
Image <Bgr, Byte> patchImage = new Image <Bgr, byte>(patchImagePath);
Image <Bgr, Byte> fullImage = new Image <Bgr, byte>(fullImagePath);
TemplateMatchingType matchMethod = TemplateMatchingType.CcoeffNormed;
Image <Gray, float> imgMatch = fullImage.MatchTemplate(patchImage, matchMethod);
//i have seen people wanting to normalize here, but i dont fully understand why
CvInvoke.Normalize(imgMatch, imgMatch, 0, 1, NormType.MinMax, DepthType.Default, new Mat());
//find the best match with minMax
double[] min, max;
Point[] pointMin, pointMax;
imgMatch.MinMax(out min, out max, out pointMin, out pointMax);
Point matchLoc;
//for Sqdiff and SqdiffNormed, the best matches are lower values. For all the other methods, the higher the better
if (matchMethod == TemplateMatchingType.Sqdiff || matchMethod == TemplateMatchingType.SqdiffNormed)
{
matchLoc = pointMin[0];
}
else
{
matchLoc = pointMax[0];
}
//draw a red box around the the match
Rectangle region = new Rectangle(matchLoc.X, matchLoc.Y, patchImage.Width, patchImage.Height);
fullImage.Draw(region, new Bgr(Color.Red));
ImageViewer.Show(fullImage, "template matching");
}
void MatchTemplaeExample()
{
//Argumentos de entrada necesarios para algoritmo.
object objectVariable1 = Image.FromFile(@"C:\Images\Debug\Test1.jpg"); //Search image
object objectVariable2 = Image.FromFile(@"C:\Images\Debug\Test2.jpg"); //Template image
string upper = "CCOEFF_NORMALIZED"; //Example:CCOEFF_NORMALIZED, CCORR_NORMALIZED, SQDIFF_NORMALIZED Matching Algorithm
bool booleanVariable = true; //Show debug image
TemplateMatchingType method = new TemplateMatchingType();
bool flag = false;
if (upper == "CCOEFF_NORMALIZED")
{
method = TemplateMatchingType.CcoeffNormed;
flag = true;
}
else if (upper == "CCORR_NORMALIZED")
{
method = TemplateMatchingType.CcorrNormed;
flag = true;
}
else if (upper == "SQDIFF_NORMALIZED")
{
method = TemplateMatchingType.SqdiffNormed;
flag = false;
}
try
{
if (objectVariable1 != null && objectVariable2 != null)
{
Image <Bgr, Byte> myImage1 = new Image <Bgr, Byte>((Bitmap)objectVariable1); //Convert from bitmap to Emgu.CV.Image
Image <Bgr, Byte> myImage2 = new Image <Bgr, Byte>((Bitmap)objectVariable2); //Convert from bitmap to Emgu.CV.Image
IImage image1 = (IImage)myImage1;
IImage image2 = (IImage)myImage2;
//IImage image1 = (IImage)objectVariable1; //Original
//IImage image2 = (IImage)objectVariable2; //Original
Image <Bgr, byte> image3 = new Image <Bgr, byte>(image1.Bitmap);
Image <Bgr, byte> template = new Image <Bgr, byte>(image2.Bitmap);
Image <Gray, float> image4 = image3.MatchTemplate(template, method);
//this.VariableSpace.UpdateStatusText("OpenCV_MatchTemplate: Locating Best Matches Found in Image...");
double[] minValues;
double[] maxValues;
Point[] minLocations;
Point[] maxLocations;
image4.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);
double x = maxValues[0];
double num1 = minValues[0];
double num2;
if (flag)
{
if (maxValues.Length > 1)
{
//this.VariableSpace.UpdateStatusText("OpenCV_MatchTemplate: Multiple Optimal Matches Have Been Found in the Image, returning the first best match.");
}
num2 = Math.Pow(x, 3.0) * 100.0;
//this.VariableSpace.UpdateStatusText(string.Format("OpenCV_MatchTemplate: First Best Match Located At [{0}, {1}]. Match Confidence = [{2}%].", (object)maxLocations[0].X, (object)maxLocations[0].Y, (object)num2));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue0", VariableType.Float, (object)num2));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue1", VariableType.Integer, (object)maxLocations[0].X));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue2", VariableType.Integer, (object)maxLocations[0].Y));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue3", VariableType.Object, (object)new Image<Gray, byte>(image4.Bitmap)));
}
else
{
if (minValues.Length > 1)
{
//this.VariableSpace.UpdateStatusText("OpenCV_MatchTemplate: Multiple Optimal Matches Have Been Found in the Image, returning the first best match.");
}
num2 = Math.Pow(1.0 - num1, 3.0) * 100.0;
//this.VariableSpace.UpdateStatusText(string.Format("OpenCV_MatchTemplate: First Best Match Located At [{0},{1}]. Match Confidence = [{2}%].", (object)minLocations[0].X, (object)minLocations[0].Y, (object)num2));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue0", VariableType.Float, (object)num2));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue1", VariableType.Integer, (object)minLocations[0].X));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue2", VariableType.Integer, (object)minLocations[0].Y));
//this.VariableSpace.setVariable(new ScriptVariable("ReturnValue3", VariableType.Object, (object)new Image<Gray, byte>(image4.Bitmap)));
}
if (booleanVariable)
{
Image <Bgr, byte> image5 = image3.ConcateVertical(new Image <Bgr, byte>(image4.Bitmap));
Point[] pts = new Point[4];
if (flag)
{
pts[0].X = maxLocations[0].X;
pts[0].Y = maxLocations[0].Y;
pts[1].X = pts[0].X;
pts[1].Y = pts[0].Y + template.Height;
pts[2].X = pts[0].X + template.Width;
pts[2].Y = pts[0].Y + template.Height;
pts[3].X = pts[0].X + template.Width;
pts[3].Y = maxLocations[0].Y;
}
else
{
pts[0].X = minLocations[0].X;
pts[0].Y = minLocations[0].Y;
pts[1].X = pts[0].X;
pts[1].Y = pts[0].Y + template.Height;
pts[2].X = pts[0].X + template.Width;
pts[2].Y = pts[0].Y + template.Height;
pts[3].X = pts[0].X + template.Width;
pts[3].Y = minLocations[0].Y;
}
image5.DrawPolyline(pts, true, new Bgr((double)byte.MaxValue, 0.0, 0.0), 2, Emgu.CV.CvEnum.LineType.EightConnected, 0);
//int num3 = (int)new ImageViewer((IImage)image5, string.Format("Pattern Match Confidence: [{0}%].", (object)num2)).ShowDialog();
ImageViewer imageViewer = new ImageViewer(image5.Bitmap, num2);
imageViewer.Show();
image5.Dispose();
}
image3.Dispose();
image4.Dispose();
}
}
catch (Exception ex)
{
}
}
