private static void CompareImages(Mat scene, Mat toolbarObject, Mat wordbrainObject)
{
SURFData toolbarSurfResults = ExecuteSurfDetection(toolbarObject);
SURFData wordbrainSurfResults = ExecuteSurfDetection(wordbrainObject);
SURFData sceneSurfResults = ExecuteSurfDetection(scene);
Mat drawnMatches = new Mat();
VectorOfVectorOfDMatch toolbarMatchResults = GetSceneMatchesForModel(sceneSurfResults, toolbarSurfResults);
VectorOfVectorOfDMatch wordbrainMatchResults = GetSceneMatchesForModel(sceneSurfResults, wordbrainSurfResults);
MKeyPoint[] sceneKeyPoints = sceneSurfResults.KeyPoints.ToArray();
Point highestKeyPoint = toolbarMatchResults.ToArrayOfArray()
.Select(m => Point.Round(sceneKeyPoints[m[0].QueryIdx].Point))
.OrderBy(kp => kp.Y)
.FirstOrDefault();
Point lowestKeyPoint = wordbrainMatchResults.ToArrayOfArray()
.Select(m => Point.Round(sceneKeyPoints[m[0].QueryIdx].Point))
.OrderByDescending(kp => kp.Y)
.FirstOrDefault();
int rectangleHeight = highestKeyPoint.Y - lowestKeyPoint.Y;
Image <Gray, byte> sceneImage = scene.ToImage <Gray, Byte>();
Console.WriteLine(sceneImage.Width);
Console.WriteLine(sceneImage.Height);
Rectangle rectangle = new Rectangle(0, lowestKeyPoint.Y, scene.Width, rectangleHeight);
//sceneImage.Draw("X", highestKeyPoint, FontFace.HersheyPlain, 5, new Gray(255), thickness: 2);
//sceneImage.Draw("X", lowestKeyPoint, FontFace.HersheyPlain, 5, new Gray(255), thickness: 2);
//sceneImage.Draw(rectangle, new Gray(10), 5);
//ImageViewer.Show(sceneImage);
//Features2DToolbox.DrawMatches(toolbarObject, toolbarSurfResults.KeyPoints, scene,
// sceneSurfResults.KeyPoints, limitMatches, drawnMatches, new MCvScalar(255), new MCvScalar(255), null, Features2DToolbox.KeypointDrawType.NotDrawSinglePoints);
Image <Gray, byte> sliced = sceneImage.Copy(rectangle);
sliced.Save("../../../characters/characters-and-clues-result.jpg");
ImageViewer.Show(sliced);
}
/// <summary>
/// Filter the matched Features, such that if a match is not unique, it is rejected.
/// </summary>
/// <param name="uniquenessThreshold">The distance different ratio which a match is consider unique, a good number will be 0.8</param>
/// <param name="mask">This is both input and output. This matrix indicates which row is valid for the matches.</param>
/// <param name="matches">Matches. Each matches[i] is k or less matches for the same query descriptor.</param>
public static void VoteForUniqueness(VectorOfVectorOfDMatch matches, double uniquenessThreshold, Mat mask)
{
MDMatch[][] mArr = matches.ToArrayOfArray();
byte[] maskData = new byte[mArr.Length];
GCHandle maskHandle = GCHandle.Alloc(maskData, GCHandleType.Pinned);
using (Mat m = new Mat(mArr.Length, 1, DepthType.Cv8U, 1, maskHandle.AddrOfPinnedObject(), 1))
{
mask.CopyTo(m);
for (int i = 0; i < mArr.Length; i++)
{
if (maskData[i] != 0 && (mArr[i][0].Distance / mArr[i][1].Distance) > uniquenessThreshold)
{
maskData[i] = (byte)0;
}
}
m.CopyTo(mask);
}
maskHandle.Free();
}
public VectorOfVectorOfDMatch GetMatchesForModel(SurfData scene, SurfData model)
{
using (FlannBasedMatcher matcher =
new FlannBasedMatcher(_hierarchicalParams, _searchParams))
{
matcher.Add(model.Descriptors);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
matcher.KnnMatch(scene.Descriptors, matches, _kConstant, null);
MDMatch[][] newMatches = matches
.ToArrayOfArray()
.OrderBy(m => m[0].Distance)
.Take(_matchLimit)
.ToArray();
VectorOfVectorOfDMatch limitMatches = new VectorOfVectorOfDMatch(newMatches);
matches.Dispose();
return(limitMatches);
}
}
private static VectorOfVectorOfDMatch GetSceneMatchesForModel(SURFData sceneData, SURFData modelData)
{
FlannBasedMatcher matcher =
new FlannBasedMatcher(new HierarchicalClusteringIndexParams(), new SearchParams());
matcher.Add(modelData.Descriptors);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
matcher.KnnMatch(sceneData.Descriptors, matches, 1, null);
MDMatch[][] newMatches = matches
.ToArrayOfArray()
.OrderBy(m => m[0].Distance)
.Take(8)
.ToArray();
VectorOfVectorOfDMatch limitMatches = new VectorOfVectorOfDMatch(newMatches);
matches.Dispose();
return(limitMatches);
}
/// <summary>
///
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void btnMatch_Click(object sender, EventArgs e)
{
long matchTime;
using (Mat modelImage = CvInvoke.Imread("Template.png", ImreadModes.Grayscale))
using (Mat observedImage = CvInvoke.Imread("1456141019_地理_p1.tif", ImreadModes.Grayscale))
{
VectorOfKeyPoint vectorOfKeyPoint = new VectorOfKeyPoint();
VectorOfKeyPoint vectorOfKeyPointObserved = new VectorOfKeyPoint();
VectorOfVectorOfDMatch vectorOfDMatch = new VectorOfVectorOfDMatch();
MDMatch[][] mDMatch = vectorOfDMatch.ToArrayOfArray();
Mat mask = null;
Mat homography = null;
DrawMatches.FindMatch(modelImage,
observedImage,
out matchTime,
out vectorOfKeyPoint,
out vectorOfKeyPointObserved,
vectorOfDMatch,
out mask,
out homography);
}
}
private string[] FindFeatureMatches(string sourceFile, string[] targetFiles, ObservableCollection <string> output)
{
//Emgu.CV.CvInvoke
// Currently we are only interested in jpg files.
targetFiles = targetFiles
.Where(targetFile =>
{
var extension = Path.GetExtension(targetFile).ToLower();
return(extension == ".jpg" || extension == ".jpeg");
})
.ToArray();
var matchingFiles = new List <string>();
using var sourceImage = CvInvoke.Imread(sourceFile, ImreadModes.Grayscale);
using var sourceMat = new GpuMat();
//CudaInvoke.CvtColor(sourceImage, sourceMat, ColorConversion.Bgr2Bgra);
sourceMat.Upload(sourceImage);
using var sourceDescriptors = new GpuMat();
using var detector = new CudaORBDetector();
var sourceKeyPoints = detector.Detect(sourceMat, null);
detector.Compute(sourceMat, new VectorOfKeyPoint(sourceKeyPoints), sourceDescriptors);
//detector.DetectAndCompute(sourceImage, null, sourceKeyPoints, sourceDescriptors, false);
Parallel.ForEach(targetFiles, new ParallelOptions {
MaxDegreeOfParallelism = 40
}, targetFile =>
{
try
{
if (targetFile == sourceFile)
{
return; // No need to match the original file.
}
if (new FileInfo(targetFile).Length == 0) // We cannot compare empty images.
{
return;
}
using var targetImage = CvInvoke.Imread(targetFile, ImreadModes.Grayscale);
using var targetMat = new GpuMat();
targetMat.Upload(targetImage);
// using var difference = new Mat();
// Cv2.Subtract(sourceImage, targetImage, difference);
//
// Cv2.Split(difference, out var split);
// var r = split[0];
// var g = split[1];
// var b = split[2];
// var completeMatch = Cv2.CountNonZero(r) == 0 && Cv2.CountNonZero(g) == 0 && Cv2.CountNonZero(b) == 0;
using var targetDescriptors = new GpuMat();
//var targetKeyPoints = new VectorOfKeyPoint();
using var detector2 = new CudaORBDetector();
var targetKeyPoints = detector2.Detect(targetMat, null);
detector2.Compute(targetMat, new VectorOfKeyPoint(targetKeyPoints), targetDescriptors);
//detector.DetectAndCompute(targetImage, null, targetKeyPoints, targetDescriptors, false);
// Needed to compensate for some crashes.
// See: https://stackoverflow.com/questions/25089393/opencv-flannbasedmatcher
if (sourceKeyPoints.Length >= 2 && targetKeyPoints.Length >= 2)
{
using var matches = new VectorOfVectorOfDMatch();
using var matcher = new CudaBFMatcher(DistanceType.Hamming);
matcher.KnnMatch(sourceDescriptors, targetDescriptors, matches, KnnMatchValue);
var goodPoints = matches.ToArrayOfArray().Where(match => match.Length > 1)
.Where(match => match[0].Distance < match[1].Distance * MatchDistance)
//.Select(match => match[0])
.ToArray();
var matchCount = sourceKeyPoints.Length >= targetKeyPoints.Length
? sourceKeyPoints.Length
: targetKeyPoints.Length;
var matchQuality = (float)goodPoints.Length / matchCount;
if (matchQuality >= MinimumMatchQuality)
{
using var outputImage = new Mat();
using var scaledOutputImage = new Mat();
Features2DToolbox.DrawMatches(
sourceImage, new VectorOfKeyPoint(sourceKeyPoints),
targetImage, new VectorOfKeyPoint(targetKeyPoints),
new VectorOfVectorOfDMatch(goodPoints), outputImage,
new Bgr(System.Drawing.Color.Yellow).MCvScalar,
new Bgr(System.Drawing.Color.Red).MCvScalar);
CvInvoke.Resize(outputImage, scaledOutputImage, System.Drawing.Size.Empty, 0.1f, 0.1f);
Application.Current?.Dispatcher?.Invoke(() => CvInvoke.Imshow("Match preview", scaledOutputImage));
//Cv2.ImWrite(targetFile + ".comparison.jpg", scaledOutputImage);
var sb = new StringBuilder();
sb.AppendLine($"{DateTime.Now} Matching:");
sb.AppendLine($"Source: {sourceFile}");
sb.AppendLine($"Target: {targetFile}");
sb.Append($"Match found with quality: {matchQuality}");
output.Add(sb.ToString());
}
}
}
catch (Exception e)
{
var sb = new StringBuilder();
var exception = e.ToString().Replace(Environment.NewLine, " ");
sb.Append($"{DateTime.Now} Unable to match file: {targetFile}: {exception}");
output.Add(sb.ToString());
}
});
return(matchingFiles.ToArray());
}
private void FindMatches(IFeatureMatcher matcher, DescriptorModel leftDescriptor, DescriptorModel rightDescriptor, bool AddToList = true, bool FilterMatches = true, bool ComputeHomography = true, bool SaveInMatchNode = true)
{
WindowsFormHelper.AddLogToConsole($"Start computing matches for: \n" +
$"\t{leftDescriptor.KeyPoint.InputFile.fileInfo.Name.ToString()}\n" +
$"\t{rightDescriptor.KeyPoint.InputFile.fileInfo.Name.ToString()}\n");
var foundedMatch = new DescriptorsMatchModel()
{
FilteredMatch = FilterMatches,
LeftDescriptor = leftDescriptor,
RightDescriptor = rightDescriptor
};
var matches = new VectorOfVectorOfDMatch();
matcher.Match(leftDescriptor.Descriptors, rightDescriptor.Descriptors, matches);
WindowsFormHelper.AddLogToConsole(
$"FINISH computing matches for: \n" +
$"\t{leftDescriptor.KeyPoint.InputFile.fileInfo.Name.ToString()}\n" +
$"\t{rightDescriptor.KeyPoint.InputFile.fileInfo.Name.ToString()}\n"
);
MDMatch[][] matchesArray = matches.ToArrayOfArray();
foundedMatch.MatchesList = matchesArray.ToList();
if (FilterMatches)
{
FindMinMaxDistInMatches(matchesArray, ref ms_MAX_DIST, ref ms_MIN_DIST);
List <MDMatch[]> filteredMatchesList = FilterMatchesByMaxDist(matchesArray);
foundedMatch.FilteredMatchesList = filteredMatchesList;
}
if (ComputeHomography)
{
var PerspectiveMatrix = new Mat();
Mat Mask = new Mat();
lock (locker)
{
var matchesForHomography = FilterMatches ? foundedMatch.FilteredMatchesList : foundedMatch.MatchesList;
if (matchesForHomography.Count > 0)
{
PerspectiveMatrix = FindHomography(leftDescriptor.KeyPoint.DetectedKeyPoints, rightDescriptor.KeyPoint.DetectedKeyPoints, FilterMatches ? foundedMatch.FilteredMatchesList : foundedMatch.MatchesList, Mask);
foundedMatch.Mask = Mask;
foundedMatch.PerspectiveMatrix = PerspectiveMatrix;
}
}
}
// Save drawing image
Mat output = new Mat();
Directory.CreateDirectory($@"{tempDirectory}\DrawMatches");
Features2DToolbox.DrawMatches(new Mat(foundedMatch.LeftDescriptor.KeyPoint.InputFile.fileInfo.FullName), foundedMatch.LeftDescriptor.KeyPoint.DetectedKeyPoints, new Mat(foundedMatch.RightDescriptor.KeyPoint.InputFile.fileInfo.FullName), foundedMatch.RightDescriptor.KeyPoint.DetectedKeyPoints, new VectorOfVectorOfDMatch(foundedMatch.FilteredMatchesList.ToArray()), output, new MCvScalar(0, 0, 255), new MCvScalar(0, 255, 0), foundedMatch.Mask);
output.Save(Path.Combine($@"{tempDirectory}\DrawMatches", $"{Path.GetFileNameWithoutExtension(foundedMatch.RightDescriptor.KeyPoint.InputFile.fileInfo.Name)}-{Path.GetFileNameWithoutExtension(foundedMatch.LeftDescriptor.KeyPoint.InputFile.fileInfo.Name)}.JPG"));
fileManager.listViewerModel._lastDrawnMatches = new Image <Bgr, byte>(output.Bitmap);
var inputFile = new InputFileModel(Path.Combine($@"{tempDirectory}\DrawMatches", $"{Path.GetFileNameWithoutExtension(foundedMatch.RightDescriptor.KeyPoint.InputFile.fileInfo.Name)}-{Path.GetFileNameWithoutExtension(foundedMatch.LeftDescriptor.KeyPoint.InputFile.fileInfo.Name)}.JPG"));
var imageList = _winForm.ImageList[(int)EListViewGroup.DrawnMatches];
var listViewer = _winForm.ListViews[(int)EListViewGroup.DrawnMatches];
fileManager.AddInputFileToList(inputFile, fileManager.listViewerModel.ListOfListInputFolder[(int)EListViewGroup.DrawnMatches], imageList, listViewer);
if (SaveInMatchNode)
{
SaveMatchString(foundedMatch, true);
}
if (AddToList)
{
FoundedMatches.Add(foundedMatch);
}
}
/// <summary>
/// Filter the matched Features, such that if a match is not unique, it is rejected.
/// </summary>
/// <param name="uniquenessThreshold">The distance different ratio which a match is consider unique, a good number will be 0.8</param>
/// <param name="mask">This is both input and output. This matrix indicates which row is valid for the matches.</param>
/// <param name="matches">Matches. Each matches[i] is k or less matches for the same query descriptor.</param>
public static void VoteForUniqueness(VectorOfVectorOfDMatch matches, double uniquenessThreshold, Mat mask)
{
MDMatch[][] mArr = matches.ToArrayOfArray();
byte[] maskData = new byte[mArr.Length];
GCHandle maskHandle = GCHandle.Alloc(maskData, GCHandleType.Pinned);
using (Mat m = new Mat(mArr.Length, 1, DepthType.Cv8U, 1, maskHandle.AddrOfPinnedObject(), 1))
{
mask.CopyTo(m);
for (int i = 0; i < mArr.Length; i++)
{
if (maskData[i] != 0 && (mArr[i][0].Distance / mArr[i][1].Distance) <= uniquenessThreshold)
{
maskData[i] = (byte)255;
}
}
m.CopyTo(mask);
}
maskHandle.Free();
}
void NewORBDetector()
{
float ms_MIN_RATIO = 100;
float ms_MAX_DIST = 100;
(Image <Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors)_imgModel = (new Image <Bgr, byte>(@"C:\Images\ImgModel.jpg").Resize(0.2, Inter.Area), new VectorOfKeyPoint(), new Mat());
(Image <Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors)_imgTest = (new Image <Bgr, byte>(@"C:\Images\ImgTest.jpg").Resize(0.2, Inter.Area), new VectorOfKeyPoint(), new Mat());
Mat imgKeypointsModel = new Mat();
Mat imgKeypointsTest = new Mat();
Mat imgMatches = new Mat();
Mat imgWarped = new Mat();
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
VectorOfVectorOfDMatch filteredMatches = new VectorOfVectorOfDMatch();
List <MDMatch[]> filteredMatchesList = new List <MDMatch[]>();
ORBDetector _ORB = new ORBDetector();
BFMatcher _BFMatcher = new BFMatcher(DistanceType.Hamming2);
_ORB.DetectAndCompute(_imgModel.Image, null, _imgModel.Keypoints, _imgModel.Descriptors, false);
_ORB.DetectAndCompute(_imgTest.Image, null, _imgTest.Keypoints, _imgTest.Descriptors, false);
_BFMatcher.Add(_imgModel.Descriptors);
_BFMatcher.KnnMatch(_imgTest.Descriptors, matches, k: 2, mask: null);
MDMatch[][] matchesArray = matches.ToArrayOfArray();
//Apply ratio test
for (int i = 0; i < matchesArray.Length; i++)
{
MDMatch first = matchesArray[i][0];
float dist1 = matchesArray[i][0].Distance;
float dist2 = matchesArray[i][1].Distance;
if (dist1 < ms_MIN_RATIO * dist2)
{
filteredMatchesList.Add(matchesArray[i]);
}
}
//Filter by threshold
MDMatch[][] defCopy = new MDMatch[filteredMatchesList.Count][];
filteredMatchesList.CopyTo(defCopy);
filteredMatchesList = new List <MDMatch[]>();
foreach (var item in defCopy)
{
if (item[0].Distance < ms_MAX_DIST)
{
filteredMatchesList.Add(item);
}
}
filteredMatches = new VectorOfVectorOfDMatch(filteredMatchesList.ToArray());
Mat homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(_imgModel.Keypoints, _imgTest.Keypoints, filteredMatches, null, 10);
CvInvoke.WarpPerspective(_imgTest.Image, imgWarped, homography, _imgTest.Image.Size);
Features2DToolbox.DrawKeypoints(_imgModel.Image, _imgModel.Keypoints, imgKeypointsModel, new Bgr(0, 0, 255));
Features2DToolbox.DrawKeypoints(_imgTest.Image, _imgTest.Keypoints, imgKeypointsTest, new Bgr(0, 0, 255));
Features2DToolbox.DrawMatches(_imgModel.Image, _imgModel.Keypoints, _imgTest.Image, _imgTest.Keypoints, filteredMatches, imgMatches, new MCvScalar(0, 255, 0), new MCvScalar(0, 0, 255), null, Features2DToolbox.KeypointDrawType.Default);
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgKeypointsModel, "Keypoints Model"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgKeypointsTest, "Keypoints Test"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgMatches, "Matches"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgWarped, "Warp"));
}
public void run(Image <Gray, byte> image)
{
Stopwatch sw = new Stopwatch();
sw.Start();
Mat mask = null;
_imageInput = image;
VectorOfKeyPoint observedKeyPoints = new VectorOfKeyPoint();
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
Image <Gray, byte> _image = imageInput.Clone();
for (int i = 0; i < actionAccurateSearchData.time; i++)
{
_image = _image.PyrDown();
}
if (0 != actionAccurateSearchData.InputAOIWidth && 0 != actionAccurateSearchData.InputAOIHeight)
{
_image.ROI = new Rectangle(actionAccurateSearchData.InputAOIX, actionAccurateSearchData.InputAOIY, actionAccurateSearchData.InputAOIWidth, actionAccurateSearchData.InputAOIHeight);
}
UMat b1 = _image.ToUMat();
UMat observedDescriptors = new UMat();
//进行检测和计算，把opencv中的两部分和到一起了，分开用也可以
surf.DetectAndCompute(b1, null, observedKeyPoints, observedDescriptors, false);
BFMatcher matcher = new BFMatcher(DistanceType.L2Sqr); //开始进行匹配
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, 2, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask); //去除重复的匹配
Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, matches, mask, 1.5, 20);
byte[] maskForSizeAndOrientation = mask.GetData();
MDMatch[][] vectorOfDMatch = matches.ToArrayOfArray();
bool keepDetect = true;
Mat result1 = new Mat();
result1 = _imageInput.Convert <Gray, byte>().Mat;
Features2DToolbox.DrawMatches(_imageTempleAOI.Convert <Gray, byte>().Mat, modelKeyPoints, _image.Convert <Gray, byte>().Mat, observedKeyPoints, matches, result1, new MCvScalar(255, 0, 255), new MCvScalar(0, 255, 255), mask);
int Count = CvInvoke.CountNonZero(mask);
actionRes = ActionResponse.NG;
List <Position> posList;
listPosition = new List <Position>();
imageCon = FindSeedPoint(observedKeyPoints, vectorOfDMatch, mask, out posList);
double maxValue = 0;
double minValue = 0;
Point maxPoint = new Point();
Point minPoint = new Point();
Exception exception = new Exception("循环超时");
while (keepDetect)
{
CvInvoke.MinMaxLoc(imageCon, ref minValue, ref maxValue, ref minPoint, ref maxPoint);
if (maxValue > 10)
{
List <double[]> angleList = new List <double[]>();
double angleSin = 0;
double angleCos = 0;
double angle = 0;
List <int> status = new List <int>();
int count = 0;
int totalCount = 0;
//求出角度
foreach (Position p in posList)
{
if (Math.Abs(p.X - maxPoint.X) < 16 && Math.Abs(p.Y - maxPoint.Y) < 16)
{
double[] item = new double[2];
item[0] = p.sin;
item[1] = p.cos;
angleList.Add(item);
status.Add(1);
}
}
bool keepCalcurateAngle = true;
int numberOfItem = status.Count;
Random random = new Random();
while (keepCalcurateAngle)
{
int a = -1;
int b = -1;
bool keepGetRd = true;
int raCount = 0;
while (keepGetRd)
{
raCount++;
if (raCount > 10000)
{
throw (new Exception("角度偏差过大1"));
}
int rd = random.Next(status.Count - 1);
if (0 != status[rd] && rd != a)
{
if (-1 == a)
{
a = rd;
}
else
{
b = rd;
keepGetRd = false;
}
}
}
if (Math.Abs(angleList[a][0] - angleList[b][0]) < 0.1 && Math.Abs(angleList[a][1] - angleList[b][1]) < 0.1)
{
++count;
angleSin = angleSin + angleList[a][0];
angleCos = angleCos + angleList[a][1];
}
else
{
angleSin = 0;
angleCos = 0;
count = 0;
status[a] = 0;
status[b] = 0;
numberOfItem = numberOfItem - 2;
}
if (count > 99)
{
keepCalcurateAngle = false;
}
if (numberOfItem < status.Count / 3 | numberOfItem < 5)
{
angleSin = 0;
angleCos = 0;
count = 0;
keepCalcurateAngle = false;
//throw (new Exception("角度计算错误"));
}
totalCount++;
if (totalCount > 2000)
{
throw (new Exception("角度偏差过大2"));
}
}
if (Math.Abs(angleSin) > Math.Abs(angleCos))
{
angle = 180 * Math.Acos(angleCos / 100) / Math.PI;
if (angleSin < 0)
{
angle = 360 - angle;
}
}
else
{
angle = 180 * Math.Asin(angleSin / 100) / Math.PI;
if (angleCos < 0)
{
angle = 180 - angle;
}
else
{
if (angle < 0)
{
angle = 360 + angle;
}
}
}
listPosition.Add(new Position(maxPoint.X, maxPoint.Y, angle, angleSin / 100, angleCos / 100));
//画框
Point[] rectPoint = new Point[4];
int CenterX = _imageTempleAOI.Width / 2;//模版尺寸
int CenterY = _imageTempleAOI.Height / 2;
double sin = angleSin / 100;
double Cos = angleCos / 100;
rectPoint[0].X = (int)(maxPoint.X - CenterX * Cos + CenterY * sin);
rectPoint[0].Y = (int)(maxPoint.Y - CenterY * Cos - CenterX * sin);
rectPoint[1].X = (int)(maxPoint.X - CenterX * Cos - CenterY * sin);
rectPoint[1].Y = (int)(maxPoint.Y + CenterY * Cos - CenterX * sin);
rectPoint[2].X = (int)(maxPoint.X + CenterX * Cos - CenterY * sin);
rectPoint[2].Y = (int)(maxPoint.Y + CenterY * Cos + CenterX * sin);
rectPoint[3].X = (int)(maxPoint.X + CenterX * Cos + CenterY * sin);
rectPoint[3].Y = (int)(maxPoint.Y - CenterY * Cos + CenterX * sin);
VectorOfPoint vp = new VectorOfPoint(rectPoint);
CvInvoke.Polylines(result1, vp, true, new MCvScalar(255, 0, 0, 255), 2);
//覆盖值
int leng = Math.Min(_imageTempleAOI.Width, _imageTempleAOI.Height);
int startPX = maxPoint.X - leng / 2;
int startPY = maxPoint.Y - leng / 2;
Parallel.For(0, leng, item =>
{
for (int i = 0; i < leng; i++)
{
imageCon.Data[startPY + i, startPX + item, 0] = 0;
}
});
}
else
{
keepDetect = false;
}
}
imageDescript = result1;
actionRes = ActionResponse.OK;
sw.Stop();
}