public Image <Bgr, byte> pointComp(Image <Bgr, byte> baseImg, Image <Bgr, byte> twistedImg)
{
Image <Gray, byte> baseImgGray = baseImg.Convert <Gray, byte>();
Image <Gray, byte> twistedImgGray = twistedImg.Convert <Gray, byte>();
Brisk descriptor = new Brisk();
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
detector.DetectRaw(bimg, GFP1);
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
matcher.Add(baseDesc);
matcher.KnnMatch(twistedDesc, matches, 2, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
//int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(GFP1, GFP1, matches, mask, 1.5, 20);
Image <Bgr, byte> res = baseImg.CopyBlank();
Features2DToolbox.DrawMatches(twistedImg, GFP1, baseImg, GFP2, matches, res, new MCvScalar(255, 0, 0), new MCvScalar(255, 0, 0), mask);
return(res);
}
private void button3_Click(object sender, EventArgs e)
{
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
var baseImgGray = baseImg.Convert <Gray, byte>();
var twistedImgGray = twistedImg.Convert <Gray, byte>();
//генератор описания ключевых точек
Brisk descriptor = new Brisk();
//поскольку в данном случае необходимо посчитать обратное преобразование
//базой будет являться изменённое изображение
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
//получение необработанной информации о характерных точках изображений
detector.DetectRaw(bimg, GFP1);
//генерация описания характерных точек изображений
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
//класс позволяющий сравнивать описания наборов ключевых точек
BFMatcher matcher = new BFMatcher(DistanceType.L2);
//массив для хранения совпадений характерных точек
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
//добавление описания базовых точек
matcher.Add(baseDesc);
//сравнение с описанием изменённых
matcher.KnnMatch(twistedDesc, matches, 2, null);
//3й параметр - количество ближайших соседей среди которых осуществляется поиск совпадений
//4й параметр - маска, в данном случае не нужна
//маска для определения отбрасываемых значений (аномальных и не уникальных)
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
//определение уникальных совпадений
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
Mat homography;
//получение матрицы гомографии
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(GFP1, GFP2, matches, mask, 2);
var destImage = new Image <Bgr, byte>(baseImg.Size);
CvInvoke.WarpPerspective(twistedImg, destImage, homography, destImage.Size);
twistedImg = destImage;
imageBox2.Image = destImage.Resize(640, 480, Inter.Linear);
}
public Image <Bgr, byte> ReturnCompared(out Image <Bgr, byte> def, out Image <Bgr, byte> twistdef)
{
var image = sourceImage.Copy();
var twistedImg = additionalImage.Copy();
//генератор описания ключевых точек
Brisk descriptor = new Brisk();
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
//поскольку в данном случае необходимо посчитать обратное преобразование
//базой будет являться изменённое изображение
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
var twistedImgGray = twistedImg.Convert <Gray, byte>();
var baseImgGray = image.Convert <Gray, byte>();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
//получение необработанной информации о характерных точках изображений
detector.DetectRaw(bimg, GFP1);
//генерация описания характерных точек изображений
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
//массив для хранения совпадений характерных точек
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
//добавление описания базовых точек
matcher.Add(baseDesc);
//сравнение с описанием изменённых
matcher.KnnMatch(twistedDesc, matches, 2, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
//определение уникальных совпадений
Mat resM = new Mat(image.Height, image.Width * 2, DepthType.Cv8U, 3);
var res = resM.ToImage <Bgr, byte>();
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(GFP1, GFP1, matches, mask, 1.5, 20);
Features2DToolbox.DrawMatches(twistedImg, GFP1, image, GFP2, matches, res, new MCvScalar(255, 0,
0), new MCvScalar(255, 0, 0), mask);
def = image;
twistdef = twistedImg;
return(res);
}
private void button_Detect_Click(object sender, EventArgs e)
{
Mat scr = imagemat;
Mat result = imagemat.Clone();
#region Detect()代码
/*
* GFTTDetector _gftd = new GFTTDetector();//以默认参数创建 GFTTDetector 类。
* MKeyPoint[] keypoints = _gftd.Detect(scr, null);//检测关键点，返回 MKeyPoint[]。
* foreach (MKeyPoint keypoint in keypoints)//遍历 MKeyPoint[]数组。
* {
* Point point = Point.Truncate(keypoint.Point);//获得关键点的坐 标位置，以 Point 类型。
* CvInvoke.Circle(result, point, 3, new MCvScalar(0, 0, 255), 1);//绘 制关键点的位置，以 Circle 形式。
* }
*/
#endregion
#region DetectRaw() code
GFTTDetector _gftd = new GFTTDetector(); //以默认参数创建 GFTTDetector 类。
VectorOfKeyPoint vector_keypoints = new VectorOfKeyPoint(); //创建 VectorOfKeyPoint 类型，存储关键点集合。
_gftd.DetectRaw(scr, vector_keypoints, null); //检测关键点。
foreach (MKeyPoint keypoint in vector_keypoints.ToArray()) //遍历 MKeyPoint[]数组。
{
Point point = Point.Truncate(keypoint.Point); //获得关键点的坐 标位置，以 Point 类型。
CvInvoke.Circle(result, point, 3, new MCvScalar(255, 255, 0), 1); //绘制关键点的位置，以 Circle 形式。
}
#endregion
imageBox1.Image = scr; //显示输入图像。
imageBox2.Image = result; //显示角点检测图像。
}
public Image <Bgr, byte> PointHomo(Image <Bgr, byte> image, Image <Bgr, byte> image2)
{
Image <Gray, byte> baseImgGray = image.Convert <Gray, byte>();
Image <Gray, byte> twistedImgGray = image2.Convert <Gray, byte>();
Brisk descriptor = new Brisk();
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
detector.DetectRaw(bimg, GFP1);
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
matcher.Add(baseDesc);
matcher.KnnMatch(twistedDesc, matches, 2, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(GFP1, GFP1, matches, mask, 1.5, 20);
Image <Bgr, byte> res = image.CopyBlank();
Features2DToolbox.DrawMatches(image2, GFP1, image, GFP2, matches, res, new MCvScalar(255, 0, 0), new MCvScalar(255, 0, 0), mask);
Mat homography;
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(GFP1, GFP2, matches, mask, 2);
var destImage = new Image <Bgr, byte>(image2.Size);
CvInvoke.WarpPerspective(image2, destImage, homography, destImage.Size);
return(destImage);
}
/// <summary>
/// Find the 4 strongest corners using the GFTT algorithm.
/// </summary>
/// <param name="pieceID">ID of the piece</param>
/// <param name="pieceImgBw">Black white image of piece</param>
/// <param name="pieceImgColor">Color image of piece</param>
/// <returns>List with corner points</returns>
/// see: http://docs.opencv.org/doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.html
public override List <Point> FindCorners(string pieceID, Bitmap pieceImgBw, Bitmap pieceImgColor)
{
PluginFactory.LogHandle.Report(new LogEventInfo(pieceID + " Finding corners with GFTT algorithm"));
double minDistance = PluginFactory.GetGeneralSettingsPlugin().PuzzleMinPieceSize; //How close can 2 corners be?
double min = 0;
double max = 1;
bool found_all_corners = false;
Image <Gray, byte> bw_clone = new Image <Gray, byte>(pieceImgBw);
List <Point> corners = new List <Point>();
//Binary search, altering quality until exactly 4 corners are found. Usually done in 1 or 2 iterations
while (0 < MaxIterations--)
{
if (PluginFactory.CancelToken.IsCancellationRequested)
{
PluginFactory.CancelToken.ThrowIfCancellationRequested();
}
double qualityLevel = (min + max) / 2;
VectorOfKeyPoint keyPoints = new VectorOfKeyPoint();
GFTTDetector featureDetector = new GFTTDetector(100, qualityLevel, minDistance, BlockSize, true, HarrisDetectorParameterK);
featureDetector.DetectRaw(bw_clone, keyPoints);
if (keyPoints.Size > 4)
{
min = qualityLevel; //Found too many corners increase quality
}
else if (keyPoints.Size < 4)
{
max = qualityLevel;
}
else
{
for (int i = 0; i < keyPoints.Size; i++)
{
corners.Add(Point.Round(keyPoints[i].Point));
}
found_all_corners = true; //found all corners
break;
}
}
//Find the sub-pixel locations of the corners.
//Size winSize = new Size(blockSize, blockSize);
//Size zeroZone = new Size(-1, -1);
//MCvTermCriteria criteria = new MCvTermCriteria(40, 0.001);
// Calculate the refined corner locations
//CvInvoke.CornerSubPix(bw_clone, corners, winSize, zeroZone, criteria);
if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
{
Image <Rgb, byte> corner_img = new Image <Rgb, byte>(pieceImgColor);
for (int i = 0; i < corners.Count; i++)
{
CvInvoke.Circle(corner_img, Point.Round(corners[i]), 7, new MCvScalar(255, 0, 0), -1);
}
PluginFactory.LogHandle.Report(new LogEventImage(pieceID + " Found Corners (" + corners.Count.ToString() + ")", corner_img.Bitmap));
corner_img.Dispose();
}
if (!found_all_corners)
{
PluginFactory.LogHandle.Report(new LogEventError(pieceID + " Failed to find correct number of corners. " + corners.Count + " found."));
}
return(corners);
}
