static (KeyPoint[], Mat) FeatureCommand(Mat source)
{
// 特徴量検出アルゴリズム
var feature = KAZE.Create();
var magnification = 2;
using var _ex = source.Resize(new Size(source.Width * magnification, source.Height * magnification));
// ガンマ補正
var gamma = 1.8;
byte[] _gammaLut = new byte[256];
for (int i = 0; i < _gammaLut.Length; i++)
{
_gammaLut[i] = (byte)(255d * Math.Pow(i / 255d, 1d / gamma));
}
using Mat _temp_gammaImage = new Mat();
Cv2.LUT(_ex, _gammaLut, _temp_gammaImage);
// 特徴量計算
KeyPoint[] keyPoints; // 特徴点
Mat descriptor = new Mat(); // 特徴量
feature.DetectAndCompute(_temp_gammaImage, null, out keyPoints, descriptor);
//var _featureImage = new Mat();
//Cv2.DrawKeypoints(_temp_gammaImage, _keypoint, _featureImage);
return(keyPoints, descriptor);
}
public override void RunTest()
{
var gray = new Mat(ImagePath.Lenna, ImreadModes.Grayscale);
var kaze = KAZE.Create();
var akaze = AKAZE.Create();
var kazeDescriptors = new Mat();
var akazeDescriptors = new Mat();
KeyPoint[] kazeKeyPoints = null, akazeKeyPoints = null;
var kazeTime = MeasureTime(() =>
kaze.DetectAndCompute(gray, null, out kazeKeyPoints, kazeDescriptors));
var akazeTime = MeasureTime(() =>
akaze.DetectAndCompute(gray, null, out akazeKeyPoints, akazeDescriptors));
var dstKaze = new Mat();
var dstAkaze = new Mat();
Cv2.DrawKeypoints(gray, kazeKeyPoints, dstKaze);
Cv2.DrawKeypoints(gray, akazeKeyPoints, dstAkaze);
using (new Window(String.Format("KAZE [{0:F2}ms]", kazeTime.TotalMilliseconds), dstKaze))
using (new Window(String.Format("AKAZE [{0:F2}ms]", akazeTime.TotalMilliseconds), dstAkaze))
{
Cv2.WaitKey();
}
}
public void New3()
{
var descriptorExtractor = KAZE.Create();
var descriptorMatcher = new BFMatcher();
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
public void New5()
{
using var ip = new LinearIndexParams();
using var sp = new SearchParams();
using (var descriptorExtractor = KAZE.Create())
using (var descriptorMatcher = new FlannBasedMatcher(ip, sp))
using (new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher)) { }
}
public void New5()
{
var descriptorExtractor = KAZE.Create();
LinearIndexParams ip = new LinearIndexParams();
SearchParams sp = new SearchParams();
var descriptorMatcher = new FlannBasedMatcher(ip, sp);
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
public void GetKeypoints(Mat gray)
{
var kaze = KAZE.Create();
var kazeDescriptors = new Mat();
kaze.DetectAndCompute(gray, null, out kazeKeyPoints, kazeDescriptors);
var dstKaze = new Mat();
Cv2.DrawKeypoints(gray, kazeKeyPoints, dstKaze);
}
private static void Feature()
{
Mat img = new Mat("data/lenna.png", ImreadModes.GrayScale);
KAZE kaze = KAZE.Create();
KeyPoint[] keyPoints;
Mat descriptors = new Mat();
kaze.DetectAndCompute(img, null, out keyPoints, descriptors);
Mat dst = new Mat();
Cv2.DrawKeypoints(img, keyPoints, dst);
Window.ShowImages(dst);
}
public Mat Run(Mat img1, Mat img2)
{
Mat img3 = new Mat(Math.Max(img1.Height, img2.Height), img2.Width + img1.Width, MatType.CV_8UC3).SetTo(0);
using (var descriptors1 = new Mat())
using (var descriptors2 = new Mat())
using (var matcher = new BFMatcher(NormTypes.L2SQR))
using (var kaze = KAZE.Create())
{
kaze.DetectAndCompute(img1, null, out keypoints1, descriptors1);
kaze.DetectAndCompute(img2, null, out keypoints2, descriptors2);
if (descriptors1.Width > 0 && descriptors2.Width > 0)
{
DMatch[][] matches = matcher.KnnMatch(descriptors1, descriptors2, 2);
using (Mat mask = new Mat(matches.Length, 1, MatType.CV_8U))
{
mask.SetTo(Scalar.White);
int nonZero = Cv2.CountNonZero(mask);
VoteForUniqueness(matches, mask);
nonZero = Cv2.CountNonZero(mask);
nonZero = VoteForSizeAndOrientation(keypoints2, keypoints1, matches, mask, 1.5f, 10);
List <Point2f> obj = new List <Point2f>();
List <Point2f> scene = new List <Point2f>();
List <DMatch> goodMatchesList = new List <DMatch>();
//iterate through the mask only pulling out nonzero items because they're matches
MatIndexer <byte> maskIndexer = mask.GetGenericIndexer <byte>();
for (int i = 0; i < mask.Rows; i++)
{
if (maskIndexer[i] > 0)
{
obj.Add(keypoints1[matches[i][0].QueryIdx].Pt);
scene.Add(keypoints2[matches[i][0].TrainIdx].Pt);
goodMatchesList.Add(matches[i][0]);
}
}
List <Point2d> objPts = obj.ConvertAll(Point2fToPoint2d);
List <Point2d> scenePts = scene.ConvertAll(Point2fToPoint2d);
if (nonZero >= 4)
{
Mat homography = Cv2.FindHomography(objPts, scenePts, HomographyMethods.Ransac, 1.5, mask);
nonZero = Cv2.CountNonZero(mask);
if (homography != null && homography.Width > 0)
{
Point2f[] objCorners = { new Point2f(0, 0),
new Point2f(img1.Cols, 0),
new Point2f(img1.Cols, img1.Rows),
new Point2f(0, img1.Rows) };
Point2d[] sceneCorners = MyPerspectiveTransform3(objCorners, homography);
//This is a good concat horizontal
using (Mat left = new Mat(img3, new Rect(0, 0, img1.Width, img1.Height)))
using (Mat right = new Mat(img3, new Rect(img1.Width, 0, img2.Width, img2.Height)))
{
img1.CopyTo(left);
img2.CopyTo(right);
byte[] maskBytes = new byte[mask.Rows * mask.Cols];
mask.GetArray(out maskBytes);
Cv2.DrawMatches(img1, keypoints1, img2, keypoints2, goodMatchesList, img3, Scalar.All(-1), Scalar.All(-1), maskBytes, DrawMatchesFlags.NotDrawSinglePoints);
List <List <Point> > listOfListOfPoint2D = new List <List <Point> >();
List <Point> listOfPoint2D = new List <Point>();
listOfPoint2D.Add(new Point(sceneCorners[0].X + img1.Cols, sceneCorners[0].Y));
listOfPoint2D.Add(new Point(sceneCorners[1].X + img1.Cols, sceneCorners[1].Y));
listOfPoint2D.Add(new Point(sceneCorners[2].X + img1.Cols, sceneCorners[2].Y));
listOfPoint2D.Add(new Point(sceneCorners[3].X + img1.Cols, sceneCorners[3].Y));
listOfListOfPoint2D.Add(listOfPoint2D);
img3.Polylines(listOfListOfPoint2D, true, Scalar.LimeGreen, 2);
//This works too
//Cv2.Line(img3, scene_corners[0] + new Point2d(img1.Cols, 0), scene_corners[1] + new Point2d(img1.Cols, 0), Scalar.LimeGreen);
//Cv2.Line(img3, scene_corners[1] + new Point2d(img1.Cols, 0), scene_corners[2] + new Point2d(img1.Cols, 0), Scalar.LimeGreen);
//Cv2.Line(img3, scene_corners[2] + new Point2d(img1.Cols, 0), scene_corners[3] + new Point2d(img1.Cols, 0), Scalar.LimeGreen);
//Cv2.Line(img3, scene_corners[3] + new Point2d(img1.Cols, 0), scene_corners[0] + new Point2d(img1.Cols, 0), Scalar.LimeGreen);
}
}
}
}
}
return(img3);
}
}
public void New3()
{
using (var descriptorExtractor = KAZE.Create())
using (var descriptorMatcher = new BFMatcher())
using (new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher)) { }
}
//img1:test image; img2:ref img
public float MatchTemplate(Mat img1, Mat img2, bool ishowImageMatchTemplate, string s = "Match")
{
float matchRate = 0.0f;
using (var descriptors1 = new Mat())
using (var descriptors2 = new Mat())
using (var matcher = new BFMatcher(NormTypes.L2SQR))
using (var kaze = KAZE.Create())
{
KeyPoint[] keypoints1, keypoints2;
kaze.DetectAndCompute(img1, null, out keypoints1, descriptors1);
kaze.DetectAndCompute(img2, null, out keypoints2, descriptors2);
DMatch[][] matches = matcher.KnnMatch(descriptors1, descriptors2, 2);
using (Mat mask = new Mat(matches.Length, 1, MatType.CV_8U))
{
mask.SetTo(new Scalar(255));
int nonZero = Cv2.CountNonZero(mask);
VoteForUniqueness(matches, mask);
nonZero = Cv2.CountNonZero(mask);
nonZero = VoteForSizeAndOrientation(keypoints2, keypoints1, matches, mask, 1.5f, 20);
List <Point2f> obj = new List <Point2f>();
List <Point2f> scene = new List <Point2f>();
List <DMatch> goodMatchesList = new List <DMatch>();
//iterate through the mask only pulling out nonzero items because they're matches
for (int i = 0; i < mask.Rows; i++)
{
MatIndexer <byte> maskIndexer = mask.GetGenericIndexer <byte>();
if (maskIndexer[i] > 0)
{
obj.Add(keypoints1[matches[i][0].QueryIdx].Pt);
scene.Add(keypoints2[matches[i][0].TrainIdx].Pt);
goodMatchesList.Add(matches[i][0]);
}
}
List <Point2d> objPts = obj.ConvertAll(Point2fToPoint2d);
List <Point2d> scenePts = scene.ConvertAll(Point2fToPoint2d);
if (nonZero >= 4)
{
Mat homography = Cv2.FindHomography(objPts, scenePts, HomographyMethods.Ransac, 1.5, mask);
nonZero = Cv2.CountNonZero(mask);
//calculate match rate by how many match points exist
//matchRate = (float)nonZero / keypoints2.Count();
matchRate = 1 - (float)(keypoints2.Count() - nonZero) / (keypoints2.Count() + nonZero);
if (homography != null && ishowImageMatchTemplate == true)
{
Point2f[] objCorners = { new Point2f(0, 0),
new Point2f(img1.Cols, 0),
new Point2f(img1.Cols, img1.Rows),
new Point2f(0, img1.Rows) };
Point2d[] sceneCorners = MyPerspectiveTransform3(objCorners, homography);
//This is a good concat horizontal
using (Mat img3 = new Mat(Math.Max(img1.Height, img2.Height), img2.Width + img1.Width, MatType.CV_8UC3))
using (Mat left = new Mat(img3, new Rect(0, 0, img1.Width, img1.Height)))
using (Mat right = new Mat(img3, new Rect(img1.Width, 0, img2.Width, img2.Height)))
{
img1.CopyTo(left);
img2.CopyTo(right);
byte[] maskBytes = new byte[mask.Rows * mask.Cols];
mask.GetArray(0, 0, maskBytes);
Cv2.DrawMatches(img1, keypoints1, img2, keypoints2, goodMatchesList, img3, Scalar.All(-1), Scalar.All(-1), maskBytes, DrawMatchesFlags.NotDrawSinglePoints);
//List<List<Point>> listOfListOfPoint2D = new List<List<Point>>();
//List<Point> listOfPoint2D = new List<Point>();
//listOfPoint2D.Add(new Point(sceneCorners[0].X + img1.Cols, sceneCorners[0].Y));
//listOfPoint2D.Add(new Point(sceneCorners[1].X + img1.Cols, sceneCorners[1].Y));
//listOfPoint2D.Add(new Point(sceneCorners[2].X + img1.Cols, sceneCorners[2].Y));
//listOfPoint2D.Add(new Point(sceneCorners[3].X + img1.Cols, sceneCorners[3].Y));
//listOfListOfPoint2D.Add(listOfPoint2D);
//img3.Polylines(listOfListOfPoint2D, true, Scalar.LimeGreen, 2);
Cv2.ImShow(s, img3.Resize(new Size(img3.Rows / 2, img3.Cols / 2)));
Cv2.WaitKey(0);
Cv2.DestroyWindow(s);
//Window.ShowImages(img3.Resize(new Size(img3.Rows / 2, img3.Cols / 2)));
//Window.WaitKey(0);
//Window.DestroyAllWindows();
}
}
}
}
}
return(matchRate);
}
