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 TestBOWKmeansTrainer()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
AKAZE detector = new AKAZE();
VectorOfKeyPoint kpts = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(box, null, kpts, descriptors, false);
BOWKMeansTrainer trainer = new BOWKMeansTrainer(100, new MCvTermCriteria(), 3, CvEnum.KMeansInitType.PPCenters);
trainer.Add(descriptors);
Mat vocabulary = new Mat();
trainer.Cluster(vocabulary);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor extractor = new BOWImgDescriptorExtractor(detector, matcher);
extractor.SetVocabulary(vocabulary);
Mat descriptors2 = new Mat();
extractor.Compute(box, kpts, descriptors2);
}
private Mat MatchBySurf(Mat src1, Mat src2)
{
using var gray1 = new Mat();
using var gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(src2, gray2, ColorConversionCodes.BGR2GRAY);
//using var surf = SURF.Create(200, 4, 2, true);
using var surf = AKAZE.Create();
// Detect the keypoints and generate their descriptors using SURF
using var descriptors1 = new Mat <float>();
using var descriptors2 = new Mat <float>();
surf.DetectAndCompute(gray1, null, out var keypoints1, descriptors1);
surf.DetectAndCompute(gray2, null, out var keypoints2, descriptors2);
// Match descriptor vectors
using var bfMatcher = new BFMatcher(NormTypes.L2, false);
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
// Draw matches
var bfView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, bfView, flags: DrawMatchesFlags.NotDrawSinglePoints);
return(bfView);
}
public void TestAkaze()
{
AKAZE detector = new AKAZE();
//ParamDef[] parameters = detector.GetParams();
EmguAssert.IsTrue(TestFeature2DTracker(detector, detector), "Unable to find homography matrix");
}
/// <summary>
/// SrcとTargetのマッチングを行う。
/// </summary>
public void RunMutching()
{
// Akazeで特徴抽出
var akaze = AKAZE.Create();
var descriptorSrc = new Mat();
var descriptorTarget = new Mat();
akaze.DetectAndCompute(SrcMat, null, out KeyPtsSrc, descriptorSrc);
akaze.DetectAndCompute(TargetMat, null, out KeyPtsTarget, descriptorTarget);
// 総当たりマッチング実行
var matcher = DescriptorMatcher.Create("BruteForce");
var matches = matcher.Match(descriptorSrc, descriptorTarget);
// 結果を昇順にソートし、上位からある割合(UseRate)の結果のみを使用する。
SelectedMatched = matches
.OrderBy(p => p.Distance)
.Take((int)(matches.Length * UseRate));
// SrcとTargetの対応する特徴点を描画する
Cv2.DrawMatches(
SrcMat, KeyPtsSrc,
TargetMat, KeyPtsTarget,
SelectedMatched, MatchingResultMat);
}
public void TestMSER()
{
MSERDetector keyPointDetector = new MSERDetector();
AKAZE descriptorGenerator = new AKAZE();
//ParamDef[] parameters = keyPointDetector.GetParams();
TestFeature2DTracker(keyPointDetector, descriptorGenerator);
}
public void GetKeypoints(Mat gray)
{
var akaze = AKAZE.Create();
var akazeDescriptors = new Mat();
akaze.DetectAndCompute(gray, null, out akazeKeyPoints, akazeDescriptors);
}
public void TestGFTTDetector()
{
GFTTDetector keyPointDetector = new GFTTDetector(1000, 0.01, 1, 3, false, 0.04);
AKAZE descriptorGenerator = new AKAZE();
//ParamDef[] parameters = keyPointDetector.GetParams();
TestFeature2DTracker(keyPointDetector, descriptorGenerator);
}
private Mat Process(ref Mat buffer)
{
Mat img = new Mat();
AKAZE akaze = AKAZE.Create();
akaze.Threshold = 0.0001;
KeyPoint[] keyPoints;
DMatch[] matches;
List <DMatch> goodMatches = new List <DMatch>();
Mat descriptor = new Mat();
DescriptorMatcher matcher = DescriptorMatcher.Create("BruteForce");
Cv2.CvtColor(buffer, buffer, ColorConversionCodes.BGR2GRAY);
akaze.DetectAndCompute(buffer, null, out keyPoints, descriptor);
Cv2.DrawKeypoints(buffer, keyPoints, img, Scalar.Black);
Cv2.ImShow("keyps", img);
if (islastSeted)
{
matches = matcher.Match(descriptor, lastDescriptor);
for (int i = 0; i < matches.Length; i++)
{
if (matches[i].Distance < distanceStandard)
{
goodMatches.Add(matches[i]);
}
}
//Cv2.DrawMatches(buffer, keyPoints, lastBuffer, lastkeyPoints, goodMatches, img);
img = buffer;
if (goodMatches.Count > 3)
{
float[] average = new float[2];
average[0] = 0; average[1] = 0;
for (int i = 0; i < goodMatches.Count; i++)
{
average[0] += keyPoints[goodMatches[0].QueryIdx].Pt.X -
lastkeyPoints[goodMatches[0].TrainIdx].Pt.X;
average[1] += keyPoints[goodMatches[0].QueryIdx].Pt.Y -
lastkeyPoints[goodMatches[0].TrainIdx].Pt.Y;
}
lastPoint = new Point(lastPoint.X + average[0] / goodMatches.Count, lastPoint.Y + average[1] / goodMatches.Count);
lastBuffer = buffer;
lastDescriptor = descriptor;
lastkeyPoints = keyPoints;
}
Cv2.Circle(img, lastPoint, 15, Scalar.Red, 3);
}
else
{
islastSeted = true;
img = buffer;
lastPoint = new Point(buffer.Cols / 2, buffer.Rows / 2);
lastBuffer = buffer;
lastDescriptor = descriptor;
lastkeyPoints = keyPoints;
}
return(img);
}
public void TestAkazeBlankImage()
{
AKAZE detector = new AKAZE();
Image <Gray, Byte> img = new Image <Gray, byte>(1024, 900);
VectorOfKeyPoint vp = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(img, null, vp, descriptors, false);
}
public void TestLATCH()
{
//SURF surf = new SURF(300);
AKAZE akaze = new AKAZE();
LATCH latch = new LATCH();
TestFeature2DTracker(akaze, latch);
//EmguAssert.IsTrue(TestFeature2DTracker(akaze, latch), "Unable to find homography matrix");
}
public void TestDAISY()
{
//SURF surf = new SURF(300);
AKAZE akaze = new AKAZE();
DAISY daisy = new DAISY();
TestFeature2DTracker(akaze, daisy);
//EmguAssert.IsTrue(TestFeature2DTracker(akaze, daisy), "Unable to find homography matrix");
}
public FeatureMatching(Mat src, Mat target)
{
// 画像初期化
SrcMat = src.Clone();
TargetMat = target.Clone();
ResultMat = new Mat();
// 重心初期化
PtSrc = new System.Drawing.PointF(0.0f, 0.0f);
PtTarget = new System.Drawing.PointF(0.0f, 0.0f);
// 特徴点抽出
var akaze = AKAZE.Create();
var descriptorSrc = new Mat();
var descriptorTarget = new Mat();
akaze.DetectAndCompute(SrcMat, null, out KeyPtsSrc, descriptorSrc);
akaze.DetectAndCompute(TargetMat, null, out KeyPtsTarget, descriptorTarget);
// マッチング実行
var matcher = DescriptorMatcher.Create("BruteForce");
var matches = matcher.Match(descriptorSrc, descriptorTarget);
// 結果を昇順にソートし、上位半分の結果を使用する。
var selectedMatches = matches
.OrderBy(p => p.Distance)
//.Take(matches.Length / 2);
.Take(1);
// Src - Target 対応画像作成
Cv2.DrawMatches(SrcMat, KeyPtsSrc, TargetMat, KeyPtsTarget, selectedMatches, ResultMat);
// 特徴点の重心を求める (Src)
foreach (var item in selectedMatches)
{
int idx = item.QueryIdx;
PtSrc.X += KeyPtsSrc[idx].Pt.X;
PtSrc.Y += KeyPtsSrc[idx].Pt.Y;
}
PtSrc.X /= (float)selectedMatches.Count();
PtSrc.Y /= (float)selectedMatches.Count();
// 特徴点の重心を求める (Target)
foreach (var item in selectedMatches)
{
int idx = item.TrainIdx;
PtTarget.X += KeyPtsTarget[idx].Pt.X;
PtTarget.Y += KeyPtsTarget[idx].Pt.Y;
}
PtTarget.X /= (float)selectedMatches.Count();
PtTarget.Y /= (float)selectedMatches.Count();
}
public void ComputeImageFeaturesTest()
{
using var featuresFinder = AKAZE.Create();
using var image = Image("abbey_road.jpg", ImreadModes.Grayscale);
using var features = CvDetail.ComputeImageFeatures(featuresFinder, image);
Assert.NotNull(features);
Assert.NotEqual(0, features.ImgIdx);
Assert.Equal(image.Size(), features.ImgSize);
Assert.NotEmpty(features.Keypoints);
Assert.NotNull(features.Descriptors);
Assert.False(features.Descriptors.Empty());
}
static (KeyPoint[], Mat) FeatureCommand(Mat source)
{
// 特徴量検出アルゴリズム
var feature = AKAZE.Create();
// 特徴量計算
KeyPoint[] keyPoints; // 特徴点
Mat descriptor = new Mat(); // 特徴量
feature.DetectAndCompute(source, null, out keyPoints, descriptor);
//var _featureImage = new Mat();
//Cv2.DrawKeypoints(_temp_gammaImage, _keypoint, _featureImage);
return(keyPoints, descriptor);
}
public void AffineBestOf2NearestMatcherTest()
{
using var featuresFinder = AKAZE.Create();
using var image1 = Image("tsukuba_left.png", ImreadModes.Grayscale);
using var image2 = Image("tsukuba_right.png", ImreadModes.Grayscale);
using var features1 = CvDetail.ComputeImageFeatures(featuresFinder, image1);
using var features2 = CvDetail.ComputeImageFeatures(featuresFinder, image2);
using var matcher = new AffineBestOf2NearestMatcher();
using var matchesInfo = matcher.Apply(features1, features2);
Assert.NotEmpty(matchesInfo.Matches);
Assert.NotEmpty(matchesInfo.InliersMask);
Assert.False(matchesInfo.H.Empty());
Assert.True(matchesInfo.Confidence > 0);
}
private static void BowTest()
{
DescriptorMatcher matcher = new BFMatcher();
Feature2D extractor = AKAZE.Create();
Feature2D detector = AKAZE.Create();
TermCriteria criteria = new TermCriteria(CriteriaType.Count | CriteriaType.Eps, 10, 0.001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(200, criteria, 1);
BOWImgDescriptorExtractor bowDescriptorExtractor = new BOWImgDescriptorExtractor(extractor, matcher);
Mat img = null;
KeyPoint[] keypoint = detector.Detect(img);
Mat features = new Mat();
extractor.Compute(img, ref keypoint, features);
bowTrainer.Add(features);
throw new NotImplementedException();
}
/// <summary>
/// Stitch images together
/// </summary>
/// <param name="images">The list of images to stitch</param>
/// <returns>A final stitched image</returns>
public static Mat StichImages(List <Mat> images)
{
//Declare the Mat object that will store the final output
Mat output = new Mat();
//Declare a vector to store all images from the list
VectorOfMat matVector = new VectorOfMat();
//Push all images in the list into a vector
foreach (Mat img in images)
{
matVector.Push(img);
}
//Declare a new stitcher
Stitcher stitcher = new Stitcher(Stitcher.Mode.Scans);
//Declare the type of detector that will be used to detect keypoints
//Brisk detector = new Brisk();
//Here are some other detectors that you can try
//ORBDetector detector = new ORBDetector();
//KAZE detector = new KAZE();
AKAZE detector = new AKAZE();
//Set the stitcher class to use the specified detector declared above
stitcher.SetFeaturesFinder(detector);
//Stitch the images together//no more than 35 images. should be around 2 to 3.
stitcher.Stitch(matVector, output);
//Return the final stiched image
return(output);
}
private void btnAKAZE_Click(object sender, EventArgs e)
{
var temproot = RootImg.Clone();
var tempimg1 = WorkingImg.Clone();
Image <Bgr, byte> colorimg = tempimg1.Convert <Bgr, byte>();
Image <Bgr, byte> tempOriImg = temproot.Convert <Bgr, byte>();
var f2d = new AKAZE(
descriptorChannels: 1);
var keypoint = f2d.Detect(WorkingImg);
foreach (var point in keypoint)
{
System.Drawing.Rectangle rect = new Rectangle();
rect.X = (int)point.Point.X;
rect.Y = (int)point.Point.Y;
rect.Width = (int)point.Size;
rect.Height = (int)point.Size;
tempOriImg.Draw(rect, new Bgr(60, 200, 10), 2);
}
rtxLog.AppendText("btnAKAZE_Click" + Environment.NewLine);
RegistHisroty(tempOriImg);
}
// http://docs.opencv.org/3.0-beta/modules/features2d/doc/features2d.html
// http://docs.opencv.org/3.0-beta/modules/features2d/doc/feature_detection_and_description.html
// http://docs.opencv.org/3.0-beta/doc/tutorials/features2d/akaze_matching/akaze_matching.html
/// <summary>
/// Compare images with a feature detection algorithm
/// </summary>
/// <param name="mat_image1"> 1st image (OpenCv Mat)</param>
/// <param name="mat_image2"> 2nd image (OpenCv Mat)</param>
/// <param name="feature_count">number of feature keypoints found</param>
/// <param name="match_count">number of matches founds</param>
/// <param name="view">image of the feature and good matches</param>
/// <returns>Similarity % (#good matches/ # matches)</returns>
private static double CompareFeatures(Mat mat_image1, Mat mat_image2, out double feature_count, out double match_count, out Bitmap view)
{
match_count = 0;
feature_count = 0;
int nmatch = 0;
int ngmatch = 0;
view = new Bitmap(1, 1);
// stop here if one of the image does not seem to be valid
if (mat_image1 == null)
{
return(0);
}
if (mat_image1.Empty())
{
return(0);
}
if (mat_image2 == null)
{
return(0);
}
if (mat_image2.Empty())
{
return(0);
}
try
{
// Detect the keypoints and generate their descriptors
var detector = AKAZE.Create();
//var detector = BRISK.Create();
//var detector = ORB.Create(); // require grayscale
/*
* // grayscale
* Cv2.CvtColor(mat_image1, mat_image1, ColorConversionCodes.BGR2GRAY);
* Cv2.CvtColor(mat_image2, mat_image2, ColorConversionCodes.BGR2GRAY);
* mat_image1.EqualizeHist();
* mat_image2.EqualizeHist();
*/
var descriptors1 = new MatOfFloat();
var descriptors2 = new MatOfFloat();
var keypoints1 = new KeyPoint[1];
var keypoints2 = new KeyPoint[1];
try
{
keypoints1 = detector.Detect(mat_image1);
keypoints2 = detector.Detect(mat_image2);
if (keypoints1 != null)
{
detector.Compute(mat_image1, ref keypoints1, descriptors1);
if (descriptors1 == null)
{
return(0);
}
}
if (keypoints2 != null)
{
detector.Compute(mat_image2, ref keypoints2, descriptors2);
if (descriptors2 == null)
{
return(0);
}
}
}
catch (System.AccessViolationException) { }
catch (Exception) { }
// Find good matches (Nearest neighbor matching ratio)
float nn_match_ratio = 0.95f;
var matcher = new BFMatcher(NormTypes.Hamming);
var nn_matches = new DMatch[1][];
try
{
nn_matches = matcher.KnnMatch(descriptors1, descriptors2, 2);
}
catch (System.AccessViolationException) { }
catch (Exception) { }
var good_matches = new List <DMatch>();
var matched1 = new List <KeyPoint>();
var matched2 = new List <KeyPoint>();
var inliers1 = new List <KeyPoint>();
var inliers2 = new List <KeyPoint>();
if (nn_matches != null && nn_matches.Length > 0)
{
for (int i = 0; i < nn_matches.GetLength(0); i++)
{
if (nn_matches[i].Length >= 2)
{
DMatch first = nn_matches[i][0];
float dist1 = nn_matches[i][0].Distance;
float dist2 = nn_matches[i][1].Distance;
if (dist1 < nn_match_ratio * dist2)
{
good_matches.Add(first);
matched1.Add(keypoints1[first.QueryIdx]);
matched2.Add(keypoints2[first.TrainIdx]);
}
}
}
}
// Count matches & features
feature_count = keypoints1.Length + keypoints2.Length;
nmatch = nn_matches.Length;
match_count = nmatch;
ngmatch = good_matches.Count;
// Draw matches view
var mview = new Mat();
// show images + good matchs
if (keypoints1.Length > 0 && keypoints2.Length > 0)
{
Cv2.DrawMatches(mat_image1, keypoints1, mat_image2, keypoints2, good_matches.ToArray(), mview);
view = BitmapConverter.ToBitmap(mview);
}
else
{
// no matchs
view = new Bitmap(1, 1);
}
}
catch (System.AccessViolationException e)
{
Console.Error.WriteLine("Access Error => CompareFeatures : \n{0}", e.Message);
}
catch (Exception)
{
// Console.Error.WriteLine("Error => CompareFeatures : \n{0}", e.Message);
}
// similarity = 0 when there was no feature or no match
if (feature_count <= 0)
{
return(0);
}
if (nmatch <= 0)
{
return(0);
}
// similarity = ratio of good matches/ # matches
var similarity = 100.0 * ngmatch / nmatch;
return(similarity);
}
public async Task Update()
{
//サーバーID等の変数の宣言
string serverfolder = $@"R:\Project\RUMM.warehouse\{Context.Guild.Id}";
string datafolder = $@"{serverfolder}\Data";
string datafolder_recenter = $@"{datafolder}\Recenter";
string datafolder_trimmode = $@"{datafolder}\Trimmode";
string uploadedfolder = $@"{serverfolder}\Uploaded";
string uploadedfolder_map = $@"{uploadedfolder}\UploadedMap";
string trimedfolder = $@"{serverfolder}\Trimed";
string trimedfolder_map = $@"{trimedfolder}\TrimedMap";
string trimedfolder_map_pre = $@"{trimedfolder}\TrimedMap[Pre]";
string trimedfolder_map_backup = $@"{trimedfolder}\TrimedMap[Backup]";
//データ用テキストファイルの指定
string recenter_txt = $@"{datafolder_recenter}\recenter.txt";
string trimmode_txt = $@"{datafolder_trimmode}\trimmode.txt";
//メッセージに画像が添付されているかどうかを判断
if (!Context.Message.Attachments.Any())
{
await Context.Channel.SendErrorAsync("エラー", "画像が添付されてないよ！必ずコマンドと併せて画像を送信してね！");
return;
}
//Discordに送信されたメッセージとそのメッセージに付いているファイルを取得
var attachments = Context.Message.Attachments;
//新しいWebClientのインスタンスを作成
WebClient myWebClient = new WebClient();
//保存先とURLの指定
string uploadedmap = $@"{uploadedfolder_map}\uploadedmap.png";
string url = attachments.ElementAt(0).Url;
//ファイルをダウンロード
myWebClient.DownloadFile(url, uploadedmap);
string trimedmap_pre = $@"{trimedfolder_map_pre}\trimedmap[pre].png";
Call.Device(uploadedmap, trimedmap_pre);
Graphic.Resize_Own(trimedmap_pre, 384);
var comparemap = Directory.EnumerateFiles(trimedfolder_map, "*", SearchOption.AllDirectories);
float ImageMatch(Mat mat1, Mat mat2, bool show)
{
using (var descriptors1 = new Mat())
using (var descriptors2 = new Mat())
{
// 特徴点を検出
var akaze = AKAZE.Create();
// キーポイントを検出
akaze.DetectAndCompute(mat1, null, out KeyPoint[] keyPoints1, descriptors1);
akaze.DetectAndCompute(mat2, null, out KeyPoint[] keyPoints2, descriptors2);
// それぞれの特徴量をマッチング
var matcher = new BFMatcher(NormTypes.Hamming, false);
var matches = matcher.Match(descriptors1, descriptors2);
// 平均距離を返却(小さい方が類似度が高い)
var sum = matches.Sum(x => x.Distance);
return(sum / matches.Length);
}
}
foreach (string comparemapnum in comparemap)
{
string mapxcoord = comparemapnum.Split(',')[0].Replace(trimedfolder_map + "\\", "");
string mapzcoord = comparemapnum.Split(',')[1].Replace(".png", "");
string trimedmap = $@"{trimedfolder_map}\{mapxcoord},{mapzcoord}.png";
string trimedmap_await = $@"{trimedfolder_map_pre}\{mapxcoord},{mapzcoord}[await].png";
using (var mat1 = new Mat(trimedmap_pre))
using (var mat2 = new Mat(trimedmap))
{
// 2つの画像を比較(平均距離をスコアとした)
float score = ImageMatch(mat1, mat2, true);
Console.WriteLine(score);
if (score < 75)
{
File.Copy(trimedmap_pre, trimedmap_await, true);
}
}
}
List <string> coordslist = new List <string>();
string searchfileword = @"*await*.png";
string[] comparemap2 = Directory.GetFiles(trimedfolder_map_pre, searchfileword);
foreach (string mapnum in comparemap2)
{
coordslist.Add(mapnum);
}
string[] filelist = Directory.GetFiles(trimedfolder_map_pre, searchfileword);
if (coordslist.Count() == 1)
{
foreach (string premapnum in filelist)
{
string mapxcoord = premapnum.Split(',')[0].Replace(trimedfolder_map_pre + "\\", "");
string mapzcoord = premapnum.Split(',')[1].Replace("[await].png", "");
string trimedmap = $@"{trimedfolder_map}\{mapxcoord},{mapzcoord}.png";
string trimedfolder_map_backup_foreach = $@"{trimedfolder_map_backup}\{mapxcoord},{mapzcoord}";
string trimedmap_backup = $@"{trimedfolder_map_backup_foreach}\{DateTime.Now.ToString("yyyyMMdd")}.png";
Directory.CreateDirectory(trimedfolder_map_backup_foreach);
File.Copy(premapnum, trimedmap, true);
File.Copy(premapnum, trimedmap_backup, true);
File.Delete(premapnum);
}
await Context.Channel.SendSuccessAsync("完了", "正常に画像を切り取ったよ！");
}
else if (coordslist.Count() > 1 || coordslist.Count() == 0)
{
foreach (string premapnum in filelist)
{
File.Delete(premapnum);
}
await Context.Channel.SendErrorAsync("エラー", "定義されている地図と類似度が高くないよ！");
}
}
public void FindContours(string sLeftPictureFile, string sRightPictureFile)
{
Mat tokuLeft = new Mat();
Mat tokuRight = new Mat();
Mat output = new Mat();
AKAZE akaze = AKAZE.Create();
KeyPoint[] keyPointsLeft;
KeyPoint[] keyPointsRight;
Mat descriptorLeft = new Mat();
Mat descriptorRight = new Mat();
DescriptorMatcher matcher; //マッチング方法
DMatch[] matches; //特徴量ベクトル同士のマッチング結果を格納する配列
//画像をグレースケールとして読み込み、平滑化する
Mat Lsrc = new Mat(sLeftPictureFile, ImreadModes.Color);
//画像をグレースケールとして読み込み、平滑化する
Mat Rsrc = new Mat(sRightPictureFile, ImreadModes.Color);
//特徴量の検出と特徴量ベクトルの計算
akaze.DetectAndCompute(Lsrc, null, out keyPointsLeft, descriptorLeft);
akaze.DetectAndCompute(Rsrc, null, out keyPointsRight, descriptorRight);
//画像1の特徴点をoutput1に出力
Cv2.DrawKeypoints(Lsrc, keyPointsLeft, tokuLeft);
Image imageLeftToku = BitmapConverter.ToBitmap(tokuLeft);
pictureBox3.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox3.Image = imageLeftToku;
tokuLeft.SaveImage("result/LeftToku.jpg");
//画像2の特徴点をoutput1に出力
Cv2.DrawKeypoints(Rsrc, keyPointsRight, tokuRight);
Image imageRightToku = BitmapConverter.ToBitmap(tokuRight);
pictureBox4.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox4.Image = imageRightToku;
tokuRight.SaveImage("result/RightToku.jpg");
//総当たりマッチング
matcher = DescriptorMatcher.Create("BruteForce");
matches = matcher.Match(descriptorLeft, descriptorRight);
Cv2.DrawMatches(Lsrc, keyPointsLeft, Rsrc, keyPointsRight, matches, output);
output.SaveImage(@"result\output.jpg");
int size = matches.Count();
var getPtsSrc = new Vec2f[size];
var getPtsTarget = new Vec2f[size];
int count = 0;
foreach (var item in matches)
{
var ptSrc = keyPointsLeft[item.QueryIdx].Pt;
var ptTarget = keyPointsRight[item.TrainIdx].Pt;
getPtsSrc[count][0] = ptSrc.X;
getPtsSrc[count][1] = ptSrc.Y;
getPtsTarget[count][0] = ptTarget.X;
getPtsTarget[count][1] = ptTarget.Y;
count++;
}
// SrcをTargetにあわせこむ変換行列homを取得する。ロバスト推定法はRANZAC。
var hom = Cv2.FindHomography(
InputArray.Create(getPtsSrc),
InputArray.Create(getPtsTarget),
HomographyMethods.Ransac);
// 行列homを用いてSrcに射影変換を適用する。
Mat WarpedSrcMat = new Mat();
Cv2.WarpPerspective(
Lsrc, WarpedSrcMat, hom,
new OpenCvSharp.Size(Rsrc.Width, Rsrc.Height));
WarpedSrcMat.SaveImage(@"result\Warap.jpg");
//画像1の特徴点をoutput1に出力
Image imageLeftSyaei = BitmapConverter.ToBitmap(WarpedSrcMat);
pictureBox5.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox5.Image = imageLeftSyaei;
//画像2の特徴点をoutput1に出力
Image imageRightSyaei = BitmapConverter.ToBitmap(Rsrc);
pictureBox6.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox6.Image = imageRightSyaei;
Mat LmatFloat = new Mat();
WarpedSrcMat.ConvertTo(LmatFloat, MatType.CV_16SC3);
Mat[] LmatPlanes = LmatFloat.Split();
Mat RmatFloat = new Mat();
Rsrc.ConvertTo(RmatFloat, MatType.CV_16SC3);
Mat[] RmatPlanes = RmatFloat.Split();
Mat diff0 = new Mat();
Mat diff1 = new Mat();
Mat diff2 = new Mat();
Cv2.Absdiff(LmatPlanes[0], RmatPlanes[0], diff0);
Cv2.Absdiff(LmatPlanes[1], RmatPlanes[1], diff1);
Cv2.Absdiff(LmatPlanes[2], RmatPlanes[2], diff2);
Cv2.MedianBlur(diff0, diff0, 5);
Cv2.MedianBlur(diff1, diff1, 5);
Cv2.MedianBlur(diff2, diff2, 5);
diff0.SaveImage("result/diff0.jpg");
diff1.SaveImage("result/diff1.jpg");
diff2.SaveImage("result/diff2.jpg");
Mat wiseMat = new Mat();
Cv2.BitwiseOr(diff0, diff1, wiseMat);
Cv2.BitwiseOr(wiseMat, diff2, wiseMat);
wiseMat.SaveImage("result/wiseMat.jpg");
Mat openingMat = new Mat();
Cv2.MorphologyEx(wiseMat, openingMat, MorphTypes.Open, new Mat());
Mat dilationMat = new Mat();
Cv2.Dilate(openingMat, dilationMat, new Mat());
Cv2.Threshold(dilationMat, dilationMat, 100, 255, ThresholdTypes.Binary);
dilationMat.SaveImage(@"result\dilationMat.jpg");
Mat LaddMat = new Mat();
Mat RaddMat = new Mat();
Console.WriteLine(dilationMat.GetType());
Console.WriteLine(Rsrc.GetType());
// dilationMatはグレースケールなので合成先のMatと同じ色空間に変換する
Mat dilationScaleMat = new Mat();
Mat dilationColorMat = new Mat();
Cv2.ConvertScaleAbs(dilationMat, dilationScaleMat);
Cv2.CvtColor(dilationScaleMat, dilationColorMat, ColorConversionCodes.GRAY2RGB);
Cv2.AddWeighted(WarpedSrcMat, 0.3, dilationColorMat, 0.7, 0, LaddMat);
Cv2.AddWeighted(Rsrc, 0.3, dilationColorMat, 0.7, 0, RaddMat);
Image LaddImage = BitmapConverter.ToBitmap(LaddMat);
pictureBox7.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox7.Image = LaddImage;
Image RaddImage = BitmapConverter.ToBitmap(RaddMat);
pictureBox8.SizeMode = PictureBoxSizeMode.Zoom;
pictureBox8.Image = RaddImage;
RaddMat.SaveImage(@"result\Result.jpg");
MessageBox.Show("Done!");
}