public DescriptorMatherWrapper(IOptionsMonitor <ConfigSettings> settings)
{
_indexIdMapImage = new Dictionary <int, string>();
_settings = settings;
_matcher = DescriptorMatcher.Create(_settings.CurrentValue.DescriptorMatcherType);
}
private void Start()
{
webCamTextureToMatHelper = gameObject.GetComponent <WebCamTextureToMatHelper>();
webCamTextureToMatHelper.Initialize();
grayMat = new Mat();
makerGrayMat = new Mat(originMakerTexture.height, originMakerTexture.width, CvType.CV_8UC1);
makerTexture = new Texture2D(originMakerTexture.width, originMakerTexture.height);
Graphics.CopyTexture(originMakerTexture, makerTexture);
detector = ORB.create();
extractor = ORB.create();
// Get Key Points of Maker
makerMat = new Mat(originMakerTexture.height, originMakerTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(makerTexture, makerMat, false);
makerKeyPoints = new MatOfKeyPoint();
makerDescriptors = new Mat();
Imgproc.cvtColor(makerMat, makerGrayMat, Imgproc.COLOR_BGR2GRAY);
detector.detect(makerGrayMat, makerKeyPoints);
extractor.compute(makerGrayMat, makerKeyPoints, makerDescriptors);
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
}
/// <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);
}
/// <summary>
/// Initializes a new instance of the <see cref="PatternDetector"/> class.
/// </summary>
/// <param name="detector">Detector.</param>
/// <param name="extractor">Extractor.</param>
/// <param name="matcher">Matcher.</param>
/// <param name="ratioTest">If set to <c>true</c> ratio test.</param>
public PatternDetector(ORB detector, ORB extractor, DescriptorMatcher matcher, bool ratioTest = false)
{
if (detector == null)
{
detector = ORB.create();
detector.setMaxFeatures(1000);
}
if (extractor == null)
{
extractor = ORB.create();
extractor.setMaxFeatures(1000);
}
if (matcher == null)
{
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
}
m_detector = detector;
m_extractor = extractor;
m_matcher = matcher;
enableRatioTest = ratioTest;
enableHomographyRefinement = true;
homographyReprojectionThreshold = 3;
m_queryKeypoints = new MatOfKeyPoint();
m_queryDescriptors = new Mat();
m_matches = new MatOfDMatch();
m_knnMatches = new List <MatOfDMatch>();
m_grayImg = new Mat();
m_warpedImg = new Mat();
m_roughHomography = new Mat();
m_refinedHomography = new Mat();
}
// Use this for initialization
void Start()
{
Texture2D imgTexture = Resources.Load("lena") as Texture2D;
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
Debug.Log("img1Mat dst ToString " + img1Mat.ToString());
Mat img2Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img2Mat);
Debug.Log("img2Mat dst ToString " + img2Mat.ToString());
float angle = UnityEngine.Random.Range(0, 360), scale = 1.0f;
Point center = new Point(img2Mat.cols() * 0.5f, img2Mat.rows() * 0.5f);
Mat affine_matrix = Imgproc.getRotationMatrix2D(center, angle, scale);
Imgproc.warpAffine(img1Mat, img2Mat, affine_matrix, img2Mat.size());
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);
Mat resultImg = new Mat();
Features2d.drawMatches(img1Mat, keypoints1, img2Mat, keypoints2, matches, resultImg);
Texture2D texture = new Texture2D(resultImg.cols(), resultImg.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(resultImg, texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
}
protected override void OnCreate(Bundle savedInstanceState)
{
base.OnCreate(savedInstanceState);
adapter = new TabsAdapter(this, SupportFragmentManager);
pager = FindViewById <ViewPager>(Resource.Id.viewpager);
var tabs = FindViewById <TabLayout>(Resource.Id.tabs);
pager.Adapter = adapter;
tabs.SetupWithViewPager(pager);
pager.OffscreenPageLimit = 3;
pager.PageSelected += (sender, args) =>
{
var fragment = adapter.InstantiateItem(pager, args.Position) as IFragmentVisible;
fragment?.BecameVisible();
};
Toolbar.MenuItemClick += (sender, e) =>
{
var intent = new Intent(this, typeof(AddItemActivity));;
StartActivity(intent);
};
SupportActionBar.SetDisplayHomeAsUpEnabled(false);
SupportActionBar.SetHomeButtonEnabled(false);
var src = new Mat[2];
var dst = new Mat[2];
var keyPoints1 = new MatOfKeyPoint();
var keyPoints2 = new MatOfKeyPoint();
var descripter1 = new Mat();
var descripter2 = new Mat();
var dmatch = new MatOfDMatch();
var output = new Mat();
src[0] = Imgcodecs.Imread("path/to/source/1.png");
src[1] = Imgcodecs.Imread("path/to/source/2.png");
dst[0] = new Mat();
dst[1] = new Mat();
Imgproc.CvtColor(src[0], dst[0], Imgproc.COLORBayerGR2GRAY);
Imgproc.CvtColor(src[1], dst[1], Imgproc.COLORBayerGR2GRAY);
var akaze = FeatureDetector.Create(FeatureDetector.Akaze);
var executor = DescriptorExtractor.Create(DescriptorExtractor.Akaze);
akaze.Detect(dst[0], keyPoints1);
akaze.Detect(dst[1], keyPoints2);
executor.Compute(dst[0], keyPoints1, descripter1);
executor.Compute(dst[1], keyPoints2, descripter2);
var matcher = DescriptorMatcher.Create(DescriptorMatcher.BruteforceHamming);
matcher.Match(descripter1, descripter2, dmatch);
Features2d.DrawMatches(src[0], keyPoints1, src[1], keyPoints2, dmatch, output);
}
public CVObjectDetector()
{
// Brisk, KAZE, AKAZE, ORBDetector, HarrisLaplaceFeatureDetector, FastDetector
featureDetector = new Brisk();
// Brisk, ORBDetector, BriefDescriptorExtractor, Freak
featureDescriptor = new Brisk();
featureMatcher = new BFMatcher(DistanceType.L2);
}
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 Matcher()
{
ip = new Emgu.CV.Flann.KdTreeIndexParams();
sp = new SearchParams();
matcher = new FlannBasedMatcher(ip, sp);
_disposables.Add(ip);
_disposables.Add(sp);
_disposables.Add(matcher);
}
public Searcher(string mainpath)
{
_descriptor = new SURFDescriptor();
_indexedDatset = Trainer.LoadFromDisk(mainpath);
_descriptorMatcher = InitMatcher();
_starFull = new Mat(Path.Combine(mainpath, "data", "starfull.png"));
_closeButton = new Mat(Path.Combine(mainpath, "data", "closebutton.png"));
}
static Mat MatcheWindow(Mat template,
double gammaPower,
double distanceThreshold)
{
using var source = new Mat();
// カメラ画像取り込み
camera.Read(source);
//Cv2.Resize(source, source, new Size(2592, 1944));
Cv2.Rotate(source, source, RotateFlags.Rotate90Clockwise);
using var trim = source;//.SubMat(new Rect(20, 195, 1880, 873));
// ガンマ補正
using var gammaDst = AdjustGamma(trim, gammaPower);
// テンプレート画像の特徴量計算
(var tempKey, var tempDesc) = FeatureCommand(template);
// カメラ画像の特徴量計算
(var srcKey, var srcDesc) = FeatureCommand(gammaDst);
// 特徴点マッチャー
DescriptorMatcher matcher = DescriptorMatcher.Create("BruteForce");
// 特徴量マッチング 上位2位
DMatch[][] matches = matcher.KnnMatch(tempDesc, srcDesc, 2);
// 閾値で対応点を絞り込む
List <DMatch> goodMatches;
List <Point2f> goodTemplateKeyPoints, goodSourceKeyPoints;
(goodMatches, goodTemplateKeyPoints, goodSourceKeyPoints) =
FilterMatchGoodScore(tempKey, srcKey, matches, distanceThreshold);
Console.WriteLine("matches: {0},goodMatches: {1}", matches.Length, goodMatches.Count);
// ロバスト推定してホモグラフィーを算出する
Mat homoGraphy = LookupHomoGraphy(goodTemplateKeyPoints, goodSourceKeyPoints);
// 対象物体画像からコーナーを取得する
var cornerPoints = LookupCornerFromTargetObjectImage(template, homoGraphy);
var rect = new Rect(cornerPoints[0].X,
cornerPoints[0].Y,
template.Width / 2,
template.Height / 2);
// 枠描画
gammaDst.Rectangle(rect, Scalar.Pink, 3);
//マッチングした特徴量同士を線でつなぐ
using var output = new Mat();
Cv2.DrawMatches(template, tempKey, gammaDst, srcKey, goodMatches, output);
var output2 = new Mat();
Cv2.Resize(output, output2, new Size(), 0.5, 0.5);
return(output2);
}
private static void Run()
{
var dm = DescriptorMatcher.Create("BruteForce");
dm.Clear();
Console.WriteLine(Cv2.GetCudaEnabledDeviceCount());
string[] algoNames = Algorithm.GetList();
Console.WriteLine(String.Join("\n", algoNames));
SIFT al1 = Algorithm.Create <SIFT>("Feature2D.SIFT");
string[] ppp = al1.GetParams();
Console.WriteLine(ppp);
var t = al1.ParamType("contrastThreshold");
double d = al1.GetDouble("contrastThreshold");
t.ToString();
d.ToString();
var src = new Mat("img/lenna.png");
var rand = new Random();
var memory = new List <long>(100);
var a1 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
var a2 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
var a3 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
a3.ToString();
for (long i = 0;; i++)
{
SIFT a = Algorithm.Create <SIFT>("Feature2D.SIFT");
a.ToString();
for (int j = 0; j < 200; j++)
{
int c1 = rand.Next(100, 400);
int c2 = rand.Next(100, 400);
Mat temp = src.Row[c1];
src.Row[c1] = src.Row[c2];
src.Row[c2] = temp;
}
memory.Add(MyProcess.WorkingSet64);
if (memory.Count >= 100)
{
double average = memory.Average();
Console.WriteLine("{0:F3}MB", average / 1024.0 / 1024.0);
memory.Clear();
GC.Collect();
}
}
}
private void FindPointButton_Click(object sender, EventArgs e)
{
MinValueHarris = Convert.ToDouble(txb_minValue.Text);
WindowSize = Convert.ToInt32(txb_WindowSize.Text);
int maxPoints;
int gridSize = Convert.ToInt32(txb_gridSize.Text);
int cellSize = Convert.ToInt32(txb_cellSize.Text);
int binsCount = Convert.ToInt32(txb_binsCount.Text);
if (filter_checkBox.Checked == true)
{
maxPoints = Convert.ToInt32(txb_Filter.Text);
}
else
{
maxPoints = 5000;
}
List <InterestingPoint> pointsA =
NonMaximumSuppression.FilterA(imageA, Harris.DoHarris(MinValueHarris, WindowSize, imageA), maxPoints);
List <InterestingPoint> pointsB =
NonMaximumSuppression.FilterA(imageB, Harris.DoHarris(MinValueHarris, WindowSize, imageB), maxPoints);
List <ForDescriptor.Descriptor> descriptorsA = RotationInvariant.Calculate(imageA, pointsA);
List <ForDescriptor.Descriptor> descriptorsB = RotationInvariant.Calculate(imageB, pointsB);
List <ValueTuple <ForDescriptor.Descriptor, ForDescriptor.Descriptor> > match;
if (rbt_usual.Checked == true)
{
match = DescriptorMatcher.Match(descriptorsA, descriptorsB);
}
else if (rbt_NNDR.Checked == true)
{
match = DescriptorMatcher.Nndr(descriptorsA, descriptorsB);
}
else
{
match = DescriptorMatcher.Match(descriptorsA, descriptorsB);
}
lbl_findPoints1.Text = "Найдено интересных точек(1): " + pointsA.Count;
lbl_findPoints2.Text = "Найдено интересных точек(2): " + pointsB.Count;
lbl_PairCount.Text = "Найдено пар точек: " + match.Count;
var image = DrawHelper.DrawTwoImages(
DrawHelper.DrawPoints(imageA, pointsA), DrawHelper.DrawPoints(imageB, pointsB), match);
IOHelper.WriteImageToFile(image, "..\\..\\..\\..\\Output\\OutputPicture.png");
pictureBox1.Image = image;
}
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();
}
private void button1_Click(object sender, EventArgs e)
{
IOHelper.DeletePictures();
MinValueHarris = Convert.ToDouble(txb_minValue.Text);
WindowSize = Convert.ToInt32(txb_WindowSize.Text);
int maxPoints = Convert.ToInt32(txb_Filter.Text);
int gridSize = Convert.ToInt32(txb_gridSize.Text);
int cellSize = Convert.ToInt32(txb_cellSize.Text);
int binsCount = Convert.ToInt32(txb_binsCount.Text);
if (filter_checkBox.Checked == true)
{
maxPoints = Convert.ToInt32(txb_Filter.Text);
}
else
{
maxPoints = 5000;
}
List <ForDescriptor.Descriptor> descriptorsA = BlobsFinder.FindBlobs(imageA, maxPoints);
List <ForDescriptor.Descriptor> descriptorsB = BlobsFinder.FindBlobs(imageB, maxPoints);
List <ValueTuple <ForDescriptor.Descriptor, ForDescriptor.Descriptor> > match;
if (rbt_usual.Checked == true)
{
match = DescriptorMatcher.Match(descriptorsA, descriptorsB);
}
else if (rbt_NNDR.Checked == true)
{
match = DescriptorMatcher.Nndr(descriptorsA, descriptorsB);
}
else
{
match = DescriptorMatcher.Match(descriptorsA, descriptorsB);
}
lbl_findPoints1.Text = "Найдено интересных точек(1): " + descriptorsA.Count;
lbl_findPoints2.Text = "Найдено интересных точек(2): " + descriptorsB.Count;
lbl_PairCount.Text = "Найдено пар точек: " + match.Count;
var image = DrawHelper.DrawTwoImages(
DrawHelper.DrawPoints(imageA, descriptorsA), DrawHelper.DrawPoints(imageB, descriptorsB), match);
// var image = DrawHelper.DrawPoints(imageA, descriptorsA);
IOHelper.WriteImageToFile(image, "..\\..\\..\\..\\Output\\OutputPicture.png");
pictureBox1.Image = image;
}
public PatternDetector(bool ratioTest)
{
m_detector = ORB.Create(1000);
m_extractor = ORB.Create(1000);
//BFMatcher bfMatcher = new BFMatcher(NormTypes.Hamming, true);
m_matcher = new BFMatcher(NormTypes.Hamming);
//m_matcher = DescriptorMatcher.Create("BRUTEFORCE_HAMMING");
enableRatioTest = ratioTest;
enableHomographyRefinement = true;
homographyReprojectionThreshold = 3;
//m_queryKeypoints = new MatOfKeyPoint ();
m_queryDescriptors = new Mat();
//m_matches = new MatOfDMatch ();
//m_knnMatches = new List<MatOfDMatch> ();
m_grayImg = new Mat();
m_warpedImg = new Mat();
m_roughHomography = new Mat();
m_refinedHomography = new Mat();
}
public Image Match(List <ImageDataResult> resultList)
{
DescriptorMatcher matcher = GetDescritorMatcher();
matcher.Add(Image1.Descriptors);
matcher.KnnMatch(Image2.Descriptors, Matches, 2, null);
var mask = GetMask();
Mat homography = GetHomography(Image1.KeyPoints, Image2.KeyPoints, Matches, mask);
Mat result = new Mat();
Features2DToolbox.DrawMatches(Image1.CvMaterial, Image1.KeyPoints, Image2.CvMaterial, Image2.KeyPoints,
Matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
Mat regged = new Mat();
if (homography != null)
{
Rectangle rect = new System.Drawing.Rectangle(Point.Empty, Image1.CvMaterial.Size);
PointF[] pts =
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
Point[] points = Array.ConvertAll(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255));
}
CvInvoke.WarpPerspective(Image1.CvOriginal, regged, homography, Image1.Size);
var image = ImageData.Subtract(regged.Bitmap, Image2.CvOriginal.Bitmap);
resultList.Add(new ImageDataResult((Bitmap)image, result, regged));
return(image);
}
return(ImageData.Subtract(Image1.CvOriginal.Bitmap, Image2.CvOriginal.Bitmap));
}
public SimpleFeature(SimpleFeatureDesc desc, SIFT featureDetector = null)
{
size = desc.size;
cropRect = new Rectangle(desc.cropPoint, size);
mask = new Mat(desc.size, DepthType.Cv8U, 1);
features = desc.features;
CvInvoke.Rectangle(mask, desc.rect, new MCvScalar(255, 255, 255), -1);
CvInvoke.Circle(mask, desc.maskPoint, 23, new MCvScalar(0, 0, 0), -1);
//SimpleFeature sf = new SimpleFeature(new Point(842, 646), size, mask);
if (desc.colorFilter != null)
{
SetColorFilter(desc.colorFilter.lowerBound, desc.colorFilter.upperBound);
}
//featureDetector = new SIFT(0,2,0.02,20,0.4);
if (featureDetector == null)
{
this.featureDetector = new SIFT(0, 3, 0.04, 10, 0.8);
}
Emgu.CV.Flann.LinearIndexParams ip = new Emgu.CV.Flann.LinearIndexParams();
Emgu.CV.Flann.SearchParams sp = new SearchParams();
matcher = new FlannBasedMatcher(ip, sp);
}
private static void Run()
{
var dm = DescriptorMatcher.Create("BruteForce");
dm.Clear();
var src = new Mat("data/lenna.png");
var rand = new Random();
var memory = new List <long>(100);
var a1 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
var a2 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
var a3 = new Mat(src, Rect.FromLTRB(0, 0, 30, 40));
a3.ToString();
for (long i = 0; ; i++)
{
for (int j = 0; j < 200; j++)
{
int c1 = rand.Next(100, 400);
int c2 = rand.Next(100, 400);
Mat temp = src.Row[c1];
src.Row[c1] = src.Row[c2];
src.Row[c2] = temp;
}
memory.Add(MyProcess.WorkingSet64);
if (memory.Count >= 100)
{
double average = memory.Average();
Console.WriteLine("{0:F3}MB", average / 1024.0 / 1024.0);
memory.Clear();
GC.Collect();
}
}
}
private void button2_Click(object sender, EventArgs e)
{
int maxPoints = Convert.ToInt32(txb_Filter.Text);
string name = PathToWriteImage + "resultPicture.jpg";
Bitmap bmp1 = new Bitmap(PathToReadImage + "bridge1.jpg");
Bitmap bmp2 = new Bitmap(PathToReadImage + "bridge2.jpg");
var descriptorsABlobs = BlobsFinder.FindBlobs(IOHelper.ImageToMat(bmp1), maxPoints);
var descriptorsBBlobs = BlobsFinder.FindBlobs(IOHelper.ImageToMat(bmp2), maxPoints);
var match = DescriptorMatcher.Nndr(descriptorsABlobs, descriptorsBBlobs);
lbl_findPoints1.Text = "Найдено интересных точек(1): " + descriptorsABlobs.Count;
lbl_findPoints2.Text = "Найдено интересных точек(2): " + descriptorsBBlobs.Count;
lbl_PairCount.Text = "Найдено пар точек: " + match.Count;
var(matrixA, matrixB) = Ransac.CalculateTransform(match);
var result = Transformer.Transform(bmp1, bmp2, matrixA, matrixB);
result.Save(name, ImageFormat.Jpeg);
pictureBox1.Image = result;
}
internal static extern void cv_features2d_DescriptorMatcher_match(
DescriptorMatcher matcher,
Arr queryDescriptors,
Arr trainDescriptors,
DMatchCollection matches,
Arr mask);
public ImageString MatchFeatures(string base64image, List <string> base64imageList)
{
List <MatOfDMatch> winnerMatches = new List <MatOfDMatch>();
MatOfKeyPoint winnerKeyPoints = new MatOfKeyPoint();
Mat winnerImage = new Mat();
int winnerIndex = -1;
int winnerValue = 0;
Texture2D imgTexture = base64ImageToTexture(base64image);
List <Texture2D> imgTextures = new List <Texture2D>();
for (int i = 0; i < base64imageList.Count; i++)
{
imgTextures.Add(base64ImageToTexture(base64imageList[i]));
}
//Create Mat from texture
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//Detect keypoints and compute descriptors from photo.
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
Debug.Log("Billede features: " + descriptors1.rows());
if (descriptors1.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit original-billede");
return(new ImageString(base64image, winnerIndex));
}
//Run through each image in list
for (int i = 0; i < imgTextures.Count; i++)
{
Texture2D imgTexture2 = imgTextures[i];
//Create Mat from texture
Mat img2Mat = new Mat(imgTexture2.height, imgTexture2.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture2, img2Mat);
//Find keypoints and descriptors from image in list
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
//Match photo with image from list
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
Debug.Log("Billede2 features: " + descriptors2.rows());
if (descriptors2.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit test billede: " + i);
continue;
}
List <MatOfDMatch> matchList = new List <MatOfDMatch>();
matcher.knnMatch(descriptors1, descriptors2, matchList, 2);
//Find the good matches and put them ind a list
List <MatOfDMatch> good = new List <MatOfDMatch>();
foreach (MatOfDMatch match in matchList)
{
DMatch[] arrayDmatch = match.toArray();
if (arrayDmatch[0].distance < 0.7f * arrayDmatch[1].distance)
{
good.Add(match);
}
}
//Find the best match image based on the good lists
if (good.Count > winnerThreshold && good.Count > winnerValue)
{
winnerImage = img2Mat;
winnerMatches = good;
winnerKeyPoints = keypoints2;
winnerIndex = i;
winnerValue = good.Count;
}
}
Debug.Log("The winner is image: " + winnerIndex + " with a value of: " + winnerValue);
//If no winner just return the original image
if (winnerIndex == -1)
{
Debug.Log("No winner");
return(new ImageString(base64image, winnerIndex));
}
Debug.Log("No winner");
//Find the matching keypoints from the winner list.
MatOfPoint2f queryPoints = new MatOfPoint2f();
MatOfPoint2f matchPoints = new MatOfPoint2f();
List <Point> queryPointsList = new List <Point>();
List <Point> matchPointsList = new List <Point>();
foreach (MatOfDMatch match in winnerMatches)
{
DMatch[] arrayDmatch = match.toArray();
queryPointsList.Add(keypoints1.toList()[arrayDmatch[0].queryIdx].pt);
matchPointsList.Add(winnerKeyPoints.toList()[arrayDmatch[0].trainIdx].pt);
}
queryPoints.fromList(queryPointsList);
matchPoints.fromList(matchPointsList);
//Calculate the homography of the best matching image
Mat homography = Calib3d.findHomography(queryPoints, matchPoints, Calib3d.RANSAC, 5.0);
Mat resultImg = new Mat();
Imgproc.warpPerspective(img1Mat, resultImg, homography, winnerImage.size());
//Show image
Texture2D texture = new Texture2D(winnerImage.cols(), winnerImage.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(resultImg, texture);
return(new ImageString(Convert.ToBase64String(texture.EncodeToPNG()), winnerIndex));
}
public List <Point[]> Find()
{
CoordinateStep = Math.Min(image.Width, image.Height) / 5;
var cellsX = (int)Math.Ceiling(image.Width * 1D / CoordinateStep);
var cellsY = (int)Math.Ceiling(image.Height * 1D / CoordinateStep);
VotesImage = IOHelper.MatToImage(image);
for (var i = 1; i < cellsX; i++)
{
var x = CoordinateStep * i;
DrawHelper.DrawLine(VotesImage, x, 0, x, image.Height - 1);
}
for (var i = 1; i < cellsY; i++)
{
var y = CoordinateStep * i;
DrawHelper.DrawLine(VotesImage, 0, y, image.Width - 1, y);
}
var descriptors1 = BlobsFinder.FindBlobs(sample, 500);
var descriptors2 = BlobsFinder.FindBlobs(image, 500);
var matches = DescriptorMatcher.Nndr(descriptors2, descriptors1);
ReverseMatches = matches.Select(x => new Match(x.Item2, x.Item1)).ToList();
var sampleCenterX = sample.Width / 2.0;
var sampleCenterY = sample.Height / 2.0;
votes = new double[cellsX, cellsY, AngleCells, CellsScale];
voters = new List <Match> [cellsX, cellsY, AngleCells, CellsScale];
foreach (var match in matches)
{
var samplePoint = match.Item2.Point;
var imagePoint = match.Item1.Point;
var scale = imagePoint.Radius / samplePoint.Radius;
var angle = match.Item2.Angle - match.Item1.Angle;
var vectorX = scale * (sampleCenterX - samplePoint.getX());
var vectorY = scale * (sampleCenterY - samplePoint.getY());
var centerX = imagePoint.getX() + vectorX * Math.Cos(angle) - vectorY * Math.Sin(angle);
var centerY = imagePoint.getY() + vectorX * Math.Sin(angle) + vectorY * Math.Cos(angle);
Vote(match, centerX, centerY, scale, angle);
DrawHelper.DrawLine(VotesImage,
imagePoint.getX(), imagePoint.getY(),
(int)Math.Round(centerX), (int)Math.Round(centerY));
}
for (var x = 0; x < cellsX; x++)
{
for (var y = 0; y < cellsY; y++)
{
for (var a = 0; a < AngleCells; a++)
{
for (var s = 0; s < CellsScale; s++)
{
if (IsVotesLocalMaximum(x, y, a, s) && voters[x, y, a, s].Count > VotersThreshold)
{
// DrawHelper.DrawPolygon(VotesImage, GetPreliminary(x, y, a, s), true);
objects.Add(GetLocation(Ransac.CalculateTransform(voters[x, y, a, s])));
}
}
}
}
}
return(objects);
}
static void Main(string[] args)
{
var _videoCapture = new VideoCapture(1);
var referenceTime = DateTime.Now;
var cnt = 0;
while (true)
{
/*
* 通常の撮像は 1600 * 1200 Fps 5
* 処理速度の都合
* テンプレート画像の撮像は 2592 * 1944 Fps 2(FA 2592 * 1944 Fps 6)
*/
var frameWidth = 2592;
var frameHeight = 1944;
var fps = 6;
if (_videoCapture.Fps != fps)
{
_videoCapture.Fps = fps;
}
if (_videoCapture.FrameWidth != frameWidth)
{
_videoCapture.FrameWidth = frameWidth;
}
if (_videoCapture.FrameHeight != frameHeight)
{
_videoCapture.FrameHeight = frameHeight;
}
cnt++;
Console.WriteLine("設定書き込み {0}回目 {1}秒", cnt, (DateTime.Now - referenceTime).TotalSeconds);
if (_videoCapture.FrameWidth == frameWidth && _videoCapture.FrameHeight == frameHeight && Math.Abs(_videoCapture.Fps - fps) < 1)
{
break;
}
if ((DateTime.Now - referenceTime).TotalSeconds > 30)
{
throw new TimeoutException("videoCaptureのフレームサイズの設定がタイムアウトしました");
}
}
int posMsec = (int)(1000 * 1 / _videoCapture.Fps);
_videoCapture.Set(VideoCaptureProperties.PosMsec, posMsec);
// 特徴点マッチャー
DescriptorMatcher matcher = DescriptorMatcher.Create("BruteForce");
// 特徴量検出アルゴリズム
using var _temp = new Mat("escalator1st_20201119115525242.bmp");
// 特徴量計算
(var _temp_keypoint, var _temp_featureImage) = FeatureCommand(_temp);
while (true)
{
using var frame = new Mat();
_videoCapture.Read(frame);
// 特徴量計算
(var _frame_keypoint, var _frame_descriptor) = FeatureCommand(frame);
// 特徴量マッチング 上位2位
DMatch[][] matches = matcher.KnnMatch(_temp_featureImage, _frame_descriptor, 2);
// 閾値で対応点を絞り込む
List <DMatch> goodMatches;
List <Point2f> goodTemplateKeyPoints, goodSourceKeyPoints;
(goodMatches, goodTemplateKeyPoints, goodSourceKeyPoints) =
FilterMatchGoodScore(_temp_keypoint, _frame_keypoint, matches);
//マッチングした特徴量同士を線でつなぐ
Mat output3 = new Mat();
Cv2.DrawMatches(_temp, _temp_keypoint, frame, _frame_keypoint, goodMatches, output3);
Cv2.ImShow("feature", output3);
Cv2.WaitKey();
}
}
public bool descriptorsORB_Old(Mat RGB, Mat cameraFeed, string targetName)//找出特徵的顏色方法三(可運行但效率不佳放棄)
{
if (RGB == null)
{
Debug.Log("RGB Mat is Null");
return(false);
}
//將傳入的RGB存入Src
Mat SrcMat = new Mat();
RGB.copyTo(SrcMat);
//比對樣本
Texture2D imgTexture = Resources.Load(targetName) as Texture2D;
// Texture2D imgTexture2 = Resources.Load("lenaK") as Texture2D;
//Texture2D轉Mat
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
//創建 ORB的特徵點裝置
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//產生存放特徵點Mat
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
MatOfKeyPoint keypointsSrc = new MatOfKeyPoint();
Mat descriptorsSrc = new Mat();
//找特徵點圖1
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
//找特徵點圖Src
detector.detect(SrcMat, keypointsSrc);
extractor.compute(SrcMat, keypointsSrc, descriptorsSrc);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptorsSrc, matches);
DMatch[] arrayDmatch = matches.toArray();
for (int i = arrayDmatch.Length - 1; i >= 0; i--)
{
// Debug.Log("match " + i + ": " + arrayDmatch[i].distance);
}
//做篩選
double max_dist = 0;
double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
double dist = new double();
for (int i = 0; i < matches.rows(); i++)
{
dist = arrayDmatch[i].distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
Debug.Log("Max dist :" + max_dist);
Debug.Log("Min dist :" + min_dist);
//只畫好的點
List <DMatch> matchesGoodList = new List <DMatch>();
for (int i = 0; i < matches.rows(); i++)
{
//if (arrayDmatch[i].distance < RateDist.value * min_dist)
//{
// //Debug.Log("match " + i + ": " + arrayDmatch[i].distance);
// matchesGoodList.Add(arrayDmatch[i]);
//}
}
MatOfDMatch matchesGood = new MatOfDMatch();
matchesGood.fromList(matchesGoodList);
//Draw Keypoints
Features2d.drawKeypoints(SrcMat, keypointsSrc, SrcMat);
//做輸出的轉換予宣告
Mat resultImg = new Mat();
// Features2d.drawMatches(img1Mat, keypoints1, SrcMat, keypointsSrc, matchesGood, resultImg);
List <Point> P1 = new List <Point>();
// List<Point> P2 = new List<Point>();
List <Point> pSrc = new List <Point>();
Debug.Log("MatchCount" + matchesGoodList.Count);
for (int i = 0; i < matchesGoodList.Count; i++)
{
P1.Add(new Point(keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.x, keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.y));
pSrc.Add(new Point(keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.x, keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.y));
//Debug.Log("ID = " + matchesGoodList[i].queryIdx );
//Debug.Log("x,y =" + (int)keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.x + "," + (int)keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.y);
//Debug.Log("x,y =" + (int)keypoints2.toArray()[matchesGoodList[i].trainIdx].pt.x + "," + (int)keypoints2.toArray()[matchesGoodList[i].trainIdx].pt.y);
}
MatOfPoint2f p2fTarget = new MatOfPoint2f(P1.ToArray());
MatOfPoint2f p2fSrc = new MatOfPoint2f(pSrc.ToArray());
Mat matrixH = Calib3d.findHomography(p2fTarget, p2fSrc, Calib3d.RANSAC, 3);
List <Point> srcPointCorners = new List <Point>();
srcPointCorners.Add(new Point(0, 0));
srcPointCorners.Add(new Point(img1Mat.width(), 0));
srcPointCorners.Add(new Point(img1Mat.width(), img1Mat.height()));
srcPointCorners.Add(new Point(0, img1Mat.height()));
Mat originalRect = Converters.vector_Point2f_to_Mat(srcPointCorners);
List <Point> srcPointCornersEnd = new List <Point>();
srcPointCornersEnd.Add(new Point(0, img1Mat.height()));
srcPointCornersEnd.Add(new Point(0, 0));
srcPointCornersEnd.Add(new Point(img1Mat.width(), 0));
srcPointCornersEnd.Add(new Point(img1Mat.width(), img1Mat.height()));
Mat changeRect = Converters.vector_Point2f_to_Mat(srcPointCornersEnd);
Core.perspectiveTransform(originalRect, changeRect, matrixH);
List <Point> srcPointCornersSave = new List <Point>();
Converters.Mat_to_vector_Point(changeRect, srcPointCornersSave);
if ((srcPointCornersSave[2].x - srcPointCornersSave[0].x) < 5 || (srcPointCornersSave[2].y - srcPointCornersSave[0].y) < 5)
{
Debug.Log("Match Out Put image is to small");
SrcMat.copyTo(cameraFeed);
SrcMat.release();
Imgproc.putText(cameraFeed, "X-S", new Point(10, 50), 0, 1, new Scalar(255, 255, 255), 2);
return(false);
}
// Features2d.drawMatches(img1Mat, keypoints1, SrcMat, keypointsSrc, matchesGood, resultImg);
Imgproc.line(SrcMat, srcPointCornersSave[0], srcPointCornersSave[1], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[1], srcPointCornersSave[2], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[2], srcPointCornersSave[3], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[3], srcPointCornersSave[0], new Scalar(255, 0, 0), 3);
SrcMat.copyTo(cameraFeed);
keypoints1.release();
img1Mat.release();
SrcMat.release();
return(true);
}
//============================================================
//=================以下為沒有再使用的函式=====================
//============================================================
//找出特徵的顏色方法三(ORB特徵點比對)
public bool descriptorsORB(Mat RGB, Mat cameraFeed, string targetName)
{
if (RGB == null)
{
Debug.Log("RGB Mat is Null");
return(false);
}
//將傳入的RGB存入Src
Mat SrcMat = new Mat();
RGB.copyTo(SrcMat);
//比對樣本載入
Texture2D imgTexture = Resources.Load(targetName) as Texture2D;
//Texture2D轉Mat
Mat targetMat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, targetMat);
//創建 ORB的特徵點裝置
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//產生存放特徵點Mat
MatOfKeyPoint keypointsTarget = new MatOfKeyPoint();
Mat descriptorsTarget = new Mat();
MatOfKeyPoint keypointsSrc = new MatOfKeyPoint();
Mat descriptorsSrc = new Mat();
//找特徵點圖Target
detector.detect(targetMat, keypointsTarget);
extractor.compute(targetMat, keypointsTarget, descriptorsTarget);
//找特徵點圖Src
detector.detect(SrcMat, keypointsSrc);
extractor.compute(SrcMat, keypointsSrc, descriptorsSrc);
//創建特徵點比對物件
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
//丟入兩影像的特徵點
matcher.match(descriptorsTarget, descriptorsSrc, matches);
DMatch[] arrayDmatch = matches.toArray();
//做篩選
double max_dist = 0;
double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
double dist = new double();
for (int i = 0; i < matches.rows(); i++)
{
dist = arrayDmatch[i].distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
Debug.Log("Max dist :" + max_dist);
Debug.Log("Min dist :" + min_dist);
List <DMatch> matchesGoodList = new List <DMatch>();
MatOfDMatch matchesGood = new MatOfDMatch();
matchesGood.fromList(matchesGoodList);
//Draw Keypoints
Features2d.drawKeypoints(SrcMat, keypointsSrc, SrcMat);
List <Point> pTarget = new List <Point>();
List <Point> pSrc = new List <Point>();
Debug.Log("MatchCount" + matchesGoodList.Count);
for (int i = 0; i < matchesGoodList.Count; i++)
{
pTarget.Add(new Point(keypointsTarget.toArray()[matchesGoodList[i].queryIdx].pt.x, keypointsTarget.toArray()[matchesGoodList[i].queryIdx].pt.y));
pSrc.Add(new Point(keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.x, keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.y));
}
MatOfPoint2f p2fTarget = new MatOfPoint2f(pTarget.ToArray());
MatOfPoint2f p2fSrc = new MatOfPoint2f(pSrc.ToArray());
Mat matrixH = Calib3d.findHomography(p2fTarget, p2fSrc, Calib3d.RANSAC, 3);
List <Point> srcPointCorners = new List <Point>();
srcPointCorners.Add(new Point(0, 0));
srcPointCorners.Add(new Point(targetMat.width(), 0));
srcPointCorners.Add(new Point(targetMat.width(), targetMat.height()));
srcPointCorners.Add(new Point(0, targetMat.height()));
Mat originalRect = Converters.vector_Point2f_to_Mat(srcPointCorners);
List <Point> srcPointCornersEnd = new List <Point>();
srcPointCornersEnd.Add(new Point(0, targetMat.height()));
srcPointCornersEnd.Add(new Point(0, 0));
srcPointCornersEnd.Add(new Point(targetMat.width(), 0));
srcPointCornersEnd.Add(new Point(targetMat.width(), targetMat.height()));
Mat changeRect = Converters.vector_Point2f_to_Mat(srcPointCornersEnd);
Core.perspectiveTransform(originalRect, changeRect, matrixH);
List <Point> srcPointCornersSave = new List <Point>();
Converters.Mat_to_vector_Point(changeRect, srcPointCornersSave);
if ((srcPointCornersSave[2].x - srcPointCornersSave[0].x) < 5 || (srcPointCornersSave[2].y - srcPointCornersSave[0].y) < 5)
{
Debug.Log("Match Out Put image is to small");
SrcMat.copyTo(cameraFeed);
SrcMat.release();
Imgproc.putText(cameraFeed, targetName, srcPointCornersSave[0], 0, 1, new Scalar(255, 255, 255), 2);
return(false);
}
//畫出框框
Imgproc.line(SrcMat, srcPointCornersSave[0], srcPointCornersSave[1], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[1], srcPointCornersSave[2], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[2], srcPointCornersSave[3], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[3], srcPointCornersSave[0], new Scalar(255, 0, 0), 3);
//畫中心
Point middlePoint = new Point((srcPointCornersSave[0].x + srcPointCornersSave[2].x) / 2, (srcPointCornersSave[0].y + srcPointCornersSave[2].y) / 2);
Imgproc.line(SrcMat, middlePoint, middlePoint, new Scalar(0, 0, 255), 10);
SrcMat.copyTo(cameraFeed);
keypointsTarget.release();
targetMat.release();
SrcMat.release();
return(true);
}
// 描画処理
private void videoRendering(object sender, NewFrameEventArgs eventArgs)
{
Bitmap img = (Bitmap)eventArgs.Frame.Clone();
// Debug.WriteLine(DateTime.Now + ":" + "描画更新");
// Debug.WriteLine(mode);
try
{
//pictureBoxCamera.Image = img;
temp = BitmapConverter.ToMat(img);//比較先画像
//特徴量の検出と特徴量ベクトルの計算
akaze.DetectAndCompute(temp, null, out key_point2, descriptor2);
//画像２の特徴点をoutput2に出力
Cv2.DrawKeypoints(temp, key_point2, output2);
//Cv2.ImShow("output2", output2);
pictureBoxCamera.Image = BitmapConverter.ToBitmap(output2);
matcher = DescriptorMatcher.Create("BruteForce");
matches = matcher.Match(descriptor1, descriptor2);
//閾値以下の要素数のカウント
for (int i = 0; i < key_point1.Length && i < key_point2.Length; ++i)
{
if (matches[i].Distance < threshold)
{
++good_match_length;
}
}
DMatch[] good_matches = new DMatch[good_match_length];//閾値以下の要素数で定義
//good_matchesに格納していく
int j = 0;
for (int i = 0; i < key_point1.Length && i < key_point2.Length; ++i)
{
if (matches[i].Distance < threshold)
{
good_matches[j] = matches[i];
++j;
}
}
//good_matchesの個数デバッグ表示
Debug.WriteLine(j);
Invoke((MethodInvoker) delegate()
{
labelMatch.Text = j.ToString();
});
//類似点の数が多ければチェックボックスの状態に応じて非常停止
if (j >= 16)
{
//非常停止
if (checkBoxStop.Checked == true)
{
//WebRequest request = WebRequest.Create("https://maker.ifttt.com/trigger/raspberry/with/key/gHPH_xDKR664IVIr2YtRRj6BbQoQi-K0mCowIJCGPF3");
//WebResponse response = request.GetResponse();
}
//アラート音
if (checkBoxAlert.Checked == true)
{
// _mediaPlayer.settings.volume = 20;
_mediaPlayer.URL = @"D:\DCIM\app\AkazeAlert\PcCameraApp\Resources\decision1.mp3";
_mediaPlayer.controls.play();
}
}
Cv2.DrawMatches(mat, key_point1, temp, key_point2, good_matches, output3);
//Cv2.ImShow("output3", output3);
pictureBoxResult.Image = BitmapConverter.ToBitmap(output3);
}
catch
{
pictureBoxCamera.Image = img;
}
}
void Orb()
{
p1Mat = Imgcodecs.imread(Application.dataPath + "/Textures/1.jpg", 1);
p2Mat = Imgcodecs.imread(Application.dataPath + "/Textures/3.jpg", 1);
Imgproc.cvtColor(p1Mat, p1Mat, Imgproc.COLOR_BGR2RGB);
Imgproc.cvtColor(p2Mat, p2Mat, Imgproc.COLOR_BGR2RGB);
Imgproc.resize(p2Mat, p2Mat, new Size(p1Mat.width(), p1Mat.height()));
Debug.Log(p2Mat);
/*
* //仿射变换（矩阵旋转）
* float angle = UnityEngine.Random.Range(0, 360), scale = 1.0f;
* Point center = new Point(img2Mat.cols() * 0.5f, img2Mat.rows() * 0.5f);
*
* Mat affine_matrix = Imgproc.getRotationMatrix2D(center, angle, scale);
* Imgproc.warpAffine(img1Mat, img2Mat, affine_matrix, img2Mat.size());
*
* Texture2D texture = new Texture2D(img2Mat.cols(), img2Mat.rows());
* Utils.matToTexture2D(img2Mat, texture);
* outputRawImage.texture = texture;
*/
ORB detector = ORB.create();
ORB extractor = ORB.create();
//提取图一特征点
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
detector.detect(p1Mat, keypoints1);
extractor.compute(p1Mat, keypoints1, descriptors1);
//提取图二特征点
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(p2Mat, keypoints2);
extractor.compute(p2Mat, keypoints2, descriptors2);
//第一次匹配结果（密密麻麻）
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);
//筛选（非官方）
//计算向量距离的最大值/最小值
double max_dist = 0;
double min_dist = 15; //通过距离控制需要的特征。
//（设到10，最终只有2个耳朵匹配。。。）
//（设到15，尾巴也开始匹配。。。。。。）
//新建两个容器存放筛选样本
List <DMatch> matchesArray = matches.toList(); //用Unity版API多转一步
//Debug.Log(matchesArray.Count); //500
List <DMatch> goodmatchesArray = new List <DMatch>();
//Debug.Log(img1Mat.rows()); //512
for (int i = 0; i < matchesArray.Count; i++)
{
Debug.Log("[" + i + "]" + matchesArray[i].distance);
if (matchesArray[i].distance > max_dist)
{
//max_dist = matchesArray[i].distance;
}
if (matchesArray[i].distance < min_dist)
{
min_dist = matchesArray[i].distance;
}
}
//Debug.Log("The max distance is: " + max_dist);
Debug.Log("The min distance is: " + min_dist);
for (int i = 0; i < matchesArray.Count; i++)
{
if (matchesArray[i].distance < 2 * min_dist) //
{
goodmatchesArray.Add(matchesArray[i]);
}
}
MatOfDMatch newMatches = new MatOfDMatch();
newMatches.fromList(goodmatchesArray);
Debug.Log(newMatches.toList().Count); //第二次筛选后符合的
//绘制第二次筛选结果
dstMat = new Mat();
Features2d.drawMatches(p1Mat, keypoints1, p2Mat, keypoints2, newMatches, dstMat);
Texture2D t2d = new Texture2D(dstMat.width(), dstMat.height());
Utils.matToTexture2D(dstMat, t2d);
Sprite sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
m_dstImage.sprite = sp;
m_dstImage.preserveAspect = true;
}
//public bool verificaImagem(Texture2D texture, Texture2D texture2)
//{
// Texture2D camFoto = texture;
// Texture2D printTela = texture2;
// // Escala de cinza. CV_8UC1
// Mat img1Mat = new Mat(camFoto.height, camFoto.width, CvType.CV_8UC1);
// Utils.texture2DToMat(camFoto, img1Mat);
// // Escala de cinza. CV_8UC1
// Mat img2Mat = new Mat(printTela.height, printTela.width, CvType.CV_8UC1);
// Utils.texture2DToMat(printTela, img2Mat);
// Imgproc.GaussianBlur(img1Mat, img1Mat, new Size(5, 5), 0);
// Imgproc.threshold(img1Mat, img1Mat, 100, 255, Imgproc.THRESH_BINARY);
// Imgproc.GaussianBlur(img2Mat, img2Mat, new Size(5, 5), 0);
// Imgproc.threshold(img2Mat, img2Mat, 240, 255, Imgproc.THRESH_BINARY);
// //Create the result mat
// int result_cols = img1Mat.cols() - img2Mat.cols() + 1;
// int result_rows = img1Mat.rows() - img2Mat.rows() + 1;
// Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);
// int match_method = Imgproc.TM_CCOEFF_NORMED;
// Imgproc.matchTemplate(img1Mat, img2Mat, result, match_method);
// Debug.Log(match_method);
// return match_method <= 1;
//}
public bool verificaImagem(Texture2D textParam, Texture2D textParam2)
{
var bytes = textParam.EncodeToJPG();
//File.WriteAllBytes("imagem1_tratamento.png", bytes);
//bytes = textParam2.EncodeToJPG();
//File.WriteAllBytes("imagem2_tratamento.png", bytes);
//Texture2D imgTexture = Resources.Load("circulo") as Texture2D;
Texture2D camFoto = textParam;
Texture2D printTela = textParam2;
// Escala de cinza. CV_8UC1
Mat img1Mat = new Mat(camFoto.height, camFoto.width, CvType.CV_8UC1);
Utils.texture2DToMat(camFoto, img1Mat);
// Escala de cinza. CV_8UC1
Mat img2Mat = new Mat(printTela.height, printTela.width, CvType.CV_8UC1);
Utils.texture2DToMat(printTela, img2Mat);
Imgproc.GaussianBlur(img1Mat, img1Mat, new Size(5, 5), 0);
Texture2D tex3 = new Texture2D(img1Mat.cols(), img1Mat.rows(), TextureFormat.RGBA32, false);
//Utils.matToTexture2D(img1Mat, tex3);
//bytes = tex3.EncodeToJPG();
//File.WriteAllBytes("imagem1_tratamento_gaussian.png", bytes);
Imgproc.threshold(img1Mat, img1Mat, 100, 255, Imgproc.THRESH_BINARY);
tex3 = new Texture2D(img1Mat.cols(), img1Mat.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(img1Mat, tex3);
bytes = tex3.EncodeToJPG();
File.WriteAllBytes("imagem1_tratamento_threshold.png", bytes);
Imgproc.GaussianBlur(img2Mat, img2Mat, new Size(5, 5), 0);
Texture2D tex4 = new Texture2D(img2Mat.cols(), img2Mat.rows(), TextureFormat.RGBA32, false);
//Utils.matToTexture2D(img2Mat, tex4);
//bytes = tex4.EncodeToJPG();
//File.WriteAllBytes("imagem2_tratamento_gaussian.png", bytes);
Imgproc.threshold(img2Mat, img2Mat, 240, 255, Imgproc.THRESH_BINARY);
tex4 = new Texture2D(img2Mat.cols(), img2Mat.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(img2Mat, tex4);
bytes = tex4.EncodeToJPG();
File.WriteAllBytes("imagem2_tratamento_threshold.png", bytes);
ORB detector = ORB.create();
ORB extractor = ORB.create();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);
List <MatOfDMatch> lista = new List <MatOfDMatch>();
lista.Add(matches);
matcher.knnMatch(descriptors1, descriptors2, lista, 2);
long total = 0;
foreach (MatOfDMatch item in lista)
{
if (item.toList()[0].distance < 0.75 * item.toList()[1].distance)
{
total++;
}
}
long number_keypoints = 0;
if (keypoints1.elemSize() <= keypoints2.elemSize())
{
number_keypoints = keypoints1.elemSize();
}
else
{
number_keypoints = keypoints2.elemSize();
}
Debug.Log(total / number_keypoints * 100);
return((total / number_keypoints * 100) >= 70);
}
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!");
}
