/// <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, bool ratioTest = false)
{
if (detector == null)
{
detector = ORB.Create();
detector.MaxFeatures = 1000;
}
if (extractor == null)
{
extractor = ORB.Create();
extractor.MaxFeatures = 1000;
}
m_detector = detector;
m_extractor = extractor;
enableRatioTest = ratioTest;
enableHomographyRefinement = true;
homographyReprojectionThreshold = 3;
m_queryKeypoints = new KeyPoint[] { };
m_queryDescriptors = new Mat();
m_grayImg = new Mat();
m_warpedImg = new Mat();
m_roughHomography = new Mat();
m_refinedHomography = new Mat();
warpedKeypoints = new KeyPoint[] { };
}
public void Mathing()
{
using (var img1 = Image("tsukuba_left.png", ImreadModes.GrayScale))
using (var img2 = Image("tsukuba_right.png", ImreadModes.GrayScale))
using (var orb = ORB.Create(500))
using (var descriptor1 = new Mat())
using (var descriptor2 = new Mat())
{
KeyPoint[] keyPoints1, keyPoints2;
orb.DetectAndCompute(img1, null, out keyPoints1, descriptor1);
orb.DetectAndCompute(img2, null, out keyPoints2, descriptor2);
// Flann needs the descriptors to be of type CV_32F
Assert.AreEqual(MatType.CV_8UC1, descriptor1.Type());
Assert.AreEqual(MatType.CV_8UC1, descriptor2.Type());
descriptor1.ConvertTo(descriptor1, MatType.CV_32F);
descriptor2.ConvertTo(descriptor2, MatType.CV_32F);
var matcher = new FlannBasedMatcher();
DMatch[] matches = matcher.Match(descriptor1, descriptor2);
/*
* using (var view = new Mat())
* using (var window = new Window())
* {
* Cv2.DrawMatches(img1, keyPoints1, img2, keyPoints2, matches, view);
* window.ShowImage(view);
* Cv2.WaitKey();
* }*/
}
}
public void MathingWithLshIndexParams()
{
using var img1 = Image("tsukuba_left.png", ImreadModes.Grayscale);
using var img2 = Image("tsukuba_right.png", ImreadModes.Grayscale);
using var orb = ORB.Create(500);
using var descriptor1 = new Mat();
using var descriptor2 = new Mat();
orb.DetectAndCompute(img1, null, out _, descriptor1);
orb.DetectAndCompute(img2, null, out _, descriptor2);
using var indexParams = new LshIndexParams(12, 20, 2);
Assert.Equal(MatType.CV_8UC1, descriptor1.Type());
Assert.Equal(MatType.CV_8UC1, descriptor2.Type());
// LshIndexParams requires Binary descriptor, so it must NOT convert to CV_32F.
//descriptor1.ConvertTo(descriptor1, MatType.CV_32F);
//descriptor2.ConvertTo(descriptor2, MatType.CV_32F);
using var matcher = new FlannBasedMatcher(indexParams);
DMatch[] matches = matcher.Match(descriptor1, descriptor2);
Assert.NotEmpty(matches);
/*
* using (var view = new Mat())
* using (var window = new Window())
* {
* Cv2.DrawMatches(img1, keyPoints1, img2, keyPoints2, matches, view);
* window.ShowImage(view);
* Cv2.WaitKey();
* }*/
}
public KeyPoint[] getKeyPoints(Mat camMat, int nKeyPoints)
{
orb = ORB.Create(nKeyPoints);
KeyPoint[] keyPoints = orb.Detect(camMat);
return(keyPoints);
}
void OnFast()
{
Mat image01 = Cv2.ImRead(Application.streamingAssetsPath + "/bryce_01.jpg");
Mat image02 = Cv2.ImRead(Application.streamingAssetsPath + "/bryce_02.jpg");
Mat image1 = new Mat(), image2 = new Mat();
Cv2.CvtColor(image01, image1, ColorConversionCodes.RGB2GRAY);
Cv2.CvtColor(image02, image2, ColorConversionCodes.RGB2GRAY);
KeyPoint[] keyPoint1 = Cv2.FAST(image1, 50, true);
KeyPoint[] keyPoint2 = Cv2.FAST(image2, 50, true);
using (Mat descriptor1 = new Mat())
using (Mat descriptor2 = new Mat())
using (var orb = ORB.Create(50))
using (var matcher = new BFMatcher())
{
orb.Compute(image1, ref keyPoint1, descriptor1);
orb.Compute(image2, ref keyPoint2, descriptor2);
Debug.Log(string.Format("keyPoints has {0},{1} items.", keyPoint1.Length, keyPoint2.Length));
Debug.Log(string.Format("descriptor has {0},{1} items.", descriptor1.Rows, descriptor2.Rows));
List <DMatch> goodMatchePoints = new List <DMatch>();
var dm = matcher.KnnMatch(descriptor1, descriptor2, 2);
#region matched 175
for (int i = 0; i < dm.Length; i++)
{
if (dm[i][0].Distance < 0.6 * dm[i][1].Distance)
{
goodMatchePoints.Add(dm[i][0]);
}
}
#endregion
#region matched 90
float minRatio = 1.0f / 1.5f;
for (int i = 0; i < dm.Length; i++)
{
DMatch bestMatch = dm[i][0];
DMatch betterMatch = dm[i][1];
float distanceRatio = bestMatch.Distance / betterMatch.Distance;
if (distanceRatio < minRatio)
{
goodMatchePoints.Add(bestMatch);
}
}
#endregion
var dstMat = new Mat();
Debug.Log(string.Format("matchePoints has {0} items", goodMatchePoints.Count));
Cv2.DrawMatches(image01, keyPoint1, image02, keyPoint2, goodMatchePoints, dstMat);
t2d = Utils.MatToTexture2D(dstMat);
}
Sprite dst_sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
SrcSprite.sprite = dst_sp;
}
public void Detect()
{
// This parameter should introduce same result of http://opencv.jp/wordpress/wp-content/uploads/lenna_SURF-150x150.png
using var gray = Image("lenna.png", 0);
using var orb = ORB.Create(500);
var keyPoints = orb.Detect(gray);
Console.WriteLine($"KeyPoint has {keyPoints.Length} items.");
}
/// <summary>
/// 测试函数
/// </summary>
void Detect()
{
var gray = new Mat(Application.streamingAssetsPath + "/Textures/p1.jpg", ImreadModes.GrayScale);
KeyPoint[] keyPoints = null;
using (var orb = ORB.Create(500))
{
keyPoints = orb.Detect(gray);
Debug.Log($"KeyPoint has {keyPoints.Length} items.");
}
}
static private void CreateORB(
Mat imgGray,
KeyPoint[] keypoints,
out MatOfFloat descriptors)
{
descriptors = new MatOfFloat();
ORB orb1 = ORB.Create();
orb1.Compute(imgGray, ref keypoints, descriptors);
}
void Detect()
{
var gray = new Mat(Application.streamingAssetsPath + "/bryce_01.jpg", ImreadModes.GrayScale);
KeyPoint[] keyPoints = null;
using (var orb = ORB.Create(500))
{
keyPoints = orb.Detect(gray);
Debug.Log(string.Format("KeyPoint has {0} items.", keyPoints.Length));
}
}
public void DetectAndCompute()
{
using (var gray = Image("lenna.png", ImreadModes.Grayscale))
using (var orb = ORB.Create(500))
using (Mat descriptor = new Mat())
{
orb.DetectAndCompute(gray, null, out var keyPoints, descriptor);
Console.WriteLine($"keyPoints has {keyPoints.Length} items.");
Console.WriteLine($"descriptor has {descriptor.Rows} items.");
}
}
static IEnumerable <DMatch> BFMatch(Mat image1, Mat image2)
{
Mat dst1 = new Mat();
Mat dst2 = new Mat();
var orb = ORB.Create();
orb.DetectAndCompute(image1, null, out var kp1, dst1);
orb.DetectAndCompute(image2, null, out var kp2, dst2);
BFMatcher matcher = new BFMatcher();
return(matcher.Match(dst1, dst2));
}
void DetectAndCompute()
{
var gray = new Mat(Application.streamingAssetsPath + "/bryce_01.jpg", ImreadModes.GrayScale);
KeyPoint[] keyPoints = null;
using (var orb = ORB.Create(500))
using (Mat descriptor = new Mat())
{
orb.DetectAndCompute(gray, new Mat(), out keyPoints, descriptor);
Debug.Log(string.Format("keyPoints has {0} items.", keyPoints.Length));
Debug.Log(string.Format("descriptor has {0} items.", descriptor.Rows));
}
}
void DetectAndCompute()
{
var gray = new Mat(Application.streamingAssetsPath + "/Textures/p1.jpg", ImreadModes.GrayScale);
KeyPoint[] keyPoints = null;
using (var orb = ORB.Create(500))
using (Mat descriptor = new Mat())
{
orb.DetectAndCompute(gray, new Mat(), out keyPoints, descriptor);
Debug.Log($"keyPoints has {keyPoints.Length} items.");
Debug.Log($"descriptor has {descriptor.Rows} items.");
}
}
public override void RunTest()
{
using var img1 = new Mat(ImagePath.Match1, ImreadModes.Color);
using var img2 = new Mat(ImagePath.Match2, ImreadModes.Color);
using var orb = ORB.Create(1000);
using var descriptors1 = new Mat();
using var descriptors2 = new Mat();
orb.DetectAndCompute(img1, null, out var keyPoints1, descriptors1);
orb.DetectAndCompute(img2, null, out var keyPoints2, descriptors2);
using var bf = new BFMatcher(NormTypes.Hamming, crossCheck: true);
var matches = bf.Match(descriptors1, descriptors2);
var goodMatches = matches
.OrderBy(x => x.Distance)
.Take(10)
.ToArray();
var srcPts = goodMatches.Select(m => keyPoints1[m.QueryIdx].Pt).Select(p => new Point2d(p.X, p.Y));
var dstPts = goodMatches.Select(m => keyPoints2[m.TrainIdx].Pt).Select(p => new Point2d(p.X, p.Y));
using var homography = Cv2.FindHomography(srcPts, dstPts, HomographyMethods.Ransac, 5, null);
int h = img1.Height, w = img1.Width;
var img2Bounds = new[]
{
new Point2d(0, 0),
new Point2d(0, h - 1),
new Point2d(w - 1, h - 1),
new Point2d(w - 1, 0),
};
var img2BoundsTransformed = Cv2.PerspectiveTransform(img2Bounds, homography);
using var view = img2.Clone();
var drawingPoints = img2BoundsTransformed.Select(p => (Point)p).ToArray();
Cv2.Polylines(view, new [] { drawingPoints }, true, Scalar.Red, 3);
using (new Window("view", view))
{
Cv2.WaitKey();
}
}
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 void Run()
{
var gray = new Mat(FilePath.Image.Lenna, ImreadModes.GrayScale);
var dst = new Mat(FilePath.Image.Lenna, ImreadModes.Color);
// ORB
var orb = ORB.Create(1000);
KeyPoint[] keypoints = orb.Detect(gray);
// FREAK
FREAK freak = FREAK.Create();
Mat freakDescriptors = new Mat();
freak.Compute(gray, ref keypoints, freakDescriptors);
if (keypoints != null)
{
var color = new Scalar(0, 255, 0);
foreach (KeyPoint kpt in keypoints)
{
float r = kpt.Size / 2;
Cv2.Circle(dst, kpt.Pt, (int)r, color);
Cv2.Line(dst,
new Point2f(kpt.Pt.X + r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X - r, kpt.Pt.Y - r),
color);
Cv2.Line(dst,
new Point2f(kpt.Pt.X - r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X + r, kpt.Pt.Y - r),
color);
}
}
using (new Window("FREAK", dst))
{
Cv2.WaitKey();
}
}
/// <summary>
/// Orb特征提取
/// </summary>
void OnOrb()
{
Mat image01 = Cv2.ImRead(Application.streamingAssetsPath + "/Textures/p1.jpg");
Mat image02 = Cv2.ImRead(Application.streamingAssetsPath + "/Textures/p2.jpg");
//灰度图转换
Mat image1 = new Mat(), image2 = new Mat();
Cv2.CvtColor(image01, image1, ColorConversionCodes.RGB2GRAY);
Cv2.CvtColor(image02, image2, ColorConversionCodes.RGB2GRAY);
KeyPoint[] keyPoint1 = null;
KeyPoint[] keyPoint2 = null;
using (ORB orb = ORB.Create(500))
using (Mat descriptor1 = new Mat())
using (Mat descriptor2 = new Mat())
using (var matcher = new BFMatcher())
{
//特征点提取并计算
orb.DetectAndCompute(image1, new Mat(), out keyPoint1, descriptor1);
orb.DetectAndCompute(image2, new Mat(), out keyPoint2, descriptor2);
Debug.Log($"keyPoints has {keyPoint1.Length},{keyPoint2.Length} items.");
Debug.Log($"descriptor has {descriptor1.Rows},{descriptor2.Rows} items.");
//特征点匹配
DMatch[] matchePoints = null;
matchePoints = matcher.Match(descriptor1, descriptor2);
dstMat = new Mat();
Cv2.DrawMatches(image01, keyPoint1, image02, keyPoint2, matchePoints, dstMat);
t2d = Utils.MatToTexture2D(dstMat);
}
Sprite dst_sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
m_srcImage.sprite = dst_sp;
m_srcImage.preserveAspect = true;
}
//--------------------------------------------------------------------------------------------------
static void Main()
{
Mat srcOri = new Mat("C:/Users/Li&Ao/Desktop/Test/5.JPG", ImreadModes.Grayscale);
Mat dstOri = new Mat("C:/Users/Li&Ao/Desktop/Test/6.JPG", ImreadModes.Grayscale);
Mat src = new Mat();
Mat dst = new Mat();
RotateAndResize(srcOri, out src, true);
RotateAndResize(dstOri, out dst, true);
// Step1: Detect the keypoints and generate their descriptors using SURF
ORB orb = ORB.Create();
KeyPoint[] kp1, kp2;
Mat desc1 = new Mat();
Mat desc2 = new Mat();
orb.DetectAndCompute(src, null, out kp1, desc1);
orb.DetectAndCompute(dst, null, out kp2, desc2);
// Step2: Matching descriptor vectors with a brute force matcher
var bfMatcher = new BFMatcher();
var matches = bfMatcher.KnnMatch(desc1, desc2, k: 2);
// Step3: Ratio test for outlier removal
var betterKp1 = new List <Point2f>();
var betterKp2 = new List <Point2f>();
var betterMatches = new List <DMatch>();
foreach (DMatch[] items in matches)
{
if (items[0].Distance < 0.8 * items[1].Distance)
{
betterKp1.Add(kp1[items[0].QueryIdx].Pt);
betterKp2.Add(kp2[items[0].TrainIdx].Pt);
betterMatches.Add(items[0]);
}
}
// Step4: RANSAC for outlier removal
Point2d Point2fToPoint2d(Point2f pf) => new Point2d(((double)pf.X), ((double)pf.Y));
var betterKp1_tmp = betterKp1.ConvertAll(Point2fToPoint2d);
var betterKp2_tmp = betterKp2.ConvertAll(Point2fToPoint2d);
var bestTuple = RansacMethod(betterKp1_tmp, betterKp2_tmp, src.Cols, src.Rows);
var bestKp1 = bestTuple.Item1;
var bestKp2 = bestTuple.Item2;
//Step5:draw matches after ransac
var plotMatches = new List <DMatch>();
foreach (DMatch[] items in matches)
{
var p1 = Point2fToPoint2d(kp1[items[0].QueryIdx].Pt);
var p2 = Point2fToPoint2d(kp2[items[0].TrainIdx].Pt);
if (bestKp1.Contains(p1) && bestKp2.Contains(p2))
{
plotMatches.Add(items[0]);
}
}
Mat outImg = new Mat();
Cv2.DrawMatches(src, kp1, dst, kp2, plotMatches, outImg);
Cv2.ImShow("outImg", outImg);
Cv2.Resize(outImg, outImg, new Size(outImg.Rows / 2, outImg.Cols / 2));
Cv2.ImWrite("C:/Users/Li&Ao/Desktop/Test/output.JPG", outImg);
//Calculate R matrix
Matrix <double> A = Matrix <double> .Build.Dense(3, bestKp1.Count);
Matrix <double> B = Matrix <double> .Build.Dense(3, bestKp2.Count);
for (int i = 0; i < bestKp1.Count; i++)
{
Vector <double> p1 = From2dTo3d(bestKp1[i], src.Cols, src.Rows);
Vector <double> p2 = From2dTo3d(bestKp2[i], src.Cols, src.Rows);
A.SetColumn(i, p1);
B.SetColumn(i, p2);
}
var R = CalcRotation(A, B);
Console.WriteLine("R matrix is:" + R);
Cv2.WaitKey();
}
public void CreateAndDispose()
{
var surf = ORB.Create(400);
surf.Dispose();
}
void OnHarris()
{
Mat image01 = Cv2.ImRead(Application.streamingAssetsPath + "/bryce_01.jpg");
Mat image02 = Cv2.ImRead(Application.streamingAssetsPath + "/bryce_02.jpg");
Mat image1 = new Mat(), image2 = new Mat();
Cv2.CvtColor(image01, image1, ColorConversionCodes.RGB2GRAY);
Cv2.CvtColor(image02, image2, ColorConversionCodes.RGB2GRAY);
KeyPoint[] keyPoint1 = null, keyPoint2 = null;
using (var gFTTDetector = GFTTDetector.Create(500))
using (var orb = ORB.Create(20))
using (Mat descriptor1 = new Mat())
using (Mat descriptor2 = new Mat())
using (var matcher = new BFMatcher(NormTypes.L2))
{
keyPoint1 = gFTTDetector.Detect(image1);
keyPoint2 = gFTTDetector.Detect(image2);
orb.Compute(image1, ref keyPoint1, descriptor1);
orb.Compute(image2, ref keyPoint2, descriptor2);
List <DMatch> goodMatchePoints = new List <DMatch>();
DMatch[][] dm = matcher.KnnMatch(descriptor1, descriptor2, 2);
#region matched 30
//for (int i = 0; i < dm.Length; i++)
//{
// if (dm[i][0].Distance < 0.6 * dm[i][1].Distance)
// {
// goodMatchePoints.Add(dm[i][0]);
// }
//}
#endregion
#region matched 48
float minRatio = 1.0f / 1.5f;
for (int i = 0; i < dm.Length; i++)
{
DMatch bestMatch = dm[i][0];
DMatch betterMatch = dm[i][1];
float distanceRatio = bestMatch.Distance / betterMatch.Distance;
if (distanceRatio < minRatio)
{
goodMatchePoints.Add(bestMatch);
}
}
#endregion
var dstMat = new Mat();
Debug.Log(string.Format("matchePoints has {0} items", goodMatchePoints.Count));
Cv2.DrawMatches(image01, keyPoint1, image02, keyPoint2, goodMatchePoints, dstMat);
t2d = VideoDetectorExample.Utils.MatToTexture2D(dstMat);
}
Sprite dst_sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
SrcSprite.sprite = dst_sp;
}
/// <summary>
/// Orb特征提取
/// </summary>
void OnOrb(string path2_)
{
Debug.Log(path2_);
Mat image01 = Cv2.ImRead(Application.streamingAssetsPath + "/Textures/p1.jpg");
Mat image02 = Cv2.ImRead(Application.streamingAssetsPath + path2_);
//灰度图转换
Mat image1 = new Mat(), image2 = new Mat();
Cv2.CvtColor(image01, image1, ColorConversionCodes.RGB2GRAY);
Cv2.CvtColor(image02, image2, ColorConversionCodes.RGB2GRAY);
KeyPoint[] keyPoint1 = null;
KeyPoint[] keyPoint2 = null;
using (ORB orb = ORB.Create(500))
using (Mat descriptor1 = new Mat())
using (Mat descriptor2 = new Mat())
using (var matcher = new BFMatcher())
{
//特征点提取并计算
orb.DetectAndCompute(image1, new Mat(), out keyPoint1, descriptor1);
orb.DetectAndCompute(image2, new Mat(), out keyPoint2, descriptor2);
Debug.Log("image1 keyPoints: " + keyPoint1.Length + " descriptor: " + descriptor1.Rows);
Debug.Log("image2 keyPoints: " + keyPoint2.Length + " descriptor: " + descriptor2.Rows);
//特征点匹配
//DMatch[] matchePoint = null;
//matchePoint = matcher.Match(descriptor2, descriptor1);
//Debug.Log(matchePoint.Length);
// Lowe's algorithm,获取优秀匹配点
DMatch[][] matchePoints = null;
DMatch[][] matchePointssss = null;
// 使用knnMatch最邻近匹配
matchePoints = matcher.KnnMatch(descriptor1, descriptor2, 2);
List <DMatch> GoodMatchePoints = new List <DMatch>();
for (int i = 0; i < matchePoints.Length; i++)
{
float minRatio = 0.9f;
float disRation = matchePoints[i][0].Distance / matchePoints[i][1].Distance;
if (disRation < minRatio)
{
GoodMatchePoints.Add(matchePoints[i][0]);
}
}
DMatch[] matchePointss = GoodMatchePoints.ToArray();
Debug.Log("手写文字图总特征点： " + descriptor2.Rows);
Debug.Log("匹配符合的文字特征点： " + matchePointss.Length);
//float zongVa = (matchePoints.Length / 2);
Debug.Log("相识度比例： " + ((float)matchePointss.Length / descriptor2.Rows));
dstMat = new Mat();
Cv2.DrawMatches(image01, keyPoint1, image02, keyPoint2, matchePointss, dstMat);
t2d = MatToTexture2D(dstMat);
}
Sprite dst_sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
m_srcImage.sprite = dst_sp;
m_srcImage.preserveAspect = true;
}
public Place(string[] images, int nORBFeatures, float scaleFactor)
{
m_orbDetector = ORB.Create(nORBFeatures);
GridDescriptor = new Dictionary <Tuple <int, int, int>, List <Mat> >();
RepImages = new Dictionary <Tuple <int, int, int>, string>();
m_nORBFeatures = nORBFeatures;
// Iterate over all image paths in images
foreach (var imagePath in images)
{
Tuple <int, int, int> currentImagePose = null;
// Read the current Image
var currentImageMat = Cv2.ImRead(imagePath, ImreadModes.Grayscale);
Cv2.Resize(currentImageMat, currentImageMat, new OpenCvSharp.Size(currentImageMat.Width * scaleFactor, currentImageMat.Height * scaleFactor));
var temp = imagePath.Split('\\');
var fileName = temp[temp.Length - 1];
// Tokenize to extract the information from the image
string[] tokens = fileName.Split('_');
if (tokens.Length == 5)
{
int x, y, rotIdx;
bool bParseSuccess = true;
bParseSuccess = Int32.TryParse(tokens[1], out x);
if (bParseSuccess == false)
{
// Parse Failed..
Console.WriteLine("Check names of images");
}
bParseSuccess = Int32.TryParse(tokens[2], out y);
if (bParseSuccess == false)
{
// Parse Failed..
Console.WriteLine("Check names of images");
}
bParseSuccess = Int32.TryParse(tokens[3], out rotIdx);
if (bParseSuccess == false)
{
// Parse Failed..
Console.WriteLine("Check names of images");
}
// Allocate the coordinate for this image
PlaceName = tokens[0];
currentImagePose = new Tuple <int, int, int>(x, y, rotIdx);
// Increment the count of images for this place
Size++;
// If there is no key (currentImageLocation), allocate
if (GridDescriptor.ContainsKey(currentImagePose) == false)
{
GridDescriptor.Add(currentImagePose, new List <Mat>(0));
RepImages.Add(currentImagePose, imagePath);
}
// Detect and compute using ORB
Mat descriptor = new Mat();
KeyPoint[] keypoints;
m_orbDetector.DetectAndCompute(currentImageMat, null, out keypoints, descriptor);
GridDescriptor[currentImagePose].Add(descriptor);
Console.WriteLine("Storing Images.. Place = {0}, (x={1}, y={2}, thetaIdx={3})", PlaceName, currentImagePose.Item1, currentImagePose.Item2, currentImagePose.Item3);
}
}
}
