private void MatchBySift(Mat src1, Mat src2)
{
Mat gray1 = new Mat();
Mat gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
SIFT sift = new SIFT();
// Detect the keypoints and generate their descriptors using SIFT
KeyPoint[] keypoints1, keypoints2;
MatOfFloat descriptors1 = new MatOfFloat();
MatOfFloat descriptors2 = new MatOfFloat();
sift.Run(gray1, null, out keypoints1, descriptors1);
sift.Run(gray2, null, out keypoints2, descriptors2);
// Matching descriptor vectors with a brute force matcher
BFMatcher matcher = new BFMatcher(NormType.L2, false);
DMatch[] matches = matcher.Match(descriptors1, descriptors2);
// Draw matches
Mat view = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, matches, view);
using (new Window("SIFT matching", WindowMode.AutoSize, view))
{
Cv2.WaitKey();
}
}
public void GetData()
{
var imPath = "D:\\PATHTOIMAGE\\Original.jpg";
var image = Cv2.ImRead(imPath);
var sift = new SIFT(100);
KeyPoint[] keypoints;
MatOfFloat descriptors = new MatOfFloat();
sift.Run(image, null, out keypoints, descriptors);
}
static void GenSIFT(string path, StreamWriter sw)
{
using (Mat input = new Mat(path, LoadMode.GrayScale)){
if (input.Empty())
{
return;
}
double h = input.Rows, w = input.Cols;
double newh, neww;
if (h > w)
{
newh = maxSize;
neww = Math.Ceiling(w / h * maxSize);
}
else
{
neww = maxSize;
newh = Math.Ceiling(h / w * maxSize);
}
Size newsize = new Size(neww, newh);
using (Mat rinput = new Mat())
{
Cv2.Resize(input, rinput, newsize);
using (MatOfFloat descriptors = new MatOfFloat())
{
sift.Run(rinput, null, out keypoint, descriptors);
var indexer = descriptors.GetIndexer();
int cnt = 0;
System.Console.Out.WriteLine(descriptors.Rows);
for (int i = 0; i < descriptors.Rows; i++)
{
String str = i.ToString();
// System.Console.Out.WriteLine(cnt);
str = str + "\t0\t";
for (int j = 0; j < descriptors.Cols; j++)
{
str += indexer[i, j].ToString() + " ";
}
sw.Write(str + "\n");
cnt++;
}
input.Release();
rinput.Release();
descriptors.Release();
}
}
}
}
private void MatchBySift(Mat src1, Mat src2)
{
var gray1 = new Mat();
var gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
var sift = new SIFT();
// Detect the keypoints and generate their descriptors using SIFT
KeyPoint[] keypoints1, keypoints2;
var descriptors1 = new MatOfFloat();
var descriptors2 = new MatOfFloat();
sift.Run(gray1, null, out keypoints1, descriptors1);
sift.Run(gray2, null, out keypoints2, descriptors2);
// Match descriptor vectors
var bfMatcher = new BFMatcher(NormType.L2, false);
var flannMatcher = new FlannBasedMatcher();
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
DMatch[] flannMatches = flannMatcher.Match(descriptors1, descriptors2);
// Draw matches
var bfView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, bfView);
var flannView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, flannMatches, flannView);
using (new Window("SIFT matching (by BFMather)", WindowMode.AutoSize, bfView))
using (new Window("SIFT matching (by FlannBasedMatcher)", WindowMode.AutoSize, flannView))
{
Cv2.WaitKey();
}
}
static void Main(string[] args)
{
// SURF surf = new SURF(500, 4, 2, true);
SIFT sift = new SIFT(1000);
KeyPoint[] keiKeyPoints2 = { };
KeyPoint[] keiKeyPoints1 = { };
MatOfFloat desc1 = new MatOfFloat();
MatOfFloat desc2 = new MatOfFloat();
Mat logo_img =
Cv2.ImRead(
@"C:\Users\Laptop\Documents\Projektai\Vaizdo-Atpazinimas\SIFTSURF\SIFTSURF\arduino2.png",
LoadMode.GrayScale);
Window videoWindow = new Window("Logo", WindowMode.FreeRatio);
// surf.Run(logo_img, null, out keiKeyPoints1, desc1);
sift.Run(logo_img, null, out keiKeyPoints1, desc1);
BFMatcher matcher = new BFMatcher(NormType.L2);
cameraWork(matcher, desc1, keiKeyPoints1, logo_img, sift);
}
public static Result Run(AirPhoto mainMap, AirPhoto image)
{
SIFT sift = new SIFT();
BFMatcher matcher = new BFMatcher();
Result res = new Result(mainMap, image);
Mat map = new Mat(res.MainMap.FileName);
Mat singleImage = new Mat(res.SingleImage.FileName);
sift.Run(map, null, out res.MainMap.keyPoints, res.MainMap.Descriptors);
sift.Run(singleImage, null, out res.SingleImage.keyPoints, res.SingleImage.Descriptors);
res.Matches = matcher.Match(res.MainMap.Descriptors, res.SingleImage.Descriptors);
for (int i = 0; i < res.MainMap.Descriptors.Rows; i++)
{
double dist = res.Matches[i].Distance;
if (dist < res.Min_dist)
{
res.Min_dist = dist;
}
if (dist > res.Max_dist)
{
res.Max_dist = dist;
}
}
for (int i = 0; i < res.MainMap.Descriptors.Rows; i++)
{
if (res.Matches[i].Distance <= Math.Max(2 * res.Min_dist, 0.25))
{
res.Good_matchesRAW.Add(res.Matches[i]);
res.Current_good_matches.Add(res.Matches[i]);
}
}
for (int i = 0; i < res.Good_matchesRAW.Count; i++)
{
res.MainMap.Keypoints_goodRAW.Add(res.MainMap.Keypoints[res.Good_matchesRAW[i].QueryIdx].Pt);
res.SingleImage.Keypoints_goodRAW.Add(res.SingleImage.Keypoints[res.Good_matchesRAW[i].TrainIdx].Pt);
res.MainMap.Current_good_keypoints.Add(res.MainMap.Keypoints[res.Good_matchesRAW[i].QueryIdx].Pt);
res.SingleImage.Current_good_keypoints.Add(res.SingleImage.Keypoints[res.Good_matchesRAW[i].TrainIdx].Pt);
}
Mat view = new Mat();
Mat map_draw = new Mat(res.MainMap.FileName);
Mat singleImage_draw = new Mat(res.SingleImage.FileName);
Cv2.DrawMatches(map, res.MainMap.Keypoints, singleImage, res.SingleImage.Keypoints, res.Current_good_matches, view);
for (int i = 0; i < res.MainMap.Current_good_keypoints.Count; i++)
{
Cv.DrawCircle((IplImage)map_draw,
new CvPoint(Convert.ToInt32(res.MainMap.Current_good_keypoints[i].X),
Convert.ToInt32(res.MainMap.Current_good_keypoints[i].Y)),
map_draw.Width / 500,
CvColor.Red, 2);
}
for (int i = 0; i < res.SingleImage.Current_good_keypoints.Count; i++)
{
Cv.DrawCircle((IplImage)singleImage_draw,
new CvPoint(Convert.ToInt32(res.SingleImage.Current_good_keypoints[i].X),
Convert.ToInt32(res.SingleImage.Current_good_keypoints[i].Y)),
singleImage_draw.Width / 100,
CvColor.Red, 2);
}
res.MainMap.RAWwithKP = map_draw;
res.SingleImage.RAWwithKP = singleImage_draw;
res.RAWmatches = view;
return(res);
}
/// <summary>
/// Feature extraction and matching on the given 2 images.
/// The result is the images concatenated together with features,
/// matching and homography drawn on it.
/// </summary>
/// <param name="image1"></param>
/// <param name="image2"></param>
/// <param name="featureType"></param>
/// <returns></returns>
private IplImage Matching(IplImage image1, IplImage image2, FeatureType featureType)
{
Mat src1 = new Mat(image1);
Mat src2 = new Mat(image2);
KeyPoint[] keypoints1;
KeyPoint[] keypoints2;
MatOfFloat descriptors1 = new MatOfFloat();
MatOfFloat descriptors2 = new MatOfFloat();
// extract features with different feature-extration methods
switch (featureType)
{
case FeatureType.Sift:
sift.Run(src1, null, out keypoints1, descriptors1);
sift.Run(src2, null, out keypoints2, descriptors2);
break;
case FeatureType.Surf:
surf.Run(src1, null, out keypoints1, descriptors1);
surf.Run(src2, null, out keypoints2, descriptors2);
break;
default:
throw new NotSupportedException("Sorry, missing feature type.");
}
// matching descriptor vectors with a brute force matcher
DMatch[] matches;
switch (FeatureMatcher)
{
case MatcherType.BruteForce:
matches = bruteForceMatcher.Match(descriptors1, descriptors2);
break;
case MatcherType.FlannBased:
matches = flannBasedMatcher.Match(descriptors1, descriptors2);
break;
default:
throw new NotSupportedException("Sorry, missing matcher type.");
}
// get only "good" matches, only good matches will be drawn
List <DMatch> goodMatches;
// // check to get only good matches or all matches
if (IsGoodMatching)
{
// quick calculation of max and min distances between keypoints
IEnumerable <float> distances = matches.Select(i => i.Distance);
double maxDistance = 0;
double minDistance = 100;
double newMinDistance = distances.Min();
double newMaxDistance = distances.Max();
minDistance = (newMinDistance < minDistance) ? newMinDistance : minDistance;
maxDistance = (newMaxDistance > maxDistance) ? newMaxDistance : maxDistance;
goodMatches = matches.Where(i => i.Distance <= GoodMatchingThreshold * minDistance).ToList();
}
else
{
goodMatches = matches.ToList();
}
// draw matches
Mat view = new Mat();
Cv2.DrawMatches(src1, keypoints1, src2, keypoints2, goodMatches, view);
// homography need at least 4 points or more
if (IsHomography && goodMatches.Count > 4)
{
// get good keypoints (localize the object)
List <Point2d> goodKeypoints1 = new List <Point2d>();
List <Point2d> goodKeypoints2 = new List <Point2d>();
Point2f pt;
// get the keypoints from the good matches
for (int i = 0; i < goodMatches.Count; i++)
{
pt = keypoints1[goodMatches[i].QueryIdx].Pt;
goodKeypoints1.Add(new Point2d(pt.X, pt.Y));
pt = keypoints2[goodMatches[i].TrainIdx].Pt;
goodKeypoints2.Add(new Point2d(pt.X, pt.Y));
}
// find the homography
Mat homography = Cv2.FindHomography(goodKeypoints2, goodKeypoints1, HomographyMethod.Ransac);
// get the corners from image1
InputArray corners1 = InputArray.Create(new Point2f[]
{
new Point2f(0, 0),
new Point2f(src1.Cols, 0),
new Point2f(src1.Cols, src1.Rows),
new Point2f(0, src1.Rows),
}.ToList());
OutputArray corners2 = OutputArray.Create(new Point2f[]
{
new Point2f(0, 0),
new Point2f(0, 0),
new Point2f(0, 0),
new Point2f(0, 0),
}.ToList());
InputArray perspectiveMatrix = InputArray.Create(homography);
Cv2.PerspectiveTransform(corners1, corners2, perspectiveMatrix);
Mat corners2Matrix = corners2.GetMat();
Point2f point1 = corners2Matrix.At <Point2f>(0, 0);
Point2f point2 = corners2Matrix.At <Point2f>(1, 0);
Point2f point3 = corners2Matrix.At <Point2f>(2, 0);
Point2f point4 = corners2Matrix.At <Point2f>(3, 0);
Scalar color = new Scalar(0, 200, 253);
// draw lines between the corners
Cv2.Line(view, point1, point2, color, 4);
Cv2.Line(view, point2, point3, color, 4);
Cv2.Line(view, point3, point4, color, 4);
Cv2.Line(view, point4, point1, color, 4);
}
IplImage result = view.ToIplImage();
return(result);
}
static void GenSIFT(string path, string filename)
{
try
{
using (Mat input = new Mat(path, LoadMode.GrayScale)){
if (input.Empty())
{
return;
}
double h = input.Rows, w = input.Cols;
double newh, neww;
if (h > w)
{
newh = maxSize;
neww = Math.Ceiling(w / h * maxSize);
}
else
{
neww = maxSize;
newh = Math.Ceiling(h / w * maxSize);
}
Size newsize = new Size(neww, newh);
using (Mat rinput = new Mat())
{
Cv2.Resize(input, rinput, newsize);
/* using (new Window("image", rinput))
* {
* Cv2.WaitKey();
* }*/
using (MatOfFloat descriptors = new MatOfFloat())
{
sift.Run(rinput, null, out keypoint, descriptors);
cnt += 1;
Console.Out.WriteLine(descriptors.Rows + " " + cnt);
var indexer = descriptors.GetIndexer();
UInt64 v = 0;
for (int i = 0; i < 16; i++)
{
v *= 16;
if (filename[i] >= '0' && filename[i] <= '9')
{
v += (UInt64)filename[i] - '0';
}
else
{
v += (UInt64)(filename[i] - 'a') + 10;
}
}
for (int i = 0; i < descriptors.Rows; i++)
{
bw.Write((UInt64)v);
for (int j = 0; j < descriptors.Cols; j++)
{
Byte b = (Byte)indexer[i, j];
bw.Write(b);
}
}
tot += descriptors.Rows;
input.Release();
rinput.Release();
descriptors.Release();
}
}
}
}
catch
{
return;
}
}
