public void FishEyeCalibrate()
{
var patternSize = new Size(10, 7);
using (var image = Image("calibration/00.jpg"))
using (var corners = new MatOfPoint2f())
{
Cv2.FindChessboardCorners(image, patternSize, corners);
var objectPointsArray = Create3DChessboardCorners(patternSize, 1.0f).ToArray();
var imagePointsArray = corners.ToArray();
using (var objectPoints = MatOfPoint3f.FromArray(objectPointsArray))
using (var imagePoints = MatOfPoint2f.FromArray(imagePointsArray))
using (var cameraMatrix = new MatOfDouble(Mat.Eye(3, 3, MatType.CV_64FC1)))
using (var distCoeffs = new MatOfDouble())
{
var rms = Cv2.FishEye.Calibrate(new[] { objectPoints }, new[] { imagePoints }, image.Size(), cameraMatrix,
distCoeffs, out var rotationVectors, out var translationVectors,
FishEyeCalibrationFlags.None);
var distCoeffValues = distCoeffs.ToArray();
Assert.Equal(55.15, rms, 2);
Assert.Contains(distCoeffValues, d => Math.Abs(d) > 1e-20);
Assert.NotEmpty(rotationVectors);
Assert.NotEmpty(translationVectors);
}
}
}
public void BuildPatternFromImage(Mat image, Pattern pattern)
{
// Store original image in pattern structure
pattern.size = new Size(image.Cols, image.Rows);
pattern.frame = image.Clone();
GetGray(image, pattern.grayImg);
// Build 2d and 3d contours (3d contour lie in XY plane since it's planar)
List <Point2f> points2dList = new List <Point2f>(4);
List <Point3f> points3dList = new List <Point3f>(4);
// Image dimensions
float w = image.Cols;
float h = image.Rows;
// Normalized dimensions:
points2dList.Add(new Point2f(0, 0));
points2dList.Add(new Point2f(w, 0));
points2dList.Add(new Point2f(w, h));
points2dList.Add(new Point2f(0, h));
pattern.points2d = MatOfPoint2f.FromArray(points2dList);
points3dList.Add(new Point3f(-0.5f, -0.5f, 0));
points3dList.Add(new Point3f(+0.5f, -0.5f, 0));
points3dList.Add(new Point3f(+0.5f, +0.5f, 0));
points3dList.Add(new Point3f(-0.5f, +0.5f, 0));
pattern.points3d = MatOfPoint3f.FromArray(points3dList);
ExtractFeatures(pattern.grayImg, ref pattern.keypoints, pattern.descriptors);
Train(pattern);
}
public static void TransformTv()
{
Mat img = Cv2.ImRead(@"C:\prog\consoleHomography\ConsoleApp1\ConsoleApp1\images\input\in.jpg");
Mat imgOut = new Mat();
Point2f[] source =
{
new Point2f(62f, 59f),
new Point2f(420f, 112f),
new Point2f(425f, 300f),
new Point2f(50f, 265f)
};
MatOfPoint2f src = MatOfPoint2f.FromArray(source);
Point2f[] destination =
{
new Point2f(0f, 0f),
new Point2f(img.Width, 0f),
new Point2f(img.Width, img.Height),
new Point2f(0f, img.Height)
};
MatOfPoint2f dest = MatOfPoint2f.FromArray(destination);
Mat m = Cv2.FindHomography(src, dest);
Cv2.WarpPerspective(img, imgOut, m, img.Size());
string imgName = $"{DateTime.Now:dd_MM_yyyy_HH_mm_ss}_out.jpg";
Cv2.ImWrite(@"C:\prog\consoleHomography\ConsoleApp1\ConsoleApp1\images\output\" + imgName, imgOut);
}
// fixed FromArray behavior
static Point2d[] MyPerspectiveTransform2(Point2f[] yourData, Mat transformationMatrix)
{
using (MatOfPoint2f s = MatOfPoint2f.FromArray(yourData))
using (MatOfPoint2f d = new MatOfPoint2f())
{
Cv2.PerspectiveTransform(s, d, transformationMatrix);
Point2f[] f = d.ToArray();
return(f.Select(Point2fToPoint2d).ToArray());
}
}
static bool RefineMatchesWithHomography(
KeyPoint[] queryKeypoints,
KeyPoint[] trainKeypoints,
float reprojectionThreshold,
DMatch[] matches,
Mat homography
)
{
int minNumberMatchesAllowed = 8;
if (matches.Length < minNumberMatchesAllowed)
{
return(false);
}
List <Point2f> srcPointsList = new List <Point2f>(matches.Length);
List <Point2f> dstPointsList = new List <Point2f>(matches.Length);
for (int i = 0; i < matches.Length; i++)
{
srcPointsList.Add(trainKeypoints[matches[i].TrainIdx].Pt);
dstPointsList.Add(queryKeypoints[matches[i].QueryIdx].Pt);
}
using (MatOfPoint2f srcPoints = MatOfPoint2f.FromArray(srcPointsList))
using (MatOfPoint2f dstPoints = MatOfPoint2f.FromArray(dstPointsList))
using (MatOfByte inliersMask = new MatOfByte())
{
Cv2.FindHomography(srcPoints, dstPoints, HomographyMethods.Ransac, reprojectionThreshold, inliersMask).CopyTo(homography);
if (homography.Rows != 3 || homography.Cols != 3)
{
return(false);
}
byte[] inliersMaskList = inliersMask.ToArray();
List <DMatch> inliers = new List <DMatch>();
for (int i = 0; i < inliersMaskList.Length; i++)
{
if (inliersMaskList[i] == 1)
{
inliers.Add(matches[i]);
}
}
matches = inliers.ToArray();
}
return(matches.Length > minNumberMatchesAllowed);
}
