FindHomography question (some points of the image go in the negative part of the x or y axis)

[MartinStokelj]

Hello, this is not an issue, I just need help with WarpPerspective.
After getting homography matrix with FindHomography() and making WarpPerspective() I can see that the output is not a full image (top of the image is missing). How to make offset for warped image?

Example code:

Mat H = getHomography(kpsA, kpsB, featuresA, featuresB, matches, 4);
if (H == null || H.Width == 0 || H.Height == 0)
	throw new Exception("No Homography!!!");

//# Apply panorama correction
double width = trainImg.Width + queryImg.Width;
double height = trainImg.Height + queryImg.Height;

Mat result = new Mat();
Cv2.WarpPerspective(trainImg, result, H, new OpenCvSharp.Size(width, height));

using (Bitmap resultBitmap = BitmapConverter.ToBitmap(result))
	resultBitmap.Save(locationFolder2 + (imageCounter++).ToString().PadLeft(3, '0') + "_trainImg.png", ImageFormat.Png); //5

private Mat getHomography(KeyPoint[] kpsA, KeyPoint[] kpsB, Mat featuresA, Mat featuresB, DMatch[] matches, int reprojThresh)
{
	//# convert the keypoints to numpy arrays
	if (matches.Length > 4)
	{
		List<Point2f> PtA = new List<Point2f>(matches.Length);
		List<Point2f> PtB = new List<Point2f>(matches.Length);
		foreach (var m in matches)
		{
			KeyPoint kpsAI = kpsA[m.QueryIdx];
			KeyPoint kpsBI = kpsB[m.TrainIdx];

			PtA.Add(new Point2f(kpsAI.Pt.X, kpsAI.Pt.Y));
			PtB.Add(new Point2f(kpsBI.Pt.X, kpsBI.Pt.Y));
		}
		InputArray ptsA = InputArray.Create(PtA);
		InputArray ptsB = InputArray.Create(PtB);

		//# estimate the homography between the sets of points
		//step 1: find the homography H with findHomography you will get a classic structure for homography
		Mat H = Cv2.FindHomography(ptsA, ptsB, HomographyMethods.Ransac, reprojThresh);

		Console.WriteLine("Homography:");
		for (var rowIndex = 0; rowIndex < H.Rows; rowIndex++)
		{
			for (var colIndex = 0; colIndex < H.Cols; colIndex++)
			{
				double hVal = H.At<double>(rowIndex, colIndex);
				Console.Write($"{hVal} ");
			}
			Console.WriteLine("");
		}
		/*
			1,00341764165136 -0,0140892817967429 22,6703275561596
			0,00180754198580635 1,00579655474349 -67,6480727682579
			-2,38456747876871E-06 -6,51185532110956E-06 1
		/*

		return H;
	}
	else
		return null;
}

Output:

The output is not a full image (the top of image is missing)

[MartinStokelj]

Any suggestions?

[MartinStokelj]

I found the solution with PerspectiveTransform, here is the full code if someone will look for it

`

Mat keyView = new Mat();
Cv2.DrawMatches(trainImg_gray, kpsA, queryImg_gray, kpsB, matches100, keyView, null, null, null, DrawMatchesFlags.NotDrawSinglePoints);
if (debugImages)
{
	using (Bitmap resultBitmap = BitmapConverter.ToBitmap(keyView))
		resultBitmap.Save(locationFolder2 + (imageCounter++).ToString().PadLeft(3, '0') + "_keyView.png", ImageFormat.Png); //4
}

Mat H = getHomography(kpsA, kpsB, featuresA, featuresB, matches, 4);
if (H == null || H.Width == 0 || H.Height == 0)
	throw new Exception("No Homography!!!");

//https://answers.opencv.org/question/144252/perspective-transform-without-crop/
//https://stackoverflow.com/a/44840212/2983443

//# Find the corners after the transform has been applied
Point2f[] corners = { new Point2f(0, 0),
		  new Point2f(0, trainImg.Height),
		  new Point2f(trainImg.Width, trainImg.Height),
		  new Point2f(trainImg.Width, 0) };

corners = Cv2.PerspectiveTransform(corners, H);

//# Find the bounding rectangle
InputArray inputArray = InputArray.Create(corners);
Rect box = Cv2.BoundingRect(inputArray); //b = {(x:12 y:-68 width:1298 height:728)}

//# Compute the translation homography that will move (bx, by) to (0, 0)
H.Set(0, 2, (H.At<double>(0, 2) + 0));
H.Set(1, 2, (H.At<double>(1, 2) - box.Y));


//# Apply panorama correction
double width = (trainImg.Width + queryImg.Width);
double height = (trainImg.Height + queryImg.Height);

Mat result = new Mat(new OpenCvSharp.Size(box.Width, box.Height), H.Type());
Cv2.WarpPerspective(trainImg, result, H, new OpenCvSharp.Size(width, height), InterpolationFlags.Cubic, BorderTypes.Constant);

using (Bitmap resultBitmap = BitmapConverter.ToBitmap(result))
	resultBitmap.Save(locationFolder2 + (imageCounter++).ToString().PadLeft(3, '0') + "_trainImg.png", ImageFormat.Png);
	
private Mat getHomography(KeyPoint[] kpsA, KeyPoint[] kpsB, Mat featuresA, Mat featuresB, DMatch[] matches, int reprojThresh)
{
	//# convert the keypoints to numpy arrays
	if (matches.Length > 4)
	{
		List<Point2f> PtA = new List<Point2f>(matches.Length);
		List<Point2f> PtB = new List<Point2f>(matches.Length);
		foreach (var m in matches)
		{
			KeyPoint kpsAI = kpsA[m.QueryIdx];
			KeyPoint kpsBI = kpsB[m.TrainIdx];

			PtA.Add(new Point2f(kpsAI.Pt.X, kpsAI.Pt.Y));
			PtB.Add(new Point2f(kpsBI.Pt.X, kpsBI.Pt.Y));
		}
		InputArray ptsA = InputArray.Create(PtA);
		InputArray ptsB = InputArray.Create(PtB);

		//# estimate the homography between the sets of points
		//step 1: find the homography H with findHomography you will get a classic structure for homography
		Mat H = Cv2.FindHomography(ptsA, ptsB, HomographyMethods.Ransac, reprojThresh);
		/*
		Console.WriteLine("Homography:");
		float[] data = new float[9];
		int counter = 0;
		for (var rowIndex = 0; rowIndex < H.Rows; rowIndex++)
		{
			for (var colIndex = 0; colIndex < H.Cols; colIndex++)
			{
				double hVal = H.At<double>(rowIndex, colIndex);
				data[counter] = (float)hVal;
				counter++;
				Console.Write($"{hVal} ");
			}
			Console.WriteLine("");
		}
		*/
		return H;
	}
	else
		return null;
}

`