public void ApplyTest()
{
var img1 = Properties.Resources.image2;
var img2 = Properties.Resources.image2;
MatrixH homography = new MatrixH(1, 0, 32,
0, 1, 0,
0, 0);
Blend blend = new Blend(homography, img1);
var actual = blend.Apply(img2);
Assert.AreEqual(64, actual.Size.Width);
Assert.AreEqual(32, actual.Size.Height);
homography = new MatrixH(1, 0, 32,
0, 1, 32,
0, 0);
blend = new Blend(homography, img1);
actual = blend.Apply(img2);
Assert.AreEqual(64, actual.Size.Width);
Assert.AreEqual(64, actual.Size.Height);
// ImageBox.Show(img3, PictureBoxSizeMode.Zoom);
}
public void Panorama_Example1()
{
Accord.Math.Tools.SetupGenerator(0);
// Let's start with two pictures that have been
// taken from slightly different points of view:
//
Bitmap img1 = Resources.dc_left;
Bitmap img2 = Resources.dc_right;
// Those pictures are shown below:
// ImageBox.Show(img1, PictureBoxSizeMode.Zoom, 640, 480);
// ImageBox.Show(img2, PictureBoxSizeMode.Zoom, 640, 480);
// Step 1: Detect feature points using Surf Corners Detector
var surf = new SpeededUpRobustFeaturesDetector();
var points1 = surf.ProcessImage(img1);
var points2 = surf.ProcessImage(img2);
// Step 2: Match feature points using a k-NN
var matcher = new KNearestNeighborMatching(5);
var matches = matcher.Match(points1, points2);
// Step 3: Create the matrix using a robust estimator
var ransac = new RansacHomographyEstimator(0.001, 0.99);
MatrixH homographyMatrix = ransac.Estimate(matches);
Assert.AreEqual(1.13583624f, homographyMatrix.Elements[0], 1e-5);
Assert.AreEqual(-0.0229569562f, homographyMatrix.Elements[1], 1e-5);
Assert.AreEqual(-255.243988f, homographyMatrix.Elements[2], 1e-2);
Assert.AreEqual(0.080111593f, homographyMatrix.Elements[3], 1e-5);
Assert.AreEqual(1.11404252f, homographyMatrix.Elements[4], 1e-5);
Assert.AreEqual(-167.362167f, homographyMatrix.Elements[5], 1e-2);
Assert.AreEqual(0.00011207442f, homographyMatrix.Elements[6], 1e-5);
Assert.AreEqual(0.0000529394056f, homographyMatrix.Elements[7], 1e-5);
Assert.AreEqual(8, homographyMatrix.Elements.Length);
// Step 4: Project and blend using the homography
Blend blend = new Blend(homographyMatrix, img1);
// Compute the blending algorithm
Bitmap result = blend.Apply(img2);
// Show on screen
// ImageBox.Show(result, PictureBoxSizeMode.Zoom, 640, 480);
#pragma warning disable 618
double[,] expected = Properties.Resources.blend_result.ToDoubleMatrix(0);
double[,] actual = result.ToDoubleMatrix(0);
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.1));
#pragma warning restore 618
}
private void btnBlend_Click(object sender, EventArgs e)
{
if (homography == null)
{
MessageBox.Show("Please, click RANSAC button first! :-)");
return;
}
// Step 4: Project and blend the second image using the homography
Blend blend = new Blend(homography, img1);
pictureBox.Image = blend.Apply(img2);
}
public void ApplyTest2()
{
var img1 = Properties.Resources.image2;
var img2 = Properties.Resources.image2;
var img3 = Properties.Resources.image2;
var img4 = Properties.Resources.image2;
MatrixH homography;
Blend blend;
homography = new MatrixH(1, 0, 32,
0, 1, 0,
0, 0);
blend = new Blend(homography, img1);
var img12 = blend.Apply(img2);
//ImageBox.Show("Blend of 1 and 2", img12, PictureBoxSizeMode.Zoom);
Assert.AreEqual(img12.PixelFormat, PixelFormat.Format32bppArgb);
blend = new Blend(homography, img3);
var img34 = blend.Apply(img4);
//ImageBox.Show("Blend of 3 and 4", img34, PictureBoxSizeMode.Zoom);
Assert.AreEqual(img34.PixelFormat, PixelFormat.Format32bppArgb);
homography = new MatrixH(1, 0, 64,
0, 1, 0,
0, 0);
blend = new Blend(homography, img12);
var img1234 = blend.Apply(img34);
//ImageBox.Show("Blend of 1, 2, 3, 4", img1234, PictureBoxSizeMode.Zoom);
Assert.AreEqual(img1234.PixelFormat, PixelFormat.Format32bppArgb);
// Blend of 1 and 5 (8bpp and 32bpp)
homography = new MatrixH(1, 0, 0,
0, 1, 32,
0, 0);
//ImageBox.Show("Image 1", img1, PictureBoxSizeMode.Zoom);
blend = new Blend(homography, img1234);
var img15 = blend.Apply(img1);
//ImageBox.Show("Blend of 1 and 5", img15, PictureBoxSizeMode.Zoom);
Assert.AreEqual(img1234.PixelFormat, PixelFormat.Format32bppArgb);
Assert.AreEqual(img1.PixelFormat, PixelFormat.Format8bppIndexed);
Assert.AreEqual(img15.PixelFormat, PixelFormat.Format32bppArgb);
Assert.AreEqual(128, img15.Width);
Assert.AreEqual(64, img15.Height);
}
/// <summary>
/// Blends two images with homography matrix.
/// </summary>
private void BlendImages()
{
Blend blend = new Blend(homography, new Bitmap(input_images[0]));
panorama = blend.Apply(new Bitmap(input_images[1]));
ShowThumbnail(panorama, true, PanoramaPhase.Blend);
}
private void BtnBlend_OnClick(object sender, RoutedEventArgs e)
{
// Step 4: Project and blend the second image using the homography
Blend blend = new Blend(homography, img1);
PictureBox.Source = blend.Apply(img2);
}
public void Panorama_Example1()
{
Accord.Math.Tools.SetupGenerator(0);
// Let's start with two pictures that have been
// taken from slightly different points of view:
//
Bitmap img1 = Resources.dc_left;
Bitmap img2 = Resources.dc_right;
// Those pictures are shown below:
// ImageBox.Show(img1, PictureBoxSizeMode.Zoom, 640, 480);
// ImageBox.Show(img2, PictureBoxSizeMode.Zoom, 640, 480);
// Step 1: Detect feature points using Surf Corners Detector
var surf = new SpeededUpRobustFeaturesDetector();
var points1 = surf.ProcessImage(img1);
var points2 = surf.ProcessImage(img2);
// Step 2: Match feature points using a k-NN
var matcher = new KNearestNeighborMatching(5);
var matches = matcher.Match(points1, points2);
// Step 3: Create the matrix using a robust estimator
var ransac = new RansacHomographyEstimator(0.001, 0.99);
MatrixH homographyMatrix = ransac.Estimate(matches);
Assert.AreEqual(1.15707409, homographyMatrix.Elements[0], 1e-5);
Assert.AreEqual(-0.0233834628, homographyMatrix.Elements[1], 1e-5);
Assert.AreEqual(-261.8217, homographyMatrix.Elements[2], 1e-2);
Assert.AreEqual(0.08801343, homographyMatrix.Elements[3], 1e-5);
Assert.AreEqual(1.12451434, homographyMatrix.Elements[4], 1e-5);
Assert.AreEqual(-171.191208, homographyMatrix.Elements[5], 1e-2);
Assert.AreEqual(0.000127789128, homographyMatrix.Elements[6], 1e-5);
Assert.AreEqual(0.00006173445, homographyMatrix.Elements[7], 1e-5);
Assert.AreEqual(8, homographyMatrix.Elements.Length);
// Step 4: Project and blend using the homography
Blend blend = new Blend(homographyMatrix, img1);
// Compute the blending algorithm
Bitmap result = blend.Apply(img2);
// Show on screen
// ImageBox.Show(result, PictureBoxSizeMode.Zoom, 640, 480);
//result.Save(@"C:\Projects\Accord.NET\net35.png", ImageFormat.Png);
#if NET35
Bitmap image = Properties.Resources.blend_net35;
#else
Bitmap image = Properties.Resources.blend_net45;
#endif
#pragma warning disable 618
double[,] expected = image.ToDoubleMatrix(0);
double[,] actual = result.ToDoubleMatrix(0);
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.1));
#pragma warning restore 618
}
// PANORAMIC STICHING CODE
// Accord.NET and AForge.NET frameworks and code examples provided from
// http://www.codeproject.com/KB/recipes/automatic_panoramas.aspx
private void panoramicStitchingToolStripMenuItem_Click(object sender, EventArgs e)
{
// Save a copy of the current image, ask user to open another image to merge with
Bitmap img2 = img;
openToolStripMenuItem_Click(sender, e);
// Check whether the current loaded image is different
// (If the user cancelled the open image operation, the current image in the viewer
// is still the same image object)
if (img2 != img)
{
Bitmap img1 = img;
AForge.IntPoint[] harrisPoints1;
AForge.IntPoint[] harrisPoints2;
AForge.IntPoint[] correlationPoints1;
AForge.IntPoint[] correlationPoints2;
MatrixH homography;
// Use Harris Corners Detector to find points of interest
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 1000f);
harrisPoints1 = harris.ProcessImage(img1).ToArray();
harrisPoints2 = harris.ProcessImage(img2).ToArray();
// This check fixes an out of bounds exception generated by matcher.Match() below when a
// monocolour image (0 harris points) is stitched with a non-monocolour image
if (harrisPoints1.Length == 0 || harrisPoints2.Length == 0)
{
MessageBox.Show("Panoramic stitching cannot continue because at least one of the images does not contain any Harris points.", "Warning");
}
else
{
// Match detected points using correlation
CorrelationMatching matcher = new CorrelationMatching(9);
AForge.IntPoint[][] matches = matcher.Match(img1, img2, harrisPoints1, harrisPoints2);
// Separate the two arrays
correlationPoints1 = matches[0];
correlationPoints2 = matches[1];
// Find homography matrix using RANSAC algorithm
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
// This check is to handle the ransac.Estimate() function
// which throws an exception if the array parameters do not contain at least 4 elements
if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4)
{
MessageBox.Show("Panoramic stitching cannot continue because at least one of the images does not contain at least 4 correlation points.", "Warning");
}
else
{
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// Merge the images
Blend blend = new Blend(homography, img1);
img = blend.Apply(img2);
//save the image properly and resize main form
origImg.Dispose();
origImg = new Bitmap(img);
pictureBox.Image = img;
mainForm.ActiveForm.Width = img.Width + widthPad;
mainForm.ActiveForm.Height = img.Height + heightPad;
}
}
}
}
public void Example1()
{
// Let's start with two pictures that have been
// taken from slightly different points of view:
//
Bitmap img1 = Resources.dc_left;
Bitmap img2 = Resources.dc_right;
// Those pictures are shown below:
ImageBox.Show(img1, PictureBoxSizeMode.Zoom, 640, 480);
ImageBox.Show(img2, PictureBoxSizeMode.Zoom, 640, 480);
// Step 1: Detect feature points using Surf Corners Detector
var surf = new SpeededUpRobustFeaturesDetector();
var points1 = surf.ProcessImage(img1);
var points2 = surf.ProcessImage(img2);
// Step 2: Match feature points using a k-NN
var matcher = new KNearestNeighborMatching(5);
var matches = matcher.Match(points1, points2);
// Step 3: Create the matrix using a robust estimator
var ransac = new RansacHomographyEstimator(0.001, 0.99);
MatrixH homographyMatrix = ransac.Estimate(matches);
// Step 4: Project and blend using the homography
Blend blend = new Blend(homographyMatrix, img1);
// Compute the blending algorithm
Bitmap result = blend.Apply(img2);
// Show on screen
ImageBox.Show(result, PictureBoxSizeMode.Zoom, 640, 480);
}
private void btnBlend_Click(object sender, EventArgs e)
{
// Step 4: Project and blend the second image using the homography
Blend blend = new Blend(homography, img1);
pictureBox.Image = blend.Apply(img2);
}
