public HelloWorldPage()
: base()
{
this.GetButton().Opacity = 0;
Mat img = new Mat(200, 400, DepthType.Cv8U, 3); //Create a 3 channel image of 400x200
img.SetTo(new Bgr(255, 0, 0).MCvScalar); // set it to Blue color
//Draw "Hello, world." on the image using the specific font
CvInvoke.PutText(
img,
"Hello, world",
new System.Drawing.Point(10, 80),
FontFace.HersheyComplex,
1.0,
new Bgr(0, 255, 0).MCvScalar);
SetImage(img);
}
private void image1_Initialized(object sender, EventArgs e)
{
Mat image = new Mat(100, 400, DepthType.Cv8U, 3);
image.SetTo(new Bgr(255, 255, 255).MCvScalar);
CvInvoke.PutText(image, "Hello, world", new System.Drawing.Point(10, 50), Emgu.CV.CvEnum.FontFace.HersheyPlain, 3.0, new Bgr(255.0, 0.0, 0.0).MCvScalar);
image1.Source = BitmapSourceConvert.ToBitmapSource(image);
}
static void Main(string[] args)
{
String win1 = "Test Window"; //The name of the window
CvInvoke.NamedWindow(win1); //Create the window using the specific name
Mat img = new Mat(200, 400, DepthType.Cv8U, 3); //Create a 3 channel image of 400x200
img.SetTo(new Bgr(255, 0, 0).MCvScalar); // set it to Blue color
//Draw "Hello, world." on the image using the specific font
CvInvoke.PutText(
img,
"Hello, world",
new System.Drawing.Point(10, 80),
FontFace.HersheyComplex,
1.0,
new Bgr(0, 255, 0).MCvScalar);
CvInvoke.Imshow(win1, img); //Show the image
CvInvoke.WaitKey(0); //Wait for the key pressing event
CvInvoke.DestroyWindow(win1); //Destroy the window if key is pressed
}
public static void FindMatch(Mat modelImage, Mat observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography, out long score)
{
int k = 2;
double uniquenessThreshold = 0.80;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
KAZE featureDetector = new KAZE();
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
// KdTree for faster results / less accuracy
using (var ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (var sp = new SearchParams())
using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
// Calculate score based on matches size
// ---------------------------------------------->
score = 0;
for (int i = 0; i < matches.Size; i++)
{
if (mask.GetData(i)[0] == 0)
{
continue;
}
foreach (var e in matches[i].ToArray())
{
++score;
}
}
// <----------------------------------------------
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, matches, mask, 2);
}
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
}
/// <summary>
/// Populates the page with content passed during navigation. Any saved state is also
/// provided when recreating a page from a prior session.
/// </summary>
/// <param name="sender">
/// The source of the event; typically <see cref="NavigationHelper"/>.
/// </param>
/// <param name="e">Event data that provides both the navigation parameter passed to
/// <see cref="Frame.Navigate(Type, Object)"/> when this page was initially requested and
/// a dictionary of state preserved by this page during an earlier
/// session. The state will be null the first time a page is visited.</param>
private async void NavigationHelper_LoadState(object sender, LoadStateEventArgs e)
{
// TODO: Create an appropriate data model for your problem domain to replace the sample data
var item = await SampleDataSource.GetItemAsync((string)e.NavigationParameter);
this.DefaultViewModel["Item"] = item;
if (item.Title.Equals("Run Hello World"))
{
Mat img = new Mat(200, 400, DepthType.Cv8U, 3);
img.SetTo(new MCvScalar(255, 0, 0));
CvInvoke.PutText(img, "Hello world.", new System.Drawing.Point(10, 80), FontFace.HersheyComplex, 1.0, new MCvScalar(0, 255, 0));
ImageView.Source = img.ToWritableBitmap();
} else if (item.Title.Equals("Run Planar Subdivision"))
{
Mat img = PlanarSubdivisionExample.DrawSubdivision.Draw(400, 30);
ImageView.Source = img.ToWritableBitmap();
} else if (item.Title.Equals("Run Face Detection"))
{
Mat img = await LoadImage(@"Assets\Images\lena.jpg");
List<Rectangle> faces = new List<Rectangle>();
List<Rectangle> eyes = new List<Rectangle>();
long detectionTime;
FaceDetection.DetectFace.Detect(img, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes, false, false, out detectionTime);
foreach (Rectangle face in faces)
CvInvoke.Rectangle(img, face, new Bgr(0, 0, 255).MCvScalar, 2);
foreach (Rectangle eye in eyes)
CvInvoke.Rectangle(img, eye, new Bgr(255, 0, 0).MCvScalar, 2);
ImageView.Source = img.ToWritableBitmap();
} else if (item.Title.Equals("Run Pedestrian Detection"))
{
Mat img = await LoadImage(@"Assets\Images\pedestrian.png");
Mat gray = new Mat();
CvInvoke.CvtColor(img, gray, ColorConversion.Bgr2Gray);
long detectionTime;
Rectangle[] pedestrians = PedestrianDetection.FindPedestrian.Find(gray, false, false, out detectionTime);
foreach (Rectangle pedestrian in pedestrians)
{
CvInvoke.Rectangle(img, pedestrian, new MCvScalar(0, 0, 255) );
}
ImageView.Source = img.ToWritableBitmap();
}
}
private void Bayer()
{
var width = 800;
var height = 600;
var header = 1024;
Mat img = new Mat(height, width, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
byte[] data = new byte[height * width];
using (Stream input = new FileStream("Schlittentest.raw", FileMode.Open, FileAccess.Read, FileShare.Read))
{
input.Read(data, 0, header); //Skip header
input.Read(data, 0, height * width);
}
img.SetTo(data);
Mat rgb = new Mat();
CvInvoke.CvtColor(img, rgb, Emgu.CV.CvEnum.ColorConversion.BayerBg2Rgb);
//Setup an own rgb image
Mat mine = new Mat(height, width, Emgu.CV.CvEnum.DepthType.Cv8U, 3);
byte[] myDat = new byte[3 * height * width];
Func<int, byte> get = (loc) => data[Math.Max(0, Math.Min(data.Length - 1, loc))];
Func<int, byte> myget = (loc) => myDat[Math.Max(0, Math.Min(myDat.Length - 1, loc))];
Func<int, int[], byte> all = (loc, permutations) => (byte) permutations.Average(p => get(loc + p));
Func<int, byte> green = (loc) => all(loc, new int[] { -width, -1, +1, width });
Func<int, byte> redBlue = (loc) => all(loc, new int[] { -width - 1, width + 1, -width + 1, width - 1 });
for (int v = 0; v < height; ++v)
{
var row = v * width;
var rowEven = v % 2 == 0;
for (int u = 0; u < width; ++u)
{
var loc = row + u;
var colEven = u % 2 == 0;
myDat[3 * loc] = colEven && rowEven ? data[loc] :
(!colEven && !rowEven ? redBlue(loc) : (byte)0);
myDat[3 * loc + 1] = colEven == rowEven ? green(loc) : data[loc];
myDat[3 * loc + 2] = !colEven && !rowEven ? data[loc] :
(colEven && rowEven ? redBlue(loc) : (byte)0);
//if (colEven && rowEven)
//{
// myDat[3 * loc] = 0;
// myDat[3 * loc + 1] = 0;
// myDat[3 * loc + 2] = get(loc);
//}
//else if (!colEven && !rowEven)
//{
// myDat[3 * loc] = get(loc);
// myDat[3 * loc + 1] = 0;
// myDat[3 * loc + 2] = 0;
//}
//else if (colEven && !rowEven)
//{
// myDat[3 * loc] = 0;
// myDat[3 * loc + 1] = get(loc);
// myDat[3 * loc + 2] = 0;
//}
//else
//{
// myDat[3 * loc] = 0;
// myDat[3 * loc + 1] = get(loc);
// myDat[3 * loc + 2] = 0;
//}
//"Perfect" Bayer
//if (colEven && rowEven)
//{
// myDat[3 * loc] = get(loc);
// myDat[3 * loc + 1] = (byte)((get(loc + 1) + get(loc + width)) / 2);
// myDat[3 * loc + 2] = get(loc + width + 1);
//}
//else if (!colEven && !rowEven)
//{
// myDat[3 * loc] = get(loc - width - 1);
// myDat[3 * loc + 1] = (byte)((get(loc - 1) + get(loc - width)) / 2);
// myDat[3 * loc + 2] = get(loc);
//}
//else if (colEven && !rowEven)
//{
// myDat[3 * loc] = get(loc - width);
// myDat[3 * loc + 1] = (byte)((get(loc) + get(loc - width + 1)) / 2);
// myDat[3 * loc + 2] = get(loc + 1);
//}
//else
//{
// myDat[3 * loc] = get(loc - 1);
// myDat[3 * loc + 1] = (byte)((get(loc) + get(loc + width - 1)) / 2);
// myDat[3 * loc + 2] = get(loc + width);
//}
}
}
for (int v = 0; v < height; ++v)
{
var row = v * width;
var rowEven = v % 2 == 0;
for (int u = 0; u < width; ++u)
{
var loc = row + u;
var colEven = u % 2 == 0;
if (colEven != rowEven)
{
myDat[3 * loc] = (byte)new int[]{ -width, -1, +1, width }.Average(p => myget(3*(loc + p)));
myDat[3 * loc + 2] = (byte)new int[] { -width, -1, +1, width }.Average(p => myget(3 * (loc + p) + 2));
}
}
}
mine.SetTo(myDat);
myImage = mine.Bitmap;
opencvImage = rgb.Bitmap;
}
private void View3DGlControl_Load(object sender, EventArgs e)
{
_glLoaded = true;
GL.ClearColor(0.0f, 0.0f, 0.0f, 0.0f);
SetupViewport();
#region Create texture for the 3D Point clouds
int repeat = (int)OpenTK.Graphics.OpenGL.All.Repeat;
int linear = (int)OpenTK.Graphics.OpenGL.All.Linear;
GL.Enable(EnableCap.CullFace);
GL.Enable(EnableCap.Texture2D);
_textures = new int[1];
GL.GenTextures(1, _textures);
GL.BindTexture(TextureTarget.Texture2D, _textures[0]);
GL.TexParameterI(TextureTarget.Texture2D, TextureParameterName.TextureWrapS, ref repeat);
GL.TexParameterI(TextureTarget.Texture2D, TextureParameterName.TextureWrapT, ref repeat);
GL.TexParameterI(TextureTarget.Texture2D, TextureParameterName.TextureMagFilter, ref linear);
GL.TexParameterI(TextureTarget.Texture2D, TextureParameterName.TextureMinFilter, ref linear);
GL.TexEnv(TextureEnvTarget.TextureEnv, TextureEnvParameter.TextureEnvMode, (int)OpenTK.Graphics.OpenGL.All.Decal);
GL.ShadeModel(ShadingModel.Smooth);
GL.Hint(HintTarget.PerspectiveCorrectionHint, HintMode.Nicest);
GL.BindTexture(TextureTarget.Texture2D, 1);
Size size = _left.Size;
int maxDim = Math.Max(size.Width, size.Height);
using (Mat squareImg = new Mat(maxDim, maxDim, DepthType.Cv8U, 3))
{
squareImg.SetTo(new MCvScalar());
Rectangle roi = new Rectangle(maxDim / 2 - size.Width / 2, maxDim / 2 - size.Height / 2, size.Width, size.Height);
Mat roiImg = new Mat(squareImg, roi);
_left.CopyTo(roiImg);
if (_texture == null)
_texture = new Mat();
CvInvoke.Resize(squareImg, _texture, new Size(256, 256), 0, 0, Emgu.CV.CvEnum.Inter.Cubic);
CvInvoke.Flip(_texture, _texture, Emgu.CV.CvEnum.FlipType.Vertical);
GL.TexImage2D(TextureTarget.Texture2D, 0, PixelInternalFormat.Rgb8, _texture.Width, _texture.Height, 0, PixelFormat.Bgr, PixelType.UnsignedByte, _texture.DataPointer);
}
#endregion
}
//Transform images according to transform matrix
// From Line 130-239
public static ReturnColorImg transformColor(Mat img1, Mat mask1, Mat img2, Mat mask2,
Mat dst, Mat dst_mask,
Point centroid1, Point centroid2, double angle, Point tweak1, Point tweak2, bool mode = true)
{
Mat E = img2.Clone();
Mat E_mask = mask2.Clone();//Don't ruin original images
double intersections = 0;
double x = centroid2.X;
double y = centroid2.Y;
double _x, _y, _y2;
double y2;
LineSegment2D centerLine = new LineSegment2D(new Point((int)x, (int)y), new Point(img2.Width - (int)x, img2.Height - (int)y));
//Rectangle r=new Rectangle((int)x,(int)y,2*(img2.Width-(int)x),2*(img2.Height-(int)y));
Mat ri = new Mat(2 * (img2.Width - (int)x), 2 * (img2.Height - (int)y),DepthType.Cv8U, 3);
ri.SetTo(new MCvScalar(255, 255, 255));
Point oldc = new Point((int)x, (int)y);
bool success = false; // if the tweaking is not successful, return false
// inverse y axis
// y2 = -y;
// rotation of centeroid
x -= img2.Width / 2;
y -= img2.Height / 2;//shift origin to (w/2,h/2)
_x = x * Math.Cos(angle / (180 / Math.PI)) - y * Math.Sin(angle / (180 / Math.PI));//rotate by theta
_y = x * Math.Sin(angle / (180 / Math.PI)) + y * Math.Cos(angle / (180 / Math.PI));
_x += img2.Width / 2;
_y += img2.Height / 2;//back to origin
//_x = x+img2.Width/2;
//_y = y+img2.Height/2;
// inverse y axis
//_y = -_y2;
centroid2.X = (int)_x;
centroid2.Y = (int)_y;
Point shift = new Point();
shift.X = centroid1.X - centroid2.X;
shift.Y = centroid1.Y - centroid2.Y;
MatImage m1 = new MatImage(E);
m1.Rotate(angle, new Bgr(255, 255, 255));
E = m1.Out();
MatImage m2 = new MatImage(E_mask);
m1.Rotate(angle, new Bgr(255, 255, 255));
E_mask = m2.Out();
//Find optimal size of canvas to hold both images and appropriate transformations
Point t1, t2;//transformation 1 and 2
t1 = new Point();
t2 = new Point();
int optimal_h = 0, optimal_w = 0;//of canvas(IplImage* dst)
switch (quadrant(shift))
{
case 1:
t1.X = 0;
t1.Y = 0;
t2 = shift;
optimal_h = Math.Max(img1.Height, img2.Height + shift.Y);
optimal_w = Math.Max(img1.Width, img2.Width + shift.X);
break;
case 2:
t1.X = -shift.X;
t1.Y = 0;
t2.X = 0;
t2.Y = shift.Y;
optimal_h = Math.Max(img1.Height, img2.Height + shift.Y);
optimal_w = Math.Max(img2.Width, img1.Width - shift.X);
break;
case 3:
t1.X = -shift.X;
t1.Y = -shift.Y;
t2.X = 0;
t2.Y = 0;
optimal_h = Math.Max(img1.Height - shift.Y, img2.Height);
optimal_w = Math.Max(img1.Width - shift.X, img2.Width);
break;
case 4:
t1.X = 0;
t1.Y = -shift.Y;
t2.X = shift.X;
t2.Y = 0;
optimal_h = Math.Max(img1.Height - shift.Y, img2.Height);
optimal_w = Math.Max(img2.Width + shift.X, img1.Width);
break;
}
// add tweak factor
t1.X += tweak1.X;
t1.Y += tweak1.Y;
t2.X += tweak2.X;
t2.Y += tweak2.Y;
//optimal_h = 1000;
//optimal_w = 1000;
dst = new Mat(optimal_h, optimal_w,DepthType.Cv8U,3);
dst_mask = new Mat(optimal_h, optimal_w,DepthType.Cv8U,3);
if (mode)
{
dst.SetTo(new MCvScalar(255, 255, 255)); // white background=255, black background=0
}
else
{
dst.SetTo(new MCvScalar(0, 0, 0)); // white background=255, black background=0
}
dst_mask.SetTo(new MCvScalar(0, 0, 0));
/*if (BKG_WHITE)
cvSet(dst, cvScalar(255));//make it white
else
cvSet(dst, cvScalar(0));//make it black*/
for (int i = 0; i < img1.Height; ++i)
{
for (int j = 0; j < img1.Width; ++j)
{
// if black background
if (mode)
{
if (mask1.GetData(i, j)[0] != 255)
{
int i_new = i + t1.Y;
int j_new = j + t1.X;
try
{
dst.SetValue(i_new, j_new, img1.GetData(i, j));
int[] vals = { 255, 255, 255 };
dst_mask.SetValue(i_new, j_new, vals);
}
catch(IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
// if white background
else
{
if (mask1.GetData(i, j) [0] != 0)
{
int i_new = i + t1.Y;
int j_new = j + t1.X;
try
{
dst.SetValue(i_new, j_new, img1.GetData(i, j));
int[] vals = { 0, 0, 0 };
dst_mask.SetValue(i_new, j_new, vals);
}
catch(IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
}
}
//Apply transformation to image2
for (int i = 0; i < img2.Height; ++i)
{
for (int j = 0; j < img2.Width; ++j)
{
// if black background
if (mode)
{
if (E_mask.GetData(i, j) [0] != 255)
{
int i_new = i + t2.Y;
int j_new = j + t2.X;
try
{
if (dst_mask.GetData(i_new,j_new)[0] != 0)
{
intersections++;
}
else
{
dst.SetValue(i_new, j_new, E.GetData(i, j));
int[] vals = { 255, 255, 255 };
dst_mask.SetValue(i_new, j_new, vals);
}
}
catch (IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
// else if white background
else
{
if (E_mask.GetData(i, j)[0] != 0)
{
int i_new = i + t2.Y;
int j_new = j + t2.X;
try
{
if (dst_mask.GetData(i_new, j_new)[0] != 0)
{
intersections++;
}
else
{
dst.SetValue(i_new, j_new, E.GetData(i, j));
int[] vals = { 0, 0, 0 };
dst_mask.SetValue(i_new, j_new, vals);
}
}
catch (IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
}
}
/*for (int i = 0; i < Adst.Length; i++)
{
for(int j=0; j < Adst[0].Length; j++)
{
dst.SetValue(i, j, Adst[i][j]);
}
}
for (int i = 0; i < Adst_mask.Length; i++)
{
for (int j = 0; j < Adst_mask[0].Length; j++)
{
dst_mask.SetValue(i, j, Adst_mask[i][j]);
}
}*/
// dst.SetTo(Adst);
//dst_mask.SetTo(Adst_mask);
success = true;
/*cvReleaseImage(&E);
cvReleaseImage(&E_mask);*/ // should not need these two lines because of garbage collection
// threshold detection is meaningless for 2-piece case, always success
ret:
if (intersections > Constants.THRESHOLD)
{
/*cvReleaseImage(&dst);//In case of failure in joining
cvReleaseImage(&dst_mask);//release memory*/
ReturnColorImg img = new ReturnColorImg();
img.img = dst;
img.img_mask = dst_mask;
img.source1 = img1;
img.source2 = E_mask;
img.center1 = centroid1;
img.center2old = oldc;
img.center2new = centroid2;
img.centerLinee = centerLine;
img.returnbool = false; // for determining if the image is matched or not
img.translate1 = t1;
img.translate2 = t2;
img.overlap = intersections;
img.success = success; // for tweak only
return img;
}
else
{
ReturnColorImg img = new ReturnColorImg();
img.img = dst;
img.img_mask = dst_mask;
img.source1 = img1;
img.source2 = E_mask;
img.center1 = centroid1;
img.center2old = oldc;
img.center2new = centroid2;
img.centerLinee = centerLine;
img.returnbool = true; // for determining if the image is matched or not
img.translate1 = t1;
img.translate2 = t2;
img.overlap = intersections;
img.success = success; // for tweak only
return img;
}
}