public override void Trigger(EditableView.ClickPosition.Sources source, EditableView pnlView, Transaction transaction)
{
IShapeContainer container = CurrentPage.SelectionContainer();
if (container == null)
{
MessageBox.Show(Strings.Item("Container_Mismatch"));
return;
}
transaction.Edit((Datum)container);
Lined mask = FindSourceShape();
ImportedImage image = FindSourceImage();
transaction.Edit(mask);
transaction.Edit(image);
(mask as Pencil).ForceClosure(transaction);
if (mask is Curve curve && !curve.Closed())
{
throw new UserException("[Mask_Not_Closed]");
}
MaskedImage result = new MaskedImage(image, mask);
transaction.Create(result);
int index = Math.Max(mask.Z, image.Z); // this will be the Z-order of the new group
result.Parent = container;
container.Contents.Insert(index + 1, result); // This index will be valid as the individual shapes are still in the list
container.Contents.Remove(mask);
container.Contents.Remove(image);
container.FinishedModifyingContents(transaction, null);
CurrentPage.SelectOnly(result);
}
/// <summary>
/// Creates processed marker file
/// </summary>
private void CreateProcessedMask(string folder, MaskedImage maskedImage)
{
try
{
StringBuilder csvContent = new StringBuilder();
string newFilePath = CreateNewPath(folder, maskedImage.csvMask);
// Getting processed mask
char[,] processedMask = FileProcess.GetProcessedMask(maskedImage, Effects);
// Writing to new File
int width = processedMask.GetLength(0);
int height = processedMask.GetLength(1);
for (int y = 0; y < height; y++)
{
string line = "";
for (int x = 0; x < width; x++)
{
line += processedMask[x, y] + ";";
}
csvContent.AppendLine(line);
}
File.AppendAllText(newFilePath, csvContent.ToString());
}
catch (Exception e)
{
System.Windows.Forms.MessageBox.Show(e.Message,
"Error",
MessageBoxButtons.OK,
MessageBoxIcon.Error);
}
}
//-----------------------------------------------------
public Inpaint(image input, bool[,] mask, int radius)
{
// initial image
this.initial = new MaskedImage(input, mask);
// patch radius
this.radius = radius;
int i = 0;
// working copies
source = new MaskedImage(initial.width, initial.height);
source = initial;
target = new MaskedImage(source.width, source.height);
Debug.Log("build pyramid of images...");
// build pyramid of downscaled images
pyramid = new MaskedImage[12];
this.pyramid[i] = source;
//Debug.Log("pyramid0:"+ pyramid[0].width+"/"+pyramid[0].height);
while(source.width>radius && source.height>radius)
{
source = source.downsample();
i++;
this.pyramid[i] = new MaskedImage(source.width, source.height);
this.pyramid[i] = source;
}
maxlevel = i+1;
}
public static char[,] GetProcessedMask(MaskedImage maskedImage, List <int> effects)
{
BitmapImage bm = new BitmapImage(new Uri(maskedImage.source));
int indent = EditPageVM.IndentToCrop;
char[,] mask = ReadMaskFromCSV(maskedImage.csvMask, bm.PixelWidth, bm.PixelHeight);
if (!effects.Contains(EditPageVM.EFFECT_CROP) && !effects.Contains(EditPageVM.EFFECT_ROTATE))
{
return(mask);
}
// Cropping mask
if (effects.Contains(EditPageVM.EFFECT_CROP))
{
int width = bm.PixelWidth - 2 * indent;
int height = bm.PixelHeight - 2 * indent;
mask = CropMask(width, height, indent, mask);
}
// Rotating mask
int rotationCount = effects.FindAll(element => element.Equals(EditPageVM.EFFECT_ROTATE)).Count;
for (int i = 0; i < rotationCount; i++)
{
mask = RotateMask90(mask);
}
return(mask);
}
/// <summary>
/// Matches images and .csv masks by compairing the names
/// </summary>
private static bool TryToParceMaskedImages()
{
List <MaskedImage> parcedImages = new List <MaskedImage>();
int imageCount = PathesToImages.Count();
int maskCount = PathesToCsvFiles.Count();
for (int imgIndx = 0; imgIndx < imageCount; imgIndx++)
{
string imageName = Path.GetFileNameWithoutExtension(PathesToImages[imgIndx]);
for (int maskIndx = 0; maskIndx < maskCount; maskIndx++)
{
string maskName = Path.GetFileNameWithoutExtension(PathesToCsvFiles[maskIndx]);
if (imageName == maskName)
{
MaskedImage image = new MaskedImage();
image.source = PathesToImages[imgIndx];
image.csvMask = PathesToCsvFiles[maskIndx];
parcedImages.Add(image);
break;
}
}
}
if (parcedImages.Count > 0)
{
MaskedImages = parcedImages;
return(true);
}
return(false);
}
private void DerivePitchEdges(Image <Bgr, Byte> image)
{
ThresholdedImage = ImageProcess.ThresholdHsv(image, 22, 89, 33, 240, 40, 250);
MaskedImage = image.Copy(ThresholdedImage);
CannyImage = MaskedImage.Canny(200, 100);
var lines = CannyImage.HoughLinesBinary(1, Math.PI / 360, 100, 200, 50)[0];
foreach (var line in lines)
{
float dirY = line.Direction.Y;
if (line.Length > 500 && dirY < 0.3 && dirY > -0.3f)
{
if (line.P1.Y < image.Height / 2 && line.P2.Y < image.Height / 2)
{
m_TopBorderCandidateLines.Enqueue(line);
if (m_TopBorderCandidateLines.Count > 20)
{
m_TopBorderCandidateLines.Dequeue();
}
}
else if (line.P1.Y > image.Height / 2 && line.P2.Y > image.Height / 2)
{
m_BottomBorderCandidates.Enqueue(line);
if (m_BottomBorderCandidates.Count > 20)
{
m_BottomBorderCandidates.Dequeue();
}
}
}
}
}
//-----------------------------------------------------
// distance between two patches in two images
public static int distance(MaskedImage source,int xs,int ys, MaskedImage target,int xt,int yt, int S)
{
long distance=0, wsum=0, ssdmax = 9*255*255;
// for each pixel in the source patch
for(int dy=-S;dy<=S;dy++) {
for(int dx=-S;dx<=S;dx++) {
wsum+=ssdmax;
int xks=xs+dx, yks=ys+dy;
if (xks<1 || xks>=source.width-1) {distance+=ssdmax; continue;}
if (yks<1 || yks>=source.height-1) {distance+=ssdmax; continue;}
// cannot use masked pixels as a valid source of information
if (source.isMasked(xks, yks)) {distance+=ssdmax; continue;}
// corresponding pixel in the target patch
int xkt=xt+dx, ykt=yt+dy;
//Debug.Log("target.width:"+(target.getWidth()));
//Debug.Log("xkt:"+xkt+" ykt:"+ykt);
if (xkt < 1 || xkt >= target.width-1)
{
distance += ssdmax; continue;
}
if (ykt<1 || ykt>=target.height-1)
{distance += ssdmax; continue;}
// cannot use masked pixels as a valid source of information
if (target.isMasked(xkt, ykt)) {distance+=ssdmax; continue;}
// SSD distance between pixels (each value is in [0,255^2])
long ssd=0;
for(int band=0; band<3; band++) {
// pixel values
int s_value = source.getSample(xks, yks, band);
int t_value = source.getSample(xkt, ykt, band);
// pixel horizontal gradients (Gx)
float s_gx = 128+(source.getSample(xks+1, yks, band) - source.getSample(xks-1, yks, band))/2;
float t_gx = 128+(target.getSample(xkt+1, ykt, band) - target.getSample(xkt-1, ykt, band))/2;
// pixel vertical gradients (Gy)
float s_gy = 128+(source.getSample(xks, yks+1, band) - source.getSample(xks, yks-1, band))/2;
float t_gy = 128+(target.getSample(xkt, ykt+1, band) - target.getSample(xkt, ykt-1, band))/2;
ssd += (long) Mathf.Pow(s_value-t_value , 2); // distance between values in [0,255^2]
ssd += (long) Mathf.Pow(s_gx-t_gx , 2); // distance between Gx in [0,255^2]
ssd += (long) Mathf.Pow(s_gy-t_gy , 2); // distance between Gy in [0,255^2]
}
// add pixel distance to global patch distance
distance += ssd;
}
}
return (int)(DSCALE*distance/wsum);
}
//-----------------------------------------------------
public MaskedImage(MaskedImage copy)
{
this.im = copy.im;
width = copy.width;
height = copy.height;
this.mask = new bool[width,height];
for(int y=0;y<height;y++)
for(int x=0;x<width;x++)
mask[x,y] = copy.mask[x,y];
}
public static Mat ImageToMat_Mask(MaskedImage image)
{
Mat mat = new Emgu.CV.Mat(
image.RowCount, image.ColumnCount, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
for(int r = 0; r < image.RowCount; ++r)
{
for(int c = 0; c < image.ColumnCount; ++c)
{
byte m = image.HaveValueAt(r, c) ? (byte)255 : (byte)0;
mat.SetByteValue(r, c, m);
}
}
return mat;
}
public override void Trigger(EditableView.ClickPosition.Sources source, EditableView pnlView, Transaction transaction)
{
MaskedImage masked = (MaskedImage)CurrentPage.SelectedShapes.First();
IShapeContainer container = (IShapeContainer)masked.Parent;
transaction.Edit((Datum)container);
transaction.Edit(masked.MaskShape);
transaction.Edit(masked.Image);
transaction.Delete(masked);
masked.Image.Parent = container;
masked.MaskShape.Parent = container;
container.Contents.Insert(masked.Z + 1, masked.Image); // This index will be valid as the individual shapes are still in the list
container.Contents.Insert(masked.Z + 2, masked.MaskShape);
container.Contents.Remove(masked);
container.FinishedModifyingContents(transaction, null);
CurrentPage.SelectOnly(masked.MaskShape); // arbitrarily seemd better to leave the shape selected
}
public MatchedImagesTab()
{
InitializeComponent();
_camImageFirst.ImageSourceChanged += (s, e) =>
{
_imgLeft = new MaskedImage();
_imgLeft.FromBitmapSource(e.NewImage);
};
_camImageSec.ImageSourceChanged += (s, e) =>
{
_imgRight = new MaskedImage();
_imgRight.FromBitmapSource(e.NewImage);
};
_featureDetector = new FeatureDetectionAlgorithmController();
_featureDetector.StatusChanged += _featureDetector_StatusChanged;
}
public FeatureImagesTab()
{
InitializeComponent();
_camImageFirst.ImageSourceChanged += (s, e) =>
{
ImageLeft = new MaskedImage();
ImageLeft.FromBitmapSource(e.NewImage);
};
_camImageSec.ImageSourceChanged += (s, e) =>
{
ImageRight = new MaskedImage();
ImageRight.FromBitmapSource(e.NewImage);
};
_matcher = new FeatureMatchingAlgorithm();
_matcher.StatusChanged += _matcher_StatusChanged;
_camImageFirst.SelectedPointChanged += OnSelectedPointChanged;
_camImageSec.SelectedPointChanged += OnSelectedPointChanged;
}
// initialize field from an existing (possibily smaller) NNF
public void initialize(NNF nnf)
{
// field
this.field = new int[input.width,input.height,3];
this.input = nnf.input;
this.output = nnf.output;
this.S = nnf.S;
int fx = input.width/nnf.input.width;
int fy = input.height/nnf.input.height;
//System.out.println("nnf upscale by "+fx+"x"+fy+" : "+nnf.input.W+","+nnf.input.H+" -> "+input.W+","+input.H);
for(int y=0;y<input.height;y++) {
for(int x=0;x<input.width;x++) {
int xlow = Mathf.Min(x/fx, nnf.input.width-1);
int ylow = Mathf.Min(y/fy, nnf.input.height-1);
field[x,y,0] = nnf.field[xlow,ylow,0]*fx;
field[x,y,1] = nnf.field[xlow,ylow,1]*fy;
field[x,y,2] = MaskedImage.DSCALE;
}
}
initialize();
}
//-----------------------------------------------------
private void weightedCopy(MaskedImage src, int xs, int ys, double[,,] vote, int xd,int yd, double w)
{
//Debug.Log("src:"+src.width+"/"+src.height+" xs/ys:"+xs+"/"+ys+" xd/yd:"+xd+"/"+yd+ " w: "+w);
if (src.isMasked(xs, ys)) return;
//Debug.Log("vote: "+vote.GetLength(0)+"/"+vote.GetLength(1));
if(xd<vote.GetLength(0) && yd<vote.GetLength(1)){
vote[xd,yd,0] += w*src.getSample(xs, ys, 0);
vote[xd,yd,1] += w*src.getSample(xs, ys, 1);
vote[xd,yd,2] += w*src.getSample(xs, ys, 2);
vote[xd,yd,3] += w;
}
else Debug.Log("en dehors");
}
//-----------------------------------------------------
// EM-Like algorithm (see "PatchMatch" - page 6)
// Returns a double sized target image
public MaskedImage ExpectationMaximization(int level)
{
int iterEM = 1+level/2;
int iterNNF = Mathf.Min(7,1+level/2);
// source = nnf_SourceToTarget.input;
// target = nnf_SourceToTarget.output;
newtarget = null;
// Debug.Log("source: "+source.width+"/"+source.height);
// Debug.Log("target: "+target.width+"/"+target.height);
Debug.Log("EM loop (em="+iterEM+",nnf="+iterNNF+") : ");
// EM Loop
for(int emloop=1;emloop<=iterEM;emloop++) {
Debug.Log((1+iterEM-emloop)+" ");
// set the new target as current target
if (newtarget!=null) {
//nnf_SourceToTarget.output = newtarget;
nnf_TargetToSource.input = newtarget;
target = newtarget;
newtarget = null;
}
// -- add constraint to the NNF
if(level>0)
{
for(int y=0;y<target.height;y++)
for(int x=0;x<target.width;x++)
if(!source.constainsMasked(x, y, radius))
{
nnf_TargetToSource.field[x,y,0] = x;
nnf_TargetToSource.field[x,y,1] = y;
nnf_TargetToSource.field[x,y,2] = 0;
}
// -- minimize the NNF
nnf_TargetToSource.minimize(iterNNF);
}
// -- Now we rebuild the target using best patches from source
bool upscaled = false;
// Instead of upsizing the final target, we build the last target from the next level source image
// So the final target is less blurry (see "Space-Time Video Completion" - page 5)
if (level>=1 && (emloop==iterEM))
{
newsource = pyramid[level-1];
newtarget = target.upscale(newsource.width,newsource.height);
//Debug.Log("newsource: "+newsource.width+"/"+newsource.height);
upscaled = true;
}
else
{
newsource = pyramid[level];
newtarget = target;
upscaled = false;
}
// --- EXPECTATION STEP ---
if(level>0)
{
// votes for best patch from NNF Source->Target (completeness) and Target->Source (coherence)
double[,,] vote = new double[newtarget.width,newtarget.width,4];
//ExpectationStep(nnf_SourceToTarget, true, vote, newsource, upscaled);
ExpectationStep(nnf_TargetToSource, false, vote, newsource, upscaled);
// --- MAXIMIZATION STEP ---
// compile votes and update pixel values
MaximizationStep(newtarget, vote);
}
// debug : display intermediary result
result = MaskedImage.resize(newtarget.getBufferedImage(), initial.width, initial.height);
//Demo.display(result);
}
return newtarget;
}
// constructor
public NNF(MaskedImage input, MaskedImage output, int patchsize)
{
this.input = input;
this.output= output;
this.S = patchsize;
}
//-----------------------------------------------------
// Maximization Step : Maximum likelihood of target pixel
public void MaximizationStep(MaskedImage target, double[,,] vote)
{
for(int y=0;y<target.height;y++) {
for(int x=0;x<target.width;x++) {
if (vote[x,y,3]>0) {
int r = (int) ( vote[x,y,0]/vote[x,y,3] );
int g = (int) ( vote[x,y,1]/vote[x,y,3] );
int b = (int) ( vote[x,y,2]/vote[x,y,3] );
target.setSample(x, y, 0, r );
target.setSample(x, y, 1, g );
target.setSample(x, y, 2, b );
target.setMask(x,y,false);
} else {
// conserve the values from previous target
//target.setMask(x,y,true);
}
}
}
}
//-----------------------------------------------------
// return a downsampled image (factor 1/2)
public MaskedImage downsample()
{
int newW=width/2, newH=height/2;
// Binomial coefficient
int[] kernel = {1,5,10,10,5,1};
MaskedImage newimage = new MaskedImage(newW, newH);
for(int y=0;y<height-1;y+=2) {
for(int x=0;x<width-1;x+=2) {
int r=0,g=0,b=0,m=0,ksum=0;
for(int dy=-2;dy<=3;dy++) {
int yk=y+dy;
if (yk<0 || yk>=height) continue;
int ky = kernel[2+dy];
for(int dx=-2;dx<=3;dx++) {
int xk = x+dx;
if (xk<0 || xk>=width) continue;
if (mask[xk,yk]) continue;
int k = kernel[2+dx]*ky;
r+= k*this.getSample(xk, yk, 0);
g+= k*this.getSample(xk, yk, 1);
b+= k*this.getSample(xk, yk, 2);
ksum+=k;
m++;
}
}
if (ksum>0) {r/=ksum; g/=ksum; b/=ksum;}
if (m!=0) {
newimage.setSample(x/2, y/2, 0, r);
newimage.setSample(x/2, y/2, 1, g);
newimage.setSample(x/2, y/2, 2, b);
newimage.setMask(x/2, y/2, false);
} else {
newimage.setMask(x/2, y/2, true);
}
}
}
return newimage;
}
//-----------------------------------------------------
// return an upscaled image
public MaskedImage upscale(int newW,int newH)
{
MaskedImage newimage = new MaskedImage(newW, newH);
for(int y=0;y<newH;y++) {
for(int x=0;x<newW;x++) {
// original pixel
int xs = (x*width)/newW;
int ys = (y*height)/newH;
// copy to new image
if (!mask[xs,ys]) {
newimage.setSample(x, y, 0, this.getSample(xs, ys, 0));
newimage.setSample(x, y, 1, this.getSample(xs, ys, 1));
newimage.setSample(x, y, 2, this.getSample(xs, ys, 2));
newimage.setMask(x, y, false);
} else {
newimage.setMask(x, y, true);
}
}
}
return newimage;
}
// Finds automatically calibration points on standard calibration image
// (that is big black dots on white background )
// In point managment one still have to set correct grid number for each point
private void FindCalibrationPoints(object sender, RoutedEventArgs e)
{
if(_imageControl.ImageSource == null)
return;
_finderChooseWindow.ShowDialog();
if(_finderChooseWindow.Accepted)
{
_currentImagePointFinder = (CalibrationPointsFinder)
_finderChooseWindow.GetSelectedProcessor("Calibration Points Finder");
_currentImagePointFinder.PrimaryShapeChecker = (ShapeChecker)
_finderChooseWindow.GetSelectedProcessor("Primary CalibShape Qualifier");
MaskedImage img = new MaskedImage();
img.FromBitmapSource(_imageControl.ImageSource);
_currentImagePointFinder.Image = img;
_currentImagePointFinder.FindCalibrationPoints();
if(_currentImagePointFinder.Points != null)
{
_currentImagePointFinder.LinesExtractor.ExtractLines();
_currentImageGrid = _currentImagePointFinder.Points;
RefreshCalibrationPoints();
_butAcceptGrid.IsEnabled = true;
}
}
}
public MaskedImage TransfromImageBackwards(IImage image, bool preserveSize = false)
{
MaskedImage undistorted;
FindTransformedImageSize(image.RowCount, image.ColumnCount);
Matrix<double>[] matrices = new Matrix<double>[image.ChannelsCount];
if(preserveSize)
{
undistorted = new MaskedImage(image.Clone());
for(int i = 0; i < image.ChannelsCount; ++i)
{
matrices[i] = new DenseMatrix(image.RowCount, image.ColumnCount);
undistorted.SetMatrix(matrices[i], i);
}
}
else
{
IImage img = image.Clone();
for(int i = 0; i < image.ChannelsCount; ++i)
{
matrices[i] = new DenseMatrix(_finalSize.Y, _finalSize.X);
img.SetMatrix(matrices[i], i);
}
undistorted = new MaskedImage(image.Clone());
}
int R = InterpolationRadius;
int R21 = R * 2 + 1;
for(int x = 0; x < matrices[0].ColumnCount; ++x)
{
for(int y = 0; y < matrices[0].RowCount; ++y)
{
// Cast point from new image to old one
Vector2 oldCoords = Transformation.TransformPointBackwards(new Vector2(x: x, y: y));
Vector2 aa = Transformation.TransformPointForwards(oldCoords);
IntVector2 oldPixel = new IntVector2(oldCoords);
// Check if point is in old image range or points to undefined point
if(oldCoords.X < 0 || oldCoords.X > image.ColumnCount ||
oldCoords.Y < 0 || oldCoords.Y > image.RowCount ||
image.HaveValueAt(oldPixel.Y, oldPixel.X) == false)
{
// Point out of range, so set to black
for(int i = 0; i < image.ChannelsCount; ++i)
{
matrices[i].At(y, x, 0.0);
undistorted.SetMaskAt(y, x, false);
}
}
else
{
// Interpolate value from patch in old image
double[,] influence = new double[R21, R21];
double totalInf = 0;
// For each pixel in neighbourhood find its distance and influence of Pu on it
for(int dx = -R; dx <= R; ++dx)
{
for(int dy = -R; dy <= R; ++dy)
{
double distance = _computeDistance(oldCoords, oldPixel.X + dx, oldPixel.Y + dy);
influence[dx + R, dy + R] = 1.0 / distance;
totalInf += influence[dx + R, dy + R];
}
}
double infScale = 1.0 / totalInf; // Scale influence, so that its sum over all pixels in radius is 1
double[] val = new double[image.ChannelsCount];
for(int dx = -R; dx <= R; ++dx)
{
for(int dy = -R; dy <= R; ++dy)
{
double inf = influence[dx + R, dy + R] * infScale;
// Store color for new point considering influence from neighbours
int ix = Math.Max(0, Math.Min(image.ColumnCount - 1, oldPixel.X + dx));
int iy = Math.Max(0, Math.Min(image.RowCount - 1, oldPixel.Y + dy));
for(int i = 0; i < image.ChannelsCount; ++i)
{
val[i] += image[iy, ix, i] * inf;
}
}
}
for(int i = 0; i < image.ChannelsCount; ++i)
{
matrices[i].At(y, x, val[i]);
}
}
}
}
return undistorted;
}
public MaskedImage TransfromImageForwards(IImage image, bool preserveSize = false)
{
MaskedImage undistorted;
var influences = FindInfluenceMatrix(image.RowCount, image.ColumnCount);
Matrix<double>[] matrices = new Matrix<double>[image.ChannelsCount];
if(preserveSize)
{
// New image is in old one's coords
undistorted = new MaskedImage(image.Clone());
for(int i = 0; i < image.ChannelsCount; ++i)
{
matrices[i] = new DenseMatrix(image.RowCount, image.ColumnCount);
undistorted.SetMatrix(matrices[i], i);
}
// Bound processing to smaller of images in each dimesions
int minX = Math.Max(0, _finalTopLeft.X);
int maxX = Math.Min(image.ColumnCount, _finalSize.X + _finalTopLeft.X);
int minY = Math.Max(0, _finalTopLeft.Y);
int maxY = Math.Min(image.RowCount, _finalSize.Y + _finalTopLeft.Y);
for(int x = minX; x < maxX; ++x)
{
for(int y = minY; y < maxY; ++y)
{
double influenceTotal = 0.0;
double[] val = new double[image.ChannelsCount];
foreach(var inf in influences[y - _finalTopLeft.Y, x - _finalTopLeft.X]) // Move Pu to influence matrix coords
{
for(int i = 0; i < image.ChannelsCount; ++i)
val[i] += image[inf.Yd, inf.Xd, i] * inf.Influence;
influenceTotal += inf.Influence;
}
if(influenceTotal > 0.25)
{
for(int i = 0; i < image.ChannelsCount; ++i)
matrices[i].At(y, x, val[i] / influenceTotal);
undistorted.SetMaskAt(y, x, true);
}
else
{
undistorted.SetMaskAt(y, x, false);
}
}
}
}
else
{
// New image is in same coords as influence matrix
for(int i = 0; i < image.ChannelsCount; ++i)
matrices[i] = new DenseMatrix(_finalSize.Y, _finalSize.X);
IImage img = image.Clone();
for(int i = 0; i < image.ChannelsCount; ++i)
img.SetMatrix(matrices[i], i);
undistorted = new MaskedImage(image.Clone());
for(int x = 0; x < _finalSize.X; ++x)
{
for(int y = 0; y < _finalSize.Y; ++y)
{
double influenceTotal = 0.0;
double[] val = new double[image.ChannelsCount];
foreach(var inf in influences[y, x])
{
for(int i = 0; i < image.ChannelsCount; ++i)
val[i] += image[inf.Yd, inf.Xd, i] * inf.Influence;
influenceTotal += inf.Influence;
}
if(influenceTotal > 0.5)
{
for(int i = 0; i < image.ChannelsCount; ++i)
matrices[i].At(y, x, val[i] / influenceTotal);
undistorted.SetMaskAt(y, x, true);
}
else
{
undistorted.SetMaskAt(y, x, false);
}
}
}
}
return undistorted;
}
//-----------------------------------------------------
// Expectation Step : vote for best estimations of each pixel
public void ExpectationStep(NNF nnf, bool sourceToTarget, double[,,] vote, MaskedImage source, bool upscale)
{
int[,,] field = nnf.getField();
int R = nnf.S;
for(int y=0;y<nnf.input.height;y++)
{
for(int x=0;x<nnf.input.width;x++)
{
// x,y = center pixel of patch in input
// xp,yp = center pixel of best corresponding patch in output
int xp=field[x,y,0], yp=field[x,y,1], dp=field[x,y,2];
// similarity measure between the two patches
double w=0;
if(dp<MaskedImage.DSCALE+1)
w = MaskedImage.similarity[dp];
// vote for each pixel inside the input patch
for(int dy=-R;dy<=R;dy++)
{
for(int dx=-R;dx<=R;dx++)
{
// get corresponding pixel in output patch
int xs,ys,xt,yt;
if (sourceToTarget)
{ xs=x+dx; ys=y+dy; xt=xp+dx; yt=yp+dy; }
else
{ xs=xp+dx; ys=yp+dy; xt=x+dx; yt=y+dy; }
if (xs<0 || xs>=nnf.input.width) continue;
if (ys<0 || ys>=nnf.input.height) continue;
if (xt<0 || xt>=nnf.output.width) continue;
if (yt<0 || yt>=nnf.output.height) continue;
// add vote for the value
if (upscale)
{
weightedCopy(source, 2*xs, 2*ys, vote, 2*xt, 2*yt, w);
weightedCopy(source, 2*xs+1, 2*ys, vote, 2*xt+1, 2*yt, w);
weightedCopy(source, 2*xs, 2*ys+1, vote, 2*xt, 2*yt+1, w);
weightedCopy(source, 2*xs+1, 2*ys+1, vote, 2*xt+1, 2*yt+1, w);
}
else
weightedCopy(source, xs, ys, vote, xt, yt, w);
}
} // vote
}
}
}
