/// <summary>
/// Creates a DepthCorrectionMap from an uncorrected raw image and the desired TableDistance
/// </summary>
/// <param name="uncorrectedImage"></param>
/// <param name="averageTableDistance"></param>
/// <returns></returns>
public DepthCorrectionMap CreateDepthCorrectionMap(DepthImage uncorrectedImage, int averageTableDistance)
{
DepthCorrectionMap correctionMap = new DepthCorrectionMap(uncorrectedImage.Width, uncorrectedImage.Height);
int lastValue = 0;
for (int x = 0; x < uncorrectedImage.Width; x++)
{
for (int y = 0; y < uncorrectedImage.Height; y++)
{
if (uncorrectedImage.Data[x, y] > 0)
{
//Correctly recognized point: calculate correction value
correctionMap.CorrectionData[x, y] = averageTableDistance - uncorrectedImage.Data[x, y];
lastValue = averageTableDistance - uncorrectedImage.Data[x, y];
}
else
{
//Not recognized point (height = 0) use recent calibration value as approximation
correctionMap.CorrectionData[x, y] = lastValue;
}
}
}
return correctionMap;
}
/// <summary>
/// Applies a correctionMap to a raw DepthImage
/// </summary>
/// <param name="rawImage"></param>
/// <param name="correctionMap"></param>
/// <returns></returns>
public DepthImage ApplyDepthCorrection(DepthImage rawImage, DepthCorrectionMap correctionMap)
{
if (rawImage.Width != correctionMap.Width ||rawImage.Height != correctionMap.Height)
{
throw new Exception("Image size does not match");
}
//calculate the values for the section that is not involved in border cutting
int BorderCutXmin = 0 + correctionMap.CutOffLeft;
int BorderCutXmax = rawImage.Width - 1 - correctionMap.CutOffRight;
int BorderCutYmin = 0 + correctionMap.CutOffTop;
int BorderCutYmax = rawImage.Height - 1 - correctionMap.CutOffBOttom;
//Apply the Depth Correction
for (int x=0; x < rawImage.Width; x++)
{
for (int y = 0; y < rawImage.Height; y++)
{
if (rawImage.Data[x,y] > 0) //Only if there wasn't a reading error
rawImage.Data[x, y] = rawImage.Data[x, y] + correctionMap.CorrectionData[x, y];
//Coordinates outside the CutOffBorder?
if (!((BorderCutXmin <= x) && (x <= BorderCutXmax) && (BorderCutYmin <= y) && (y <= BorderCutYmax)))
rawImage.Data[x, y] = 0;
}
}
return rawImage;
}
public RecognitionDataPacket()
{
rawDepthImage = null;
correctedDepthImage = null;
TableObjects = new List<TableObject>();
HandObj = null;
objectmap = null;
neighbourmap = null;
bmpCorrectedDepth = null;
bmpRawDepth = null;
bmpVideoFrame = null;
RecognitionDuration = 0;
}
/// <summary>
/// Creates a boolmap out of an depth image. Every pixel that has an height > 0 is set to true
/// </summary>
/// <param name="source"></param>
/// <returns></returns>
private bool[,] CreateBoolMap(DepthImage source)
{
bool[,] boolmap = new bool[source.Width,source.Height];
for (int y = 0; y < source.Height; y++)
{
for (int x = 0; x < source.Width; x++)
{
if (source.Data[x,y] > 0)
{
boolmap[x, y] = true;
}
else
{
boolmap[x, y] = false;
}
}
}
return boolmap;
}
public List<TableObject> SeperateObjects(ref DepthImage image, out bool[,] boolmap_object, out int[,,] neighbourmap)
{
//Create 2 seperate depth-images, remove the objects (for hand recognition) and the hand (for object recognition)
DepthImage HandImage = image.Clone();
DepthImage ObjectImage = image.Clone();
SplitUpDepthImages(ref HandImage, ref ObjectImage);
//For object recognition, create a BooleanXY-Map (true for all objects with a height > 0)
bool[,] boolmap_hand = CreateBoolMap(HandImage);
boolmap_object = CreateBoolMap(ObjectImage);
if (SettingsManager.RecognitionSet.SaveDebugMaps)
{
Bitmap dm_obj = MapVisualizer.VisualizeDepthImage(ObjectImage, false, false);
dm_obj.Save("ObjectDepthImage.bmp");
Bitmap dm_h = MapVisualizer.VisualizeDepthImage(HandImage, false, false);
dm_h.Save("HandDepthImage.bmp");
Bitmap bobj = MapVisualizer.VisualizeBoolMap(boolmap_hand, image.Width, image.Height);
bobj.Save("boolmap_hand.bmp");
Bitmap bmpo = MapVisualizer.VisualizeBoolMap(boolmap_object, image.Width, image.Height);
bmpo.Save("boolmap_object.bmp");
Bitmap ci = MapVisualizer.VisualizeDepthImage(image, false, false);
ci.Save("normalized_depthimage.bmp");
}
//Run the Hand Recognition
HandRecognizer hrec = new HandRecognizer();
HandObject hand = hrec.RecognizeHands(ref boolmap_hand, image.Width, image.Height);
//RunTheObjectRecognition
ObjectRecognizer orec = new ObjectRecognizer();
List<TableObject> tableObjects = orec.RecognizeObjects(boolmap_object, image, out neighbourmap);
//If there is a hand, add it to the TableObjects
if (hand != null)
tableObjects.Add(hand);
//Return the ObjectList
return tableObjects;
}
private void b_aktualisieren_Click(object sender, RoutedEventArgs e)
{
if (_tmgr.LastKinectDepthFrame == null)
{
MessageBox.Show("Kinect muss zuerst initialisiert werden!", "Fehler!");
return;
}
int width, height;
SettingsManager.KinectSet.GetDepthResolution(out width, out height);
_dimage = new DepthImage(_tmgr.LastKinectDepthFrame, width, height);
Bitmap bmp = MapVisualizer.VisualizeDepthImage(_dimage,false,false);
image.Source = bmp.ToWpfBitmap();
s_cutoff_down.Value = SettingsManager.PreprocessingSet.DefaultCorrectionMap.CutOffBOttom;
s_cutoff_left.Value = SettingsManager.PreprocessingSet.DefaultCorrectionMap.CutOffLeft;
s_cutoff_right.Value = SettingsManager.PreprocessingSet.DefaultCorrectionMap.CutOffRight;
s_cutoff_top.Value = SettingsManager.PreprocessingSet.DefaultCorrectionMap.CutOffTop;
l_höhe.Text = SettingsManager.RecognitionSet.TableDistance.ToString();
}
/// <summary>
/// Recognizes the Objects via a depth image and a prepared boolmap
/// </summary>
/// <param name="prepared_boolmap">a prepared boolmap, with only objects marked as true</param>
/// <param name="image">the depth image, used to calculate the height of the recognized objects</param>
/// <returns></returns>
public List<TableObject> RecognizeObjects(bool[,] prepared_boolmap, DepthImage image, out int[,,] neighbourmap)
{
//Check in a raster whether there are any true pixels
List<TPoint> TrueRasterPoints = GetTrueRasterPoints(prepared_boolmap, image);
//Create a neighbourmap - an int[x,y,n] array, where the int stores the count of the whitepixel-neighbours of the object (n=0) and the size of the circle/rect used
//to count these neighbours (n=1). The circle/rect is scaled up until a defined percentage of the pixels is false, meaning no part of the object
neighbourmap = CreateNeighbourMap(prepared_boolmap, image, TrueRasterPoints);
//DEBUG
if (SettingsManager.RecognitionSet.SaveDebugMaps)
{
Bitmap bmp = MapVisualizer.VisualizeNeighbourMap(neighbourmap, image.Width, image.Height);
bmp.Save("neigbourmap.bmp");
}
//Now select the ObjectCenters (maximum neigbour-values on the neigbourmap)
List<ObjectPoint> pointlist = SelectObjectCenters(neighbourmap, image.Width, image.Height);
//Create TableObjects from the ObjectCenters List
return GenerateTableObjects(pointlist, image);
}
public DepthImage NormalizeHeights(DepthImage source)
{
for (int y = 0; y < source.Height; y++)
{
for (int x = 0; x < source.Width; x++)
{
int height = source.Data[x, y];
//Normalize Height (Calculate the object height in respect to the table surface)
height = SettingsManager.RecognitionSet.TableDistance - height;
//is the height within the Range of the table surface? then set to 0
//also set to 0 if height is negative -> "under" the table
if (height < 0 || SettingsManager.RecognitionSet.TableDistanceRange > height)
height = 0;
//is the height too high, set it to 0
if (height > SettingsManager.RecognitionSet.HandMaximalHeight)
height = 0;
source.Data[x, y] = height;
}
}
return source;
}
private void DoRecognitionWork(object data)
{
object[] dataArray = (object[]) data;
PlanarImage pimg = (PlanarImage) dataArray[0];
int[] deptharray = (int[]) dataArray[1];
Bitmap colorFrame = (Bitmap) dataArray[2];
RecognitionDataPacket rpacket = new DataStructures.RecognitionDataPacket();
DateTime dtBegin = DateTime.Now;
//Create DepthImage
DepthImage dimg = new DepthImage(deptharray,pimg.Width,pimg.Height);
rpacket.rawDepthImage = dimg.Clone();
//Correct the image
DepthMapPreprocessor dmp = new DepthMapPreprocessor();
dimg = dmp.ApplyDepthCorrection(dimg, SettingsManager.PreprocessingSet.DefaultCorrectionMap);
dimg = dmp.NormalizeHeights(dimg);
ObjectSeperator objectSeperator = new ObjectSeperator();
//Seperate objects
bool[,] boolmap_object;
int[,,] neighbourmap;
List<TableObject> objects = objectSeperator.SeperateObjects(ref dimg,out boolmap_object,out neighbourmap);
//if supplied, extract the relevant bitmap parts from the ColorFrame
if (colorFrame != null)
{
ObjectVideoBitmapAssigner ovba = new ObjectVideoBitmapAssigner();
ovba.AssignVideoBitmap(objects, colorFrame);
}
//Extract hand object from table objects
if (objects.Where( o => o.GetType() == typeof(HandObject)).Count() > 0)
{
rpacket.HandObj = (HandObject)objects.Where(o => o.GetType() == typeof (HandObject)).ToArray()[0];
}
//Fill DataPacket with Data
rpacket.correctedDepthImage = dimg;
rpacket.TableObjects = objects;
rpacket.objectmap = boolmap_object;
rpacket.neighbourmap = neighbourmap;
rpacket.bmpVideoFrame = colorFrame;
TimeSpan ts = DateTime.Now - dtBegin;
rpacket.RecognitionDuration = (int)Math.Round(ts.TotalMilliseconds);
if (SettingsManager.RecognitionSet.SaveDebugMaps)
{
Bitmap bmp = MapVisualizer.VisualizeDepthImage(rpacket.rawDepthImage);
bmp.Save("rawDepthImage.bmp");
}
//Event
OnRecognitionFinished(rpacket);
}
private void SplitUpDepthImages(ref DepthImage HandImage, ref DepthImage objectImage)
{
for (int x=0;x<HandImage.Width;x++)
{
for(int y=0;y<HandImage.Height;y++)
{
int heigt = HandImage.Data[x, y];
if (HandImage.Data[x, y] > SettingsManager.RecognitionSet.HandMaximalHeight || HandImage.Data[x, y] <= SettingsManager.RecognitionSet.ObjectMaximalHeight)
HandImage.Data[x, y] = 0;
if (objectImage.Data[x, y] > SettingsManager.RecognitionSet.ObjectMaximalHeight)
objectImage.Data[x, y] = 0;
}
}
}
public static Bitmap VisualizeDepthImage(DepthImage img, bool invert=false, bool printDepthValues = false)
{
//Calculate Min Value
int[] RawData = img.RawData;
int min = 0;
for (int i = 1; i < RawData.Count(); i++)
{
if (min == 0)
min = RawData[i];
if ((RawData[i] < min) && (RawData[i] != 0))
min = RawData[i];
}
//Calculate max
int max = RawData[0];
for (int i = 1; i < RawData.Count(); i++)
{
if (RawData[i] > max)
max = RawData[i];
}
if (max == 0)
max = 1;
//The range between min and max has to fit into 255 bit (grayscale image)
double scale_multiplicator = (float)255.0 / Convert.ToDouble(max - min);
//Convert each pixel
Bitmap grayscale = new Bitmap(img.Width, img.Height);
int indexc = 0;
for (var y = 0; y < img.Height; y++)
{
for (var x = 0; x < img.Width; x++)
{
int value = img.Data[x, y];
int rgb_value = (int)Math.Round((value - min) * scale_multiplicator);
if (rgb_value < 0)
rgb_value = 0;
if (rgb_value > 255)
rgb_value = 255;
if (invert)
rgb_value = 255 - rgb_value;
Color col = new Color();
col = Color.FromArgb(rgb_value, rgb_value, rgb_value);
grayscale.SetPixel(x, y, col);
indexc++;
}
}
//Add information if desired
if (printDepthValues)
{
/* Graphics g = Graphics.FromImage(grayscale);
g.DrawString("min: " + min.ToString() + "mm max: " + max.ToString() + "mm", new Font("Tahoma", 10),
Brushes.Red, new PointF(0, 0));
return new Bitmap(img.Width, img.Height, g);
* */
//TODO: Bild wird schwarz, bug beheben
}
return grayscale;
}
private List<TableObject> GenerateTableObjects(List<ObjectPoint> pointlist, DepthImage image)
{
List<TableObject> tableobjects = new List<TableObject>();
foreach (ObjectPoint op in pointlist)
{
TableObject tobj = new TableObject();
tobj.Center = new TPoint(op.X, op.Y, TPoint.PointCreationType.depth);
tobj.CenterDefined = true;
tobj.Radius = op.RectSize;
tobj.Height = image.Data[op.X, op.Y];
tableobjects.Add(tobj);
}
return tableobjects;
}
/// <summary>
/// Check in a raster whether there are any true pixels
/// </summary>
/// <param name="prepared_boolmap"></param>
/// <param name="image"></param>
/// <returns></returns>
private static List<TPoint> GetTrueRasterPoints(bool[,] prepared_boolmap, DepthImage image)
{
List<TPoint> TrueRasterPoints = new List<TPoint>();
for (int x = 0; x < image.Width; x = x + SettingsManager.RecognitionSet.ObjectRecognitionGridSpacing)
{
for (int y = 0; y < image.Height; y = y + SettingsManager.RecognitionSet.ObjectRecognitionGridSpacing)
{
if (prepared_boolmap[x, y])
TrueRasterPoints.Add(new TPoint(x, y, TPoint.PointCreationType.depth));
}
}
return TrueRasterPoints;
}
/// <summary>
/// Creates a neighbourmap - an int[x,y,n] array, where the int stores the count of the whitepixel-neighbours of the object (n=0) and the size of the circle/rect used
/// to count these neighbours (n=1). The circle/rect is scaled up until a defined percentage of the pixels is false, meaning no part of the object. the neigbourmap is only created around true rasterpoints to be eficcent
/// </summary>
/// <param name="boolmap">The boolmap</param>
/// <param name="image">Depth Image</param>
/// <param name="rasterpoints">The calculated Rasterpoints</param>
/// <returns></returns>
private static int[,,] CreateNeighbourMap(bool[,] boolmap, DepthImage image, List<TPoint> rasterpoints)
{
int[,,] neighbourmap = new int[image.Width,image.Height,2];
//Do this for each region arount the true rasterpoints
foreach (TPoint rasterpoint in rasterpoints)
{
//calculate the area: the Gridsize after the points Position
int xmin = rasterpoint.DepthX;
int ymin = rasterpoint.DepthY;
int xmax = rasterpoint.DepthX + SettingsManager.RecognitionSet.ObjectRecognitionGridSpacing;
if (xmax >= image.Width)
xmax = image.Width - 1;
int ymax = rasterpoint.DepthY + SettingsManager.RecognitionSet.ObjectRecognitionGridSpacing;
if (ymax >= image.Height)
ymax = image.Height - 1;
TRectangle area = new TRectangle(xmin, ymin, xmax, ymax);
//Now calculate the values for every point in the area
CalculateNeigbourValues(boolmap, area, ref neighbourmap, image.Width, image.Height);
}
return neighbourmap;
}
