public Dictionary<Card, Point> LocateCards(Image<Bgr, Byte> table, Settings settings)
{
#region process image
//Convert the image to grayscale and filter out the noise
Image<Gray, Byte> gray = table.Convert<Gray, Byte>();
Gray cannyThreshold = new Gray(180);
Gray cannyThresholdLinking = new Gray(120);
Gray circleAccumulatorThreshold = new Gray(120);
Image<Gray, Byte> cannyEdges = gray.Canny(cannyThreshold, cannyThresholdLinking);
StructuringElementEx el = new StructuringElementEx(3, 3, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
cannyEdges = cannyEdges.MorphologyEx(el, CV_MORPH_OP.CV_MOP_CLOSE, 1);
#endregion
Contour<Point> contours = cannyEdges.FindContours(
CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, //was CV_CHAIN_APPROX_SIMPLE
RETR_TYPE.CV_RETR_TREE);
ContourNode tree = new ContourNode(contours);
Dictionary<Card, Point> cardlocs = new Dictionary<Card, Point>();
foreach (KeyValuePair<Card, CardContour> pair in GiveCards(tree))
{
ContourNode node = pair.Value.Node;
Card card = pair.Key;
PointF fcenter = node.Contour.GetMinAreaRect().center;
Point center = new Point((int)fcenter.X, (int)fcenter.Y);
cardlocs.Add(card, center);
}
#region draw
#if DEBUG
TreeViz.VizualizeTree(tree);
ContourAnalyzer.DrawContours(tree, table);
//ImageViewer.Show(table);
#endif
#endregion
return cardlocs;
}
private Image<Bgr, Byte> Run(string filename)
{
Stopwatch watch = new Stopwatch();
watch.Start();
ContourAnalyzer ca = new ContourAnalyzer();
Image<Bgr, Byte> table = new Image<Bgr, byte>(filename);
table = table.PyrDown().PyrDown();
int debuglevel = cmbDebuglevel.SelectedIndex;
Settings settings = new Settings(debuglevel);
Dictionary<Card, System.Drawing.Point> cards = ca.LocateCards(table, settings);
Logic logic = new Logic();
HashSet<List<Card>> sets = logic.FindSets(new List<Card>(cards.Keys));
Random rnd = new Random();
foreach (List<Card> set in sets)
{
DrawSet(table, cards, rnd, set);
}
watch.Stop();
this.Title = String.Format("Done. Elapsed time: {0}", watch.Elapsed.ToString());
ImageSource bitmap = TreeViz.ToBitmapSource(table);
Shower.Source = bitmap;
return table;
}
public Dictionary<SetVision.Gamelogic.Card, Point> LocateCards(Image<Bgr, byte> table, Settings settings)
{
throw new NotImplementedException();
}
private static CardColor RecognizeColor(ContourNode node, Settings settings)
{
Image<Bgr, Byte> image = node.Image;
image.ROI = node.Contour.BoundingRectangle;
Image<Bgr, Byte> debugBest = null;
Point bestpos = FindBestColoredPixel(image, out debugBest);
Bgr bgr = image[bestpos];
#region classification
CardColor classification = classifier.Classify(bgr);
CardColor verdict = classification;
if (verdict == CardColor.Other)
{
if (!isGray(bgr, 10))
{
verdict = ClassifyBgr2(bgr);
}
else
{
verdict = CardColor.White;
}
}
#endregion
#if DEBUG
//save for training:
writer.Write((int)bgr.Blue, (int)bgr.Green, (int)bgr.Red);
#endif
#region debug
if (settings.debuglevel >= 4)
{
ImageViewer.Show(debugBest, verdict.ToString());
}
else if (settings.debuglevel >= 2 && verdict == CardColor.Other)
{
ImageViewer.Show(debugBest, verdict.ToString());
}
#endregion
return verdict;
}
private static void AssignColors(ContourNode tree, Image<Bgr, Byte> image, Settings settings)
{
AssignColor(tree, image, settings);
foreach (ContourNode child in tree.Children)
{
//AssignColor(child, image, settings);
AssignColors(child, image, settings);
}
}
private static void AssignColor(ContourNode node, Image<Bgr, Byte> image, Settings settings)
{
if (node.Contour.Area > 100
&& ((node.Shape == Shape.Squiggle)
|| (node.Shape == Shape.Diamond)
|| (node.Shape == Shape.Oval)))
{
//CardColor color = RecognizeColor(image, node);
//CardColor color = RecognizeColor(node);
CardColor color = RecognizeColor(node, settings);
node.Color = color;
AssignAverageColors(node, settings);
}
else if (node.Shape == Shape.Card)
{
AssignAverageColors(node, settings);
}
}
private static void AssignAverageColors(ContourNode node, Settings settings)
{
//Then get the average color of the card (should be white or gray)
Bgr avgBgr = new Bgr();
MCvScalar scr1 = new MCvScalar();
node.Image.AvgSdv(out avgBgr, out scr1, node.AttentionMask);
node.averageBgr = avgBgr;
Hsv avgHsv = new Hsv();
MCvScalar scr2 = new MCvScalar();
node.Image.Convert<Hsv, Byte>().AvgSdv(out avgHsv, out scr2, node.AttentionMask);
node.averageBgr = avgBgr;
node.averageHsv = avgHsv;
}
/// <summary>
/// LocateCards works by analyzing the contours in the image.
/// For instance, the Diamond in Set is a polygon with exactly 4 vertices.
/// The Oval has no such features yet.
/// The Squiggle is not convex, but concave and has edges in a 'right bend', instead of only 'left bends'
///
/// All these shapes are inside the contour of a (white) card, which is a rounded square.
/// Cards may also be the (only) exterior boundaries
/// </summary>
/// <param name="table">An image displaying the table with the Set cards</param>
/// <returns>A dict locating which cards are present where in the image</returns>
public Dictionary<Card, Point> LocateCards(Image<Bgr, Byte> table, Settings settings)
{
classifier = new BgrHsvClassifier();
classifier.Train();
#region process image
//Convert the image to grayscale and filter out the noise
Image<Gray, Byte> gray = table.Convert<Gray, Byte>();
Gray cannyThreshold = new Gray(50); //180
Gray cannyThresholdLinking = new Gray(30); //120
Gray circleAccumulatorThreshold = new Gray(100); //120
#region old
Image<Gray, Byte> cannyEdges = gray.Canny(cannyThreshold, cannyThresholdLinking);
if (settings.debuglevel >= 3)
{
ImageViewer.Show(cannyEdges, "cannyEdges before Closing");
}
#endregion
//#region new
//Image<Gray, Byte> thresholded = new Image<Gray, byte>(gray.Size);
//CvInvoke.cvAdaptiveThreshold(gray.Ptr, thresholded.Ptr,
// 255,
// ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_GAUSSIAN_C,
// THRESH.CV_THRESH_BINARY_INV, 9, 5);
//StructuringElementEx el1 = new StructuringElementEx(3, 3, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//thresholded = thresholded.Erode(1);//thresholded.MorphologyEx(el1, CV_MORPH_OP.CV_MOP_CLOSE, 1);//
//Image<Gray, Byte> cannyEdges = thresholded;//.Canny(new Gray(1), new Gray(10));
//#endregion
//StructuringElementEx el = new StructuringElementEx(5, 5, 2, 2, CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
StructuringElementEx el = new StructuringElementEx(3, 3, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
cannyEdges = cannyEdges.MorphologyEx(el, CV_MORPH_OP.CV_MOP_CLOSE, 1);
if (settings.debuglevel >= 3)
{
ImageViewer.Show(cannyEdges, "cannyEdges after Closing");
}
#endregion
Contour<Point> contours = cannyEdges.FindContours(
CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, //was CV_CHAIN_APPROX_SIMPLE
RETR_TYPE.CV_RETR_TREE);
ContourNode tree = new ContourNode(contours);
FilterTree(tree);
#region debug
if (settings.debuglevel >= 3)
{
var debug = table.Clone();
DrawContours(tree, debug);
ImageViewer.Show(debug, "Contours after filtering");
}
#endregion
AssignShapes(tree);
AssignImages(tree, table, true);
#region debug
if (settings.debuglevel >= 3)
{
var debug1 = table.Clone();
DrawContours(tree, debug1);
ImageViewer.Show(debug1);
}
#endregion
FilterTree(tree);
#region debug
if (settings.debuglevel >= 3)
{
var debug2 = table.Clone();
DrawContours(tree, debug2);
ImageViewer.Show(debug2);
}
#endregion
AssignColors(tree, table, settings);
#region debug
if (settings.debuglevel >= 3)
{
TreeViz.VizualizeTree(tree);
}
#endregion
AssignFills(tree);
Dictionary<Card, Point> cardlocs = new Dictionary<Card, Point>();
foreach (KeyValuePair<Card, ContourNode> pair in GiveCards(tree))
{
ContourNode node = pair.Value;
Card card = pair.Key;
PointF fcenter = node.Contour.GetMinAreaRect().center;
Point center = new Point((int)fcenter.X, (int)fcenter.Y);
cardlocs.Add(card, center);
}
#region debug
if (settings.debuglevel >= 1)
{
TreeViz.VizualizeTree(tree);
}
if (settings.debuglevel >= 2)
{
DrawContours(tree, table);
ImageViewer.Show(table);
}
#endregion
return cardlocs;
}