public HistogramPieceSegmenter(HistChessboardModel histChessboardModel)
{
this._histChessboardModel = histChessboardModel;
difference = new Mat(histChessboardModel.BackgroundModel.Size(), MatType.CV_16SC3); // histChessboardModel.BackgroundModel.Type());
states = difference.Clone();
diffWin = new Window("Difference from BG");
}
// TODO: Optional<Mat>
public HistChessboardModel Do(IVideo video)
{
HistChessboardModel model = null;
Mat frame;
Mat gray = new Mat();
Mat thresh = new Mat();
var corners = new Point2f[4];
var patternSize = new Size(7, 3);
var threshWin = new Window("Adaptive Threshold");
// TODO: each iteration, try different block sizes for the adaptive threshold (height / 4, height / 2, etc)
do
{
frame = video.GetNextFrame();
if (frame != null && frame.Width + frame.Height > 0)
{
Cv2.CvtColor(frame, gray, ColorConversionCodes.BGR2GRAY);
//Cv2.MedianBlur(gray, gray, 5); // Disallows chessboard to be found, because of how it opens/closes corners
Cv2.AdaptiveThreshold(gray, thresh,
maxValue: 255.0,
adaptiveMethod: AdaptiveThresholdTypes.GaussianC,
thresholdType: ThresholdTypes.Binary,
blockSize: (gray.Height / 4) | 1,
c: 0.0);
threshWin.ShowImage(thresh);
var found = Cv2.FindChessboardCorners(thresh, patternSize, out corners,
ChessboardFlags.None); //, ChessboardFlags.AdaptiveThresh | ChessboardFlags.NormalizeImage);
//frame.CopyTo(output);
//Cv2.DrawChessboardCorners(output, patternSize, corners, found);
//if (!found) Console.Out.WriteLine("Chessboard not found :( ");
if (found)
{
var boardPoints = new Point2d[21];
Point2d[] foundPoints = OCVUtil.Point2fTo2d(corners);
for (int c = 0; c < 7; c++)
{
for (int r = 0; r < 3; r++)
{
boardPoints[r * 7 + c] = new Point2d((c + 1.0), (r + 3.0));
}
}
var boardToImageTransform = Cv2.FindHomography(boardPoints, foundPoints);
var imageToBoardTransform =
boardToImageTransform.Inv(); //Cv2.FindHomography(foundPoints, boardPoints);
model = new HistChessboardModel(boardToImageTransform, imageToBoardTransform, frame);
}
}
} while (frame != null && model == null);
return(model);
}
public void Render(bool[,] state)
{
for (int row = 0; row < 8; row++)
{
for (int col = 0; col < 8; col++)
{
var color = state[row, col] ? Scalar.White :
(HistChessboardModel.IsWhiteSquare(row, col) ? WhiteColor : BlackColor);
Cv2.Rectangle(
img: Output,
pt1: new Point(row * SquareSize, col * SquareSize),
pt2: new Point((row + 1) * SquareSize, (col + 1) * SquareSize),
color: color,
thickness: -1);
}
}
outWin.ShowImage(Output);
}
SquareClassificationScores[,] CalcSquareClassifications(Mat frame)
{
var result = new SquareClassificationScores[8, 8];
Mat squareMask = new Mat(frame.Size(), MatType.CV_8U);
var histSize = new[] { 16, 16, 16 };
var histRanges = new[] { new Rangef(0f, 256f), new Rangef(0f, 256f), new Rangef(0f, 256f) };
for (int row = 0; row < 8; row++)
{
for (int col = 0; col < 8; col++)
{
squareMask.SetTo(Scalar.Black);
_histChessboardModel.DrawSquareMask(row, col, squareMask, HistChessboardModel.SquareMaskScale);
Mat squareHist = new Mat();
Cv2.CalcHist(
images: new[] { frame },
channels: new int[] { 0, 1, 2 },
mask: squareMask,
hist: squareHist,
dims: 3,
histSize: histSize,
ranges: histRanges);
var method = HistCompMethods.Correl;
var squareColor = HistChessboardModel.SquareColor(row, col);
var likeWhitePiece = Cv2.CompareHist(
h1: squareHist,
h2: _histChessboardModel.XOnYHist[(int)ChessColor.White, squareColor],
method: method);
var likeBlackPiece = Cv2.CompareHist(
h1: squareHist,
h2: _histChessboardModel.XOnYHist[(int)ChessColor.Black, squareColor],
method: method);
var likeEmpty = Cv2.CompareHist(
h1: squareHist,
h2: _histChessboardModel.XOnYHist[(int)SquareState.Empty, squareColor],
method: method);
result[row, col] = new SquareClassificationScores(new [] { likeWhitePiece, likeBlackPiece, likeEmpty });
}
}
return(result);
}
public void Render(SquareClassificationScores[,] scores)
{
for (int row = 0; row < 8; row++)
{
for (int col = 0; col < 8; col++)
{
var squareState = scores[row, col].MostLikely;
var color = squareState ==
SquareState.White ? Scalar.White :
(squareState == SquareState.Black ? Scalar.Black :
(HistChessboardModel.IsWhiteSquare(row, col) ? WhiteColor : BlackColor));
Cv2.Rectangle(
img: Output,
pt1: new Point(row * SquareSize, col * SquareSize),
pt2: new Point((row + 1) * SquareSize, (col + 1) * SquareSize),
color: color,
thickness: -1);
}
}
outWin.ShowImage(Output);
}
