HawkeyeVision/Plugins/LindtLeerformPlugin/Services/LeerformPatternRecognitionService.cs

using LindtLeerformPlugin.Models;
using OpenCvSharp;

namespace LindtLeerformPlugin.Services;

public class LeerformPatternRecognitionService
{
    private static readonly Scalar GoodColor = new(0, 255, 0); // BGR Green
    private static readonly Scalar BadColor = new(0, 0, 255);  // BGR Red

    public IReadOnlyList<CellRegion> ComputeCells(CellPattern pattern)
    {
        var cells = new List<CellRegion>();
        if (pattern.Rows <= 0 || pattern.Cols <= 0 || pattern.RoiWidth <= 0 || pattern.RoiHeight <= 0)
            return cells;

        var cellW = pattern.RoiWidth / pattern.Cols;
        var cellH = pattern.RoiHeight / pattern.Rows;
        var padding = Math.Max(0, pattern.Padding);

        var index = 0;
        for (var r = 0; r < pattern.Rows; r++)
        {
            for (var c = 0; c < pattern.Cols; c++)
            {
                var x = pattern.RoiX + c * cellW + padding;
                var y = pattern.RoiY + r * cellH + padding;
                var w = Math.Max(1, cellW - 2 * padding);
                var h = Math.Max(1, cellH - 2 * padding);
                var rect = new Rect(x, y, w, h);
                var center = new Point(x + w / 2, y + h / 2);
                var radius = Math.Max(1, Math.Min(w, h) / 2);

                cells.Add(new CellRegion
                {
                    Index = index++,
                    Row = r,
                    Col = c,
                    BoundingBox = rect,
                    Center = center,
                    Radius = radius
                });
            }
        }
        return cells;
    }

    public Mat BuildMask(CellPattern pattern, Size imageSize, int? cellIndex = null)
    {
        var mask = new Mat(imageSize, MatType.CV_8UC1, Scalar.All(0));
        var cells = ComputeCells(pattern);
        var color = Scalar.All(255);
        var imageRect = new Rect(0, 0, imageSize.Width, imageSize.Height);

        foreach (var cell in cells)
        {
            if (cellIndex.HasValue && cellIndex.Value != cell.Index)
                continue;

            var clipped = cell.BoundingBox & imageRect;
            if (clipped.Width <= 0 || clipped.Height <= 0)
                continue;

            if (pattern.Shape == CellShape.Square)
            {
                Cv2.Rectangle(mask, clipped, color, thickness: -1);
            }
            else
            {
                Cv2.Circle(mask, cell.Center, cell.Radius, color, thickness: -1);
            }
        }
        return mask;
    }

    /// <summary>
    /// Build a single foreground mask covering both white and dark chocolate flecks on the pink mold,
    /// using the LAB-channel thresholding approach from <c>jupiter/grid_test.ipynb</c>.
    /// <list type="bullet">
    /// <item><description><b>White chocolate</b> — pink mold has a* well above 128 (red), white sits near 128. Inverse-threshold a* so neutral pixels become foreground.</description></item>
    /// <item><description><b>Dark chocolate</b> — pink mold is bright (high L*), dark chocolate is dark. Inverse-threshold L* so dark pixels become foreground.</description></item>
    /// </list>
    /// The two masks are OR-merged, then morphologically opened (3×3 ELLIPSE) and closed (5×5 ELLIPSE)
    /// to drop single-pixel noise and merge speck fragments. Caller owns the returned <see cref="Mat"/>.
    /// </summary>
    public static Mat DetectChocolateMask(Mat bgrImage, int aThreshold, int lThreshold)
    {
        using var lab = new Mat();
        Cv2.CvtColor(bgrImage, lab, ColorConversionCodes.BGR2Lab);
        var channels = Cv2.Split(lab);
        try
        {
            var lChan = channels[0];
            var aChan = channels[1];

            using var whiteMask = new Mat();
            Cv2.Threshold(aChan, whiteMask, aThreshold, 255, ThresholdTypes.BinaryInv);

            using var darkMask = new Mat();
            Cv2.Threshold(lChan, darkMask, lThreshold, 255, ThresholdTypes.BinaryInv);

            var combined = new Mat();
            Cv2.BitwiseOr(whiteMask, darkMask, combined);

            using var openKernel = Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(3, 3));
            using var closeKernel = Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(5, 5));
            Cv2.MorphologyEx(combined, combined, MorphTypes.Open, openKernel);
            Cv2.MorphologyEx(combined, combined, MorphTypes.Close, closeKernel);

            return combined;
        }
        finally
        {
            foreach (var ch in channels)
                ch.Dispose();
        }
    }

    public IReadOnlyList<CellResult> EvaluateCells(Mat bgrImage, LeerformRecipe recipe)
    {
        using var chocolateMask = DetectChocolateMask(bgrImage, recipe.AThreshold, recipe.LThreshold);

        var cells = ComputeCells(recipe.Pattern);
        var imageSize = new Size(bgrImage.Cols, bgrImage.Rows);
        var results = new List<CellResult>(cells.Count);

        foreach (var cell in cells)
        {
            using var cellMask = BuildMask(recipe.Pattern, imageSize, cell.Index);
            using var perCell = new Mat();
            Cv2.BitwiseAnd(chocolateMask, cellMask, perCell);

            Cv2.FindContours(
                perCell,
                out var contours,
                out _,
                RetrievalModes.External,
                ContourApproximationModes.ApproxSimple);

            var blobs = 0;
            var area = 0;
            foreach (var contour in contours)
            {
                var contourArea = (int)Cv2.ContourArea(contour);
                if (contourArea >= recipe.MinBlobArea && contourArea <= recipe.MaxBlobArea)
                {
                    blobs++;
                    area += contourArea;
                }
            }

            results.Add(new CellResult
            {
                Index = cell.Index,
                IsGood = blobs == 0,
                BlobCount = blobs,
                DetectedArea = area
            });
        }

        return results;
    }

    public Mat RenderOverlay(Mat bgrImage, CellPattern pattern, IReadOnlyList<CellResult> results)
    {
        var overlay = bgrImage.Clone();
        var cells = ComputeCells(pattern);
        var resultByIndex = results.ToDictionary(r => r.Index);
        const int thickness = 2;

        foreach (var cell in cells)
        {
            if (!resultByIndex.TryGetValue(cell.Index, out var result))
                continue;

            var color = result.IsGood ? GoodColor : BadColor;

            if (pattern.Shape == CellShape.Square)
                Cv2.Rectangle(overlay, cell.BoundingBox, color, thickness);
            else
                Cv2.Circle(overlay, cell.Center, cell.Radius, color, thickness);
        }
        return overlay;
    }

    public void DrawPattern(Mat target, CellPattern pattern, Scalar color, int thickness = 2)
    {
        var cells = ComputeCells(pattern);
        foreach (var cell in cells)
        {
            if (pattern.Shape == CellShape.Square)
                Cv2.Rectangle(target, cell.BoundingBox, color, thickness);
            else
                Cv2.Circle(target, cell.Center, cell.Radius, color, thickness);
        }
    }

    public int? FindCellAtPoint(int x, int y, CellPattern pattern)
    {
        var cells = ComputeCells(pattern);
        foreach (var cell in cells)
        {
            if (pattern.Shape == CellShape.Square)
            {
                if (cell.BoundingBox.Contains(x, y))
                    return cell.Index;
            }
            else
            {
                var dx = x - cell.Center.X;
                var dy = y - cell.Center.Y;
                if (dx * dx + dy * dy <= cell.Radius * cell.Radius)
                    return cell.Index;
            }
        }
        return null;
    }
}