cell histogram
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EugeneTes
@@ -0,0 +1,258 @@
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using LindtLeerformPlugin.Models;
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using OpenCvSharp;
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namespace LindtLeerformPlugin.Services;
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public class LeerformPatternRecognitionService
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{
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private static readonly Scalar GoodColor = new(0, 255, 0); // BGR Green
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private static readonly Scalar BadColor = new(0, 0, 255); // BGR Red
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public IReadOnlyList<CellRegion> ComputeCells(CellPattern pattern)
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{
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var cells = new List<CellRegion>();
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if (pattern.Rows <= 0 || pattern.Cols <= 0 || pattern.RoiWidth <= 0 || pattern.RoiHeight <= 0)
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return cells;
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var cellW = pattern.RoiWidth / pattern.Cols;
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var cellH = pattern.RoiHeight / pattern.Rows;
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var padding = Math.Max(0, pattern.Padding);
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var index = 0;
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for (var r = 0; r < pattern.Rows; r++)
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{
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for (var c = 0; c < pattern.Cols; c++)
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{
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var x = pattern.RoiX + c * cellW + padding;
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var y = pattern.RoiY + r * cellH + padding;
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var w = Math.Max(1, cellW - 2 * padding);
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var h = Math.Max(1, cellH - 2 * padding);
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var rect = new Rect(x, y, w, h);
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var center = new Point(x + w / 2, y + h / 2);
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var radius = Math.Max(1, Math.Min(w, h) / 2);
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cells.Add(new CellRegion
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{
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Index = index++,
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Row = r,
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Col = c,
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BoundingBox = rect,
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Center = center,
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Radius = radius
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});
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}
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}
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return cells;
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}
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public Mat BuildMask(CellPattern pattern, Size imageSize, int? cellIndex = null)
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{
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var mask = new Mat(imageSize, MatType.CV_8UC1, Scalar.All(0));
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var cells = ComputeCells(pattern);
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var color = Scalar.All(255);
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var imageRect = new Rect(0, 0, imageSize.Width, imageSize.Height);
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foreach (var cell in cells)
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{
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if (cellIndex.HasValue && cellIndex.Value != cell.Index)
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continue;
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var clipped = cell.BoundingBox & imageRect;
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if (clipped.Width <= 0 || clipped.Height <= 0)
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continue;
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if (pattern.Shape == CellShape.Square)
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{
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Cv2.Rectangle(mask, clipped, color, thickness: -1);
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}
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else
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{
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Cv2.Circle(mask, cell.Center, cell.Radius, color, thickness: -1);
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}
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}
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return mask;
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}
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public float[] ComputeReferenceHistogram(Mat bgrImage, CellPattern pattern, int[]? bins = null)
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{
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bins ??= [5, 5, 5];
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using var mask = BuildMask(pattern, new Size(bgrImage.Cols, bgrImage.Rows));
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using var hist = CalcHistogram(bgrImage, mask, bins);
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return HistogramToArray(hist);
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}
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public IReadOnlyList<CellResult> EvaluateCells(Mat bgrImage, LeerformRecipe recipe)
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{
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var cells = ComputeCells(recipe.Pattern);
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var bins = recipe.HistogramBins is { Length: 3 } ? recipe.HistogramBins : [5, 5, 5];
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var imageSize = new Size(bgrImage.Cols, bgrImage.Rows);
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var results = new List<CellResult>(cells.Count);
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using var refHist = ArrayToHistogram(recipe.ReferenceHistogram, bins);
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foreach (var cell in cells)
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{
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using var mask = BuildMask(recipe.Pattern, imageSize, cell.Index);
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using var hist = CalcHistogram(bgrImage, mask, bins);
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var distance = Cv2.CompareHist(refHist, hist, HistCompMethods.Bhattacharyya);
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results.Add(new CellResult
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{
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Index = cell.Index,
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IsGood = distance <= recipe.Tolerance,
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Distance = distance
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});
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}
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return results;
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}
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public Mat RenderOverlay(Mat bgrImage, CellPattern pattern, IReadOnlyList<CellResult> results)
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{
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var overlay = bgrImage.Clone();
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var cells = ComputeCells(pattern);
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var resultByIndex = results.ToDictionary(r => r.Index);
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const int thickness = 2;
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foreach (var cell in cells)
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{
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if (!resultByIndex.TryGetValue(cell.Index, out var result))
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continue;
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var color = result.IsGood ? GoodColor : BadColor;
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if (pattern.Shape == CellShape.Square)
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{
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Cv2.Rectangle(overlay, cell.BoundingBox, color, thickness);
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}
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else
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{
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Cv2.Circle(overlay, cell.Center, cell.Radius, color, thickness);
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}
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}
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return overlay;
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}
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public void DrawPattern(Mat target, CellPattern pattern, Scalar color, int thickness = 2)
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{
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var cells = ComputeCells(pattern);
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foreach (var cell in cells)
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{
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if (pattern.Shape == CellShape.Square)
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Cv2.Rectangle(target, cell.BoundingBox, color, thickness);
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else
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Cv2.Circle(target, cell.Center, cell.Radius, color, thickness);
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}
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}
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public int? FindCellAtPoint(int x, int y, CellPattern pattern)
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{
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var cells = ComputeCells(pattern);
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foreach (var cell in cells)
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{
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if (pattern.Shape == CellShape.Square)
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{
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if (cell.BoundingBox.Contains(x, y))
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return cell.Index;
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}
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else
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{
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var dx = x - cell.Center.X;
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var dy = y - cell.Center.Y;
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if (dx * dx + dy * dy <= cell.Radius * cell.Radius)
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return cell.Index;
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}
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}
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return null;
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}
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public float[] ComputeCellHistogram(Mat bgrImage, CellPattern pattern, int cellIndex, int[]? bins = null)
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{
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bins ??= [5, 5, 5];
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using var mask = BuildMask(pattern, new Size(bgrImage.Cols, bgrImage.Rows), cellIndex);
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using var hist = CalcHistogram(bgrImage, mask, bins);
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return HistogramToArray(hist);
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}
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public double CompareHistograms(float[] reference, float[] candidate, int[]? bins = null)
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{
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bins ??= [5, 5, 5];
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using var refMat = ArrayToHistogram(reference, bins);
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using var candMat = ArrayToHistogram(candidate, bins);
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return Cv2.CompareHist(refMat, candMat, HistCompMethods.Bhattacharyya);
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}
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public static Mat RenderHistogramChart(float[] histogram, int[] bins, int width = 600, int height = 220)
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{
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var chart = new Mat(height, width, MatType.CV_8UC3, new Scalar(30, 30, 30));
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if (histogram == null || histogram.Length == 0)
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return chart;
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var totalBins = bins[0] * bins[1] * bins[2];
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var max = 0f;
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for (var i = 0; i < histogram.Length; i++)
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if (histogram[i] > max) max = histogram[i];
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if (max <= 0)
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return chart;
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const int leftPad = 10;
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const int bottomPad = 15;
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var barWidth = Math.Max(1, (width - 2 * leftPad) / totalBins);
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var maxBarHeight = height - bottomPad - 10;
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for (var i = 0; i < totalBins && i < histogram.Length; i++)
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{
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var binB = i / (bins[1] * bins[2]);
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var binG = (i / bins[2]) % bins[1];
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var binR = i % bins[2];
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var b = (int)((binB + 0.5) * 256 / bins[0]);
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var g = (int)((binG + 0.5) * 256 / bins[1]);
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var r = (int)((binR + 0.5) * 256 / bins[2]);
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var barHeight = (int)(histogram[i] / max * maxBarHeight);
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var x = leftPad + i * barWidth;
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var y = height - bottomPad - barHeight;
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Cv2.Rectangle(chart, new Rect(x, y, barWidth, barHeight), new Scalar(b, g, r), -1);
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}
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return chart;
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}
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private static Mat CalcHistogram(Mat bgrImage, Mat mask, int[] bins)
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{
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var hist = new Mat();
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Cv2.CalcHist(
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images: new[] { bgrImage },
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channels: new[] { 0, 1, 2 },
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mask: mask,
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hist: hist,
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dims: 3,
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histSize: bins,
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ranges: new[] { new Rangef(0, 256), new Rangef(0, 256), new Rangef(0, 256) });
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Cv2.Normalize(hist, hist);
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var totalBins = bins[0] * bins[1] * bins[2];
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var reshaped = hist.Reshape(1, totalBins).Clone();
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hist.Dispose();
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return reshaped;
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}
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private static float[] HistogramToArray(Mat hist)
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{
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var totalBins = hist.Rows * hist.Cols;
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var arr = new float[totalBins];
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for (var i = 0; i < totalBins; i++)
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arr[i] = hist.At<float>(i, 0);
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return arr;
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}
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private static Mat ArrayToHistogram(float[] data, int[] bins)
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{
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var totalBins = bins[0] * bins[1] * bins[2];
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var mat = new Mat(totalBins, 1, MatType.CV_32F, Scalar.All(0));
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var count = Math.Min(data.Length, totalBins);
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for (var i = 0; i < count; i++)
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mat.Set<float>(i, 0, data[i]);
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return mat;
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}
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}
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