// ============================================================================
// Copyright © 2026 Hexagon Technology Center GmbH. All Rights Reserved.
// 文件名: EllipseDetectionProcessor.cs
// 描述: 椭圆检测算子，基于轮廓分析和椭圆拟合检测图像中的椭圆
// 功能:
//   - 阈值分割 + 轮廓提取
//   - 椭圆拟合（FitEllipse）
//   - 面积/轴长/离心率/拟合误差多维过滤
//   - 支持双阈值分割和 Otsu 自动阈值
// 算法: 阈值分割 + OpenCV FitEllipse
// 作者: 李伟 wei.lw.li@hexagon.com
// ============================================================================

using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.Util;
using XP.ImageProcessing.Core;
using Serilog;
using System.Drawing;

namespace XP.ImageProcessing.Processors;

/// <summary>
/// 椭圆检测结果
/// </summary>
public class EllipseInfo
{
    /// <summary>序号</summary>
    public int Index { get; set; }
    /// <summary>中心点X</summary>
    public float CenterX { get; set; }
    /// <summary>中心点Y</summary>
    public float CenterY { get; set; }
    /// <summary>长轴长度</summary>
    public float MajorAxis { get; set; }
    /// <summary>短轴长度</summary>
    public float MinorAxis { get; set; }
    /// <summary>旋转角度（度）</summary>
    public float Angle { get; set; }
    /// <summary>面积</summary>
    public double Area { get; set; }
    /// <summary>周长</summary>
    public double Perimeter { get; set; }
    /// <summary>离心率 (0=圆, 接近1=扁椭圆)</summary>
    public double Eccentricity { get; set; }
    /// <summary>拟合误差（像素）</summary>
    public double FitError { get; set; }
    /// <summary>轮廓点集</summary>
    public Point[] ContourPoints { get; set; } = Array.Empty<Point>();
    /// <summary>外接矩形</summary>
    public Rectangle BoundingBox { get; set; }
}

/// <summary>
/// 椭圆检测器
/// </summary>
public class EllipseDetector
{
    private static readonly ILogger _logger = Log.ForContext<EllipseDetector>();

    public int MinThreshold { get; set; } = 64;
    public int MaxThreshold { get; set; } = 192;
    public bool UseOtsu { get; set; } = false;
    public int MinContourPoints { get; set; } = 30;
    public double MinArea { get; set; } = 100;
    public double MaxArea { get; set; } = 1000000;
    public float MinMajorAxis { get; set; } = 10;
    public double MaxEccentricity { get; set; } = 0.95;
    public double MaxFitError { get; set; } = 5.0;
    public int Thickness { get; set; } = 2;

    /// <summary>执行椭圆检测</summary>
    public List<EllipseInfo> Detect(Image<Gray, byte> inputImage, Image<Gray, byte>? roiMask = null)
    {
        _logger.Debug("Ellipse detection started: UseOtsu={UseOtsu}, MinThreshold={Min}, MaxThreshold={Max}",
            UseOtsu, MinThreshold, MaxThreshold);
        var results = new List<EllipseInfo>();

        using var binary = new Image<Gray, byte>(inputImage.Size);

        if (UseOtsu)
        {
            CvInvoke.Threshold(inputImage, binary, MinThreshold, 255, ThresholdType.Otsu);
            _logger.Debug("Using Otsu auto threshold");
        }
        else
        {
            // 双阈值分割：介于MinThreshold和MaxThreshold之间的为前景(255)，其他为背景(0)
            byte[,,] inputData = inputImage.Data;
            byte[,,] outputData = binary.Data;
            int height = inputImage.Height;
            int width = inputImage.Width;

            for (int y = 0; y < height; y++)
            {
                for (int x = 0; x < width; x++)
                {
                    byte pixelValue = inputData[y, x, 0];
                    outputData[y, x, 0] = (pixelValue >= MinThreshold && pixelValue <= MaxThreshold)
                        ? (byte)255
                        : (byte)0;
                }
            }

            _logger.Debug("Dual threshold segmentation: MinThreshold={Min}, MaxThreshold={Max}", MinThreshold, MaxThreshold);
        }

        // 应用ROI掩码
        if (roiMask != null)
        {
            CvInvoke.BitwiseAnd(binary, roiMask, binary);
        }

        using var contours = new VectorOfVectorOfPoint();
        using var hierarchy = new Mat();
        CvInvoke.FindContours(binary, contours, hierarchy, RetrType.List, ChainApproxMethod.ChainApproxNone);
        _logger.Debug("Found {Count} contours", contours.Size);

        int index = 0;
        for (int i = 0; i < contours.Size; i++)
        {
            var contour = contours[i];
            if (contour.Size < Math.Max(5, MinContourPoints)) continue;

            double area = CvInvoke.ContourArea(contour);
            if (area < MinArea || area > MaxArea) continue;

            RotatedRect ellipseRect = CvInvoke.FitEllipse(contour);
            float majorAxis = Math.Max(ellipseRect.Size.Width, ellipseRect.Size.Height);
            float minorAxis = Math.Min(ellipseRect.Size.Width, ellipseRect.Size.Height);
            if (majorAxis < MinMajorAxis) continue;

            double eccentricity = 0;
            if (majorAxis > 0)
            {
                double ratio = minorAxis / majorAxis;
                eccentricity = Math.Sqrt(1.0 - ratio * ratio);
            }
            if (eccentricity > MaxEccentricity) continue;

            double fitError = ComputeFitError(contour.ToArray(), ellipseRect);
            if (fitError > MaxFitError) continue;

            results.Add(new EllipseInfo
            {
                Index = index++,
                CenterX = ellipseRect.Center.X,
                CenterY = ellipseRect.Center.Y,
                MajorAxis = majorAxis,
                MinorAxis = minorAxis,
                Angle = ellipseRect.Angle,
                Area = area,
                Perimeter = CvInvoke.ArcLength(contour, true),
                Eccentricity = eccentricity,
                FitError = fitError,
                ContourPoints = contour.ToArray(),
                BoundingBox = CvInvoke.BoundingRectangle(contour)
            });
        }

        _logger.Information("Ellipse detection completed: detected {Count} ellipses", results.Count);
        return results;
    }

    private static double ComputeFitError(Point[] contourPoints, RotatedRect ellipse)
    {
        double cx = ellipse.Center.X, cy = ellipse.Center.Y;
        double a = Math.Max(ellipse.Size.Width, ellipse.Size.Height) / 2.0;
        double b = Math.Min(ellipse.Size.Width, ellipse.Size.Height) / 2.0;
        double angleRad = ellipse.Angle * Math.PI / 180.0;
        double cosA = Math.Cos(angleRad), sinA = Math.Sin(angleRad);
        if (a < 1e-6) return double.MaxValue;

        double totalError = 0;
        foreach (var pt in contourPoints)
        {
            double dx = pt.X - cx, dy = pt.Y - cy;
            double localX = dx * cosA + dy * sinA;
            double localY = -dx * sinA + dy * cosA;
            double ellipseVal = (localX * localX) / (a * a) + (localY * localY) / (b * b);
            totalError += Math.Abs(Math.Sqrt(ellipseVal) - 1.0) * Math.Sqrt(a * b);
        }
        return totalError / contourPoints.Length;
    }
}

/// <summary>
/// 椭圆检测算子
/// </summary>
public class EllipseDetectionProcessor : ImageProcessorBase
{
    private static readonly ILogger _logger = Log.ForContext<EllipseDetectionProcessor>();

    public EllipseDetectionProcessor()
    {
        Name = LocalizationHelper.GetString("EllipseDetectionProcessor_Name");
        Description = LocalizationHelper.GetString("EllipseDetectionProcessor_Description");
    }

    protected override void InitializeParameters()
    {
        // ── 多边形ROI（由UI注入，最多32个点） ──
        Parameters.Add("PolyCount", new ProcessorParameter("PolyCount", "PolyCount", typeof(int), 0, null, null, "") { IsVisible = false });
        for (int i = 0; i < 32; i++)
        {
            Parameters.Add($"PolyX{i}", new ProcessorParameter($"PolyX{i}", $"PolyX{i}", typeof(int), 0, null, null, "") { IsVisible = false });
            Parameters.Add($"PolyY{i}", new ProcessorParameter($"PolyY{i}", $"PolyY{i}", typeof(int), 0, null, null, "") { IsVisible = false });
        }

        Parameters.Add("MinThreshold", new ProcessorParameter(
            "MinThreshold", LocalizationHelper.GetString("EllipseDetectionProcessor_MinThreshold"),
            typeof(int), 64, 0, 255,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MinThreshold_Desc")));

        Parameters.Add("MaxThreshold", new ProcessorParameter(
            "MaxThreshold", LocalizationHelper.GetString("EllipseDetectionProcessor_MaxThreshold"),
            typeof(int), 192, 0, 255,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MaxThreshold_Desc")));

        Parameters.Add("UseOtsu", new ProcessorParameter(
            "UseOtsu", LocalizationHelper.GetString("EllipseDetectionProcessor_UseOtsu"),
            typeof(bool), false, null, null,
            LocalizationHelper.GetString("EllipseDetectionProcessor_UseOtsu_Desc")));

        Parameters.Add("MinContourPoints", new ProcessorParameter(
            "MinContourPoints", LocalizationHelper.GetString("EllipseDetectionProcessor_MinContourPoints"),
            typeof(int), 30, 5, 1000,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MinContourPoints_Desc")));

        Parameters.Add("MinArea", new ProcessorParameter(
            "MinArea", LocalizationHelper.GetString("EllipseDetectionProcessor_MinArea"),
            typeof(double), 100.0, 0.0, 1000000.0,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MinArea_Desc")));

        Parameters.Add("MaxArea", new ProcessorParameter(
            "MaxArea", LocalizationHelper.GetString("EllipseDetectionProcessor_MaxArea"),
            typeof(double), 1000000.0, 0.0, 10000000.0,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MaxArea_Desc")));

        Parameters.Add("MaxEccentricity", new ProcessorParameter(
            "MaxEccentricity", LocalizationHelper.GetString("EllipseDetectionProcessor_MaxEccentricity"),
            typeof(double), 0.95, 0.0, 1.0,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MaxEccentricity_Desc")));

        Parameters.Add("MaxFitError", new ProcessorParameter(
            "MaxFitError", LocalizationHelper.GetString("EllipseDetectionProcessor_MaxFitError"),
            typeof(double), 5.0, 0.0, 50.0,
            LocalizationHelper.GetString("EllipseDetectionProcessor_MaxFitError_Desc")));

        Parameters.Add("Thickness", new ProcessorParameter(
            "Thickness", LocalizationHelper.GetString("EllipseDetectionProcessor_Thickness"),
            typeof(int), 2, 1, 10,
            LocalizationHelper.GetString("EllipseDetectionProcessor_Thickness_Desc")));

        _logger.Debug("InitializeParameters");
    }

    public override Image<Gray, byte> Process(Image<Gray, byte> inputImage)
    {
        int thickness = GetParameter<int>("Thickness");

        _logger.Debug("Ellipse detection started");
        OutputData.Clear();

        // 构建多边形ROI掩码
        int polyCount = GetParameter<int>("PolyCount");
        Image<Gray, byte>? roiMask = null;
        if (polyCount >= 3)
        {
            var pts = new Point[polyCount];
            for (int i = 0; i < polyCount; i++)
                pts[i] = new Point(GetParameter<int>($"PolyX{i}"), GetParameter<int>($"PolyY{i}"));
            roiMask = new Image<Gray, byte>(inputImage.Width, inputImage.Height);
            using var vop = new VectorOfPoint(pts);
            using var vvop = new VectorOfVectorOfPoint(vop);
            CvInvoke.DrawContours(roiMask, vvop, 0, new MCvScalar(255), -1);
        }

        var detector = new EllipseDetector
        {
            MinThreshold = GetParameter<int>("MinThreshold"),
            MaxThreshold = GetParameter<int>("MaxThreshold"),
            UseOtsu = GetParameter<bool>("UseOtsu"),
            MinContourPoints = GetParameter<int>("MinContourPoints"),
            MinArea = GetParameter<double>("MinArea"),
            MaxArea = GetParameter<double>("MaxArea"),
            MaxEccentricity = GetParameter<double>("MaxEccentricity"),
            MaxFitError = GetParameter<double>("MaxFitError"),
            Thickness = thickness
        };

        var ellipses = detector.Detect(inputImage, roiMask);

        OutputData["Ellipses"] = ellipses;
        OutputData["EllipseCount"] = ellipses.Count;
        OutputData["Thickness"] = thickness;

        roiMask?.Dispose();
        _logger.Information("Ellipse detection completed: detected {Count} ellipses", ellipses.Count);
        return inputImage.Clone();
    }
}