// ============================================================================
// 文件名: VoidMeasurementProcessor.cs
// 描述: 空隙测量算子
//
// 处理流程:
//   1. 构建多边形ROI掩码，计算ROI面积
//   2. 在ROI内进行双阈值分割提取气泡区域
//   3. 形态学膨胀合并相邻气泡
//   4. 轮廓检测，计算每个气泡面积
//   5. 计算空隙率 = 总气泡面积 / ROI面积
// ============================================================================

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;

public class VoidMeasurementProcessor : ImageProcessorBase
{
    private static readonly ILogger _logger = Log.ForContext<VoidMeasurementProcessor>();

    public VoidMeasurementProcessor()
    {
        Name = LocalizationHelper.GetString("VoidMeasurementProcessor_Name");
        Description = LocalizationHelper.GetString("VoidMeasurementProcessor_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 });
        }

        // ── 气泡检测参数 ──
        Parameters.Add("MinThreshold", new ProcessorParameter(
            "MinThreshold",
            LocalizationHelper.GetString("VoidMeasurementProcessor_MinThreshold"),
            typeof(int), 128, 0, 255,
            LocalizationHelper.GetString("VoidMeasurementProcessor_MinThreshold_Desc")));

        Parameters.Add("MaxThreshold", new ProcessorParameter(
            "MaxThreshold",
            LocalizationHelper.GetString("VoidMeasurementProcessor_MaxThreshold"),
            typeof(int), 255, 0, 255,
            LocalizationHelper.GetString("VoidMeasurementProcessor_MaxThreshold_Desc")));

        Parameters.Add("MinVoidArea", new ProcessorParameter(
            "MinVoidArea",
            LocalizationHelper.GetString("VoidMeasurementProcessor_MinVoidArea"),
            typeof(int), 10, 1, 100000,
            LocalizationHelper.GetString("VoidMeasurementProcessor_MinVoidArea_Desc")));

        Parameters.Add("MergeRadius", new ProcessorParameter(
            "MergeRadius",
            LocalizationHelper.GetString("VoidMeasurementProcessor_MergeRadius"),
            typeof(int), 3, 0, 30,
            LocalizationHelper.GetString("VoidMeasurementProcessor_MergeRadius_Desc")));

        Parameters.Add("BlurSize", new ProcessorParameter(
            "BlurSize",
            LocalizationHelper.GetString("VoidMeasurementProcessor_BlurSize"),
            typeof(int), 3, 1, 31,
            LocalizationHelper.GetString("VoidMeasurementProcessor_BlurSize_Desc")));

        Parameters.Add("VoidLimit", new ProcessorParameter(
            "VoidLimit",
            LocalizationHelper.GetString("VoidMeasurementProcessor_VoidLimit"),
            typeof(double), 25.0, 0.0, 100.0,
            LocalizationHelper.GetString("VoidMeasurementProcessor_VoidLimit_Desc")));
    }

    public override Image<Gray, byte> Process(Image<Gray, byte> inputImage)
    {
        int minThresh = GetParameter<int>("MinThreshold");
        int maxThresh = GetParameter<int>("MaxThreshold");
        int minVoidArea = GetParameter<int>("MinVoidArea");
        int mergeRadius = GetParameter<int>("MergeRadius");
        int blurSize = GetParameter<int>("BlurSize");
        double voidLimit = GetParameter<double>("VoidLimit");

        if (blurSize % 2 == 0) blurSize++;

        OutputData.Clear();
        int w = inputImage.Width, h = inputImage.Height;

        // ── 构建多边形ROI掩码 ──
        int polyCount = GetParameter<int>("PolyCount");
        Image<Gray, byte>? roiMask = null;
        Point[]? roiPoints = null;

        if (polyCount >= 3)
        {
            roiPoints = new Point[polyCount];
            for (int i = 0; i < polyCount; i++)
                roiPoints[i] = new Point(GetParameter<int>($"PolyX{i}"), GetParameter<int>($"PolyY{i}"));
            roiMask = new Image<Gray, byte>(w, h);
            using var vop = new VectorOfPoint(roiPoints);
            using var vvop = new VectorOfVectorOfPoint(vop);
            CvInvoke.DrawContours(roiMask, vvop, 0, new MCvScalar(255), -1);
        }
        else
        {
            // 无ROI时使用全图
            roiMask = new Image<Gray, byte>(w, h);
            roiMask.SetValue(new Gray(255));
        }

        int roiArea = CvInvoke.CountNonZero(roiMask);

        _logger.Debug("VoidMeasurement: ROI area={Area}, Thresh=[{Min},{Max}], MergeR={MR}",
            roiArea, minThresh, maxThresh, mergeRadius);

        // ── 高斯模糊降噪 ──
        var blurred = new Image<Gray, byte>(w, h);
        CvInvoke.GaussianBlur(inputImage, blurred, new Size(blurSize, blurSize), 0);

        // ── 双阈值分割提取气泡（亮区域） ──
        var voidImg = new Image<Gray, byte>(w, h);
        byte[,,] srcData = blurred.Data;
        byte[,,] dstData = voidImg.Data;
        byte[,,] maskData = roiMask.Data;

        for (int y = 0; y < h; y++)
        {
            for (int x = 0; x < w; x++)
            {
                if (maskData[y, x, 0] > 0)
                {
                    byte val = srcData[y, x, 0];
                    dstData[y, x, 0] = (val >= minThresh && val <= maxThresh) ? (byte)255 : (byte)0;
                }
            }
        }

        // ── 形态学膨胀合并相邻气泡 ──
        if (mergeRadius > 0)
        {
            int kernelSize = mergeRadius * 2 + 1;
            using var kernel = CvInvoke.GetStructuringElement(ElementShape.Ellipse,
                new Size(kernelSize, kernelSize), new Point(-1, -1));
            CvInvoke.Dilate(voidImg, voidImg, kernel, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(0));
            // 与ROI掩码取交集，防止膨胀超出ROI
            CvInvoke.BitwiseAnd(voidImg, roiMask, voidImg);
        }

        // ── 轮廓检测 ──
        using var contours = new VectorOfVectorOfPoint();
        using var hierarchy = new Mat();
        CvInvoke.FindContours(voidImg, contours, hierarchy, RetrType.External, ChainApproxMethod.ChainApproxSimple);

        var voids = new List<VoidRegionInfo>();
        int totalVoidArea = 0;

        for (int i = 0; i < contours.Size; i++)
        {
            double area = CvInvoke.ContourArea(contours[i]);
            if (area < minVoidArea) continue;

            var moments = CvInvoke.Moments(contours[i]);
            if (moments.M00 < 1) continue;

            int intArea = (int)Math.Round(area);
            totalVoidArea += intArea;

            voids.Add(new VoidRegionInfo
            {
                Index = voids.Count + 1,
                CenterX = moments.M10 / moments.M00,
                CenterY = moments.M01 / moments.M00,
                Area = intArea,
                AreaPercent = roiArea > 0 ? area / roiArea * 100.0 : 0,
                BoundingBox = CvInvoke.BoundingRectangle(contours[i]),
                ContourPoints = contours[i].ToArray()
            });
        }

        // 按面积从大到小排序
        voids.Sort((a, b) => b.Area.CompareTo(a.Area));
        for (int i = 0; i < voids.Count; i++) voids[i].Index = i + 1;

        double voidRate = roiArea > 0 ? (double)totalVoidArea / roiArea * 100.0 : 0;
        string classification = voidRate <= voidLimit ? "PASS" : "FAIL";
        int maxVoidArea = voids.Count > 0 ? voids[0].Area : 0;

        _logger.Information("VoidMeasurement: VoidRate={Rate:F1}%, Voids={Count}, MaxArea={Max}, {Class}",
            voidRate, voids.Count, maxVoidArea, classification);

        // ── 输出数据 ──
        OutputData["VoidMeasurementResult"] = true;
        OutputData["RoiArea"] = roiArea;
        OutputData["RoiPoints"] = roiPoints;
        OutputData["TotalVoidArea"] = totalVoidArea;
        OutputData["VoidRate"] = voidRate;
        OutputData["VoidLimit"] = voidLimit;
        OutputData["VoidCount"] = voids.Count;
        OutputData["MaxVoidArea"] = maxVoidArea;
        OutputData["Classification"] = classification;
        OutputData["Voids"] = voids;
        OutputData["ResultText"] = $"Void: {voidRate:F1}% | {classification} | {voids.Count} voids | ROI: {roiArea}px";

        blurred.Dispose();
        voidImg.Dispose();
        roiMask.Dispose();

        return inputImage.Clone();
    }
}

/// <summary>
/// 单个空隙区域信息
/// </summary>
public class VoidRegionInfo
{
    public int Index { get; set; }
    public double CenterX { get; set; }
    public double CenterY { get; set; }
    public int Area { get; set; }
    public double AreaPercent { get; set; }
    public Rectangle BoundingBox { get; set; }
    public Point[] ContourPoints { get; set; } = Array.Empty<Point>();
}