// ============================================================================
// Copyright © 2026 Hexagon Technology Center GmbH. All Rights Reserved.
// 文件名: ThresholdProcessor.cs
// 描述: 阈值分割算子，用于图像二值化处理
// 功能:
//   - 固定阈值二值化
//   - Otsu自动阈值计算
//   - 可调节阈值和最大值
//   - 将灰度图像转换为二值图像
// 算法: 阈值分割、Otsu算法
// 作者: 李伟 wei.lw.li@hexagon.com
// ============================================================================

using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using XP.ImageProcessing.Core;
using Serilog;

namespace XP.ImageProcessing.Processors;

/// <summary>
/// 阈值分割算子（支持 8 位和 16 位灰度图像）
/// </summary>
public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
    where TDepth : struct, IComparable
{
    private static readonly ILogger _logger = Log.ForContext<ThresholdProcessor<TDepth>>();

    public ThresholdProcessor()
    {
        Name = LocalizationHelper.GetString("ThresholdProcessor_Name");
        Description = LocalizationHelper.GetString("ThresholdProcessor_Description");
    }

    protected override void InitializeParameters()
    {
        // 参数范围必须跟随当前算子的位深。主流程使用 ushort，因此默认阈值也按
        // 16 位满量程计算，避免把 8 位的 64/192 直接套用到 0~65535。
        int quarterValue = MaxPixelValue / 4;
        int threeQuarterValue = MaxPixelValue * 3 / 4;
        Parameters.Add("MinThreshold", new ProcessorParameter(
            "MinThreshold",
            LocalizationHelper.GetString("ThresholdProcessor_MinThreshold"),
            typeof(int), quarterValue, 0, MaxPixelValue,
            LocalizationHelper.GetString("ThresholdProcessor_MinThreshold_Desc")));

        Parameters.Add("MaxThreshold", new ProcessorParameter(
            "MaxThreshold",
            LocalizationHelper.GetString("ThresholdProcessor_MaxThreshold"),
            typeof(int), threeQuarterValue, 0, MaxPixelValue,
            LocalizationHelper.GetString("ThresholdProcessor_MaxThreshold_Desc")));

        Parameters.Add("UseOtsu", new ProcessorParameter(
            "UseOtsu",
            LocalizationHelper.GetString("ThresholdProcessor_UseOtsu"),
            typeof(bool), false, null, null,
            LocalizationHelper.GetString("ThresholdProcessor_UseOtsu_Desc")));
        _logger.Debug("InitializeParameters");
    }

    public override Image<Gray, TDepth> Process(Image<Gray, TDepth> inputImage)
    {
        int minThreshold = GetParameter<int>("MinThreshold");
        int maxThreshold = GetParameter<int>("MaxThreshold");
        bool useOtsu = GetParameter<bool>("UseOtsu");

        int height = inputImage.Height;
        int width = inputImage.Width;
        int maxVal = MaxPixelValue;

        var result = new Image<Gray, TDepth>(inputImage.Size);

        if (useOtsu)
        {
            // 8 位直接用 OpenCV Otsu；16 位在完整位深直方图上原生计算 Otsu 阈值，避免降位
            if (typeof(TDepth) == typeof(byte))
            {
                using var res8 = new Image<Gray, byte>(inputImage.Size);
                CvInvoke.Threshold(inputImage as Image<Gray, byte> ?? inputImage.Convert<Gray, byte>(),
                    res8, minThreshold, 255, ThresholdType.Otsu);
                _logger.Debug("Process: UseOtsu=true (8bit native)");
                return res8 as Image<Gray, TDepth> ?? PixelDepthHelper.FromByteImage<TDepth>(res8);
            }
            else
            {
                int otsuThreshold = ComputeOtsuThreshold16((inputImage as Image<Gray, ushort>)!, maxVal);
                Parallel.For(0, height, y =>
                {
                    for (int x = 0; x < width; x++)
                    {
                        int val = PixelDepthHelper.ReadPixel(inputImage, y, x);
                        PixelDepthHelper.WritePixel(result, y, x, val > otsuThreshold ? maxVal : 0);
                    }
                });
                _logger.Debug("Process: UseOtsu=true (16bit native), threshold={Threshold}", otsuThreshold);
                return result;
            }
        }

        // 手工双阈值分割（支持全位深）
        Parallel.For(0, height, y =>
        {
            for (int x = 0; x < width; x++)
            {
                int val = PixelDepthHelper.ReadPixel(inputImage, y, x);
                PixelDepthHelper.WritePixel(result, y, x,
                    (val >= minThreshold && val <= maxThreshold) ? maxVal : 0);
            }
        });

        _logger.Debug("Process: MinThreshold={Min}, MaxThreshold={Max}", minThreshold, maxThreshold);
        return result;
    }

    /// <summary>
    /// 在完整 16 位直方图（65536 bin）上计算 Otsu 最优阈值（最大化类间方差）。
    /// 返回值为灰度阈值：像素值 &gt; 阈值 判为前景。
    /// </summary>
    private static int ComputeOtsuThreshold16(Image<Gray, ushort> image, int maxVal)
    {
        int levels = maxVal + 1;
        var histogram = new long[levels];
        int h = image.Height, w = image.Width;
        var data = image.Data;
        for (int y = 0; y < h; y++)
            for (int x = 0; x < w; x++)
                histogram[data[y, x, 0]]++;

        long totalPixels = (long)w * h;
        if (totalPixels == 0) return maxVal / 2;

        double totalSum = 0;
        for (int i = 0; i < levels; i++)
            totalSum += (double)i * histogram[i];

        double bgSum = 0;
        long bgPixels = 0;
        double maxVariance = -1;
        int bestThreshold = maxVal / 2; // Default to midpoint if no valid threshold found

        for (int t = 0; t < levels; t++)
        {
            bgPixels += histogram[t];
            if (bgPixels == 0) continue;

            long fgPixels = totalPixels - bgPixels;
            if (fgPixels == 0) break;

            bgSum += (double)t * histogram[t];
            double bgMean = bgSum / bgPixels;
            double fgMean = (totalSum - bgSum) / fgPixels;
            double variance = (double)bgPixels * fgPixels * (bgMean - fgMean) * (bgMean - fgMean);

            if (variance > maxVariance)
            {
                maxVariance = variance;
                bestThreshold = t;
            }
        }

        return bestThreshold;
    }
}