// ============================================================================
// Copyright © 2026 Hexagon Technology Center GmbH. All Rights Reserved.
// 文件名: HorizontalEdgeProcessor.cs
// 描述: 水平边缘检测算子，专门用于检测水平方向的边缘
// 功能:
//   - 检测水平边缘
//   - 支持Prewitt和Sobel算子
//   - 可调节检测灵敏度
//   - 适用于检测水平线条和纹理
// 算法: Prewitt/Sobel水平算子
// 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>
/// 水平边缘检测算子
/// </summary>
public class HorizontalEdgeProcessor : ImageProcessorBase
{
    private static readonly ILogger _logger = Log.ForContext<HorizontalEdgeProcessor>();

    public HorizontalEdgeProcessor()
    {
        Name = LocalizationHelper.GetString("HorizontalEdgeProcessor_Name");
        Description = LocalizationHelper.GetString("HorizontalEdgeProcessor_Description");
    }

    protected override void InitializeParameters()
    {
        Parameters.Add("Method", new ProcessorParameter(
            "Method",
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Method"),
            typeof(string),
            "Sobel",
            null,
            null,
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Method_Desc"),
            new string[] { "Sobel", "Prewitt", "Simple" }));

        Parameters.Add("Sensitivity", new ProcessorParameter(
            "Sensitivity",
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Sensitivity"),
            typeof(double),
            1.0,
            0.1,
            5.0,
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Sensitivity_Desc")));

        Parameters.Add("Threshold", new ProcessorParameter(
            "Threshold",
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Threshold"),
            typeof(int),
            20,
            0,
            255,
            LocalizationHelper.GetString("HorizontalEdgeProcessor_Threshold_Desc")));
        _logger.Debug("InitializeParameters");
    }

    public override Image<Gray, byte> Process(Image<Gray, byte> inputImage)
    {
        string method = GetParameter<string>("Method");
        double sensitivity = GetParameter<double>("Sensitivity");
        int threshold = GetParameter<int>("Threshold");

        Image<Gray, byte> result;

        if (method == "Sobel")
        {
            result = ApplySobel(inputImage, sensitivity, threshold);
        }
        else if (method == "Prewitt")
        {
            result = ApplyPrewitt(inputImage, sensitivity, threshold);
        }
        else // Simple
        {
            result = ApplySimple(inputImage, sensitivity, threshold);
        }

        _logger.Debug("Process: Method = {Method}, Sensitivity = {Sensitivity}, Threshold = {Threshold}",
            method, sensitivity, threshold);
        return result;
    }

    private Image<Gray, byte> ApplySobel(Image<Gray, byte> inputImage, double sensitivity, int threshold)
    {
        // 使用Sobel算子检测水平边缘（Y方向导数）
        Image<Gray, float> sobelY = new Image<Gray, float>(inputImage.Size);
        CvInvoke.Sobel(inputImage, sobelY, DepthType.Cv32F, 0, 1, 3);

        // 转换为绝对值并应用灵敏度
        Image<Gray, byte> result = new Image<Gray, byte>(inputImage.Size);
        CvInvoke.ConvertScaleAbs(sobelY, result, sensitivity, 0);

        // 应用阈值
        if (threshold > 0)
        {
            CvInvoke.Threshold(result, result, threshold, 255, ThresholdType.Binary);
            CvInvoke.Threshold(result, result, 0, 255, ThresholdType.ToZero);
        }

        sobelY.Dispose();
        return result;
    }

    private Image<Gray, byte> ApplyPrewitt(Image<Gray, byte> inputImage, double sensitivity, int threshold)
    {
        // Prewitt水平算子
        // [ 1  1  1]
        // [ 0  0  0]
        // [-1 -1 -1]

        int width = inputImage.Width;
        int height = inputImage.Height;
        byte[,,] inputData = inputImage.Data;

        Image<Gray, byte> result = new Image<Gray, byte>(width, height);
        byte[,,] outputData = result.Data;

        for (int y = 1; y < height - 1; y++)
        {
            for (int x = 1; x < width - 1; x++)
            {
                int sum = 0;

                // 上行
                sum += inputData[y - 1, x - 1, 0];
                sum += inputData[y - 1, x, 0];
                sum += inputData[y - 1, x + 1, 0];

                // 下行
                sum -= inputData[y + 1, x - 1, 0];
                sum -= inputData[y + 1, x, 0];
                sum -= inputData[y + 1, x + 1, 0];

                // 取绝对值并应用灵敏度
                int value = (int)(Math.Abs(sum) * sensitivity);

                // 应用阈值
                if (value > threshold)
                {
                    outputData[y, x, 0] = (byte)Math.Min(255, value);
                }
                else
                {
                    outputData[y, x, 0] = 0;
                }
            }
        }

        return result;
    }

    private Image<Gray, byte> ApplySimple(Image<Gray, byte> inputImage, double sensitivity, int threshold)
    {
        // 简单差分算子
        // [ 1  1  1]
        // [ 0  0  0]
        // [-1 -1 -1]
        // 但权重更简单

        int width = inputImage.Width;
        int height = inputImage.Height;
        byte[,,] inputData = inputImage.Data;

        Image<Gray, byte> result = new Image<Gray, byte>(width, height);
        byte[,,] outputData = result.Data;

        for (int y = 1; y < height - 1; y++)
        {
            for (int x = 0; x < width; x++)
            {
                // 简单的上下差分
                int diff = inputData[y - 1, x, 0] - inputData[y + 1, x, 0];
                int value = (int)(Math.Abs(diff) * sensitivity);

                // 应用阈值
                if (value > threshold)
                {
                    outputData[y, x, 0] = (byte)Math.Min(255, value);
                }
                else
                {
                    outputData[y, x, 0] = 0;
                }
            }
        }

        return result;
    }
}