using TeamAAS_VP.Enums;
using TeamAAS_VP.Models;
using OpenCvSharp;
using System;

namespace TeamAAS_VP.Services
{
    /// <summary>
    /// 图像分析服务
    /// </summary>
    public class ImageAnalysisService
    {
        /// <summary>
        /// 分析图像清晰度和对比度
        /// </summary>
        public AnalysisResult AnalyzeImage(Mat image, FocusMethod method, Rect? roi = null)
        {
            var result = new AnalysisResult { Timestamp = DateTime.Now };

            if (image == null || image.Empty())
            {
                result.Message = "图像为空";
                return result;
            }

            try
            {
                Mat analyzeMat = image;

                // 如果指定了ROI，则裁剪图像
                if (roi.HasValue && roi.Value.Width > 0 && roi.Value.Height > 0)
                {
                    var roiRect = roi.Value;
                    // 确保ROI在图像范围内
                    roiRect.X = Math.Max(0, Math.Min(roiRect.X, image.Width - 1));
                    roiRect.Y = Math.Max(0, Math.Min(roiRect.Y, image.Height - 1));
                    roiRect.Width = Math.Min(roiRect.Width, image.Width - roiRect.X);
                    roiRect.Height = Math.Min(roiRect.Height, image.Height - roiRect.Y);

                    if (roiRect.Width > 0 && roiRect.Height > 0)
                    {
                        analyzeMat = new Mat(image, roiRect);
                    }
                }

                // 转换为灰度图
                Mat gray = new Mat();
                if (analyzeMat.Channels() == 3)
                {
                    Cv2.CvtColor(analyzeMat, gray, ColorConversionCodes.BGR2GRAY);
                }
                else
                {
                    gray = analyzeMat.Clone();
                }

                // 计算清晰度
                result.Sharpness = CalculateSharpness(gray, method);

                // 计算对比度
                result.Contrast = CalculateContrast(gray);

                // 计算质量评分 (0-100)
                result.QualityScore = CalculateQualityScore(result.Sharpness, result.Contrast, method);

                result.Message = "分析完成";

                gray?.Dispose();
                if (roi.HasValue && analyzeMat != image)
                {
                    analyzeMat?.Dispose();
                }
            }
            catch (Exception ex)
            {
                result.Message = $"分析错误: {ex.Message}";
            }

            return result;
        }

        /// <summary>
        /// 计算清晰度
        /// </summary>
        private double CalculateSharpness(Mat gray, FocusMethod method)
        {
            switch (method)
            {
                case FocusMethod.Laplacian:
                    return CalculateLaplacianVariance(gray);

                case FocusMethod.Sobel:
                    return CalculateSobelVariance(gray);

                case FocusMethod.Variance:
                    return CalculateImageVariance(gray);

                case FocusMethod.Tenengrad:
                    return CalculateTenengrad(gray);

                case FocusMethod.FrequencyDomain:
                    return CalculateFrequencyDomain(gray);

                default:
                    return CalculateLaplacianVariance(gray);
            }
        }

        /// <summary>
        /// Laplacian方差法
        /// </summary>
        private double CalculateLaplacianVariance(Mat gray)
        {
            using (var laplacian = new Mat())
            {
                Cv2.Laplacian(gray, laplacian, MatType.CV_64F);
                Cv2.MeanStdDev(laplacian, out _, out Scalar stddev);
                return stddev.Val0 * stddev.Val0;
            }
        }

        /// <summary>
        /// Sobel方差法
        /// </summary>
        private double CalculateSobelVariance(Mat gray)
        {
            using (var sobelX = new Mat())
            using (var sobelY = new Mat())
            using (var sobel = new Mat())
            {
                Cv2.Sobel(gray, sobelX, MatType.CV_64F, 1, 0, 3);
                Cv2.Sobel(gray, sobelY, MatType.CV_64F, 0, 1, 3);
                
                Cv2.Magnitude(sobelX, sobelY, sobel);
                
                return Cv2.Mean(sobel).Val0;
            }
        }

        /// <summary>
        /// 图像方差法
        /// </summary>
        private double CalculateImageVariance(Mat gray)
        {
            Cv2.MeanStdDev(gray, out _, out Scalar stddev);
            return stddev.Val0 * stddev.Val0;
        }

        /// <summary>
        /// Tenengrad算子
        /// </summary>
        private double CalculateTenengrad(Mat gray)
        {
            using (var sobelX = new Mat())
            using (var sobelY = new Mat())
            {
                Cv2.Sobel(gray, sobelX, MatType.CV_64F, 1, 0, 3);
                Cv2.Sobel(gray, sobelY, MatType.CV_64F, 0, 1, 3);

                double sum = 0;
                for (int y = 0; y < gray.Rows; y++)
                {
                    for (int x = 0; x < gray.Cols; x++)
                    {
                        double gx = sobelX.At<double>(y, x);
                        double gy = sobelY.At<double>(y, x);
                        double magnitude = Math.Sqrt(gx * gx + gy * gy);
                        
                        // 只计算超过阈值的梯度
                        if (magnitude > 50)
                        {
                            sum += magnitude * magnitude;
                        }
                    }
                }

                return sum / (gray.Rows * gray.Cols);
            }
        }

        /// <summary>
        /// 频域分析法
        /// </summary>
        private double CalculateFrequencyDomain(Mat gray)
        {
            try
            {
                // 转换为浮点型
                Mat floatMat = new Mat();
                gray.ConvertTo(floatMat, MatType.CV_32F);

                // 执行DFT
                Mat dft = new Mat();
                Cv2.Dft(floatMat, dft, DftFlags.ComplexOutput);

                // 计算幅度谱
                Mat[] planes = Cv2.Split(dft);
                Mat magnitude = new Mat();
                Cv2.Magnitude(planes[0], planes[1], magnitude);

                // 计算高频能量占比
                int centerX = magnitude.Cols / 2;
                int centerY = magnitude.Rows / 2;
                int radius = Math.Min(centerX, centerY) / 3;

                double totalEnergy = Cv2.Sum(magnitude).Val0;
                
                // 屏蔽低频区域
                Cv2.Circle(magnitude, new Point(centerX, centerY), radius, Scalar.All(0), -1);
                
                double highFreqEnergy = Cv2.Sum(magnitude).Val0;

                floatMat?.Dispose();
                dft?.Dispose();
                foreach (var plane in planes) plane?.Dispose();
                magnitude?.Dispose();

                return totalEnergy > 0 ? (highFreqEnergy / totalEnergy) * 100 : 0;
            }
            catch
            {
                return 0;
            }
        }

        /// <summary>
        /// 计算对比度
        /// </summary>
        private double CalculateContrast(Mat gray)
        {
            try
            {
                // 使用标准差作为对比度指标
                Cv2.MeanStdDev(gray, out Scalar mean, out Scalar stddev);
                
                // 归一化对比度 (0-100)
                double contrast = (stddev.Val0 / 128.0) * 100;
                
                return Math.Min(100, contrast);
            }
            catch
            {
                return 0;
            }
        }

        /// <summary>
        /// 计算质量评分
        /// </summary>
        private double CalculateQualityScore(double sharpness, double contrast, FocusMethod method)
        {
            // 根据不同的算法，设置不同的归一化参数
            double normalizedSharpness = 0;

            switch (method)
            {
                case FocusMethod.Laplacian:
                    normalizedSharpness = Math.Min(100, (sharpness / 50.0) * 100);
                    break;
                case FocusMethod.Sobel:
                    normalizedSharpness = Math.Min(100, (sharpness / 30.0) * 100);
                    break;
                case FocusMethod.Variance:
                    normalizedSharpness = Math.Min(100, (sharpness / 5000.0) * 100);
                    break;
                case FocusMethod.Tenengrad:
                    normalizedSharpness = Math.Min(100, (sharpness / 1000.0) * 100);
                    break;
                case FocusMethod.FrequencyDomain:
                    normalizedSharpness = sharpness;
                    break;
            }

            // 综合清晰度和对比度
            double score = (normalizedSharpness * 0.7 + contrast * 0.3);
            
            return Math.Min(100, Math.Max(0, score));
        }

        /// <summary>
        /// 获取ROI区域图像
        /// </summary>
        public Mat GetRoiImage(Mat image, Rect roi)
        {
            if (image == null || image.Empty())
                return null;

            try
            {
                // 确保ROI在图像范围内
                roi.X = Math.Max(0, Math.Min(roi.X, image.Width - 1));
                roi.Y = Math.Max(0, Math.Min(roi.Y, image.Height - 1));
                roi.Width = Math.Min(roi.Width, image.Width - roi.X);
                roi.Height = Math.Min(roi.Height, image.Height - roi.Y);

                if (roi.Width > 0 && roi.Height > 0)
                {
                    return new Mat(image, roi).Clone();
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine($"获取ROI图像错误: {ex.Message}");
            }

            return null;
        }
    }
}