using SixLabors.ImageSharp;
using SixLabors.ImageSharp.PixelFormats;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using SixLabors.ImageSharp.Processing;

namespace VideoAnalysisCore.Common
{
    /// <summary>
    /// ssim计算器
    /// </summary>
    public class SSIMCalculator
    {
        // SSIM计算常量 (基于8-bit图像范围0-255)
        private const double C1 = (0.01 * 255) * (0.01 * 255);
        private const double C2 = (0.03 * 255) * (0.03 * 255);

        /// <summary>
        /// 计算连续帧的SSIM 值
        /// </summary>
        /// <param name="img1"></param>
        /// <param name="img2"></param>
        /// <returns>返回阈值 0-1 越小变化越大<para>清晰视频：阈值 0.90-0.95 </para> <para>低质量视频：阈值 0.85-0.90</para></returns>
        public static double CalculateFrameSSIM(Image<Rgb24> img1, Image<Rgb24> img2)
        {
            // 转换为灰度图
            var gray1 = CreateResizedGrayImage(img1);
            var gray2 = CreateResizedGrayImage(img2);

            // 计算全局统计量
            CalculateStats(gray1, gray2, out double mean1, out double mean2,
                          out double var1, out double var2, out double covar);

            // 计算SSIM分量
            double luminance = (2 * mean1 * mean2 + C1) / (mean1 * mean1 + mean2 * mean2 + C1);
            double contrast = (2 * Math.Sqrt(var1) * Math.Sqrt(var2) + C2) / (var1 + var2 + C2);
            double structure = (covar + C2 / 2) / (Math.Sqrt(var1) * Math.Sqrt(var2) + C2 / 2);

            // 返回SSIM值 (值越接近1表示越相似)
            return luminance * contrast * structure;
        }

        private static Image<L8> CreateResizedGrayImage(Image<Rgb24> image)
        {
            return image
                .Clone(x => x.Grayscale())
                .CloneAs<L8>(); // 转换为8位灰度格式
        }

        private static void CalculateStats(
            Image<L8> img1,
            Image<L8> img2,
            out double mean1,
            out double mean2,
            out double var1,
            out double var2,
            out double covar)
        {
            int width = img1.Width;
            int height = img1.Height;
            int totalPixels = width * height;

            double sum1 = 0, sum2 = 0;
            double sum1Sq = 0, sum2Sq = 0, sumProduct = 0;

            // 单次遍历计算所有统计量
            for (int y = 0; y < height; y++)
            {
                for (int x = 0; x < width; x++)
                {
                    double val1 = img1[x, y].PackedValue;
                    double val2 = img2[x, y].PackedValue;

                    sum1 += val1;
                    sum2 += val2;
                    sum1Sq += val1 * val1;
                    sum2Sq += val2 * val2;
                    sumProduct += val1 * val2;
                }
            }

            // 计算均值
            mean1 = sum1 / totalPixels;
            mean2 = sum2 / totalPixels;

            // 计算方差: Var(X) = E[X²] - E[X]²
            var1 = (sum1Sq / totalPixels) - (mean1 * mean1);
            var2 = (sum2Sq / totalPixels) - (mean2 * mean2);

            // 计算协方差: Cov(X,Y) = E[XY] - E[X]E[Y]
            covar = (sumProduct / totalPixels) - (mean1 * mean2);
        }
    }
}