98 lines
3.3 KiB
C#
98 lines
3.3 KiB
C#
using SixLabors.ImageSharp;
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using SixLabors.ImageSharp.PixelFormats;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using SixLabors.ImageSharp.Processing;
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namespace VideoAnalysisCore.Common
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{
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/// <summary>
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/// ssim计算器
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/// </summary>
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public class SSIMCalculator
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{
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// SSIM计算常量 (基于8-bit图像范围0-255)
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private const double C1 = (0.01 * 255) * (0.01 * 255);
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private const double C2 = (0.03 * 255) * (0.03 * 255);
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/// <summary>
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/// 计算连续帧的SSIM 值
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/// </summary>
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/// <param name="img1"></param>
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/// <param name="img2"></param>
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/// <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>
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public static double CalculateFrameSSIM(Image<Rgb24> img1, Image<Rgb24> img2)
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{
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// 转换为灰度图
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var gray1 = CreateResizedGrayImage(img1);
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var gray2 = CreateResizedGrayImage(img2);
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// 计算全局统计量
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CalculateStats(gray1, gray2, out double mean1, out double mean2,
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out double var1, out double var2, out double covar);
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// 计算SSIM分量
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double luminance = (2 * mean1 * mean2 + C1) / (mean1 * mean1 + mean2 * mean2 + C1);
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double contrast = (2 * Math.Sqrt(var1) * Math.Sqrt(var2) + C2) / (var1 + var2 + C2);
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double structure = (covar + C2 / 2) / (Math.Sqrt(var1) * Math.Sqrt(var2) + C2 / 2);
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// 返回SSIM值 (值越接近1表示越相似)
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return luminance * contrast * structure;
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}
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private static Image<L8> CreateResizedGrayImage(Image<Rgb24> image)
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{
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return image
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.Clone(x => x.Grayscale())
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.CloneAs<L8>(); // 转换为8位灰度格式
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}
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private static void CalculateStats(
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Image<L8> img1,
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Image<L8> img2,
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out double mean1,
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out double mean2,
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out double var1,
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out double var2,
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out double covar)
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{
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int width = img1.Width;
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int height = img1.Height;
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int totalPixels = width * height;
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double sum1 = 0, sum2 = 0;
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double sum1Sq = 0, sum2Sq = 0, sumProduct = 0;
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// 单次遍历计算所有统计量
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for (int y = 0; y < height; y++)
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{
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for (int x = 0; x < width; x++)
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{
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double val1 = img1[x, y].PackedValue;
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double val2 = img2[x, y].PackedValue;
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sum1 += val1;
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sum2 += val2;
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sum1Sq += val1 * val1;
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sum2Sq += val2 * val2;
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sumProduct += val1 * val2;
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}
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}
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// 计算均值
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mean1 = sum1 / totalPixels;
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mean2 = sum2 / totalPixels;
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// 计算方差: Var(X) = E[X²] - E[X]²
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var1 = (sum1Sq / totalPixels) - (mean1 * mean1);
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var2 = (sum2Sq / totalPixels) - (mean2 * mean2);
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// 计算协方差: Cov(X,Y) = E[XY] - E[X]E[Y]
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covar = (sumProduct / totalPixels) - (mean1 * mean2);
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}
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}
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}
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