发布！

LightCurve/Core/Help.cs

@@ -16,7 +16,7 @@ internal static void Show()
             var help = sb.AppendLine("欢迎使用像素值统计工具！\n")
                          .AppendLine("本工具会统计一组图片或一段视频中，")
                          .AppendLine("特定区域像素的被测指标的平均值，")
-                         .AppendLine("然后输出为一个列表，或绘制为一个折线图。")
+                         .AppendLine("然后绘制为一个折线图，或输出为一个列表。")
                          .AppendLine("详见README.md。\n")
                          .AppendLine($"版本号：{version}")
                          .AppendLine("作者：GarthTB\n")

LightCurve/Core/ImgAna.cs

@@ -22,26 +22,14 @@ internal void Run()
                 var values = new double[files.Count];
                 _ = Parallel.For(0, values.Length, i =>
                 {
-                    using var image = Cv2.ImRead(files[i].FullName);
-                    using var roi = ImgProc.GetROI(image, x, y, w, h);
+                    using Mat image = Cv2.ImRead(files[i].FullName);
+                    using Mat roi = ImgProc.GetROI(image, x, y, w, h);
                     values[i] = ImgProc.GetValue(roi, channel);
                 });
 
                 var outName = Tools.File.GenOutName(files);
                 outName = ValCvt.AppendSuff(outName, channel);
-                switch (outputType)
-                {
-                    case 0:
-                        Tools.File.OutputTxt(values, outputDir, outName);
-                        break;
-                    case 1:
-                        Tools.File.OutputPlot(values, outputDir, outName);
-                        break;
-                    default:
-                        Tools.File.OutputTxt(values, outputDir, outName);
-                        Tools.File.OutputPlot(values, outputDir, outName);
-                        break;
-                }
+                Tools.File.OutputValues(outputType, values, outputDir, outName);
 
                 Tools.MsgB.OkInfo("分析完成", "提示");
             }

LightCurve/Core/ImgProc.cs

@@ -14,7 +14,7 @@ internal static Mat GetROI(Mat image, uint? x, uint? y, uint? w, uint? h)
                 return image; // 直接返回全帧
             if (x + w > image.Cols || y + h > image.Rows)
                 throw new ArgumentException("选区超出图像范围");
-            Rect roi = new((int)x, (int)y, (int)w, (int)h);
+            Rect roi = new((int)x, (int)y, (int)w, (int)h); // 若无法转换，应该会在上一步筛掉
             return new Mat(image, roi);
         }
 

LightCurve/Core/Tools/File.cs

@@ -17,7 +17,7 @@ internal static string SelectOutputDir(int typeIndex)
                 FileName = "临时文件名，最后会根据原文件名修改",
                 Filter = typeIndex switch
                 {
-                    1 => "png图片文件(*.png)|*.png",
+                    0 => "png图片文件(*.png)|*.png",
                     _ => "txt文本文件(*.txt)|*.txt",
                 },
             };
@@ -87,8 +87,26 @@ private static string DistinctPath(string dir, string name, string ext)
             return path;
         }
 
+        /// <summary> 将结果输出到指定路径 </summary>
+        internal static void OutputValues(int outputType, double[] values, string outputDir, string outName)
+        {
+            switch (outputType)
+            {
+                case 0:
+                    OutputPlot(values, outputDir, outName);
+                    break;
+                case 1:
+                    OutputTxt(values, outputDir, outName);
+                    break;
+                default:
+                    OutputPlot(values, outputDir, outName);
+                    OutputTxt(values, outputDir, outName);
+                    break;
+            }
+        }
+
         /// <summary> 将结果列表输出到指定路径 </summary>
-        internal static void OutputTxt(double[] values, string dir, string name)
+        private static void OutputTxt(double[] values, string dir, string name)
         {
             var path = DistinctPath(dir, name, "txt");
 
@@ -101,7 +119,7 @@ internal static void OutputTxt(double[] values, string dir, string name)
         }
 
         /// <summary> 将结果折线图输出到指定路径 </summary>
-        internal static void OutputPlot(double[] values, string dir, string name)
+        private static void OutputPlot(double[] values, string dir, string name)
         {
             var path = DistinctPath(dir, name, "png");
 
@@ -111,10 +129,17 @@ internal static void OutputPlot(double[] values, string dir, string name)
             plot.XLabel("Frame Number");
             plot.YLabel("Value");
             plot.ScaleFactor = 2;
-            plot.Axes.SetLimits(1, indexes[^1], 0, 1);
+            if (values.Length > 1)
+                plot.Axes.SetLimits(1, indexes[^1], 0, 1);
             plot.Axes.AntiAlias(true);
+            var plotWidth = values.Length switch
+            {
+                <= 80 => 800,
+                >= 480 => 4800,
+                _ => values.Length * 10,
+            };
 
-            _ = plot.SavePng(path, 3240, 2000);
+            _ = plot.SavePng(path, plotWidth, 2000);
         }
     }
 }

LightCurve/Core/ValCvt.cs

@@ -7,7 +7,7 @@ internal static class ValCvt
     {
         /// <summary> 获取单色图像指定通道的均值 </summary>
         internal static double MeanValue1(Mat image, int channel)
-            => channel is >= 0 and <= 6 ? image.Mean().Val0 : 0;
+            => channel is >= 0 and <= 6 ? Cv2.Mean(image).Val0 : 0;
 
         /// <summary> 获取3通道图像指定通道的均值 </summary>
         internal static double MeanValue3(Mat image, int channel)
@@ -50,48 +50,33 @@ internal static string AppendSuff(string name, int channel)
             _ => name,
         };
 
-        /// <summary> 获取Mat图允许的最大值 </summary>
-        private static double Normalize(double value, int depth)
-        => depth switch
+        /// <summary> 获取单通道图像归一化后的均值 </summary>
+        private static double NormalizedMean(Mat image)
+        => image.Depth() switch
         {
-            MatType.CV_8U => value / 255.0,
-            MatType.CV_16U => value / 65535.0,
-            MatType.CV_32F or MatType.CV_64F => value, // 浮点图已经归一化
+            MatType.CV_8U => Mean(image) / 255.0,
+            MatType.CV_16U => Mean(image) / 65535.0,
+            MatType.CV_32F or MatType.CV_64F => Mean(image), // 浮点图已经归一化
             _ => throw new ArgumentException("不支持的位深度！")
         };
 
+        /// <summary> 单通道图像的均值 </summary>
+        private static double Mean(Mat image) => Cv2.Mean(image).Val0;
+
         /// <summary> 提取R通道的均值 </summary>
-        private static double MeanR(Mat image)
-        {
-            var r = Cv2.Split(image)[2];
-            var mean = Cv2.Mean(r).Val0;
-            return Normalize(mean, image.Depth());
-        }
+        private static double MeanR(Mat image) => NormalizedMean(Cv2.Split(image)[2]);
 
         /// <summary> 提取G通道的均值 </summary>
-        private static double MeanG(Mat image)
-        {
-            var g = Cv2.Split(image)[1];
-            var mean = Cv2.Mean(g).Val0;
-            return Normalize(mean, image.Depth());
-        }
+        private static double MeanG(Mat image) => NormalizedMean(Cv2.Split(image)[1]);
 
         /// <summary> 提取B通道的均值 </summary>
-        private static double MeanB(Mat image)
-        {
-            var b = Cv2.Split(image)[0];
-            var mean = Cv2.Mean(b).Val0;
-            return Normalize(mean, image.Depth());
-        }
+        private static double MeanB(Mat image) => NormalizedMean(Cv2.Split(image)[0]);
 
         /// <summary> 提取CIEL通道的均值 </summary>
         private static double MeanCIEL(Mat image)
         {
-            using Mat labImage = new();
-            Cv2.CvtColor(image, labImage, ColorConversionCodes.BGR2Lab);
-            var l = Cv2.Split(labImage)[0];
-            var mean = Cv2.Mean(l).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat labImage = image.CvtColor(ColorConversionCodes.BGR2Lab);
+            return NormalizedMean(Cv2.Split(labImage)[0]);
         }
 
         /// <summary> 提取I通道的均值 </summary>
@@ -101,51 +86,36 @@ private static double MeanI(Mat image)
         /// <summary> 提取L通道的均值 </summary>
         private static double MeanL(Mat image)
         {
-            using Mat hlsImage = new();
-            Cv2.CvtColor(image, hlsImage, ColorConversionCodes.BGR2HLS);
-            var l = Cv2.Split(hlsImage)[1];
-            var mean = Cv2.Mean(l).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat hlsImage = image.CvtColor(ColorConversionCodes.BGR2HLS);
+            return NormalizedMean(Cv2.Split(hlsImage)[1]);
         }
 
         /// <summary> 提取V通道的均值 </summary>
         private static double MeanV(Mat image)
         {
-            using Mat hsvImage = new();
-            Cv2.CvtColor(image, hsvImage, ColorConversionCodes.BGR2HSV);
-            var v = Cv2.Split(hsvImage)[2];
-            var mean = Cv2.Mean(v).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat hsvImage = image.CvtColor(ColorConversionCodes.BGR2HSV);
+            return NormalizedMean(Cv2.Split(hsvImage)[2]);
         }
 
         /// <summary> 提取Sl通道的均值 </summary>
         private static double MeanSl(Mat image)
         {
-            using Mat hlsImage = new();
-            Cv2.CvtColor(image, hlsImage, ColorConversionCodes.BGR2HLS);
-            var s = Cv2.Split(hlsImage)[2];
-            var mean = Cv2.Mean(s).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat hlsImage = image.CvtColor(ColorConversionCodes.BGR2HLS);
+            return NormalizedMean(Cv2.Split(hlsImage)[2]);
         }
 
         /// <summary> 提取Sv通道的均值 </summary>
         private static double MeanSv(Mat image)
         {
-            using Mat hsvImage = new();
-            Cv2.CvtColor(image, hsvImage, ColorConversionCodes.BGR2HSV);
-            var s = Cv2.Split(hsvImage)[1];
-            var mean = Cv2.Mean(s).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat hsvImage = image.CvtColor(ColorConversionCodes.BGR2HSV);
+            return NormalizedMean(Cv2.Split(hsvImage)[1]);
         }
 
         /// <summary> 提取H通道的均值 </summary>
         private static double MeanH(Mat image)
         {
-            using Mat hsvImage = new();
-            Cv2.CvtColor(image, hsvImage, ColorConversionCodes.BGR2HSV);
-            var h = Cv2.Split(hsvImage)[0];
-            var mean = Cv2.Mean(h).Val0;
-            return Normalize(mean, image.Depth());
+            using Mat hsvImage = image.CvtColor(ColorConversionCodes.BGR2HSV);
+            return NormalizedMean(Cv2.Split(hsvImage)[0]);
         }
     }
 }

LightCurve/Core/VidAna.cs

@@ -21,7 +21,7 @@ internal void Run()
             {
                 try
                 {
-                    using var vid = new VideoCapture(file.FullName);
+                    using VideoCapture vid = new(file.FullName);
                     var values = new double[vid.FrameCount];
 
                     Mat frame = new(), roi = new();
@@ -33,19 +33,7 @@ internal void Run()
 
                     var outName = Tools.File.GenOutName([file]);
                     outName = ValCvt.AppendSuff(outName, channel);
-                    switch (outputType)
-                    {
-                        case 0:
-                            Tools.File.OutputTxt(values, outputDir, outName);
-                            break;
-                        case 1:
-                            Tools.File.OutputPlot(values, outputDir, outName);
-                            break;
-                        default:
-                            Tools.File.OutputTxt(values, outputDir, outName);
-                            Tools.File.OutputPlot(values, outputDir, outName);
-                            break;
-                    }
+                    Tools.File.OutputValues(outputType, values, outputDir, outName);
                 }
                 catch (Exception e)
                 {

LightCurve/MainWindow.xaml

@@ -216,8 +216,8 @@
             VerticalAlignment="Top"
             VerticalContentAlignment="Center"
             SelectedIndex="0">
-            <ComboBoxItem Content="txt列表" />
             <ComboBoxItem Content="png折线" />
+            <ComboBoxItem Content="txt列表" />
             <ComboBoxItem Content="列表和折线" />
         </ComboBox>
         <Label

LightCurve/MainWindow.xaml.cs

@@ -137,10 +137,13 @@ private void BtRemovePaths_Click(object sender, RoutedEventArgs e)
             paths = files.Select(x => x.FullName).ToList();
             LBPaths.ItemsSource = paths;
             if (files.Count == 0)
+            {
+                mode = 0;
                 BtRemovePaths.IsEnabled =
                 BtRun.IsEnabled =
                 CBOrder.IsEnabled =
                 CBDescending.IsEnabled = false;
+            }
         }
 
         #endregion
@@ -177,7 +180,6 @@ private void ReOrderFiles(int orderIndex, bool descending)
             files = [.. descending
                 ? files.OrderByDescending(judge)
                 : files.OrderBy(judge)];
-
             paths = files.Select(x => x.FullName).ToList();
             LBPaths.ItemsSource = paths;
         }

README.md

@@ -5,20 +5,22 @@
 ## 功能
 
 - 统计一组图片或一段视频中，特定区域像素的被测指标的平均值。
-- 将这个平均值的变化轨迹输出为一个txt列表，或绘制为一个折线图。
+- 将这个平均值的变化轨迹绘制为一个折线图，或输出为一个txt列表。
 
 ## 环境要求
 
 - [.NET 9.0运行时](https://dotnet.microsoft.com/zh-cn/download/dotnet/9.0)
 
 ## 注意
 
-- 每一组照片或一个视频文件进行一次统计。照片和视频不能混合处理。多个视频可以并行处理。
+- 每一组照片或一个视频文件进行一次统计。照片和视频不能混合处理。
 - 选择输出位置时的文件名不是最终的文件名。最终的文件根据原文件来命名。
+- 此程序不使用GPU。在统计大文件时可能卡顿。
+- 折线图的高度固定为2000像素，宽度在800至4800之间浮动。
 
 ## 快捷键
 
-- F1：帮助
+- F1：查看帮助和软件信息
 
 ## Credits