xhouse/XplorePlane
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: 修复算子代码审查发现的关键问题

Browse Source 
1. 资源泄漏：HighPass/LowPass滤波器Image对象未dispose(改用using+Clone)
2. 线程安全：GammaProcessor LUT从实例字段改为方法局部变量
3. 资源泄漏：BandPassFilter补充floatImage和mask的Dispose
4. 边界情况：ThresholdProcessor Otsu16初始阈值改为中值(防全黑/全白)
5. 异常安全：RemoveOutliers的medianImage用try-finally保证释放
6. 空引用：SuperResolution InputMetadata空检查
This commit is contained in:
wei.lw.li
2026-08-17 09:40:31 +08:00
parent 3ffcb2452a
commit bbb3bf5326
7 changed files with 62 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files
XP.ImageProcessing.Processors/图像变换/ThresholdProcessor.cs
+11 -9
View File
@@ -35,20 +35,20 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
protected override void InitializeParameters()
{
// 参数范围必须跟随当前算子的位深。主流程使用 ushort，因此默认阈值也按
// 16 位满量程计算，避免把 8 位的 64/192 直接套用到 0~65535。
int quarterValue = MaxPixelValue / 4;
int threeQuarterValue = MaxPixelValue * 3 / 4;
Parameters.Add("MinThreshold", new ProcessorParameter(
// 参数范围必须跟随当前算子的位深。主流程使用 ushort，因此默认阈值也按
// 16 位满量程计算，避免把 8 位的 64/192 直接套用到 0~65535。
int quarterValue = MaxPixelValue / 4;
int threeQuarterValue = MaxPixelValue * 3 / 4;
Parameters.Add("MinThreshold", new ProcessorParameter(
"MinThreshold",
LocalizationHelper.GetString("ThresholdProcessor_MinThreshold"),
typeof(int), quarterValue, 0, MaxPixelValue,
typeof(int), quarterValue, 0, MaxPixelValue,
LocalizationHelper.GetString("ThresholdProcessor_MinThreshold_Desc")));
Parameters.Add("MaxThreshold", new ProcessorParameter(
"MaxThreshold",
LocalizationHelper.GetString("ThresholdProcessor_MaxThreshold"),
typeof(int), threeQuarterValue, 0, MaxPixelValue,
typeof(int), threeQuarterValue, 0, MaxPixelValue,
LocalizationHelper.GetString("ThresholdProcessor_MaxThreshold_Desc")));
Parameters.Add("UseOtsu", new ProcessorParameter(
@@ -128,6 +128,8 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
histogram[data[y, x, 0]]++;
long totalPixels = (long)w * h;
if (totalPixels == 0) return maxVal / 2;
double totalSum = 0;
for (int i = 0; i < levels; i++)
totalSum += (double)i * histogram[i];
@@ -135,7 +137,7 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
double bgSum = 0;
long bgPixels = 0;
double maxVariance = -1;
int bestThreshold = 0;
int bestThreshold = maxVal / 2; // Default to midpoint if no valid threshold found
for (int t = 0; t < levels; t++)
{
@@ -159,4 +161,4 @@ public class ThresholdProcessor<TDepth> : ImageProcessorBase<TDepth>
return bestThreshold;
}
}
}
XP.ImageProcessing.Processors/图像增强/GammaProcessor.cs
+12 -10
View File
@@ -25,8 +25,6 @@ namespace XP.ImageProcessing.Processors;
public class GammaProcessor<TDepth> : ImageProcessorBase<TDepth>
where TDepth : struct, IComparable
{
private byte[] _lookupTable8 = new byte[256];
private ushort[] _lookupTable16 = new ushort[65536];
private static readonly ILogger _logger = Log.ForContext<GammaProcessor<TDepth>>();
public GammaProcessor()
@@ -64,49 +62,53 @@ public class GammaProcessor<TDepth> : ImageProcessorBase<TDepth>
if (typeof(TDepth) == typeof(ushort))
{
BuildLUT16(gamma, gain);
var lut16 = BuildLUT16(gamma, gain);
var img16 = inputImage as Image<Gray, ushort>;
var result = new Image<Gray, ushort>(inputImage.Width, inputImage.Height);
Parallel.For(0, inputImage.Height, y =>
{
for (int x = 0; x < inputImage.Width; x++)
result.Data[y, x, 0] = _lookupTable16[img16!.Data[y, x, 0]];
result.Data[y, x, 0] = lut16[img16!.Data[y, x, 0]];
});
_logger.Debug("Process(16bit): Gamma={G}, Gain={Gain}", gamma, gain);
return (result as Image<Gray, TDepth>)!;
}
else
{
BuildLUT8(gamma, gain);
var lut8 = BuildLUT8(gamma, gain);
var result = (inputImage as Image<Gray, byte>)!.Clone();
int h = inputImage.Height, w = inputImage.Width;
for (int y = 0; y < h; y++)
for (int x = 0; x < w; x++)
result.Data[y, x, 0] = _lookupTable8[result.Data[y, x, 0]];
result.Data[y, x, 0] = lut8[result.Data[y, x, 0]];
_logger.Debug("Process(8bit): Gamma={G}, Gain={Gain}", gamma, gain);
return (result as Image<Gray, TDepth>)!;
}
}
private void BuildLUT8(double gamma, double gain)
private static byte[] BuildLUT8(double gamma, double gain)
{
var lut = new byte[256];
double invGamma = 1.0 / gamma;
for (int i = 0; i < 256; i++)
{
double normalized = i / 255.0;
double corrected = Math.Pow(normalized, invGamma) * gain;
_lookupTable8[i] = (byte)Math.Clamp((int)(corrected * 255.0), 0, 255);
lut[i] = (byte)Math.Clamp((int)(corrected * 255.0), 0, 255);
}
return lut;
}
private void BuildLUT16(double gamma, double gain)
private static ushort[] BuildLUT16(double gamma, double gain)
{
var lut = new ushort[65536];
double invGamma = 1.0 / gamma;
for (int i = 0; i < 65536; i++)
{
double normalized = i / 65535.0;
double corrected = Math.Pow(normalized, invGamma) * gain;
_lookupTable16[i] = (ushort)Math.Clamp((int)(corrected * 65535.0), 0, 65535);
lut[i] = (ushort)Math.Clamp((int)(corrected * 65535.0), 0, 65535);
}
return lut;
}
}
XP.ImageProcessing.Processors/图像增强/SuperResolutionProcessor.cs
+2 -1
View File
@@ -145,7 +145,8 @@ public class SuperResolutionProcessor : ImageProcessorBase<byte>
int w = inputImage.Width;
// 获取模型输入信息
string inputName = session.InputMetadata.Keys.First();
string inputName = session.InputMetadata.Keys.FirstOrDefault()
?? throw new InvalidOperationException("ONNX model has no input metadata");
var inputMeta = session.InputMetadata[inputName];
int[] dims = inputMeta.Dimensions;
// dims 格式: [1, H, W, C] (NHWC)，C 可能是 1 或 3
XP.ImageProcessing.Processors/滤波处理/BandPassFilterProcessor.cs
+3 -1
View File
@@ -85,7 +85,7 @@ public class BandPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
if (highCutoff <= lowCutoff) highCutoff = lowCutoff + 10;
var floatImage = inputImage.Convert<Gray, float>();
var imaginaryImage = new Image<Gray, float>(floatImage.Size);
using var imaginaryImage = new Image<Gray, float>(floatImage.Size);
imaginaryImage.SetZero();
using (var planes = new Emgu.CV.Util.VectorOfMat())
@@ -139,6 +139,8 @@ public class BandPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
result = (result - minVal) * (255.0 / (maxVal - minVal));
}
floatImage.Dispose();
mask.Dispose();
complexMat.Dispose();
dftMat.Dispose();
filteredDft.Dispose();
XP.ImageProcessing.Processors/滤波处理/HighPassFilterProcessor.cs
+2 -2
View File
@@ -126,7 +126,7 @@ public class HighPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
/// </summary>
private Mat CreateHighPassFilter(int rows, int cols, double d0)
{
var filter = new Image<Gray, float>(cols, rows);
using var filter = new Image<Gray, float>(cols, rows);
int centerX = cols / 2;
int centerY = rows / 2;
@@ -141,6 +141,6 @@ public class HighPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
}
}
return filter.Mat;
return filter.Mat.Clone();
}
}
XP.ImageProcessing.Processors/滤波处理/LowPassFilterProcessor.cs
+2 -2
View File
@@ -122,7 +122,7 @@ public class LowPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
/// </summary>
private Mat CreateLowPassFilter(int rows, int cols, double d0)
{
var filter = new Image<Gray, float>(cols, rows);
using var filter = new Image<Gray, float>(cols, rows);
int centerX = cols / 2;
int centerY = rows / 2;
@@ -137,6 +137,6 @@ public class LowPassFilterProcessor<TDepth> : ImageProcessorBase<TDepth>
}
}
return filter.Mat;
return filter.Mat.Clone();
}
}
XP.ImageProcessing.Processors/滤波处理/RemoveOutliersProcessor.cs
+30 -25
View File
@@ -106,38 +106,43 @@ public class RemoveOutliersProcessor<TDepth> : ImageProcessorBase<TDepth>
// 逐像素比较并替换离群点
var result = inputImage.Clone();
for (int y = 0; y < height; y++)
try
{
for (int x = 0; x < width; x++)
for (int y = 0; y < height; y++)
{
double original = Convert.ToDouble(inputImage.Data[y, x, 0]);
double median = Convert.ToDouble(medianImage.Data[y, x, 0]);
double diff = original - median;
bool isOutlier = false;
switch (outlierType)
for (int x = 0; x < width; x++)
{
case "Bright":
isOutlier = diff > threshold; // 亮离群点：比邻域中值亮太多
break;
case "Dark":
isOutlier = -diff > threshold; // 暗离群点：比邻域中值暗太多
break;
case "Both":
default:
isOutlier = System.Math.Abs(diff) > threshold;
break;
}
double original = Convert.ToDouble(inputImage.Data[y, x, 0]);
double median = Convert.ToDouble(medianImage.Data[y, x, 0]);
double diff = original - median;
if (isOutlier)
{
result.Data[y, x, 0] = medianImage.Data[y, x, 0];
bool isOutlier = false;
switch (outlierType)
{
case "Bright":
isOutlier = diff > threshold; // 亮离群点：比邻域中值亮太多
break;
case "Dark":
isOutlier = -diff > threshold; // 暗离群点：比邻域中值暗太多
break;
case "Both":
default:
isOutlier = System.Math.Abs(diff) > threshold;
break;
}
if (isOutlier)
{
result.Data[y, x, 0] = medianImage.Data[y, x, 0];
}
}
}
}
medianImage.Dispose();
finally
{
medianImage.Dispose();
}
_logger.Debug("Process: KernelSize={K}, Threshold={T}, Type={Type}",
kernelSize, threshold, outlierType);