李伟
231 lines
9.1 KiB
C#
231 lines
9.1 KiB
C#
// ============================================================================
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// 文件名: VoidMeasurementProcessor.cs
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// 描述: 空隙测量算子
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//
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// 处理流程:
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// 1. 构建多边形ROI掩码，计算ROI面积
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// 2. 在ROI内进行双阈值分割提取气泡区域
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// 3. 形态学膨胀合并相邻气泡
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// 4. 轮廓检测，计算每个气泡面积
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// 5. 计算空隙率 = 总气泡面积 / ROI面积
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// ============================================================================
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using Emgu.CV;
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using Emgu.CV.CvEnum;
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using Emgu.CV.Structure;
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using Emgu.CV.Util;
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using XP.ImageProcessing.Core;
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using Serilog;
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using System.Drawing;
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namespace XP.ImageProcessing.Processors;
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public class VoidMeasurementProcessor : ImageProcessorBase
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{
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private static readonly ILogger _logger = Log.ForContext<VoidMeasurementProcessor>();
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public VoidMeasurementProcessor()
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{
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Name = LocalizationHelper.GetString("VoidMeasurementProcessor_Name");
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Description = LocalizationHelper.GetString("VoidMeasurementProcessor_Description");
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}
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protected override void InitializeParameters()
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{
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// ── 多边形ROI（由UI注入，最多32个点） ──
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Parameters.Add("PolyCount", new ProcessorParameter("PolyCount", "PolyCount", typeof(int), 0, null, null, "") { IsVisible = false });
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for (int i = 0; i < 32; i++)
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{
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Parameters.Add($"PolyX{i}", new ProcessorParameter($"PolyX{i}", $"PolyX{i}", typeof(int), 0, null, null, "") { IsVisible = false });
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Parameters.Add($"PolyY{i}", new ProcessorParameter($"PolyY{i}", $"PolyY{i}", typeof(int), 0, null, null, "") { IsVisible = false });
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}
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// ── 气泡检测参数 ──
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Parameters.Add("MinThreshold", new ProcessorParameter(
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"MinThreshold",
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LocalizationHelper.GetString("VoidMeasurementProcessor_MinThreshold"),
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typeof(int), 128, 0, 255,
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LocalizationHelper.GetString("VoidMeasurementProcessor_MinThreshold_Desc")));
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Parameters.Add("MaxThreshold", new ProcessorParameter(
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"MaxThreshold",
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LocalizationHelper.GetString("VoidMeasurementProcessor_MaxThreshold"),
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typeof(int), 255, 0, 255,
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LocalizationHelper.GetString("VoidMeasurementProcessor_MaxThreshold_Desc")));
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Parameters.Add("MinVoidArea", new ProcessorParameter(
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"MinVoidArea",
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LocalizationHelper.GetString("VoidMeasurementProcessor_MinVoidArea"),
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typeof(int), 10, 1, 100000,
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LocalizationHelper.GetString("VoidMeasurementProcessor_MinVoidArea_Desc")));
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Parameters.Add("MergeRadius", new ProcessorParameter(
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"MergeRadius",
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LocalizationHelper.GetString("VoidMeasurementProcessor_MergeRadius"),
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typeof(int), 3, 0, 30,
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LocalizationHelper.GetString("VoidMeasurementProcessor_MergeRadius_Desc")));
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Parameters.Add("BlurSize", new ProcessorParameter(
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"BlurSize",
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LocalizationHelper.GetString("VoidMeasurementProcessor_BlurSize"),
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typeof(int), 3, 1, 31,
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LocalizationHelper.GetString("VoidMeasurementProcessor_BlurSize_Desc")));
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Parameters.Add("VoidLimit", new ProcessorParameter(
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"VoidLimit",
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LocalizationHelper.GetString("VoidMeasurementProcessor_VoidLimit"),
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typeof(double), 25.0, 0.0, 100.0,
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LocalizationHelper.GetString("VoidMeasurementProcessor_VoidLimit_Desc")));
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}
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public override Image<Gray, byte> Process(Image<Gray, byte> inputImage)
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{
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int minThresh = GetParameter<int>("MinThreshold");
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int maxThresh = GetParameter<int>("MaxThreshold");
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int minVoidArea = GetParameter<int>("MinVoidArea");
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int mergeRadius = GetParameter<int>("MergeRadius");
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int blurSize = GetParameter<int>("BlurSize");
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double voidLimit = GetParameter<double>("VoidLimit");
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if (blurSize % 2 == 0) blurSize++;
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OutputData.Clear();
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int w = inputImage.Width, h = inputImage.Height;
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// ── 构建多边形ROI掩码 ──
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int polyCount = GetParameter<int>("PolyCount");
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Image<Gray, byte>? roiMask = null;
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Point[]? roiPoints = null;
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if (polyCount >= 3)
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{
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roiPoints = new Point[polyCount];
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for (int i = 0; i < polyCount; i++)
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roiPoints[i] = new Point(GetParameter<int>($"PolyX{i}"), GetParameter<int>($"PolyY{i}"));
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roiMask = new Image<Gray, byte>(w, h);
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using var vop = new VectorOfPoint(roiPoints);
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using var vvop = new VectorOfVectorOfPoint(vop);
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CvInvoke.DrawContours(roiMask, vvop, 0, new MCvScalar(255), -1);
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}
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else
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{
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// 无ROI时使用全图
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roiMask = new Image<Gray, byte>(w, h);
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roiMask.SetValue(new Gray(255));
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}
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int roiArea = CvInvoke.CountNonZero(roiMask);
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_logger.Debug("VoidMeasurement: ROI area={Area}, Thresh=[{Min},{Max}], MergeR={MR}",
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roiArea, minThresh, maxThresh, mergeRadius);
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// ── 高斯模糊降噪 ──
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var blurred = new Image<Gray, byte>(w, h);
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CvInvoke.GaussianBlur(inputImage, blurred, new Size(blurSize, blurSize), 0);
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// ── 双阈值分割提取气泡（亮区域） ──
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var voidImg = new Image<Gray, byte>(w, h);
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byte[,,] srcData = blurred.Data;
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byte[,,] dstData = voidImg.Data;
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byte[,,] maskData = roiMask.Data;
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for (int y = 0; y < h; y++)
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{
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for (int x = 0; x < w; x++)
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{
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if (maskData[y, x, 0] > 0)
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{
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byte val = srcData[y, x, 0];
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dstData[y, x, 0] = (val >= minThresh && val <= maxThresh) ? (byte)255 : (byte)0;
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}
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}
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}
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// ── 形态学膨胀合并相邻气泡 ──
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if (mergeRadius > 0)
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{
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int kernelSize = mergeRadius * 2 + 1;
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using var kernel = CvInvoke.GetStructuringElement(ElementShape.Ellipse,
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new Size(kernelSize, kernelSize), new Point(-1, -1));
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CvInvoke.Dilate(voidImg, voidImg, kernel, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(0));
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// 与ROI掩码取交集，防止膨胀超出ROI
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CvInvoke.BitwiseAnd(voidImg, roiMask, voidImg);
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}
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// ── 轮廓检测 ──
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using var contours = new VectorOfVectorOfPoint();
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using var hierarchy = new Mat();
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CvInvoke.FindContours(voidImg, contours, hierarchy, RetrType.External, ChainApproxMethod.ChainApproxSimple);
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var voids = new List<VoidRegionInfo>();
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int totalVoidArea = 0;
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for (int i = 0; i < contours.Size; i++)
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{
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double area = CvInvoke.ContourArea(contours[i]);
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if (area < minVoidArea) continue;
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var moments = CvInvoke.Moments(contours[i]);
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if (moments.M00 < 1) continue;
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int intArea = (int)Math.Round(area);
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totalVoidArea += intArea;
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voids.Add(new VoidRegionInfo
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{
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Index = voids.Count + 1,
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CenterX = moments.M10 / moments.M00,
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CenterY = moments.M01 / moments.M00,
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Area = intArea,
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AreaPercent = roiArea > 0 ? area / roiArea * 100.0 : 0,
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BoundingBox = CvInvoke.BoundingRectangle(contours[i]),
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ContourPoints = contours[i].ToArray()
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});
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}
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// 按面积从大到小排序
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voids.Sort((a, b) => b.Area.CompareTo(a.Area));
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for (int i = 0; i < voids.Count; i++) voids[i].Index = i + 1;
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double voidRate = roiArea > 0 ? (double)totalVoidArea / roiArea * 100.0 : 0;
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string classification = voidRate <= voidLimit ? "PASS" : "FAIL";
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int maxVoidArea = voids.Count > 0 ? voids[0].Area : 0;
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_logger.Information("VoidMeasurement: VoidRate={Rate:F1}%, Voids={Count}, MaxArea={Max}, {Class}",
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voidRate, voids.Count, maxVoidArea, classification);
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// ── 输出数据 ──
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OutputData["VoidMeasurementResult"] = true;
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OutputData["RoiArea"] = roiArea;
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OutputData["RoiPoints"] = roiPoints;
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OutputData["TotalVoidArea"] = totalVoidArea;
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OutputData["VoidRate"] = voidRate;
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OutputData["VoidLimit"] = voidLimit;
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OutputData["VoidCount"] = voids.Count;
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OutputData["MaxVoidArea"] = maxVoidArea;
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OutputData["Classification"] = classification;
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OutputData["Voids"] = voids;
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OutputData["ResultText"] = $"Void: {voidRate:F1}% | {classification} | {voids.Count} voids | ROI: {roiArea}px";
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blurred.Dispose();
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voidImg.Dispose();
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roiMask.Dispose();
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return inputImage.Clone();
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}
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}
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/// <summary>
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/// 单个空隙区域信息
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/// </summary>
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public class VoidRegionInfo
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{
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public int Index { get; set; }
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public double CenterX { get; set; }
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public double CenterY { get; set; }
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public int Area { get; set; }
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public double AreaPercent { get; set; }
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public Rectangle BoundingBox { get; set; }
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public Point[] ContourPoints { get; set; } = Array.Empty<Point>();
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}
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