增加校准通用代码
This commit is contained in:
Tanghaowei
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using System.Drawing;
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using Emgu.CV;
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using Emgu.CV.Util;
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using Emgu.CV.Structure;
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using XP.Calibration.Core;
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using XP.Calibration.Models;
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namespace XP.Calibration;
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/// <summary>
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/// 中心校准: 椭圆拟合 + 透视修正 | Center calibration via ellipse fitting + perspective correction
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/// </summary>
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public class CenterCalibration
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{
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/// <summary>
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/// 从投影序列执行中心校准
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/// </summary>
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/// <param name="projections">投影帧列表 (CV_32F)</param>
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/// <param name="geo">几何参数</param>
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/// <returns>校准结果</returns>
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public CenterCalibrationResult Calibrate(List<Mat> projections, GeoParams geo)
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{
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// 检测各帧球心
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var centers = new List<PointF>();
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for (int i = 0; i < projections.Count; i++)
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{
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if (BallDetector.DetectCenter(projections[i], out var c))
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centers.Add(c);
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}
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if (centers.Count <= 10)
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throw new InvalidOperationException(
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$"有效检测帧数不足: {centers.Count}, 至少需要 10 帧");
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// 椭圆拟合
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var ellipse = FitCentersEllipse(centers);
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// 放大倍率
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double M = geo.DSD / geo.DSO;
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// 从椭圆参数反算倾斜角和 R
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double ratio = ellipse.ShortAxis / ellipse.LongAxis;
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ratio = Math.Clamp(ratio, 0.0, 1.0);
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double alphaRad = Math.Acos(ratio);
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double alphaDeg = alphaRad * 180.0 / Math.PI;
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// 长轴 = 2 * R * M / pixelSize → R = longAxis * pixelSize / (2 * M)
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double R = ellipse.LongAxis * geo.PixelSize / (2.0 * M);
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// 透视修正
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double deltaPx = R * R * Math.Sin(2.0 * alphaRad)
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/ (2.0 * geo.DSO * geo.DSO)
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* geo.DSD / geo.PixelSize;
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// 长轴方向角
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var rawEllipse = FitEllipseRaw(centers);
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double angleDeg = rawEllipse.Angle;
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float w = rawEllipse.Size.Width;
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float h = rawEllipse.Size.Height;
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if (h > w) angleDeg += 90.0f;
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double thetaDeg = 90.0 - angleDeg;
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double thetaRad = thetaDeg * Math.PI / 180.0;
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double deltaU = deltaPx * Math.Cos(thetaRad);
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double deltaV = deltaPx * (-Math.Sin(thetaRad));
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double u0 = ellipse.Center.X - deltaU;
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double v0 = ellipse.Center.Y - deltaV;
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return new CenterCalibrationResult
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{
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Ellipse = ellipse,
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AlphaDeg = alphaDeg,
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R_mm = R,
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DeltaPx = deltaPx,
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FocalU = u0,
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FocalV = v0,
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DetectedCenters = centers
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};
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}
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/// <summary>
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/// 从 RAW 文件执行中心校准 (便捷方法)
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/// </summary>
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public CenterCalibrationResult CalibrateFromRaw(
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string rawPath, int width, int height, int count, GeoParams geo)
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{
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var projections = RawReader.ReadFloat32(rawPath, width, height, count);
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try
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{
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return Calibrate(projections, geo);
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}
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finally
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{
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foreach (var p in projections) p.Dispose();
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}
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}
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/// <summary>
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/// 对检测到的球心序列做椭圆拟合, 返回长短轴和角度
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/// </summary>
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private static EllipseResult FitCentersEllipse(List<PointF> pts)
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{
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var rotRect = FitEllipseRaw(pts);
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float a = rotRect.Size.Width;
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float b = rotRect.Size.Height;
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return new EllipseResult
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{
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Center = rotRect.Center,
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LongAxis = Math.Max(a, b),
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ShortAxis = Math.Min(a, b),
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Angle = rotRect.Angle
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};
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}
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/// <summary>
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/// 调用 OpenCV fitEllipse 返回原始 RotatedRect
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/// </summary>
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private static RotatedRect FitEllipseRaw(List<PointF> pts)
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{
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using var vp = new VectorOfPointF(pts.ToArray());
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return CvInvoke.FitEllipse(vp);
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}
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}
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@@ -0,0 +1,158 @@
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using System.Drawing;
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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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namespace XP.Calibration.Core;
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/// <summary>
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/// 球心检测器 | Ball center detector from projection images
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/// </summary>
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public static class BallDetector
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{
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/// <summary>
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/// 是否启用亚像素质心 | Enable sub-pixel centroid
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/// </summary>
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public static bool EnableSubPixel { get; set; } = true;
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/// <summary>
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/// 从单帧投影中检测球心 (自适应阈值 + 亚像素质心法)
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/// </summary>
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public static bool DetectCenter(Mat srcFloat, out PointF center)
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{
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center = new PointF(-1, -1);
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// 归一化到 0-255
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using var img8 = new Mat();
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CvInvoke.Normalize(srcFloat, img8, 0, 255, NormType.MinMax);
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img8.ConvertTo(img8, DepthType.Cv8U);
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CvInvoke.GaussianBlur(img8, img8, new Size(3, 3), 0.8);
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// 自适应阈值
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using var bin = new Mat();
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CvInvoke.AdaptiveThreshold(img8, bin, 255,
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AdaptiveThresholdType.MeanC, ThresholdType.Binary, 31, -5);
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// 查找轮廓
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using var contours = new VectorOfVectorOfPoint();
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CvInvoke.FindContours(bin, contours, null,
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RetrType.External, ChainApproxMethod.ChainApproxNone);
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if (contours.Size == 0)
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return false;
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// 找最大轮廓
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int maxIdx = 0;
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double maxArea = 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 > maxArea)
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{
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maxArea = area;
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maxIdx = i;
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}
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}
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// 创建掩膜
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using var mask = new Mat(srcFloat.Size, DepthType.Cv8U, 1);
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mask.SetTo(new MCvScalar(0));
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CvInvoke.DrawContours(mask, contours, maxIdx, new MCvScalar(255), -1);
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if (EnableSubPixel)
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{
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center = SubPixelCentroid(srcFloat, mask);
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}
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else
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{
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var moments = CvInvoke.Moments(contours[maxIdx]);
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center = new PointF(
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(float)(moments.M10 / moments.M00),
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(float)(moments.M01 / moments.M00));
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}
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return center.X >= 0 && center.Y >= 0;
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}
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/// <summary>
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/// 从单帧投影中检测球心 (Canny 边缘 + 椭圆拟合法)
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/// </summary>
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public static bool DetectCenterByEllipse(Mat srcFloat, out PointF center)
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{
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center = new PointF(-1, -1);
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using var img8 = new Mat();
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CvInvoke.Normalize(srcFloat, img8, 0, 255, NormType.MinMax);
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img8.ConvertTo(img8, DepthType.Cv8U);
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CvInvoke.GaussianBlur(img8, img8, new Size(3, 3), 0.8);
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using var edges = new Mat();
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CvInvoke.Canny(img8, edges, 30, 80);
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using var contours = new VectorOfVectorOfPoint();
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CvInvoke.FindContours(edges, contours, null,
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RetrType.External, ChainApproxMethod.ChainApproxNone);
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if (contours.Size == 0)
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return false;
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int maxIdx = 0;
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double maxArea = 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 > maxArea)
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{
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maxArea = area;
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maxIdx = i;
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}
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}
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if (contours[maxIdx].Size < 5)
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return false;
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using var points = new VectorOfPointF(
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Array.ConvertAll(contours[maxIdx].ToArray(), p => new PointF(p.X, p.Y)));
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var ellipse = CvInvoke.FitEllipse(points);
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center = ellipse.Center;
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return true;
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}
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/// <summary>
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/// 亚像素质心计算 | Sub-pixel centroid using intensity weighting
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/// </summary>
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private static PointF SubPixelCentroid(Mat imgFloat, Mat mask)
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{
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double sumI = 0, sumX = 0, sumY = 0;
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var imgData = imgFloat.GetData() as float[,];
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var maskData = mask.GetData() as byte[,];
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if (imgData == null || maskData == null)
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return new PointF(-1, -1);
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int rows = imgFloat.Rows;
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int cols = imgFloat.Cols;
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for (int y = 0; y < rows; y++)
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{
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for (int x = 0; x < cols; x++)
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{
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if (maskData[y, x] != 0)
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{
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double intensity = imgData[y, x];
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sumI += intensity;
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sumX += x * intensity;
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sumY += y * intensity;
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}
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}
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}
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if (sumI == 0)
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return new PointF(-1, -1);
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return new PointF((float)(sumX / sumI), (float)(sumY / sumI));
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}
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}
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@@ -0,0 +1,161 @@
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using System.Drawing;
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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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namespace XP.Calibration.Core;
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/// <summary>
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/// 双球心检测结果
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/// </summary>
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public record DualBallResult
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{
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/// <summary>第一个球心 (面积较大的)</summary>
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public PointF Center1 { get; init; }
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/// <summary>第二个球心 (面积较小的)</summary>
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public PointF Center2 { get; init; }
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/// <summary>两球心距离 (像素)</summary>
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public double DistancePx { get; init; }
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/// <summary>两球心距离 (mm), 需提供 pixelSize 才有效</summary>
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public double DistanceMm { get; init; }
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/// <summary>第一个球的轮廓面积</summary>
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public double Area1 { get; init; }
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/// <summary>第二个球的轮廓面积</summary>
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public double Area2 { get; init; }
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}
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/// <summary>
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/// 双球心检测器: 从单帧图像中检测两个球心并计算距离
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/// </summary>
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public static class DualBallDetector
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{
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/// <summary>
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/// 检测图像中两个最大球体的中心及距离
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/// </summary>
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/// <param name="srcFloat">输入图像 (CV_32F)</param>
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/// <param name="pixelSize">像素物理尺寸 (mm), 0 表示不计算物理距离</param>
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/// <param name="minArea">最小轮廓面积阈值, 过滤噪声</param>
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/// <param name="enableSubPixel">是否启用亚像素质心</param>
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public static DualBallResult? Detect(
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Mat srcFloat,
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double pixelSize = 0,
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double minArea = 50,
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bool enableSubPixel = true)
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{
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// 归一化 + 预处理
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using var img8 = new Mat();
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CvInvoke.Normalize(srcFloat, img8, 0, 255, NormType.MinMax);
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img8.ConvertTo(img8, DepthType.Cv8U);
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CvInvoke.GaussianBlur(img8, img8, new Size(3, 3), 0.8);
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// 自适应阈值
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using var bin = new Mat();
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CvInvoke.AdaptiveThreshold(img8, bin, 255,
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AdaptiveThresholdType.MeanC, ThresholdType.Binary, 31, -5);
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// 查找轮廓
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using var contours = new VectorOfVectorOfPoint();
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CvInvoke.FindContours(bin, contours, null,
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RetrType.External, ChainApproxMethod.ChainApproxNone);
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// 按面积排序, 取最大的两个
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var candidates = new List<(int Index, double Area)>();
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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 >= minArea)
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candidates.Add((i, area));
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}
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if (candidates.Count < 2)
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return null;
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candidates.Sort((a, b) => b.Area.CompareTo(a.Area));
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var top2 = candidates.Take(2).ToList();
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// 计算两个球心
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var centers = new PointF[2];
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var areas = new double[2];
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for (int k = 0; k < 2; k++)
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{
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int idx = top2[k].Index;
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areas[k] = top2[k].Area;
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if (enableSubPixel)
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{
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using var mask = new Mat(srcFloat.Size, DepthType.Cv8U, 1);
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mask.SetTo(new MCvScalar(0));
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CvInvoke.DrawContours(mask, contours, idx, new MCvScalar(255), -1);
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centers[k] = SubPixelCentroid(srcFloat, mask);
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}
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else
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{
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var moments = CvInvoke.Moments(contours[idx]);
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centers[k] = new PointF(
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(float)(moments.M10 / moments.M00),
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(float)(moments.M01 / moments.M00));
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}
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}
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// 计算距离
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double dx = centers[0].X - centers[1].X;
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double dy = centers[0].Y - centers[1].Y;
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double distPx = Math.Sqrt(dx * dx + dy * dy);
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double distMm = pixelSize > 0 ? distPx * pixelSize : 0;
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return new DualBallResult
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{
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Center1 = centers[0],
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Center2 = centers[1],
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DistancePx = distPx,
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DistanceMm = distMm,
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Area1 = areas[0],
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Area2 = areas[1]
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};
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}
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/// <summary>
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/// 从 8bit 图像检测 (便捷重载, 支持 CV_8U 输入)
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/// </summary>
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public static DualBallResult? DetectFrom8U(
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Mat src8U,
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double pixelSize = 0,
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double minArea = 50,
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bool enableSubPixel = true)
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{
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using var floatMat = new Mat();
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src8U.ConvertTo(floatMat, DepthType.Cv32F);
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return Detect(floatMat, pixelSize, minArea, enableSubPixel);
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}
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private static PointF SubPixelCentroid(Mat imgFloat, Mat mask)
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{
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double sumI = 0, sumX = 0, sumY = 0;
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var imgData = imgFloat.GetData() as float[,];
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var maskData = mask.GetData() as byte[,];
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if (imgData == null || maskData == null)
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return new PointF(-1, -1);
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for (int y = 0; y < imgFloat.Rows; y++)
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{
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for (int x = 0; x < imgFloat.Cols; x++)
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{
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if (maskData[y, x] != 0)
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{
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double intensity = imgData[y, x];
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sumI += intensity;
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sumX += x * intensity;
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sumY += y * intensity;
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}
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}
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}
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if (sumI == 0)
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return new PointF(-1, -1);
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return new PointF((float)(sumX / sumI), (float)(sumY / sumI));
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}
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}
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@@ -0,0 +1,139 @@
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using XP.Calibration.Models;
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namespace XP.Calibration.Core;
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/// <summary>
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/// TIGRE 投影模型 (角点法) | TIGRE projection model (corner-based, Siddon)
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/// </summary>
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public static class Projection
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{
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private struct Point3D
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{
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public double X, Y, Z;
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public Point3D(double x, double y, double z) { X = x; Y = y; Z = z; }
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}
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/// <summary>
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/// 计算物体原点在探测器上的投影像素坐标
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/// </summary>
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/// <param name="scanAngle">扫描角 (rad)</param>
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/// <param name="ay">旋转轴倾斜角 (rad)</param>
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/// <param name="detX">探测器 dYaw/Rx (rad)</param>
|
||||
/// <param name="detY">探测器 dPitch/Ry (rad)</param>
|
||||
/// <param name="detZ">探测器 dRoll/Rz (rad)</param>
|
||||
/// <param name="offOrigX">物体 X 偏移 = -R (mm)</param>
|
||||
/// <param name="offDetecU">探测器 U 偏移 (mm)</param>
|
||||
/// <param name="offDetecV">探测器 V 偏移 (mm)</param>
|
||||
/// <param name="gp">几何参数</param>
|
||||
/// <param name="uPx">输出: U 像素坐标</param>
|
||||
/// <param name="vPx">输出: V 像素坐标</param>
|
||||
public static void ProjectPoint(
|
||||
double scanAngle, double ay,
|
||||
double detX, double detY, double detZ,
|
||||
double offOrigX, double offDetecU, double offDetecV,
|
||||
GeoParams gp,
|
||||
out double uPx, out double vPx)
|
||||
{
|
||||
double ODD = gp.DSD - gp.DSO;
|
||||
var S = new Point3D(gp.DSO, 0, 0);
|
||||
|
||||
// 探测器角点初始化
|
||||
double py = gp.PixelSize * (0 - (double)gp.NDetecU / 2 + 0.5);
|
||||
double pz = gp.PixelSize * ((double)gp.NDetecV / 2 - 0.5 - 0);
|
||||
double puy = gp.PixelSize * (1 - (double)gp.NDetecU / 2 + 0.5);
|
||||
double pvz = gp.PixelSize * ((double)gp.NDetecV / 2 - 0.5 - 1);
|
||||
|
||||
var P = new Point3D(0, py, pz);
|
||||
var Pu0 = new Point3D(0, puy, pz);
|
||||
var Pv0 = new Point3D(0, py, pvz);
|
||||
|
||||
// Step 1: rollPitchYaw (探测器自身旋转)
|
||||
RollPitchYaw(detZ, detY, detX, ref P);
|
||||
RollPitchYaw(detZ, detY, detX, ref Pu0);
|
||||
RollPitchYaw(detZ, detY, detX, ref Pv0);
|
||||
|
||||
// 平移回探测器位置
|
||||
P.X -= ODD; Pu0.X -= ODD; Pv0.X -= ODD;
|
||||
|
||||
// Step 2: offDetecU/V 偏移
|
||||
P.Y += offDetecU; P.Z += offDetecV;
|
||||
Pu0.Y += offDetecU; Pu0.Z += offDetecV;
|
||||
Pv0.Y += offDetecU; Pv0.Z += offDetecV;
|
||||
|
||||
// Step 3: eulerZYZ 扫描旋转
|
||||
EulerZYZ(scanAngle, ay, 0, ref P);
|
||||
EulerZYZ(scanAngle, ay, 0, ref Pu0);
|
||||
EulerZYZ(scanAngle, ay, 0, ref Pv0);
|
||||
EulerZYZ(scanAngle, ay, 0, ref S);
|
||||
|
||||
// Step 4: offOrigin 偏移
|
||||
P.X -= offOrigX; Pu0.X -= offOrigX; Pv0.X -= offOrigX;
|
||||
S.X -= offOrigX;
|
||||
|
||||
// deltaU, deltaV
|
||||
var deltaU = new Point3D(Pu0.X - P.X, Pu0.Y - P.Y, Pu0.Z - P.Z);
|
||||
var deltaV = new Point3D(Pv0.X - P.X, Pv0.Y - P.Y, Pv0.Z - P.Z);
|
||||
|
||||
// 射线: S → 原点
|
||||
var ray = new Point3D(-S.X, -S.Y, -S.Z);
|
||||
|
||||
// 探测器平面法线
|
||||
var normal = new Point3D(
|
||||
deltaU.Y * deltaV.Z - deltaU.Z * deltaV.Y,
|
||||
deltaU.Z * deltaV.X - deltaU.X * deltaV.Z,
|
||||
deltaU.X * deltaV.Y - deltaU.Y * deltaV.X);
|
||||
|
||||
// 射线与探测器平面交点
|
||||
double pfsDotN = (P.X - S.X) * normal.X + (P.Y - S.Y) * normal.Y + (P.Z - S.Z) * normal.Z;
|
||||
double rayDotN = ray.X * normal.X + ray.Y * normal.Y + ray.Z * normal.Z;
|
||||
|
||||
if (Math.Abs(rayDotN) < 1e-15)
|
||||
{
|
||||
uPx = vPx = -1;
|
||||
return;
|
||||
}
|
||||
|
||||
double t = pfsDotN / rayDotN;
|
||||
var hit = new Point3D(S.X + t * ray.X, S.Y + t * ray.Y, S.Z + t * ray.Z);
|
||||
var hitP = new Point3D(hit.X - P.X, hit.Y - P.Y, hit.Z - P.Z);
|
||||
|
||||
// 投影到像素坐标
|
||||
double dU2 = deltaU.X * deltaU.X + deltaU.Y * deltaU.Y + deltaU.Z * deltaU.Z;
|
||||
double dV2 = deltaV.X * deltaV.X + deltaV.Y * deltaV.Y + deltaV.Z * deltaV.Z;
|
||||
double pixelU = (hitP.X * deltaU.X + hitP.Y * deltaU.Y + hitP.Z * deltaU.Z) / dU2;
|
||||
double pixelV = (hitP.X * deltaV.X + hitP.Y * deltaV.Y + hitP.Z * deltaV.Z) / dV2;
|
||||
|
||||
uPx = pixelU;
|
||||
vPx = (gp.NDetecV - 1) - pixelV; // TIGRE v 翻转
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// rollPitchYaw: Rz(dRoll) * Ry(dPitch) * Rx(dYaw)
|
||||
/// </summary>
|
||||
private static void RollPitchYaw(double dRoll, double dPitch, double dYaw, ref Point3D p)
|
||||
{
|
||||
double cr = Math.Cos(dRoll), sr = Math.Sin(dRoll);
|
||||
double cp = Math.Cos(dPitch), sp = Math.Sin(dPitch);
|
||||
double cy = Math.Cos(dYaw), sy = Math.Sin(dYaw);
|
||||
|
||||
double x = p.X, y = p.Y, z = p.Z;
|
||||
p.X = cr * cp * x + (cr * sp * sy - sr * cy) * y + (cr * sp * cy + sr * sy) * z;
|
||||
p.Y = sr * cp * x + (sr * sp * sy + cr * cy) * y + (sr * sp * cy - cr * sy) * z;
|
||||
p.Z = -sp * x + cp * sy * y + cp * cy * z;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// eulerZYZ: Rz(alpha) * Ry(theta) * Rz(psi)
|
||||
/// </summary>
|
||||
private static void EulerZYZ(double alpha, double theta, double psi, ref Point3D p)
|
||||
{
|
||||
double ca = Math.Cos(alpha), sa = Math.Sin(alpha);
|
||||
double ct = Math.Cos(theta), st = Math.Sin(theta);
|
||||
double cp = Math.Cos(psi), sp = Math.Sin(psi);
|
||||
|
||||
double x = p.X, y = p.Y, z = p.Z;
|
||||
p.X = (ca * ct * cp - sa * sp) * x + (-ca * ct * sp - sa * cp) * y + ca * st * z;
|
||||
p.Y = (sa * ct * cp + ca * sp) * x + (-sa * ct * sp + ca * cp) * y + sa * st * z;
|
||||
p.Z = -st * cp * x + st * sp * y + ct * z;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
using System.Drawing;
|
||||
using Emgu.CV;
|
||||
using Emgu.CV.CvEnum;
|
||||
|
||||
namespace XP.Calibration.Core;
|
||||
|
||||
/// <summary>
|
||||
/// RAW 投影数据读取器 | RAW projection data reader
|
||||
/// </summary>
|
||||
public static class RawReader
|
||||
{
|
||||
/// <summary>
|
||||
/// 从 RAW 文件读取 float32 投影序列
|
||||
/// </summary>
|
||||
/// <param name="path">文件路径</param>
|
||||
/// <param name="width">图像宽度</param>
|
||||
/// <param name="height">图像高度</param>
|
||||
/// <param name="count">投影帧数</param>
|
||||
/// <returns>Mat 列表 (CV_32F)</returns>
|
||||
public static List<Mat> ReadFloat32(string path, int width, int height, int count)
|
||||
{
|
||||
var projections = new List<Mat>(count);
|
||||
int frameBytes = width * height * sizeof(float);
|
||||
|
||||
using var stream = new FileStream(path, FileMode.Open, FileAccess.Read);
|
||||
byte[] buffer = new byte[frameBytes];
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
int bytesRead = stream.Read(buffer, 0, frameBytes);
|
||||
if (bytesRead < frameBytes)
|
||||
throw new EndOfStreamException(
|
||||
$"帧 {i} 数据不足: 期望 {frameBytes} 字节, 实际 {bytesRead} 字节");
|
||||
|
||||
var mat = new Mat(height, width, DepthType.Cv32F, 1);
|
||||
System.Runtime.InteropServices.Marshal.Copy(buffer, 0, mat.DataPointer, frameBytes);
|
||||
projections.Add(mat);
|
||||
}
|
||||
|
||||
return projections;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 读取单张 TIFF 图像, 返回 CV_32F 灰度 Mat
|
||||
/// </summary>
|
||||
/// <param name="path">TIFF 文件路径</param>
|
||||
/// <returns>Mat (CV_32F, 单通道)</returns>
|
||||
public static Mat ReadTiff(string path)
|
||||
{
|
||||
if (!File.Exists(path))
|
||||
throw new FileNotFoundException($"TIFF 文件不存在: {path}");
|
||||
|
||||
// 以原始深度读取 (支持 8/16/32bit TIFF)
|
||||
var src = CvInvoke.Imread(path, ImreadModes.AnyDepth | ImreadModes.Grayscale);
|
||||
if (src.IsEmpty)
|
||||
throw new InvalidOperationException($"无法读取 TIFF: {path}");
|
||||
|
||||
// 统一转为 CV_32F
|
||||
if (src.Depth != DepthType.Cv32F)
|
||||
{
|
||||
var dst = new Mat();
|
||||
src.ConvertTo(dst, DepthType.Cv32F);
|
||||
src.Dispose();
|
||||
return dst;
|
||||
}
|
||||
|
||||
return src;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 批量读取多张 TIFF 图像 (按文件名排序)
|
||||
/// </summary>
|
||||
/// <param name="directory">TIFF 文件所在目录</param>
|
||||
/// <param name="pattern">搜索模式, 默认 "*.tif"</param>
|
||||
/// <returns>Mat 列表 (CV_32F)</returns>
|
||||
public static List<Mat> ReadTiffSequence(string directory, string pattern = "*.tif")
|
||||
{
|
||||
if (!Directory.Exists(directory))
|
||||
throw new DirectoryNotFoundException($"目录不存在: {directory}");
|
||||
|
||||
var files = Directory.GetFiles(directory, pattern)
|
||||
.Concat(Directory.GetFiles(directory, pattern + "f")) // 同时匹配 .tif 和 .tiff
|
||||
.Distinct()
|
||||
.OrderBy(f => f)
|
||||
.ToList();
|
||||
|
||||
if (files.Count == 0)
|
||||
throw new FileNotFoundException($"目录中未找到 TIFF 文件: {directory}");
|
||||
|
||||
return files.Select(ReadTiff).ToList();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,363 @@
|
||||
using System.Drawing;
|
||||
using Emgu.CV;
|
||||
using Emgu.CV.CvEnum;
|
||||
using Emgu.CV.Structure;
|
||||
using Emgu.CV.Util;
|
||||
using XP.Calibration.Core;
|
||||
using XP.Calibration.Models;
|
||||
|
||||
namespace XP.Calibration;
|
||||
|
||||
/// <summary>
|
||||
/// 完整几何校准: TIGRE 投影模型 + LM 优化
|
||||
/// Full geometric calibration: TIGRE projection model + Levenberg-Marquardt optimization
|
||||
/// </summary>
|
||||
public class FullCalibration
|
||||
{
|
||||
// 参数索引常量
|
||||
private const int IDX_AY = 0;
|
||||
private const int IDX_DET_Y = 1;
|
||||
private const int IDX_DET_X = 2;
|
||||
private const int IDX_DET_Z = 3;
|
||||
private const int IDX_R = 4;
|
||||
private const int BASE_PARAMS = 5;
|
||||
|
||||
// 参数边界
|
||||
private static readonly double[] LowerBase = { -Math.PI / 2, -Math.PI / 3, -Math.PI / 3, -Math.PI, 0.1 };
|
||||
private static readonly double[] UpperBase = { Math.PI / 2, Math.PI / 3, Math.PI / 3, Math.PI, 100 };
|
||||
|
||||
/// <summary>
|
||||
/// 进度回调 | Progress callback (iteration, rms, params)
|
||||
/// </summary>
|
||||
public Action<int, double, double[]>? OnProgress { get; set; }
|
||||
|
||||
/// <summary>
|
||||
/// 从投影序列执行完整几何校准
|
||||
/// </summary>
|
||||
public FullCalibrationResult Calibrate(
|
||||
List<Mat> projections, GeoParams geo, FullCalibrationOptions? options = null)
|
||||
{
|
||||
options ??= new FullCalibrationOptions();
|
||||
|
||||
// 检测各帧球心
|
||||
var thetas = new List<double>();
|
||||
var uObs = new List<double>();
|
||||
var vObs = new List<double>();
|
||||
var pts = new List<PointF>();
|
||||
|
||||
for (int i = 0; i < projections.Count; i++)
|
||||
{
|
||||
if (BallDetector.DetectCenter(projections[i], out var c))
|
||||
{
|
||||
thetas.Add((double)i / (projections.Count - 1) * 2.0 * Math.PI);
|
||||
uObs.Add(c.X);
|
||||
vObs.Add(c.Y);
|
||||
pts.Add(c);
|
||||
}
|
||||
}
|
||||
|
||||
if (pts.Count <= 10)
|
||||
throw new InvalidOperationException($"有效检测帧数不足: {pts.Count}");
|
||||
|
||||
// 椭圆拟合估算初值
|
||||
using var vp = new VectorOfPointF(pts.ToArray());
|
||||
var ell = CvInvoke.FitEllipse(vp);
|
||||
double longAxis = Math.Max(ell.Size.Width, ell.Size.Height);
|
||||
double shortAxis = Math.Min(ell.Size.Width, ell.Size.Height);
|
||||
double mag = geo.DSD / geo.DSO;
|
||||
double rInit = longAxis * geo.PixelSize / (2.0 * mag);
|
||||
double ayInit = Math.Acos(Math.Min(1.0, shortAxis / longAxis));
|
||||
|
||||
// 确定参数个数和索引
|
||||
int np = BASE_PARAMS;
|
||||
int idxOffDU = -1, idxOffDV = -1, idxDSO = -1, idxDSD = -1;
|
||||
|
||||
if (options.OptimizeDetectorOffset)
|
||||
{
|
||||
idxOffDU = np; idxOffDV = np + 1; np += 2;
|
||||
}
|
||||
if (options.OptimizeDistances)
|
||||
{
|
||||
idxDSO = np; idxDSD = np + 1; np += 2;
|
||||
}
|
||||
|
||||
// 构造初始参数
|
||||
var p = new double[np];
|
||||
p[IDX_AY] = ayInit;
|
||||
p[IDX_DET_Y] = 0;
|
||||
p[IDX_DET_X] = 0;
|
||||
p[IDX_DET_Z] = 0;
|
||||
p[IDX_R] = rInit;
|
||||
if (options.OptimizeDetectorOffset)
|
||||
{
|
||||
p[idxOffDU] = 0; p[idxOffDV] = 0;
|
||||
}
|
||||
if (options.OptimizeDistances)
|
||||
{
|
||||
p[idxDSO] = geo.DSO; p[idxDSD] = geo.DSD;
|
||||
}
|
||||
|
||||
// LM 优化
|
||||
var ctx = new LmContext(thetas, uObs, vObs, geo, np,
|
||||
options.OptimizeDetectorOffset, options.OptimizeDistances,
|
||||
idxOffDU, idxOffDV, idxDSO, idxDSD);
|
||||
|
||||
bool converged = SolveLM(p, ctx, options.MaxIterations, options.Tolerance);
|
||||
|
||||
// 计算最终 RMS
|
||||
var finalRes = ComputeResiduals(p, ctx);
|
||||
double rms = Math.Sqrt(finalRes.Sum(r => r * r) / finalRes.Length);
|
||||
|
||||
double dsoOut = options.OptimizeDistances ? p[idxDSO] : geo.DSO;
|
||||
double dsdOut = options.OptimizeDistances ? p[idxDSD] : geo.DSD;
|
||||
|
||||
return new FullCalibrationResult
|
||||
{
|
||||
AyDeg = p[IDX_AY] * 180.0 / Math.PI,
|
||||
DetYDeg = p[IDX_DET_Y] * 180.0 / Math.PI,
|
||||
DetXDeg = p[IDX_DET_X] * 180.0 / Math.PI,
|
||||
DetZDeg = p[IDX_DET_Z] * 180.0 / Math.PI,
|
||||
R_mm = p[IDX_R],
|
||||
OffDetecU_mm = options.OptimizeDetectorOffset ? p[idxOffDU] : 0,
|
||||
OffDetecV_mm = options.OptimizeDetectorOffset ? p[idxOffDV] : 0,
|
||||
DSO_mm = dsoOut,
|
||||
DSD_mm = dsdOut,
|
||||
RmsPx = rms,
|
||||
Converged = converged
|
||||
};
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 从 RAW 文件执行完整几何校准 (便捷方法)
|
||||
/// </summary>
|
||||
public FullCalibrationResult CalibrateFromRaw(
|
||||
string rawPath, int width, int height, int count,
|
||||
GeoParams geo, FullCalibrationOptions? options = null)
|
||||
{
|
||||
var projections = RawReader.ReadFloat32(rawPath, width, height, count);
|
||||
try
|
||||
{
|
||||
return Calibrate(projections, geo, options);
|
||||
}
|
||||
finally
|
||||
{
|
||||
foreach (var p in projections) p.Dispose();
|
||||
}
|
||||
}
|
||||
|
||||
#region LM Solver
|
||||
|
||||
private record LmContext(
|
||||
List<double> Thetas, List<double> UObs, List<double> VObs,
|
||||
GeoParams Geo, int NP,
|
||||
bool OptOffset, bool OptDist,
|
||||
int IdxOffDU, int IdxOffDV, int IdxDSO, int IdxDSD);
|
||||
|
||||
private static double[] ComputeResiduals(double[] p, LmContext ctx)
|
||||
{
|
||||
double ay = p[IDX_AY], detY = p[IDX_DET_Y];
|
||||
double detX = p[IDX_DET_X], detZ = p[IDX_DET_Z], R = p[IDX_R];
|
||||
double offU = ctx.OptOffset ? p[ctx.IdxOffDU] : 0;
|
||||
double offV = ctx.OptOffset ? p[ctx.IdxOffDV] : 0;
|
||||
|
||||
var gp = new GeoParams
|
||||
{
|
||||
DSO = ctx.OptDist ? p[ctx.IdxDSO] : ctx.Geo.DSO,
|
||||
DSD = ctx.OptDist ? p[ctx.IdxDSD] : ctx.Geo.DSD,
|
||||
PixelSize = ctx.Geo.PixelSize,
|
||||
NDetecU = ctx.Geo.NDetecU,
|
||||
NDetecV = ctx.Geo.NDetecV
|
||||
};
|
||||
|
||||
int N = ctx.Thetas.Count;
|
||||
var res = new double[2 * N];
|
||||
for (int i = 0; i < N; i++)
|
||||
{
|
||||
Projection.ProjectPoint(
|
||||
ctx.Thetas[i], ay, detX, detY, detZ,
|
||||
-R, offU, offV, gp,
|
||||
out double u, out double v);
|
||||
res[2 * i] = u - ctx.UObs[i];
|
||||
res[2 * i + 1] = v - ctx.VObs[i];
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
private static double[,] ComputeJacobian(double[] p, LmContext ctx)
|
||||
{
|
||||
int nObs = 2 * ctx.Thetas.Count;
|
||||
int np = ctx.NP;
|
||||
var J = new double[nObs, np];
|
||||
double eps = 1e-7;
|
||||
|
||||
for (int j = 0; j < np; j++)
|
||||
{
|
||||
double[] pp = (double[])p.Clone();
|
||||
double[] pm = (double[])p.Clone();
|
||||
double step = Math.Max(eps, Math.Abs(p[j]) * eps);
|
||||
pp[j] = p[j] + step;
|
||||
pm[j] = p[j] - step;
|
||||
|
||||
var rp = ComputeResiduals(pp, ctx);
|
||||
var rm = ComputeResiduals(pm, ctx);
|
||||
|
||||
double denom = 2.0 * step;
|
||||
for (int i = 0; i < nObs; i++)
|
||||
J[i, j] = (rp[i] - rm[i]) / denom;
|
||||
}
|
||||
return J;
|
||||
}
|
||||
|
||||
private static void ClampParams(double[] p, LmContext ctx)
|
||||
{
|
||||
for (int i = 0; i < BASE_PARAMS; i++)
|
||||
p[i] = Math.Clamp(p[i], LowerBase[i], UpperBase[i]);
|
||||
|
||||
if (ctx.OptOffset)
|
||||
{
|
||||
p[ctx.IdxOffDU] = Math.Clamp(p[ctx.IdxOffDU], -50, 50);
|
||||
p[ctx.IdxOffDV] = Math.Clamp(p[ctx.IdxOffDV], -50, 50);
|
||||
}
|
||||
if (ctx.OptDist)
|
||||
{
|
||||
p[ctx.IdxDSO] = Math.Clamp(p[ctx.IdxDSO], 50, 1000);
|
||||
p[ctx.IdxDSD] = Math.Clamp(p[ctx.IdxDSD], 100, 2000);
|
||||
if (p[ctx.IdxDSD] <= p[ctx.IdxDSO])
|
||||
p[ctx.IdxDSD] = p[ctx.IdxDSO] + 10;
|
||||
}
|
||||
}
|
||||
|
||||
private bool SolveLM(double[] p, LmContext ctx, int maxIter, double tol)
|
||||
{
|
||||
double lambda = 1e-3;
|
||||
var res = ComputeResiduals(p, ctx);
|
||||
int m = res.Length;
|
||||
int np = ctx.NP;
|
||||
double cost = res.Sum(r => r * r);
|
||||
|
||||
for (int iter = 0; iter < maxIter; iter++)
|
||||
{
|
||||
var J = ComputeJacobian(p, ctx);
|
||||
|
||||
// JtJ = J^T * J, Jtr = J^T * r
|
||||
var JtJ = new double[np, np];
|
||||
var Jtr = new double[np];
|
||||
for (int i = 0; i < np; i++)
|
||||
{
|
||||
for (int j = i; j < np; j++)
|
||||
{
|
||||
double sum = 0;
|
||||
for (int k = 0; k < m; k++)
|
||||
sum += J[k, i] * J[k, j];
|
||||
JtJ[i, j] = sum;
|
||||
JtJ[j, i] = sum;
|
||||
}
|
||||
double s = 0;
|
||||
for (int k = 0; k < m; k++)
|
||||
s += J[k, i] * res[k];
|
||||
Jtr[i] = s;
|
||||
}
|
||||
|
||||
// A = JtJ + lambda * diag(1 + JtJ)
|
||||
var A = new double[np, np];
|
||||
var b = new double[np];
|
||||
Array.Copy(JtJ, A, JtJ.Length);
|
||||
for (int i = 0; i < np; i++)
|
||||
{
|
||||
A[i, i] += lambda * (1.0 + JtJ[i, i]);
|
||||
b[i] = -Jtr[i];
|
||||
}
|
||||
|
||||
// 解线性方程组 (Cholesky)
|
||||
if (!SolveLinear(A, b, np, out var delta))
|
||||
{
|
||||
lambda *= 10;
|
||||
continue;
|
||||
}
|
||||
|
||||
// 尝试更新
|
||||
var np2 = new double[np];
|
||||
for (int i = 0; i < np; i++)
|
||||
np2[i] = p[i] + delta[i];
|
||||
ClampParams(np2, ctx);
|
||||
|
||||
var newRes = ComputeResiduals(np2, ctx);
|
||||
double newCost = newRes.Sum(r => r * r);
|
||||
|
||||
if (newCost < cost)
|
||||
{
|
||||
double improvement = cost - newCost;
|
||||
Array.Copy(np2, p, np);
|
||||
res = newRes;
|
||||
cost = newCost;
|
||||
lambda *= 0.3;
|
||||
if (lambda < 1e-12) lambda = 1e-12;
|
||||
|
||||
OnProgress?.Invoke(iter, Math.Sqrt(cost / m), p);
|
||||
|
||||
if (improvement < tol)
|
||||
return true;
|
||||
}
|
||||
else
|
||||
{
|
||||
lambda *= 10;
|
||||
if (lambda > 1e12) lambda = 1e12;
|
||||
}
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 简单的 Cholesky 分解求解 Ax = b
|
||||
/// </summary>
|
||||
private static bool SolveLinear(double[,] A, double[] b, int n, out double[] x)
|
||||
{
|
||||
x = new double[n];
|
||||
var L = new double[n, n];
|
||||
|
||||
// Cholesky: A = L * L^T
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
for (int j = 0; j <= i; j++)
|
||||
{
|
||||
double sum = 0;
|
||||
for (int k = 0; k < j; k++)
|
||||
sum += L[i, k] * L[j, k];
|
||||
|
||||
if (i == j)
|
||||
{
|
||||
double val = A[i, i] - sum;
|
||||
if (val <= 0) return false;
|
||||
L[i, j] = Math.Sqrt(val);
|
||||
}
|
||||
else
|
||||
{
|
||||
L[i, j] = (A[i, j] - sum) / L[j, j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 前代: L * y = b
|
||||
var y = new double[n];
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
double sum = 0;
|
||||
for (int k = 0; k < i; k++)
|
||||
sum += L[i, k] * y[k];
|
||||
y[i] = (b[i] - sum) / L[i, i];
|
||||
}
|
||||
|
||||
// 回代: L^T * x = y
|
||||
for (int i = n - 1; i >= 0; i--)
|
||||
{
|
||||
double sum = 0;
|
||||
for (int k = i + 1; k < n; k++)
|
||||
sum += L[k, i] * x[k];
|
||||
x[i] = (y[i] - sum) / L[i, i];
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endregion
|
||||
}
|
||||
@@ -0,0 +1,52 @@
|
||||
using System.Drawing;
|
||||
|
||||
namespace XP.Calibration.Models;
|
||||
|
||||
/// <summary>
|
||||
/// 椭圆拟合结果 | Ellipse fitting result
|
||||
/// </summary>
|
||||
public record EllipseResult
|
||||
{
|
||||
public PointF Center { get; init; }
|
||||
public float LongAxis { get; init; }
|
||||
public float ShortAxis { get; init; }
|
||||
public float Angle { get; init; }
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 几何参数 | Geometry parameters for CT system
|
||||
/// </summary>
|
||||
public class GeoParams
|
||||
{
|
||||
/// <summary>焦点到旋转中心距离 (mm) | Distance Source to Origin</summary>
|
||||
public double DSO { get; set; }
|
||||
/// <summary>焦点到探测器距离 (mm) | Distance Source to Detector</summary>
|
||||
public double DSD { get; set; }
|
||||
/// <summary>探测器像素大小 (mm) | Detector pixel size</summary>
|
||||
public double PixelSize { get; set; }
|
||||
/// <summary>探测器水平像素数 | Detector horizontal pixel count</summary>
|
||||
public int NDetecU { get; set; }
|
||||
/// <summary>探测器垂直像素数 | Detector vertical pixel count</summary>
|
||||
public int NDetecV { get; set; }
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 中心校准结果 | Center calibration result
|
||||
/// </summary>
|
||||
public record CenterCalibrationResult
|
||||
{
|
||||
/// <summary>椭圆拟合结果</summary>
|
||||
public EllipseResult Ellipse { get; init; } = null!;
|
||||
/// <summary>倾斜角 (度) | Tilt angle in degrees</summary>
|
||||
public double AlphaDeg { get; init; }
|
||||
/// <summary>反算半径 R (mm)</summary>
|
||||
public double R_mm { get; init; }
|
||||
/// <summary>透视偏移量 (像素) | Perspective offset in pixels</summary>
|
||||
public double DeltaPx { get; init; }
|
||||
/// <summary>修正后焦点投影 U 坐标</summary>
|
||||
public double FocalU { get; init; }
|
||||
/// <summary>修正后焦点投影 V 坐标</summary>
|
||||
public double FocalV { get; init; }
|
||||
/// <summary>各帧检测到的球心坐标</summary>
|
||||
public List<PointF> DetectedCenters { get; init; } = new();
|
||||
}
|
||||
@@ -0,0 +1,16 @@
|
||||
namespace XP.Calibration.Models;
|
||||
|
||||
/// <summary>
|
||||
/// 完整几何校准选项 | Options for full geometric calibration
|
||||
/// </summary>
|
||||
public class FullCalibrationOptions
|
||||
{
|
||||
/// <summary>是否优化探测器偏移 offDetecU/V</summary>
|
||||
public bool OptimizeDetectorOffset { get; set; } = false;
|
||||
/// <summary>是否优化 DSO/DSD 距离</summary>
|
||||
public bool OptimizeDistances { get; set; } = false;
|
||||
/// <summary>LM 最大迭代次数</summary>
|
||||
public int MaxIterations { get; set; } = 5000;
|
||||
/// <summary>收敛阈值</summary>
|
||||
public double Tolerance { get; set; } = 1e-16;
|
||||
}
|
||||
@@ -0,0 +1,30 @@
|
||||
namespace XP.Calibration.Models;
|
||||
|
||||
/// <summary>
|
||||
/// 完整几何校准结果 | Full geometric calibration result
|
||||
/// </summary>
|
||||
public record FullCalibrationResult
|
||||
{
|
||||
/// <summary>旋转轴倾斜角 ay (度) | Rotation axis tilt, angles[:,1]</summary>
|
||||
public double AyDeg { get; init; }
|
||||
/// <summary>探测器 dPitch (度) | Detector pitch, rotDet[:,1]</summary>
|
||||
public double DetYDeg { get; init; }
|
||||
/// <summary>探测器 dYaw (度) | Detector yaw, rotDet[:,0]</summary>
|
||||
public double DetXDeg { get; init; }
|
||||
/// <summary>探测器 dRoll (度) | Detector roll, rotDet[:,2]</summary>
|
||||
public double DetZDeg { get; init; }
|
||||
/// <summary>物体偏移 R (mm) | Object offset radius</summary>
|
||||
public double R_mm { get; init; }
|
||||
/// <summary>探测器水平偏移 (mm) | Detector U offset</summary>
|
||||
public double OffDetecU_mm { get; init; }
|
||||
/// <summary>探测器垂直偏移 (mm) | Detector V offset</summary>
|
||||
public double OffDetecV_mm { get; init; }
|
||||
/// <summary>优化后 DSO (mm)</summary>
|
||||
public double DSO_mm { get; init; }
|
||||
/// <summary>优化后 DSD (mm)</summary>
|
||||
public double DSD_mm { get; init; }
|
||||
/// <summary>RMS 残差 (像素) | RMS residual in pixels</summary>
|
||||
public double RmsPx { get; init; }
|
||||
/// <summary>是否收敛 | Whether optimization converged</summary>
|
||||
public bool Converged { get; init; }
|
||||
}
|
||||
@@ -0,0 +1,159 @@
|
||||
# XP.Calibration
|
||||
|
||||
平面 CT 系统几何校准库，基于 .NET 8 + Emgu.CV，从 C++ OpenCV 实现移植而来。
|
||||
|
||||
## 功能
|
||||
|
||||
提供两种校准方法：
|
||||
|
||||
- **中心校准 (CenterCalibration)** — 从投影序列检测球心轨迹，椭圆拟合后反算倾斜角和焦点投影偏移，适用于快速估算
|
||||
- **完整几何校准 (FullCalibration)** — 基于 TIGRE 投影模型（角点法），使用 Levenberg-Marquardt 优化器同时优化 5~9 个几何参数
|
||||
|
||||
## 项目结构
|
||||
|
||||
```
|
||||
XP.Calibration/
|
||||
├── Models/
|
||||
│ ├── CalibrationModels.cs # EllipseResult, GeoParams, CenterCalibrationResult
|
||||
│ ├── FullCalibrationResult.cs # 完整校准输出 (各参数 + RMS)
|
||||
│ └── FullCalibrationOptions.cs # 优化模式选项
|
||||
├── Core/
|
||||
│ ├── RawReader.cs # RAW float32 投影数据读取
|
||||
│ ├── BallDetector.cs # 球心检测 (自适应阈值+亚像素质心 / Canny+椭圆拟合)
|
||||
│ └── TigreProjection.cs # TIGRE 投影模型 (rollPitchYaw + eulerZYZ)
|
||||
├── CenterCalibration.cs # 中心校准
|
||||
└── FullCalibration.cs # 完整几何校准 (LM 优化)
|
||||
```
|
||||
|
||||
## 校准参数
|
||||
|
||||
完整几何校准支持三种模式：
|
||||
|
||||
| 模式 | 参数数 | 优化参数 |
|
||||
|------|--------|----------|
|
||||
| 基础 | 5 | ay, det_y, det_x, det_z, R |
|
||||
| +探测器偏移 | 7 | 基础 + offDetecU, offDetecV |
|
||||
| +距离 | 9 | 基础 + offDetecU, offDetecV + DSO, DSD |
|
||||
|
||||
参数含义：
|
||||
|
||||
| 参数 | 说明 | TIGRE 对应 |
|
||||
|------|------|-----------|
|
||||
| ay | 旋转轴倾斜角 | angles[:,1] |
|
||||
| det_x | 探测器 dYaw (Rx) | rotDetector[:,0] |
|
||||
| det_y | 探测器 dPitch (Ry) | rotDetector[:,1] |
|
||||
| det_z | 探测器 dRoll (Rz) | rotDetector[:,2] |
|
||||
| R | 物体偏移半径 | offOrigin[:,2] = -R |
|
||||
| offDetecU/V | 探测器平移偏移 | offDetector[:,0]/[:,1] |
|
||||
| DSO | 焦点到旋转中心距离 | geo.DSO |
|
||||
| DSD | 焦点到探测器距离 | geo.DSD |
|
||||
|
||||
## 使用示例
|
||||
|
||||
### 中心校准
|
||||
|
||||
```csharp
|
||||
var geo = new GeoParams
|
||||
{
|
||||
DSO = 200, DSD = 500, PixelSize = 0.6,
|
||||
NDetecU = 512, NDetecV = 512
|
||||
};
|
||||
|
||||
var calib = new CenterCalibration();
|
||||
var result = calib.CalibrateFromRaw("projections.raw", 512, 512, 360, geo);
|
||||
|
||||
Console.WriteLine($"倾斜角: {result.AlphaDeg:F2}°");
|
||||
Console.WriteLine($"R: {result.R_mm:F2} mm");
|
||||
Console.WriteLine($"焦点投影: ({result.FocalU:F2}, {result.FocalV:F2})");
|
||||
```
|
||||
|
||||
### 完整几何校准 (5 参数)
|
||||
|
||||
```csharp
|
||||
var geo = new GeoParams
|
||||
{
|
||||
DSO = 250, DSD = 450, PixelSize = 0.6,
|
||||
NDetecU = 512, NDetecV = 512
|
||||
};
|
||||
|
||||
var full = new FullCalibration();
|
||||
var result = full.CalibrateFromRaw("projections.raw", 512, 512, 360, geo);
|
||||
|
||||
Console.WriteLine($"ay: {result.AyDeg:F4}°");
|
||||
Console.WriteLine($"det_z: {result.DetZDeg:F4}°");
|
||||
Console.WriteLine($"R: {result.R_mm:F4} mm");
|
||||
Console.WriteLine($"RMS: {result.RmsPx:F6} px");
|
||||
```
|
||||
|
||||
### 完整几何校准 (9 参数)
|
||||
|
||||
```csharp
|
||||
var options = new FullCalibrationOptions
|
||||
{
|
||||
OptimizeDetectorOffset = true, // +offDetecU/V
|
||||
OptimizeDistances = true, // +DSO/DSD
|
||||
MaxIterations = 5000,
|
||||
Tolerance = 1e-16
|
||||
};
|
||||
|
||||
var full = new FullCalibration();
|
||||
// 可选: 监听优化进度
|
||||
full.OnProgress = (iter, rms, p) =>
|
||||
Console.WriteLine($"Iter {iter}: RMS={rms:F6} px");
|
||||
|
||||
var result = full.CalibrateFromRaw("projections.raw", 512, 512, 360, geo, options);
|
||||
```
|
||||
|
||||
### 直接传入 Mat 列表
|
||||
|
||||
如果投影数据已经在内存中（比如从相机采集），可以直接传 `List<Mat>`：
|
||||
|
||||
```csharp
|
||||
List<Mat> projections = ...; // 已有的 CV_32F Mat 列表
|
||||
var result = new FullCalibration().Calibrate(projections, geo, options);
|
||||
```
|
||||
|
||||
### 切换球心检测方法
|
||||
|
||||
默认使用自适应阈值 + 亚像素质心法。如需使用 Canny + 椭圆拟合法：
|
||||
|
||||
```csharp
|
||||
// 关闭亚像素质心，改用矩心
|
||||
BallDetector.EnableSubPixel = false;
|
||||
|
||||
// 或直接调用椭圆拟合检测
|
||||
BallDetector.DetectCenterByEllipse(mat, out var center);
|
||||
```
|
||||
|
||||
## 算法说明
|
||||
|
||||
### 球心检测流程
|
||||
|
||||
1. 归一化 float32 → 8bit
|
||||
2. 高斯模糊 (3×3, σ=0.8)
|
||||
3. 自适应阈值二值化 (blockSize=31, C=-5)
|
||||
4. 查找最大轮廓
|
||||
5. 亚像素质心 (强度加权) 或矩心
|
||||
|
||||
### TIGRE 投影模型
|
||||
|
||||
完全按照 TIGRE `computeDeltas_Siddon` 流程实现：
|
||||
|
||||
1. 初始化探测器角点 (P, Pu0, Pv0) 和射线源 S
|
||||
2. `rollPitchYaw` — 探测器自身旋转 (det_x, det_y, det_z)
|
||||
3. 探测器偏移 (offDetecU/V)
|
||||
4. `eulerZYZ` — 扫描旋转 (scanAngle, ay)
|
||||
5. 物体偏移 (offOrigX = -R)
|
||||
6. 射线-平面交点 → 像素坐标
|
||||
|
||||
### LM 优化器
|
||||
|
||||
- 数值差分雅可比矩阵 (中心差分, ε=1e-7)
|
||||
- Cholesky 分解求解法方程
|
||||
- 自适应阻尼因子 (成功 ×0.3, 失败 ×10)
|
||||
- 参数边界约束 (clamp)
|
||||
|
||||
## 依赖
|
||||
|
||||
- .NET 8.0-windows
|
||||
- Emgu.CV 4.10.0.5680
|
||||
@@ -0,0 +1,16 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<TargetFramework>net8.0-windows</TargetFramework>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<Nullable>enable</Nullable>
|
||||
<RootNamespace>XP.Calibration</RootNamespace>
|
||||
<AssemblyName>XP.Calibration</AssemblyName>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Emgu.CV" Version="4.10.0.5680" />
|
||||
<PackageReference Include="Emgu.CV.runtime.windows" Version="4.10.0.5680" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -64,6 +64,10 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "XP.Scan", "XP.Scan\XP.Scan.
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "XP.ScanMode", "XP.ScanMode", "{E208A5EA-7E3B-46B4-B045-A703F6274218}"
|
||||
EndProject
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "XP.Calibration", "XP.Calibration\XP.Calibration.csproj", "{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}"
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "XP.Calibration", "XP.Calibration", "{D4E5F6A7-B8C9-0123-4567-89ABCDEF0123}"
|
||||
EndProject
|
||||
Global
|
||||
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||
Debug|Any CPU = Debug|Any CPU
|
||||
@@ -266,6 +270,18 @@ Global
|
||||
{F40C71DC-7639-CD57-6183-2EAA78980EC5}.Release|x64.Build.0 = Release|Any CPU
|
||||
{F40C71DC-7639-CD57-6183-2EAA78980EC5}.Release|x86.ActiveCfg = Release|Any CPU
|
||||
{F40C71DC-7639-CD57-6183-2EAA78980EC5}.Release|x86.Build.0 = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|Any CPU.Build.0 = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|x64.ActiveCfg = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|x64.Build.0 = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|x86.ActiveCfg = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Debug|x86.Build.0 = Debug|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|Any CPU.ActiveCfg = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|Any CPU.Build.0 = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|x64.ActiveCfg = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|x64.Build.0 = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|x86.ActiveCfg = Release|Any CPU
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890}.Release|x86.Build.0 = Release|Any CPU
|
||||
EndGlobalSection
|
||||
GlobalSection(SolutionProperties) = preSolution
|
||||
HideSolutionNode = FALSE
|
||||
@@ -284,6 +300,7 @@ Global
|
||||
{B8F5E3A1-7C2D-4E9F-A1B3-6D8E4F2C9A01} = {29E2D405-341A-4445-B788-3E77A677C2BA}
|
||||
{6170AF9F-A792-6BDC-4E25-072EA87FAA15} = {29E2D405-341A-4445-B788-3E77A677C2BA}
|
||||
{F40C71DC-7639-CD57-6183-2EAA78980EC5} = {E208A5EA-7E3B-46B4-B045-A703F6274218}
|
||||
{A1B2C3D4-E5F6-7890-ABCD-EF1234567890} = {D4E5F6A7-B8C9-0123-4567-89ABCDEF0123}
|
||||
EndGlobalSection
|
||||
GlobalSection(ExtensibilityGlobals) = postSolution
|
||||
SolutionGuid = {DB6D69BA-49FD-432F-8069-2A8F64933CDE}
|
||||
|
||||
Reference in New Issue
Block a user