aivfo-tl-3.0/ivf_tl_operate_2.0/control/IvfTl.AutoFocus/Imaging/ExposureMeter.cs

using System;

namespace IvfTl.AutoFocus.Imaging
{
    /// <summary>曝光评价结果。</summary>
    public class ExposureInfo
    {
        public double Mean;           // ROI内平均灰度
        public double SaturatedPct;   // 饱和(>=250)像素百分比
        public double DarkPct;        // 死黑(<=5)像素百分比
        public Verdict State;
        public enum Verdict { Good, Over, Under }
        public override string ToString()
            => $"均值={Mean:F0} 饱和={SaturatedPct:F1}% 死黑={DarkPct:F1}% 判定={State}";
    }

    /// <summary>
    /// 曝光评价（在 well 圆内 ROI 上测）。
    /// 合格判据(参考图)：well内中灰、无大片饱和。
    ///   - 目标均值区间 [meanLo, meanHi]
    ///   - 饱和像素比 < satMax
    /// 提供二分调节建议：给当前曝光返回下一个该试的曝光值。
    /// 【移植说明 M2-01】逐字搬自 autofocustool/Imaging/ExposureMeter.cs，仅改命名空间；
    /// MeanLo=95/MeanHi=150/SatMax=2.0 一字不动(03 §1)。
    /// </summary>
    public static class ExposureMeter
    {
        public const double MeanLo = 95, MeanHi = 150;  // 目标亮度区间(参考图well内约中灰)
        public const double SatMax = 2.0;               // 饱和像素超过2%判过曝

        /// <summary>在 well 圆内(取内部 ratio 半径)评价曝光。circle 为 null 则用全图中心圆。</summary>
        public static ExposureInfo Measure(byte[] bgr24, int width, int height, WellCircle circle, double ratio = 0.7)
        {
            double cx, cy, r;
            if (circle != null && circle.Found)
            {
                cx = circle.Cx; cy = circle.Cy; r = circle.Radius * ratio;
            }
            else
            {
                cx = width / 2.0; cy = height / 2.0; r = Math.Min(width, height) * 0.25;
            }
            double r2 = r * r;
            int x0 = Math.Max(0, (int)(cx - r)), x1 = Math.Min(width - 1, (int)(cx + r));
            int y0 = Math.Max(0, (int)(cy - r)), y1 = Math.Min(height - 1, (int)(cy + r));
            int stride = width * 3;

            long sum = 0, n = 0, sat = 0, dark = 0;
            for (int y = y0; y <= y1; y++)
            {
                double dy = y - cy;
                for (int x = x0; x <= x1; x++)
                {
                    double dx = x - cx;
                    if (dx * dx + dy * dy > r2) continue;
                    int p = y * stride + x * 3;
                    int g = (bgr24[p] * 29 + bgr24[p + 1] * 150 + bgr24[p + 2] * 77) >> 8;
                    sum += g; n++;
                    if (g >= 250) sat++;
                    if (g <= 5) dark++;
                }
            }
            var info = new ExposureInfo();
            if (n == 0) { info.State = ExposureInfo.Verdict.Under; return info; }
            info.Mean = (double)sum / n;
            info.SaturatedPct = 100.0 * sat / n;
            info.DarkPct = 100.0 * dark / n;

            if (info.SaturatedPct > SatMax || info.Mean > MeanHi) info.State = ExposureInfo.Verdict.Over;
            else if (info.Mean < MeanLo) info.State = ExposureInfo.Verdict.Under;
            else info.State = ExposureInfo.Verdict.Good;
            return info;
        }

        /// <summary>
        /// 二分法找合适曝光。给定 [lo,hi] 曝光范围 和 抓帧+评价的回调，
        /// 返回让 well 内亮度落入目标区间的曝光值。
        /// grabMeasure: 输入曝光值 → 设曝光抓帧并返回 ExposureInfo。
        /// </summary>
        public static int BinarySearch(int lo, int hi, Func<int, ExposureInfo> grabMeasure,
            Action<string> log = null, int maxIter = 8)
        {
            int best = (lo + hi) / 2; double bestDist = double.MaxValue;
            double target = (MeanLo + MeanHi) / 2;
            for (int i = 0; i < maxIter && lo <= hi; i++)
            {
                int mid = (lo + hi) / 2;
                var info = grabMeasure(mid);
                double dist = Math.Abs(info.Mean - target);
                if (dist < bestDist) { bestDist = dist; best = mid; }
                log?.Invoke($"  二分#{i + 1} 曝光={mid} {info}");
                if (info.State == ExposureInfo.Verdict.Good) return mid;
                if (info.State == ExposureInfo.Verdict.Over) hi = mid - 1; // 太亮→降曝光
                else lo = mid + 1;                                         // 太暗→升曝光
            }
            return best;
        }
    }
}