tests started and tuning

tools/OcrDaemon/Daemon.cs

@@ -63,6 +63,8 @@ static class Daemon
                     "capture"     => ocrHandler.HandleCapture(request),
                     "snapshot"    => ocrHandler.HandleSnapshot(request),
                     "diff-ocr"    => ocrHandler.HandleDiffOcr(request),
+                    "test"        => ocrHandler.HandleTest(request),
+                    "tune"        => ocrHandler.HandleTune(request),
                     "grid"        => gridHandler.HandleGrid(request),
                     "detect-grid" => detectGridHandler.HandleDetectGrid(request),
                     _             => new ErrorResponse($"Unknown command: {request.Cmd}"),

tools/OcrDaemon/ImagePreprocessor.cs

@@ -9,17 +9,16 @@ static class ImagePreprocessor
     /// <summary>
     /// Pre-process an image for OCR using morphological white top-hat filtering.
     /// Isolates bright tooltip text, suppresses dim background text visible through overlay.
-    /// Pipeline: grayscale → morphological top-hat → Otsu binary → 2x upscale
+    /// Pipeline: grayscale → morphological top-hat → Otsu binary → upscale
     /// </summary>
-    public static Bitmap PreprocessForOcr(Bitmap src)
+    public static Bitmap PreprocessForOcr(Bitmap src, int kernelSize = 25, int upscale = 2)
     {
         using var mat = BitmapConverter.ToMat(src);
         using var gray = new Mat();
         Cv2.CvtColor(mat, gray, ColorConversionCodes.BGRA2GRAY);
 
         // Morphological white top-hat: isolates bright text on dark background
-        // Kernel size 25x25 captures text strokes, suppresses dim background text
-        using var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(25, 25));
+        using var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(kernelSize, kernelSize));
         using var tophat = new Mat();
         Cv2.MorphologyEx(gray, tophat, MorphTypes.TopHat, kernel);
 
@@ -27,11 +26,15 @@ static class ImagePreprocessor
         using var binary = new Mat();
         Cv2.Threshold(tophat, binary, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
 
-        // 2x upscale for better LSTM recognition
-        using var upscaled = new Mat();
-        Cv2.Resize(binary, upscaled, new OpenCvSharp.Size(binary.Width * 2, binary.Height * 2),
-            interpolation: InterpolationFlags.Cubic);
+        // Upscale for better LSTM recognition
+        if (upscale > 1)
+        {
+            using var upscaled = new Mat();
+            Cv2.Resize(binary, upscaled, new OpenCvSharp.Size(binary.Width * upscale, binary.Height * upscale),
+                interpolation: InterpolationFlags.Cubic);
+            return BitmapConverter.ToBitmap(upscaled);
+        }
 
-        return BitmapConverter.ToBitmap(upscaled);
+        return BitmapConverter.ToBitmap(binary);
     }
 }

tools/OcrDaemon/Models.cs

@@ -208,3 +208,101 @@ class DetectGridResponse
     [JsonPropertyName("cellHeight")]
     public double CellHeight { get; set; }
 }
+
+class DiffOcrParams
+{
+    [JsonPropertyName("diffThresh")]
+    public int DiffThresh { get; set; } = 10;
+
+    [JsonPropertyName("rowThreshDiv")]
+    public int RowThreshDiv { get; set; } = 30;
+
+    [JsonPropertyName("colThreshDiv")]
+    public int ColThreshDiv { get; set; } = 8;
+
+    [JsonPropertyName("maxGap")]
+    public int MaxGap { get; set; } = 20;
+
+    [JsonPropertyName("trimCutoff")]
+    public double TrimCutoff { get; set; } = 0.4;
+
+    [JsonPropertyName("kernelSize")]
+    public int KernelSize { get; set; } = 41;
+
+    [JsonPropertyName("upscale")]
+    public int Upscale { get; set; } = 2;
+
+    public DiffOcrParams Clone() => (DiffOcrParams)MemberwiseClone();
+
+    public override string ToString() =>
+        $"diffThresh={DiffThresh} rowThreshDiv={RowThreshDiv} colThreshDiv={ColThreshDiv} maxGap={MaxGap} trimCutoff={TrimCutoff:F2} kernelSize={KernelSize} upscale={Upscale}";
+}
+
+class TestCase
+{
+    [JsonPropertyName("id")]
+    public string Id { get; set; } = "";
+
+    [JsonPropertyName("image")]
+    public string Image { get; set; } = "";
+
+    [JsonPropertyName("fullImage")]
+    public string FullImage { get; set; } = "";
+
+    [JsonPropertyName("expected")]
+    public List<string> Expected { get; set; } = [];
+}
+
+class TestCaseResult
+{
+    [JsonPropertyName("id")]
+    public string Id { get; set; } = "";
+
+    [JsonPropertyName("passed")]
+    public bool Passed { get; set; }
+
+    [JsonPropertyName("score")]
+    public double Score { get; set; }
+
+    [JsonPropertyName("matched")]
+    public List<string> Matched { get; set; } = [];
+
+    [JsonPropertyName("missed")]
+    public List<string> Missed { get; set; } = [];
+
+    [JsonPropertyName("extra")]
+    public List<string> Extra { get; set; } = [];
+}
+
+class TestResponse
+{
+    [JsonPropertyName("ok")]
+    public bool Ok => true;
+
+    [JsonPropertyName("passed")]
+    public int Passed { get; set; }
+
+    [JsonPropertyName("failed")]
+    public int Failed { get; set; }
+
+    [JsonPropertyName("total")]
+    public int Total { get; set; }
+
+    [JsonPropertyName("results")]
+    public List<TestCaseResult> Results { get; set; } = [];
+}
+
+class TuneResponse
+{
+    [JsonPropertyName("ok")]
+    public bool Ok => true;
+
+    [JsonPropertyName("bestScore")]
+    public double BestScore { get; set; }
+
+    [JsonPropertyName("bestParams")]
+    public DiffOcrParams BestParams { get; set; } = new();
+
+    [JsonPropertyName("iterations")]
+    public int Iterations { get; set; }
+}

tools/OcrDaemon/OcrDaemon.csproj

@@ -22,6 +22,12 @@
     <None Update="tessdata\poe2.traineddata" Condition="Exists('tessdata\poe2.traineddata')">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Update="tessdata\cases.json">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Include="tessdata\images\*">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
   </ItemGroup>
 
 </Project>

tools/OcrDaemon/OcrHandler.cs

@@ -3,6 +3,7 @@ namespace OcrDaemon;
 using System.Drawing;
 using System.Drawing.Imaging;
 using System.Runtime.InteropServices;
+using System.Text.Json;
 using Tesseract;
 using SdImageFormat = System.Drawing.Imaging.ImageFormat;
 
@@ -54,7 +55,12 @@ class OcrHandler(TesseractEngine engine)
         return new OkResponse();
     }
 
-    public object HandleDiffOcr(Request req)
+    public object HandleDiffOcr(Request req) => HandleDiffOcr(req, new DiffOcrParams
+    {
+        DiffThresh = req.Threshold > 0 ? req.Threshold : 30,
+    });
+
+    public object HandleDiffOcr(Request req, DiffOcrParams p)
     {
         if (_referenceFrame == null)
             return new ErrorResponse("No reference snapshot stored. Send 'snapshot' first.");
@@ -78,7 +84,7 @@ class OcrHandler(TesseractEngine engine)
 
         // Detect pixels that got DARKER (tooltip = dark overlay).
         // This filters out item highlight glow (brighter) and cursor changes.
-        int diffThresh = req.Threshold > 0 ? req.Threshold : 30;
+        int diffThresh = p.DiffThresh;
         bool[] changed = new bool[w * h];
         int totalChanged = 0;
 
@@ -110,7 +116,7 @@ class OcrHandler(TesseractEngine engine)
         // Pass 1: Find row range using full-width row counts
         // Pass 2: Find column range using only pixels within detected row range
         // This makes the column threshold relative to tooltip height, not screen height.
-        int maxGap = 15;
+        int maxGap = p.MaxGap;
 
         // Pass 1: count changed pixels per row, find longest active run
         int[] rowCounts = new int[h];
@@ -119,7 +125,7 @@ class OcrHandler(TesseractEngine engine)
                 if (changed[y * w + x])
                     rowCounts[y]++;
 
-        int rowThresh = w / 30; // ~3% of width
+        int rowThresh = w / p.RowThreshDiv;
         int bestRowStart = 0, bestRowEnd = 0, bestRowLen = 0;
         int curRowStart = -1, lastActiveRow = -1;
         for (int y = 0; y < h; y++)
@@ -150,7 +156,7 @@ class OcrHandler(TesseractEngine engine)
                     colCounts[x]++;
 
         int tooltipHeight = bestRowEnd - bestRowStart + 1;
-        int colThresh = tooltipHeight / 15; // ~7% of tooltip height
+        int colThresh = tooltipHeight / p.ColThreshDiv;
 
         int bestColStart = 0, bestColEnd = 0, bestColLen = 0;
         int curColStart = -1, lastActiveCol = -1;
@@ -202,7 +208,7 @@ class OcrHandler(TesseractEngine engine)
             int midCount = 0;
             for (int x = q1; x <= q3; x++) { midSum += colCounts[x]; midCount++; }
             double avgMidDensity = (double)midSum / midCount;
-            double cutoff = avgMidDensity * 0.3; // column must have >=30% of avg density
+            double cutoff = avgMidDensity * p.TrimCutoff;
 
             // Trim from right while below cutoff
             while (maxX > minX + 100 && colCounts[maxX] < cutoff)
@@ -227,8 +233,8 @@ class OcrHandler(TesseractEngine engine)
             if (debug) Console.Error.WriteLine($"  diff-ocr: saved raw to {req.Path}");
         }
 
-        // Pre-process for OCR: boost contrast, invert colors
-        using var processed = ImagePreprocessor.PreprocessForOcr(cropped);
+        // Pre-process for OCR: top-hat + binarize + upscale
+        using var processed = ImagePreprocessor.PreprocessForOcr(cropped, p.KernelSize, p.Upscale);
 
         // Save fullscreen and preprocessed versions alongside raw
         if (!string.IsNullOrEmpty(req.Path))
@@ -255,4 +261,257 @@ class OcrHandler(TesseractEngine engine)
             Region = new RegionRect { X = minX, Y = minY, Width = rw, Height = rh },
         };
     }
+
+    public object HandleTest(Request req) => RunTestCases(new DiffOcrParams(), verbose: true);
+
+    public object HandleTune(Request req)
+    {
+        // Coordinate descent: optimize one parameter at a time, repeat until stable.
+        var best = new DiffOcrParams();
+        double bestScore = ScoreParams(best);
+        Console.Error.WriteLine($"\n=== Tuning start === baseline score={bestScore:F3}  {best}\n");
+
+        // Define search ranges for each parameter
+        var sweeps = new (string Name, int[] Values, Action<DiffOcrParams, int> Set)[]
+        {
+            ("diffThresh",   [10, 15, 20, 25, 30, 40, 50, 60], (p, v) => p.DiffThresh = v),
+            ("rowThreshDiv", [10, 15, 20, 25, 30, 40, 50, 60], (p, v) => p.RowThreshDiv = v),
+            ("colThreshDiv", [5, 8, 10, 12, 15, 20, 25, 30],   (p, v) => p.ColThreshDiv = v),
+            ("maxGap",       [5, 8, 10, 12, 15, 20, 25, 30],   (p, v) => p.MaxGap = v),
+            ("kernelSize",   [11, 15, 19, 21, 25, 31, 35, 41],  (p, v) => p.KernelSize = v),
+            ("upscale",      [1, 2, 3],                          (p, v) => p.Upscale = v),
+        };
+
+        // trimCutoff needs double values — handle separately
+        double[] trimValues = [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5];
+
+        int totalEvals = 0;
+        const int maxRounds = 3;
+
+        for (int round = 0; round < maxRounds; round++)
+        {
+            bool improved = false;
+            Console.Error.WriteLine($"--- Round {round + 1} ---");
+
+            // Sweep integer params
+            foreach (var (name, values, set) in sweeps)
+            {
+                Console.Error.Write($"  {name}: ");
+                int bestVal = 0;
+                double bestValScore = -1;
+
+                foreach (int v in values)
+                {
+                    var trial = best.Clone();
+                    set(trial, v);
+                    double score = ScoreParams(trial);
+                    totalEvals++;
+                    Console.Error.Write($"{v}={score:F3} ");
+
+                    if (score > bestValScore) { bestValScore = score; bestVal = v; }
+                }
+                Console.Error.WriteLine();
+
+                if (bestValScore > bestScore)
+                {
+                    set(best, bestVal);
+                    bestScore = bestValScore;
+                    improved = true;
+                    Console.Error.WriteLine($"    → {name}={bestVal} score={bestScore:F3}");
+                }
+            }
+
+            // Sweep trimCutoff
+            {
+                Console.Error.Write($"  trimCutoff: ");
+                double bestTrim = best.TrimCutoff;
+                double bestTrimScore = bestScore;
+
+                foreach (double v in trimValues)
+                {
+                    var trial = best.Clone();
+                    trial.TrimCutoff = v;
+                    double score = ScoreParams(trial);
+                    totalEvals++;
+                    Console.Error.Write($"{v:F2}={score:F3} ");
+
+                    if (score > bestTrimScore) { bestTrimScore = score; bestTrim = v; }
+                }
+                Console.Error.WriteLine();
+
+                if (bestTrimScore > bestScore)
+                {
+                    best.TrimCutoff = bestTrim;
+                    bestScore = bestTrimScore;
+                    improved = true;
+                    Console.Error.WriteLine($"    → trimCutoff={bestTrim:F2} score={bestScore:F3}");
+                }
+            }
+
+            Console.Error.WriteLine($"  End of round {round + 1}: score={bestScore:F3}  {best}");
+            if (!improved) break;
+        }
+
+        Console.Error.WriteLine($"\n=== Tuning done === evals={totalEvals} bestScore={bestScore:F3}\n  {best}\n");
+
+        // Run verbose test with best params for final report
+        var finalResult = RunTestCases(best, verbose: true);
+
+        return new TuneResponse
+        {
+            BestScore = bestScore,
+            BestParams = best,
+            Iterations = totalEvals,
+        };
+    }
+
+    /// <summary>Score a param set: average match ratio across all test cases (0-1).</summary>
+    private double ScoreParams(DiffOcrParams p)
+    {
+        var result = RunTestCases(p, verbose: false);
+        if (result is TestResponse tr && tr.Total > 0)
+            return tr.Results.Average(r => r.Score);
+        return 0;
+    }
+
+    private object RunTestCases(DiffOcrParams p, bool verbose)
+    {
+        var tessdataDir = Path.Combine(AppContext.BaseDirectory, "tessdata");
+        var casesPath = Path.Combine(tessdataDir, "cases.json");
+        if (!File.Exists(casesPath))
+            return new ErrorResponse($"cases.json not found at {casesPath}");
+
+        var json = File.ReadAllText(casesPath);
+        var cases = JsonSerializer.Deserialize<List<TestCase>>(json);
+        if (cases == null || cases.Count == 0)
+            return new ErrorResponse("No test cases found in cases.json");
+
+        var results = new List<TestCaseResult>();
+        int passCount = 0;
+
+        foreach (var tc in cases)
+        {
+            if (verbose) Console.Error.WriteLine($"\n=== Test: {tc.Id} ===");
+
+            var fullPath = Path.Combine(tessdataDir, tc.FullImage);
+            var imagePath = Path.Combine(tessdataDir, tc.Image);
+
+            if (!File.Exists(fullPath))
+            {
+                if (verbose) Console.Error.WriteLine($"  SKIP: full image not found: {fullPath}");
+                results.Add(new TestCaseResult { Id = tc.Id, Passed = false, Score = 0, Missed = tc.Expected });
+                continue;
+            }
+            if (!File.Exists(imagePath))
+            {
+                if (verbose) Console.Error.WriteLine($"  SKIP: tooltip image not found: {imagePath}");
+                results.Add(new TestCaseResult { Id = tc.Id, Passed = false, Score = 0, Missed = tc.Expected });
+                continue;
+            }
+
+            // Run the same pipeline: snapshot (reference) then diff-ocr (with tooltip)
+            HandleSnapshot(new Request { File = fullPath });
+            var diffResult = HandleDiffOcr(new Request { File = imagePath, Debug = verbose }, p);
+
+            // Extract actual lines from the response
+            List<string> actualLines;
+            if (diffResult is DiffOcrResponse diffResp)
+                actualLines = diffResp.Lines.Select(l => l.Text.Trim()).Where(l => l.Length > 0).ToList();
+            else if (diffResult is OcrResponse ocrResp)
+                actualLines = ocrResp.Lines.Select(l => l.Text.Trim()).Where(l => l.Length > 0).ToList();
+            else
+            {
+                if (verbose) Console.Error.WriteLine($"  ERROR: unexpected response type");
+                results.Add(new TestCaseResult { Id = tc.Id, Passed = false, Score = 0, Missed = tc.Expected });
+                continue;
+            }
+
+            // Fuzzy match expected vs actual
+            var matched = new List<string>();
+            var missed = new List<string>();
+            var usedActual = new HashSet<int>();
+
+            foreach (var expected in tc.Expected)
+            {
+                int bestIdx = -1;
+                double bestSim = 0;
+                for (int i = 0; i < actualLines.Count; i++)
+                {
+                    if (usedActual.Contains(i)) continue;
+                    double sim = LevenshteinSimilarity(expected, actualLines[i]);
+                    if (sim > bestSim) { bestSim = sim; bestIdx = i; }
+                }
+
+                if (bestIdx >= 0 && bestSim >= 0.75)
+                {
+                    matched.Add(expected);
+                    usedActual.Add(bestIdx);
+                    if (verbose && bestSim < 1.0)
+                        Console.Error.WriteLine($"  ~ {expected}  →  {actualLines[bestIdx]}  (sim={bestSim:F2})");
+                }
+                else
+                {
+                    missed.Add(expected);
+                    if (verbose)
+                        Console.Error.WriteLine($"  MISS: {expected}" + (bestIdx >= 0 ? $"  (best: {actualLines[bestIdx]}, sim={bestSim:F2})" : ""));
+                }
+            }
+
+            var extra = actualLines.Where((_, i) => !usedActual.Contains(i)).ToList();
+            if (verbose)
+                foreach (var e in extra)
+                    Console.Error.WriteLine($"  EXTRA: {e}");
+
+            double score = tc.Expected.Count > 0 ? (double)matched.Count / tc.Expected.Count : 1.0;
+            bool passed = missed.Count == 0;
+            if (passed) passCount++;
+
+            if (verbose)
+                Console.Error.WriteLine($"  Result: {(passed ? "PASS" : "FAIL")}  matched={matched.Count}/{tc.Expected.Count}  extra={extra.Count}  score={score:F2}");
+
+            results.Add(new TestCaseResult
+            {
+                Id = tc.Id,
+                Passed = passed,
+                Score = score,
+                Matched = matched,
+                Missed = missed,
+                Extra = extra,
+            });
+        }
+
+        if (verbose)
+            Console.Error.WriteLine($"\n=== Summary: {passCount}/{cases.Count} passed ===\n");
+
+        return new TestResponse
+        {
+            Passed = passCount,
+            Failed = cases.Count - passCount,
+            Total = cases.Count,
+            Results = results,
+        };
+    }
+
+    private static double LevenshteinSimilarity(string a, string b)
+    {
+        a = a.ToLowerInvariant();
+        b = b.ToLowerInvariant();
+        if (a == b) return 1.0;
+
+        int la = a.Length, lb = b.Length;
+        if (la == 0 || lb == 0) return 0.0;
+
+        var d = new int[la + 1, lb + 1];
+        for (int i = 0; i <= la; i++) d[i, 0] = i;
+        for (int j = 0; j <= lb; j++) d[0, j] = j;
+
+        for (int i = 1; i <= la; i++)
+            for (int j = 1; j <= lb; j++)
+            {
+                int cost = a[i - 1] == b[j - 1] ? 0 : 1;
+                d[i, j] = Math.Min(Math.Min(d[i - 1, j] + 1, d[i, j - 1] + 1), d[i - 1, j - 1] + cost);
+            }
+
+        return 1.0 - (double)d[la, lb] / Math.Max(la, lb);
+    }
 }

tools/OcrDaemon/tessdata/cases.json

@@ -2,6 +2,7 @@
     {
         "id": "vertex1",
         "image": "images/vertex1.png",
+        "fullImage": "images/vertex-snapshot.png",
         "expected": [
             "The Vertex",
             "Tribal Mask",
@@ -26,6 +27,7 @@
     {
         "id": "vertex2",
         "image": "images/vertex2.png",
+        "fullImage": "images/vertex-snapshot.png",
         "expected": [
             "The Vertex",
             "Tribal Mask",