using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Diagnostics;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;

namespace AIFotoONLUS.Core
{
    /// <summary>
    /// NumberRecognitionEngine: loads Darknet models via OpenCvSharp and
    /// provides methods to detect text regions and recognize digits.
    /// </summary>
    using Microsoft.Extensions.Logging;

    public class NumberRecognitionEngine : IDisposable
    {
        private readonly Net _detectionNet;
        private readonly Net _recognitionNet;
        private readonly object _detectionLock = new();
        private readonly object _recognitionLock = new();
        private readonly ModelConfiguration _cfg;
        private readonly ILogger? _logger;
        private bool _disposed;

        public NumberRecognitionEngine(ModelConfiguration cfg)
            : this(cfg, logger: null)
        {
        }

        public NumberRecognitionEngine(ModelConfiguration cfg, ILogger? logger)
        {
            _logger = logger;
            _cfg = cfg ?? throw new ArgumentNullException(nameof(cfg));

            if (!File.Exists(_cfg.DetectionCfg) || !File.Exists(_cfg.DetectionWeights))
                throw new FileNotFoundException("Detection model files not found.", _cfg.DetectionCfg);
            if (!File.Exists(_cfg.RecognitionCfg) || !File.Exists(_cfg.RecognitionWeights))
                throw new FileNotFoundException("Recognition model files not found.", _cfg.RecognitionCfg);

            _detectionNet = CvDnn.ReadNetFromDarknet(_cfg.DetectionCfg, _cfg.DetectionWeights);
            _recognitionNet = CvDnn.ReadNetFromDarknet(_cfg.RecognitionCfg, _cfg.RecognitionWeights);

            _detectionNet.SetPreferableBackend(Backend.OPENCV);
            _detectionNet.SetPreferableTarget(Target.CPU);
            _recognitionNet.SetPreferableBackend(Backend.OPENCV);
            _recognitionNet.SetPreferableTarget(Target.CPU);
            // Let OpenCV use multiple threads internally (use number of logical processors)
            try
            {
                Cv2.SetNumThreads(Environment.ProcessorCount);
            }
            catch
            {
                // Ignore if not supported by OpenCvSharp build
            }
        }

        public void Dispose()
        {
            if (_disposed) return;
            _detectionNet?.Dispose();
            _recognitionNet?.Dispose();
            _disposed = true;
            GC.SuppressFinalize(this);
        }

        private static string SanitizeFileName(string name)
        {
            foreach (var c in Path.GetInvalidFileNameChars()) name = name.Replace(c, '_');
            return name;
        }

        private string[] GetOutputLayerNames(Net net) => net.GetUnconnectedOutLayersNames();

        public IEnumerable<DetectedRegion> DetectTextRegions(Mat image)
        {
            if (image is null) throw new ArgumentNullException(nameof(image));

            return DetectTextRegions(_detectionNet, image);
        }

        // Internal variant that accepts a Net instance so it can be used from parallel workers
        private IEnumerable<DetectedRegion> DetectTextRegions(Net detectionNet, Mat image)
        {
            using var blob = CvDnn.BlobFromImage(image, 0.00392, _cfg.DetectionInputSize, new Scalar(0, 0, 0), true, false);
            detectionNet.SetInput(blob);

            var outNames = GetOutputLayerNames(detectionNet);
            var outsList = new List<Mat>();
            detectionNet.Forward(outsList, outNames);
            
            Mat[] outs = outsList.ToArray();
            if (outs.Length == 0)
            {
                // Try per-output Forward calls as a fallback; use their results for detection
                if (outNames != null)
                {
                    var fallback = new List<Mat>();
                    for (int on = 0; on < outNames.Length; on++)
                    {
                            try
                            {
                                var single = detectionNet.Forward(outNames[on]);
                                fallback.Add(single);
                            }
                            catch (Exception ex)
                            {
                                _logger?.LogError(ex, "Fallback Forward failed for {name}", outNames[on]);
                            }
                    }
                    if (fallback.Count > 0)
                    {
                        outs = fallback.ToArray();
                    }
                }
            }

            // Diagnostic: dump outs shapes and a sample of values to help debugging
            try
            {
                // diagnostic dumping removed for performance; keep errors only
            }
            catch (Exception ex)
            {
                _logger?.LogError(ex, "Error dumping outs");
            }

            var boxes = new List<Rect>();
            var confidences = new List<float>();
            var classIds = new List<int>();
            var centerXList = new List<double>();

            int imgW = image.Width;
            int imgH = image.Height;
            foreach (var outMat in outs)
            {
                for (int i = 0; i < outMat.Rows; i++)
                {
                    float cx = outMat.At<float>(i, 0) * imgW;
                    float cy = outMat.At<float>(i, 1) * imgH;
                    float w = outMat.At<float>(i, 2) * imgW;
                    float h = outMat.At<float>(i, 3) * imgH;

                    // YOLO output layout: [cx, cy, w, h, objectness, class1, class2, ...]
                    float objectness = outMat.At<float>(i, 4);
                    float maxScore = 0f;
                    int bestClass = -1;
                    for (int c = 5; c < outMat.Cols; c++)
                    {
                        float classProb = outMat.At<float>(i, c);
                        float score = objectness * classProb; // combine objectness and class probability
                        if (score > maxScore)
                        {
                            maxScore = score;
                            bestClass = c - 5;
                        }
                    }

                    if (maxScore > _cfg.ConfidenceThreshold)
                        {
                            int x = (int)Math.Max(0, Math.Round(cx - w / 2));
                            int y = (int)Math.Max(0, Math.Round(cy - h / 2));
                            var rect = new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h));
                            boxes.Add(rect);
                            confidences.Add(maxScore);
                            classIds.Add(bestClass);
                            centerXList.Add(cx);
                        }
                }
            }

            if (boxes.Count == 0) return Enumerable.Empty<DetectedRegion>();

            

            CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] indices);

            var results = new List<DetectedRegion>();
            foreach (var idx in indices)
            {
                var b = boxes[idx];
                double centerX = b.X + b.Width / 2.0;
                results.Add(new DetectedRegion(b, confidences[idx], classIds[idx], centerX));
            }
            return results;
        }

        public string RecognizeDigits(Mat croppedImage, string? context = null)
        {
            if (croppedImage is null) throw new ArgumentNullException(nameof(croppedImage));

            // Optionally save crop image for diagnostics when enabled in configuration
            if (_cfg.EnableCropSaving)
            {
                try
                {
                    var cropsDir = Path.Combine("logs", "crops");
                    Directory.CreateDirectory(cropsDir);
                    var fname = $"{(string.IsNullOrEmpty(context) ? "crop" : SanitizeFileName(context))}_{DateTime.UtcNow:yyyyMMdd_HHmmss_fff}_{Guid.NewGuid():N}.jpg";
                    var full = Path.Combine(cropsDir, fname);
                    Cv2.ImWrite(full, croppedImage);
                }
                catch (Exception ex)
                {
                    _logger?.LogError(ex, "Failed saving crop for diagnostics");
                }
            }

            using var blob = CvDnn.BlobFromImage(croppedImage, 0.00392, _cfg.RecognitionInputSize, new Scalar(0, 0, 0), true, false);
            _recognitionNet.SetInput(blob);

            var outNames = GetOutputLayerNames(_recognitionNet);
            var outsList = new List<Mat>();
            _recognitionNet.Forward(outsList, outNames);
            Mat[] outs = outsList.ToArray();

            // Fallback: try per-output Forward if no mats were returned
            if (outs.Length == 0 && outNames != null)
            {
                var fallback = new List<Mat>();
                foreach (var n in outNames)
                {
                    try
                    {
                        var m = _recognitionNet.Forward(n);
                        fallback.Add(m);
                    }
                    catch (Exception ex)
                    {
                        _logger?.LogError(ex, "Recognition fallback forward failed for {name}", n);
                    }
                }
                if (fallback.Count > 0) outs = fallback.ToArray();
            }

            var boxes = new List<Rect>();
            var confidences = new List<float>();
            var classIds = new List<int>();
            var centerXList = new List<double>();
            int imgW = croppedImage.Width;
            int imgH = croppedImage.Height;

            foreach (var outMat in outs)
            {
                for (int i = 0; i < outMat.Rows; i++)
                {
                    float cx = outMat.At<float>(i, 0) * imgW;
                    float cy = outMat.At<float>(i, 1) * imgH;
                    float w = outMat.At<float>(i, 2) * imgW;
                    float h = outMat.At<float>(i, 3) * imgH;
                    float objectness = outMat.At<float>(i, 4);
                    float maxScore = 0f;
                    int bestClass = -1;
                    for (int c = 5; c < outMat.Cols; c++)
                    {
                        float classProb = outMat.At<float>(i, c);
                        float score = objectness * classProb;
                        if (score > maxScore)
                        {
                            maxScore = score;
                            bestClass = c - 5;
                        }
                    }
                    if (maxScore > _cfg.ConfidenceThreshold)
                    {
                        int x = (int)Math.Max(0, Math.Round(cx - w / 2));
                        int y = (int)Math.Max(0, Math.Round(cy - h / 2));
                        boxes.Add(new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h)));
                        confidences.Add(maxScore);
                        classIds.Add(bestClass);
                        centerXList.Add(cx);
                    }
                }
            }

            if (classIds.Count == 0) return string.Empty;

            CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] keep);
            var ordered = keep.Select(i => new { Idx = i, Cx = centerXList[i], ClassId = classIds[i] })
                              .OrderBy(x => x.Cx)
                              .Select(x => _cfg.NumberClasses[x.ClassId]);
            return string.Concat(ordered);
        }

        public record DetectionOutput(string Name, int Rows, int Cols);
        public record DiagnosticResult(ImageResult Result, DetectionOutput[] DetectionOutputs);

        /// <summary>
        /// Process a single image file and return the recognition result together with
        /// detection network forward output shapes for diagnostics.
        /// </summary>
        public DiagnosticResult ProcessFileWithDiagnostics(string filePath)
        {
            if (!File.Exists(filePath)) throw new FileNotFoundException("Image not found", filePath);

            using var image = Cv2.ImRead(filePath);

            // prepare input blob for detection net
            using var blob = CvDnn.BlobFromImage(image, 0.00392, _cfg.DetectionInputSize, new Scalar(0, 0, 0), true, false);
            _detectionNet.SetInput(blob);

            var outNames = GetOutputLayerNames(_detectionNet);
            var outsList = new List<Mat>();
            _detectionNet.Forward(outsList, outNames);

            // fallback: if no mats produced, try per-name Forward
            if (outsList.Count == 0 && outNames != null)
            {
                foreach (var n in outNames)
                {
                    try
                    {
                        var m = _detectionNet.Forward(n);
                        outsList.Add(m);
                    }
                    catch { }
                }
            }

            var outputs = outsList.Select((m, i) => new DetectionOutput(outNames != null && i < outNames.Length ? outNames[i] : $"out{i}", m.Rows, m.Cols)).ToArray();

            // run the normal processing to get recognized text
            var imgRes = ProcessImage(filePath);

            return new DiagnosticResult(imgRes, outputs);
        }

        public ImageResult ProcessImage(string filePath)
        {
            if (!File.Exists(filePath)) throw new FileNotFoundException("Image not found", filePath);
            using var image = Cv2.ImRead(filePath);
            var regions = DetectTextRegions(image).ToArray();
            var texts = new List<string>();
            foreach (var r in regions)
            {
                using var crop = new Mat(image, r.BoundingBox);
                var ctx = $"{Path.GetFileName(filePath)}_{r.BoundingBox.X}_{r.BoundingBox.Y}_{r.BoundingBox.Width}x{r.BoundingBox.Height}";
                var txt = RecognizeDigits(crop, ctx);
                if (!string.IsNullOrEmpty(txt)) texts.Add(txt);
            }
            var result = new ImageResult(Path.GetFileName(filePath), string.Join(",", texts), filePath);
            if (!string.IsNullOrEmpty(result.Text))
                _logger?.LogInformation("Processed image {file} -> {text}", result.FileName, result.Text);
            else
                _logger?.LogDebug("Processed image {file} -> (no text)", result.FileName);
            return result;
        }

        public IEnumerable<ImageResult> ProcessDirectory(string directoryPath, bool skipTextNegative = false)
        {
            // Simple wrapper over async implementation
            return ProcessDirectoryAsync(directoryPath, skipTextNegative).GetAwaiter().GetResult();
        }

        public async Task<IEnumerable<ImageResult>> ProcessDirectoryAsync(string directoryPath, bool skipTextNegative = false, bool recursive = false, IProgress<ProcessingStats>? progress = null, IProgress<ImageResult>? resultProgress = null, CancellationToken cancellationToken = default)
        {
            if (!Directory.Exists(directoryPath)) throw new DirectoryNotFoundException(directoryPath);
            var searchOption = recursive ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly;
            var files = Directory.EnumerateFiles(directoryPath, "*.*", searchOption)
                .Where(f => f.EndsWith(".jpg", StringComparison.OrdinalIgnoreCase) || f.EndsWith(".jpeg", StringComparison.OrdinalIgnoreCase))
                .ToArray();

            var bag = new ConcurrentBag<ImageResult>();

            var dop = Environment.ProcessorCount;
            var total = files.Length;
            var processed = 0;
            var sw = System.Diagnostics.Stopwatch.StartNew();

            // Per-thread nets (each worker gets its own pair) to allow parallel forward calls
            var netsBag = new ConcurrentBag<(Net detNet, Net recNet)>();
            var threadLocalNets = new ThreadLocal<(Net detNet, Net recNet)>(() =>
            {
                var det = CvDnn.ReadNetFromDarknet(_cfg.DetectionCfg, _cfg.DetectionWeights);
                var rec = CvDnn.ReadNetFromDarknet(_cfg.RecognitionCfg, _cfg.RecognitionWeights);
                det.SetPreferableBackend(Backend.OPENCV);
                det.SetPreferableTarget(Target.CPU);
                rec.SetPreferableBackend(Backend.OPENCV);
                rec.SetPreferableTarget(Target.CPU);
                netsBag.Add((det, rec));
                return (det, rec);
            });

            await Task.Run(() =>
            {
                try
                {
                    Parallel.ForEach(files, new ParallelOptions { MaxDegreeOfParallelism = dop, CancellationToken = cancellationToken }, f =>
                    {
                        cancellationToken.ThrowIfCancellationRequested();
                        var filename = Path.GetFileName(f);
                        if (skipTextNegative && filename.StartsWith("tn_", StringComparison.OrdinalIgnoreCase))
                            return;

                        try
                        {
                            var nets = threadLocalNets.Value;
                            using var image = Cv2.ImRead(f);

                            var regions = DetectTextRegions(nets.detNet, image).ToArray();
                            var texts = new List<string>();
                            // minimal logging for performance
                            foreach (var r in regions)
                            {
                                using var crop = new Mat(image, r.BoundingBox);
                                var ctx = $"{filename}_{r.BoundingBox.X}_{r.BoundingBox.Y}_{r.BoundingBox.Width}x{r.BoundingBox.Height}";
                                var txt = RecognizeDigits(crop, nets.recNet, ctx);
                                // minimal logging for performance
                                // Fallback: if empty, try a fresh net (diagnostic)
                                if (string.IsNullOrEmpty(txt))
                                {
                                    try
                                    {
                                        using var tempRec = CvDnn.ReadNetFromDarknet(_cfg.RecognitionCfg, _cfg.RecognitionWeights);
                                        tempRec.SetPreferableBackend(Backend.OPENCV);
                                        tempRec.SetPreferableTarget(Target.CPU);
                                        var alt = RecognizeDigits(crop, tempRec, ctx);
                                        if (!string.IsNullOrEmpty(alt)) txt = alt;
                                    }
                                    catch { }
                                }
                                if (!string.IsNullOrEmpty(txt)) texts.Add(txt);
                            }

                            // If no text was recognized with per-thread nets, try one more time using the shared nets under a lock
                            if (texts.Count == 0)
                            {
                                try
                                {
                                    DetectedRegion[] sharedRegions;
                                    lock (_detectionLock)
                                    {
                                        sharedRegions = DetectTextRegions(image).ToArray();
                                    }
                                    var sharedTexts = new List<string>();
                                    foreach (var r2 in sharedRegions)
                                    {
                                        using var crop2 = new Mat(image, r2.BoundingBox);
                                        var ctx2 = $"{filename}_{r2.BoundingBox.X}_{r2.BoundingBox.Y}_{r2.BoundingBox.Width}x{r2.BoundingBox.Height}";
                                        string txt2;
                                        lock (_recognitionLock)
                                        {
                                            txt2 = RecognizeDigits(crop2, ctx2);
                                        }
                                        if (!string.IsNullOrEmpty(txt2))
                                        {
                                            sharedTexts.Add(txt2);
                                        }
                                    }
                                    if (sharedTexts.Count > 0)
                                    {
                                        texts = sharedTexts;
                                    }
                                }
                                catch
                                {
                                    // ignore fallback errors
                                }
                            }

                            var imgRes = new ImageResult(filename, string.Join(",", texts), f);
                            if (!string.IsNullOrEmpty(imgRes.Text))
                                _logger?.LogInformation("[{file}] Result: {text}", imgRes.FileName, imgRes.Text);
                            bag.Add(imgRes);
                            resultProgress?.Report(imgRes);
                        }
                        catch (Exception ex)
                        {
                            _logger?.LogError(ex, "Error processing image {file}", filename);
                            bag.Add(new ImageResult(filename, string.Empty, f));
                        }
                        finally
                        {
                            var proc = Interlocked.Increment(ref processed);
                            if (progress != null)
                            {
                                var elapsed = Math.Max(1, sw.ElapsedMilliseconds);
                                var ips = proc * 1000.0 / elapsed;
                                progress.Report(new ProcessingStats(total, proc, ips));
                            }
                        }
                    });
                }
                catch (OperationCanceledException)
                {
                    // Cancellation requested — exit gracefully and return partial results
                }
            }, cancellationToken).ConfigureAwait(false);

            // dispose created nets
            while (netsBag.TryTake(out var pair))
            {
                try { pair.detNet.Dispose(); } catch { }
                try { pair.recNet.Dispose(); } catch { }
            }
            threadLocalNets.Dispose();

            return bag.OrderBy(b => b.FileName).ToList();
        }

        // Overload RecognizeDigits that accepts a Net for worker threads
        private string RecognizeDigits(Mat croppedImage, Net recognitionNet, string? context = null)
        {
            if (croppedImage is null) throw new ArgumentNullException(nameof(croppedImage));

            // Optionally save crop image for diagnostics when enabled in configuration
            if (_cfg.EnableCropSaving)
            {
                try
                {
                    var cropsDir = Path.Combine("logs", "crops");
                    Directory.CreateDirectory(cropsDir);
                    var fname = $"{(string.IsNullOrEmpty(context) ? "crop" : SanitizeFileName(context))}_{DateTime.UtcNow:yyyyMMdd_HHmmss_fff}_{Guid.NewGuid():N}.jpg";
                    var full = Path.Combine(cropsDir, fname);
                    Cv2.ImWrite(full, croppedImage);
                }
                catch (Exception ex)
                {
                    _logger?.LogError(ex, "Failed saving crop for diagnostics");
                }
            }

            using var blob = CvDnn.BlobFromImage(croppedImage, 0.00392, _cfg.RecognitionInputSize, new Scalar(0, 0, 0), true, false);
            recognitionNet.SetInput(blob);

            var outNames = GetOutputLayerNames(recognitionNet);
            var outsList = new List<Mat>();
            recognitionNet.Forward(outsList, outNames);
            Mat[] outs = outsList.ToArray();

            var boxes = new List<Rect>();
            var confidences = new List<float>();
            var classIds = new List<int>();
            var centerXList = new List<double>();
            int imgW = croppedImage.Width;
            int imgH = croppedImage.Height;

            // Diagnostic: if no outs, try per-output Forward
            if (outs.Length == 0 && outNames != null)
            {
                var fallback = new List<Mat>();
                foreach (var n in outNames)
                {
                    try
                    {
                        var m = recognitionNet.Forward(n);
                        fallback.Add(m);
                    }
                    catch (Exception ex)
                    {
                        _logger?.LogError(ex, "Recognition fallback forward failed for {name}", n);
                    }
                }
                if (fallback.Count > 0) outs = fallback.ToArray();
            }

            // Diagnostic: dump outs shapes and a sample of values to help debugging
            try
            {
                // diagnostic dumping removed for performance; keep errors only
            }
            catch (Exception ex)
            {
                _logger?.LogError(ex, "Error dumping recognition outs");
            }

            foreach (var outMat in outs)
            {
                for (int i = 0; i < outMat.Rows; i++)
                {
                    float cx = outMat.At<float>(i, 0) * imgW;
                    float cy = outMat.At<float>(i, 1) * imgH;
                    float w = outMat.At<float>(i, 2) * imgW;
                    float h = outMat.At<float>(i, 3) * imgH;
                    float objectness = outMat.At<float>(i, 4);
                    float maxScore = 0f;
                    int bestClass = -1;
                    for (int c = 5; c < outMat.Cols; c++)
                    {
                        float classProb = outMat.At<float>(i, c);
                        float score = objectness * classProb;
                        if (score > maxScore)
                        {
                            maxScore = score;
                            bestClass = c - 5;
                        }
                    }
                    if (maxScore > _cfg.ConfidenceThreshold)
                    {
                        int x = (int)Math.Max(0, Math.Round(cx - w / 2));
                        int y = (int)Math.Max(0, Math.Round(cy - h / 2));
                        boxes.Add(new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h)));
                        confidences.Add(maxScore);
                        classIds.Add(bestClass);
                        centerXList.Add(cx);
                    }
                }
            }

            if (classIds.Count == 0) return string.Empty;

            CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] keep);
            var ordered = keep.Select(i => new { Idx = i, Cx = centerXList[i], ClassId = classIds[i] })
                              .OrderBy(x => x.Cx)
                              .Select(x => _cfg.NumberClasses[x.ClassId]);
            return string.Concat(ordered);
        }
    }
}