bugfixes
This commit is contained in:
Linrador
parent
f0eef673d9
commit
4320f040ff
@ -1,2 +1,2 @@
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HTTPS_ENABLED=1
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HTTPS_ENABLED=0
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AUTH_RP_ORIGINS=https://l14pbbk95100006.tegdssd.de:9999,https://l14pbbk95100006.tegdssd.de:5173,https://localhost:9999,https://127.0.0.1:9999,https://10.0.1.25:9999,http://localhost:5173,http://127.0.0.1:5173,http://10.0.1.25:5173
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@ -1,3 +0,0 @@
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{
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"items": []
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}
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@ -126,9 +126,22 @@ func appLogf(format string, args ...any) {
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appLogWriteLine(fmt.Sprintf(format, args...))
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}
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// appErrorLogSuppressed unterdrückt das automatische ❌-Logging für bekannte,
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// erwartbare und sehr häufige Fehler (z.B. abgelaufene HLS-Edge-URLs während
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// eines Stream-Reconnects). Der Fehler wird weiterhin zurückgegeben, damit die
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// Retry-/Reconnect-Logik unverändert funktioniert – nur die Log-Zeile entfällt.
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func appErrorLogSuppressed(msg string) bool {
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m := strings.ToLower(msg)
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return strings.Contains(m, "leere hls url") ||
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strings.Contains(m, "http 403")
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}
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func appErrorf(format string, args ...any) error {
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err := fmt.Errorf(format, args...)
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appLogln("❌", err)
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if !appErrorLogSuppressed(err.Error()) {
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appLogln("❌", err)
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}
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return err
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}
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BIN
backend/ml/__pycache__/train_detector_model.cpython-314.pyc
Normal file
BIN
backend/ml/__pycache__/train_detector_model.cpython-314.pyc
Normal file
Binary file not shown.
@ -3,7 +3,7 @@
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import argparse
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import json
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import shutil
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from ultralytics import YOLO
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@ -53,23 +53,27 @@ def safe_int(value, fallback):
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return fallback
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def batch_sample_id(batch):
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def batch_sample_ids(batch):
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if not isinstance(batch, dict):
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return ""
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return []
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image_paths = batch.get("im_file")
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if not image_paths:
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return ""
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return []
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if isinstance(image_paths, (str, Path)):
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image_path = image_paths
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else:
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try:
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image_path = image_paths[0]
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except (IndexError, KeyError, TypeError):
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return ""
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image_paths = [image_paths]
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return Path(str(image_path)).stem.strip()
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out = []
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seen = set()
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for image_path in image_paths:
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stem = Path(str(image_path)).stem.strip()
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if stem and stem not in seen:
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seen.add(stem)
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out.append(stem)
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return out
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def progress_detection_trainer(train_count, val_count, train_device, fallback_epochs):
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@ -78,7 +82,6 @@ def progress_detection_trainer(train_count, val_count, train_device, fallback_ep
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super().__init__(*args, **kwargs)
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self._preview_epoch = 0
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self._preview_batch = 0
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self._preview_emitted_at = 0.0
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def preprocess_batch(self, batch):
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epoch = int(getattr(self, "epoch", 0)) + 1
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@ -90,27 +93,25 @@ def progress_detection_trainer(train_count, val_count, train_device, fallback_ep
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self._preview_batch += 1
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now = time.monotonic()
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if self._preview_batch == 1 or now - self._preview_emitted_at >= 0.5:
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sample_id = batch_sample_id(batch)
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# Pro Batch das gerade trainierte Bild melden – ohne Drosselung, damit
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# das Frontend live immer das aktuell trainierte Bild anzeigen kann.
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sample_ids = batch_sample_ids(batch)
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if sample_ids:
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total_batches = max(1, len(self.train_loader))
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completed = (epoch - 1) + min(1.0, self._preview_batch / total_batches)
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progress = 0.04 + 0.90 * (completed / max(1, total_epochs))
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if sample_id:
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total_batches = max(1, len(self.train_loader))
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completed = (epoch - 1) + min(1.0, self._preview_batch / total_batches)
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emit_progress(
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"detector",
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0.04 + 0.90 * (completed / max(1, total_epochs)),
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"Object Detector trainiert…",
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epoch=epoch,
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epochs=total_epochs,
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sampleId=sample_id,
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trainSamples=train_count,
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valSamples=val_count,
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device=str(train_device),
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)
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self._preview_emitted_at = now
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emit_progress(
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"detector",
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progress,
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"Object Detector trainiert…",
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epoch=epoch,
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epochs=total_epochs,
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sampleId=sample_ids[0],
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trainSamples=train_count,
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valSamples=val_count,
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device=str(train_device),
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)
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return super().preprocess_batch(batch)
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@ -181,6 +182,8 @@ def main():
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best_epoch = 0
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last_epoch = 0
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best_map50 = 0.0
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best_map5095 = 0.0
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def on_train_epoch_start(trainer):
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epoch = int(getattr(trainer, "epoch", 0)) + 1
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@ -216,7 +219,7 @@ def main():
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)
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def on_fit_epoch_end(trainer):
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nonlocal best_epoch
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nonlocal best_epoch, best_map50, best_map5095
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epoch = int(getattr(trainer, "epoch", 0)) + 1
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total = int(getattr(trainer, "epochs", epochs) or epochs)
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@ -237,6 +240,13 @@ def main():
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if map50 is not None or map5095 is not None:
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best_epoch = epoch
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# Bestes Modell merken (YOLO speichert best.pt nach Fitness ~ mAP).
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m50 = float(map50) if map50 is not None else 0.0
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if m50 >= best_map50:
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best_map50 = m50
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if map5095 is not None:
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best_map5095 = float(map5095)
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emit_progress(
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"detector",
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0.04 + 0.90 * (epoch / max(1, total)),
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@ -310,11 +320,15 @@ def main():
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"model": str(final_model),
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"sourceModel": str(best),
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"runs": str(result_path),
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"trainedAt": datetime.now(timezone.utc).isoformat(),
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"trainSamples": train_count,
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"valSamples": val_count,
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"epochs": epochs,
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"imgsz": imgsz,
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"device": str(train_device),
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"mAP50": round(best_map50, 4),
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"mAP5095": round(best_map5095, 4),
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"bestEpoch": best_epoch,
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}
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with (out_dir / "status.json").open("w", encoding="utf-8") as f:
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@ -96,6 +96,7 @@ func registerRoutes(mux *http.ServeMux, auth *AuthManager) *ModelStore {
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api.HandleFunc("/api/training/cancel", trainingCancelHandler)
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api.HandleFunc("/api/training/status", trainingStatusHandler)
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api.HandleFunc("/api/training/stats", trainingStatsHandler)
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api.HandleFunc("/api/training/history", trainingHistoryHandler)
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api.HandleFunc("/api/training/delete-all", trainingDeleteAllHandler)
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api.HandleFunc("/api/training/skip", trainingSkipHandler)
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api.HandleFunc("/api/training/import-video", trainingImportVideoHandler)
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@ -102,9 +102,18 @@ func buildStartupNotificationMessage() string {
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}
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type aiServerProcess struct {
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cmd *exec.Cmd
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cmd *exec.Cmd
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done chan struct{}
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}
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// Globales Handle auf den laufenden AI-Server, damit er (z.B. nach einem neuen
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// Modell) sauber neu gestartet werden kann.
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var (
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aiServerMu sync.Mutex
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aiServerCurrent *aiServerProcess
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aiServerCtx context.Context
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)
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func aiServerAutostartEnabled() bool {
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raw := strings.ToLower(strings.TrimSpace(os.Getenv("AI_SERVER_AUTOSTART")))
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@ -572,7 +581,8 @@ func startAIServer(ctx context.Context) (*aiServerProcess, error) {
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}
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proc := &aiServerProcess{
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cmd: cmd,
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cmd: cmd,
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done: make(chan struct{}),
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}
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go func() {
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@ -583,6 +593,8 @@ func startAIServer(ctx context.Context) (*aiServerProcess, error) {
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} else {
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appLogln("🛑 AI Server beendet.")
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}
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close(proc.done)
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}()
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waitCtx, cancel := context.WithTimeout(ctx, 30*time.Second)
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@ -606,6 +618,52 @@ func (p *aiServerProcess) Stop() {
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if p.cmd != nil && p.cmd.Process != nil {
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_ = p.cmd.Process.Kill()
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}
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// Auf das tatsächliche Prozessende warten, damit der Port frei ist,
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// bevor ggf. ein neuer Server gestartet wird.
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if p.done != nil {
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select {
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case <-p.done:
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case <-time.After(10 * time.Second):
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appLogln("⚠️ AI Server reagiert nicht auf Stop (Timeout).")
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}
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}
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}
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// restartAIServer beendet den laufenden AI-Server und startet ihn frisch.
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// Wird z.B. nach einem erfolgreichen Training aufgerufen, damit das neue Modell
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// in einem sauberen Prozess geladen wird.
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func restartAIServer() {
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aiServerMu.Lock()
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defer aiServerMu.Unlock()
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ctx := aiServerCtx
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if ctx == nil {
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ctx = context.Background()
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}
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if ctx.Err() != nil {
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appLogln("ℹ️ AI Server Neustart übersprungen (App wird beendet).")
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return
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}
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if aiServerCurrent != nil {
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appLogln("🔄 AI Server wird neu gestartet…")
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aiServerCurrent.Stop()
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aiServerCurrent = nil
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}
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proc, err := startAIServer(ctx)
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if err != nil {
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appLogln("⚠️ AI Server Neustart fehlgeschlagen:", err)
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return
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}
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aiServerCurrent = proc
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if proc != nil {
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appLogln("✅ AI Server neu gestartet.")
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}
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}
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func setEnvIfMissing(key, value string) {
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@ -801,6 +859,13 @@ func main() {
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appLogln("⚠️ AI Server konnte nicht gestartet werden:", err)
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}
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// Handle/Context global hinterlegen, damit der Server später neu gestartet
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// werden kann (z.B. nach einem neuen Modell).
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aiServerMu.Lock()
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aiServerCtx = appCtx
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aiServerCurrent = aiProc
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aiServerMu.Unlock()
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// ✅ Hier: alte manuelle Autostart-Pause beim echten App-Start zurücksetzen.
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resetAutostartPauseOnStartup()
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@ -882,8 +947,13 @@ func main() {
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var shutdownOnce sync.Once
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shutdown := func() {
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shutdownOnce.Do(func() {
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if aiProc != nil {
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aiProc.Stop()
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aiServerMu.Lock()
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current := aiServerCurrent
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aiServerCurrent = nil
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aiServerMu.Unlock()
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if current != nil {
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current.Stop()
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}
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appCancel()
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@ -527,16 +527,8 @@ func cleanupDeleteRecordOrphanFile(ctx context.Context, jobID string, path strin
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return
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}
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updateCleanupJobState(jobID, func(st *CleanupTaskState) {
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st.Queued = false
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st.Running = true
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st.Done = 0
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st.Total = 0
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st.CurrentFile = name
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st.Text = "Räume Record-Reste auf…"
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st.Error = ""
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st.FinishedAt = nil
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})
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// Fortschritt (Done/Total/CurrentFile) steuert der Aufrufer
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// cleanupRecordDirOrphanAVFiles, damit die Progress-Bar sichtbar bleibt.
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if err := removeWithRetry(path); err != nil && !os.IsNotExist(err) {
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resp.ErrorCount++
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@ -582,6 +574,34 @@ func cleanupRecordDirOrphanAVFiles(ctx context.Context, jobID string, recordAbs
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return nil
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}
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// Gesamtzahl der zu prüfenden Dateien für einen sichtbaren Fortschritt.
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total := 0
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for _, e := range entries {
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if e == nil || e.IsDir() {
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continue
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}
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if strings.TrimSpace(e.Name()) == "" {
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continue
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}
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total++
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}
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updateCleanupJobState(jobID, func(st *CleanupTaskState) {
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st.Queued = false
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st.Running = true
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if st.StartedAt.IsZero() {
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st.StartedAt = time.Now()
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}
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st.Done = 0
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st.Total = total
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st.CurrentFile = ""
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st.Text = "Räume Record-Reste auf…"
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st.Error = ""
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st.FinishedAt = nil
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})
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processed := 0
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for _, e := range entries {
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select {
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case <-ctx.Done():
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@ -598,6 +618,13 @@ func cleanupRecordDirOrphanAVFiles(ctx context.Context, jobID string, recordAbs
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continue
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}
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processed++
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updateCleanupJobState(jobID, func(st *CleanupTaskState) {
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st.Done = processed
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st.Total = total
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st.CurrentFile = name
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})
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full := filepath.Join(recordAbs, name)
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low := strings.ToLower(name)
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@ -153,6 +153,9 @@ type TrainingJobStatus struct {
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Stage string `json:"stage,omitempty"`
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Epoch int `json:"epoch,omitempty"`
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Epochs int `json:"epochs,omitempty"`
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MAP50 float64 `json:"map50,omitempty"`
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MAP5095 float64 `json:"map5095,omitempty"`
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}
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type TrainingConfidence struct {
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@ -175,6 +178,18 @@ type TrainingStatsLabels struct {
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Clothing []TrainingLabelStat `json:"clothing"`
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}
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type TrainingModelInfo struct {
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TrainedAt string `json:"trainedAt,omitempty"`
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TrainedAtMs int64 `json:"trainedAtMs,omitempty"`
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Epochs int `json:"epochs,omitempty"`
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TrainSamples int `json:"trainSamples,omitempty"`
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ValSamples int `json:"valSamples,omitempty"`
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Imgsz int `json:"imgsz,omitempty"`
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Device string `json:"device,omitempty"`
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MAP50 float64 `json:"map50,omitempty"`
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MAP5095 float64 `json:"map5095,omitempty"`
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}
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type TrainingStatsResponse struct {
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OK bool `json:"ok"`
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FeedbackCount int `json:"feedbackCount"`
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@ -184,18 +199,21 @@ type TrainingStatsResponse struct {
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SampleCount int `json:"sampleCount"`
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BoxCount int `json:"boxCount"`
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ModelAvailable bool `json:"modelAvailable"`
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ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`
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Confidence TrainingConfidence `json:"confidence"`
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Labels TrainingStatsLabels `json:"labels"`
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}
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type trainingProgressEvent struct {
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Type string `json:"type"`
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Stage string `json:"stage"`
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Progress float64 `json:"progress"` // 0..1
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Message string `json:"message,omitempty"`
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Epoch int `json:"epoch,omitempty"`
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Epochs int `json:"epochs,omitempty"`
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SampleID string `json:"sampleId,omitempty"`
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Type string `json:"type"`
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Stage string `json:"stage"`
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Progress float64 `json:"progress"` // 0..1
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Message string `json:"message,omitempty"`
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Epoch int `json:"epoch,omitempty"`
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Epochs int `json:"epochs,omitempty"`
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SampleID string `json:"sampleId,omitempty"`
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MAP50 *float64 `json:"mAP50,omitempty"`
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MAP5095 *float64 `json:"mAP5095,omitempty"`
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}
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type TrainingFeedbackListResponse struct {
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@ -689,6 +707,13 @@ func trainingHandleProgressLine(line string, start int, end int, defaultStep str
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s.Epochs = ev.Epochs
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}
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if ev.MAP50 != nil && *ev.MAP50 > 0 {
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s.MAP50 = *ev.MAP50
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}
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if ev.MAP5095 != nil && *ev.MAP5095 > 0 {
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s.MAP5095 = *ev.MAP5095
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}
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sampleID := strings.TrimSpace(ev.SampleID)
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if sampleID != "" &&
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!strings.Contains(sampleID, "/") &&
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@ -2499,7 +2524,13 @@ func trainingRunJob(ctx context.Context, root string, count int) {
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}
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if detectorStatus == "trained" {
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reloadAIServerModelAfterTraining()
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// Verlaufseintrag schreiben (vor dem Neustart, solange die Job-Startzeit
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// für die Dauer noch verfügbar ist).
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trainingAppendRunHistory(root)
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// Neues Modell → AI-Server frisch neu starten (sauberer Zustand,
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// statt nur das Modell im laufenden Prozess neu zu laden).
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restartAIServer()
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}
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trainingSetJobStatus(func(s *TrainingJobStatus) {
|
||||
@ -2591,6 +2622,7 @@ func trainingBuildStats(root string) (*TrainingStatsResponse, error) {
|
||||
if os.IsNotExist(err) {
|
||||
stats.SampleCount = trainingCountSampleFiles(filepath.Join(root, "samples"))
|
||||
stats.ModelAvailable = trainingStatsModelAvailable(root)
|
||||
stats.ModelInfo = trainingReadModelInfo(root)
|
||||
return stats, nil
|
||||
}
|
||||
|
||||
@ -2684,6 +2716,7 @@ func trainingBuildStats(root string) (*TrainingStatsResponse, error) {
|
||||
|
||||
stats.SampleCount = trainingCountSampleFiles(filepath.Join(root, "samples"))
|
||||
stats.ModelAvailable = trainingStatsModelAvailable(root)
|
||||
stats.ModelInfo = trainingReadModelInfo(root)
|
||||
|
||||
stats.Labels = TrainingStatsLabels{
|
||||
// Personen/Box-Labels brauchen mehr Beispiele, weil der Detector Boxen lernen muss.
|
||||
@ -2927,6 +2960,188 @@ func trainingStatsModelAvailable(root string) bool {
|
||||
return fileExistsNonEmpty(detectorModelPath)
|
||||
}
|
||||
|
||||
// trainingReadModelInfo liest Versions-/Datums-Infos zum aktuell trainierten
|
||||
// Modell. Datum/Version stammen primär aus status.json (vom Trainingsskript),
|
||||
// Fallback ist die Änderungszeit der best.pt-Datei.
|
||||
func trainingReadModelInfo(root string) *TrainingModelInfo {
|
||||
modelPath := filepath.Join(root, "detector", "model", "best.pt")
|
||||
|
||||
fi, err := os.Stat(modelPath)
|
||||
if err != nil || fi.IsDir() || fi.Size() <= 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
info := &TrainingModelInfo{
|
||||
TrainedAt: fi.ModTime().UTC().Format(time.RFC3339),
|
||||
TrainedAtMs: fi.ModTime().UnixMilli(),
|
||||
}
|
||||
|
||||
statusPath := filepath.Join(root, "detector", "model", "status.json")
|
||||
if b, err := os.ReadFile(statusPath); err == nil {
|
||||
var raw struct {
|
||||
TrainedAt string `json:"trainedAt"`
|
||||
Epochs int `json:"epochs"`
|
||||
TrainSamples int `json:"trainSamples"`
|
||||
ValSamples int `json:"valSamples"`
|
||||
Imgsz int `json:"imgsz"`
|
||||
Device string `json:"device"`
|
||||
MAP50 float64 `json:"mAP50"`
|
||||
MAP5095 float64 `json:"mAP5095"`
|
||||
}
|
||||
|
||||
if json.Unmarshal(b, &raw) == nil {
|
||||
if trimmed := strings.TrimSpace(raw.TrainedAt); trimmed != "" {
|
||||
if t, err := time.Parse(time.RFC3339, trimmed); err == nil {
|
||||
info.TrainedAt = t.UTC().Format(time.RFC3339)
|
||||
info.TrainedAtMs = t.UnixMilli()
|
||||
}
|
||||
}
|
||||
|
||||
info.Epochs = raw.Epochs
|
||||
info.TrainSamples = raw.TrainSamples
|
||||
info.ValSamples = raw.ValSamples
|
||||
info.Imgsz = raw.Imgsz
|
||||
info.Device = strings.TrimSpace(raw.Device)
|
||||
info.MAP50 = raw.MAP50
|
||||
info.MAP5095 = raw.MAP5095
|
||||
}
|
||||
}
|
||||
|
||||
return info
|
||||
}
|
||||
|
||||
type TrainingHistoryEntry struct {
|
||||
TrainedAt string `json:"trainedAt,omitempty"`
|
||||
TrainedAtMs int64 `json:"trainedAtMs,omitempty"`
|
||||
DurationMs int64 `json:"durationMs,omitempty"`
|
||||
Epochs int `json:"epochs,omitempty"`
|
||||
TrainSamples int `json:"trainSamples,omitempty"`
|
||||
ValSamples int `json:"valSamples,omitempty"`
|
||||
Imgsz int `json:"imgsz,omitempty"`
|
||||
Device string `json:"device,omitempty"`
|
||||
MAP50 float64 `json:"map50,omitempty"`
|
||||
MAP5095 float64 `json:"map5095,omitempty"`
|
||||
}
|
||||
|
||||
type TrainingHistoryResponse struct {
|
||||
OK bool `json:"ok"`
|
||||
Entries []TrainingHistoryEntry `json:"entries"`
|
||||
}
|
||||
|
||||
func trainingHistoryPath(root string) string {
|
||||
return filepath.Join(root, "detector", "training_history.jsonl")
|
||||
}
|
||||
|
||||
// trainingAppendRunHistory hängt nach einem erfolgreichen Trainingslauf einen
|
||||
// Verlaufseintrag an (Datum, mAP, Samples, Epochen, Dauer).
|
||||
func trainingAppendRunHistory(root string) {
|
||||
info := trainingReadModelInfo(root)
|
||||
if info == nil {
|
||||
return
|
||||
}
|
||||
|
||||
entry := TrainingHistoryEntry{
|
||||
TrainedAt: info.TrainedAt,
|
||||
TrainedAtMs: info.TrainedAtMs,
|
||||
Epochs: info.Epochs,
|
||||
TrainSamples: info.TrainSamples,
|
||||
ValSamples: info.ValSamples,
|
||||
Imgsz: info.Imgsz,
|
||||
Device: info.Device,
|
||||
MAP50: info.MAP50,
|
||||
MAP5095: info.MAP5095,
|
||||
}
|
||||
|
||||
// Dauer aus der Startzeit des aktuellen Jobs ableiten.
|
||||
job := trainingGetJobStatus()
|
||||
if startedAt, err := time.Parse(time.RFC3339, strings.TrimSpace(job.StartedAt)); err == nil {
|
||||
if ms := time.Now().UTC().Sub(startedAt).Milliseconds(); ms > 0 {
|
||||
entry.DurationMs = ms
|
||||
}
|
||||
}
|
||||
|
||||
b, err := json.Marshal(entry)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
path := trainingHistoryPath(root)
|
||||
if err := os.MkdirAll(filepath.Dir(path), 0o755); err != nil {
|
||||
appLogln("⚠️ training history dir konnte nicht erstellt werden:", err)
|
||||
return
|
||||
}
|
||||
|
||||
f, err := os.OpenFile(path, os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0o644)
|
||||
if err != nil {
|
||||
appLogln("⚠️ training history konnte nicht geöffnet werden:", err)
|
||||
return
|
||||
}
|
||||
defer f.Close()
|
||||
|
||||
if _, err := f.Write(append(b, '\n')); err != nil {
|
||||
appLogln("⚠️ training history konnte nicht geschrieben werden:", err)
|
||||
}
|
||||
}
|
||||
|
||||
// trainingReadHistory liest die Verlaufseinträge, neueste zuerst.
|
||||
func trainingReadHistory(root string, limit int) []TrainingHistoryEntry {
|
||||
b, err := os.ReadFile(trainingHistoryPath(root))
|
||||
if err != nil {
|
||||
return nil
|
||||
}
|
||||
|
||||
lines := strings.Split(string(b), "\n")
|
||||
entries := make([]TrainingHistoryEntry, 0, len(lines))
|
||||
|
||||
for _, line := range lines {
|
||||
line = strings.TrimSpace(line)
|
||||
if line == "" {
|
||||
continue
|
||||
}
|
||||
|
||||
var e TrainingHistoryEntry
|
||||
if json.Unmarshal([]byte(line), &e) != nil {
|
||||
continue
|
||||
}
|
||||
|
||||
entries = append(entries, e)
|
||||
}
|
||||
|
||||
// Datei ist chronologisch → umdrehen, damit der neueste Lauf oben steht.
|
||||
for i, j := 0, len(entries)-1; i < j; i, j = i+1, j-1 {
|
||||
entries[i], entries[j] = entries[j], entries[i]
|
||||
}
|
||||
|
||||
if limit > 0 && len(entries) > limit {
|
||||
entries = entries[:limit]
|
||||
}
|
||||
|
||||
return entries
|
||||
}
|
||||
|
||||
func trainingHistoryHandler(w http.ResponseWriter, r *http.Request) {
|
||||
if r.Method != http.MethodGet {
|
||||
trainingWriteError(w, http.StatusMethodNotAllowed, "method not allowed")
|
||||
return
|
||||
}
|
||||
|
||||
root, err := trainingRootDir()
|
||||
if err != nil {
|
||||
trainingWriteError(w, http.StatusInternalServerError, err.Error())
|
||||
return
|
||||
}
|
||||
|
||||
entries := trainingReadHistory(root, 50)
|
||||
if entries == nil {
|
||||
entries = []TrainingHistoryEntry{}
|
||||
}
|
||||
|
||||
trainingWriteJSON(w, http.StatusOK, TrainingHistoryResponse{
|
||||
OK: true,
|
||||
Entries: entries,
|
||||
})
|
||||
}
|
||||
|
||||
func trainingConfidenceFromScore(score float64) TrainingConfidence {
|
||||
if math.IsNaN(score) || math.IsInf(score, 0) {
|
||||
score = 0
|
||||
|
||||
@ -68,6 +68,8 @@ type TrainingJobStatus = {
|
||||
epoch?: number
|
||||
epochs?: number
|
||||
previewUrl?: string
|
||||
map50?: number
|
||||
map5095?: number
|
||||
}
|
||||
|
||||
type TrainingPrediction = {
|
||||
@ -165,6 +167,31 @@ type TrainingLabelStat = {
|
||||
confidence?: TrainingConfidence
|
||||
}
|
||||
|
||||
type TrainingModelInfo = {
|
||||
trainedAt?: string
|
||||
trainedAtMs?: number
|
||||
epochs?: number
|
||||
trainSamples?: number
|
||||
valSamples?: number
|
||||
imgsz?: number
|
||||
device?: string
|
||||
map50?: number
|
||||
map5095?: number
|
||||
}
|
||||
|
||||
type TrainingHistoryEntry = {
|
||||
trainedAt?: string
|
||||
trainedAtMs?: number
|
||||
durationMs?: number
|
||||
epochs?: number
|
||||
trainSamples?: number
|
||||
valSamples?: number
|
||||
imgsz?: number
|
||||
device?: string
|
||||
map50?: number
|
||||
map5095?: number
|
||||
}
|
||||
|
||||
type TrainingStats = {
|
||||
feedbackCount: number
|
||||
acceptedCount: number
|
||||
@ -173,6 +200,7 @@ type TrainingStats = {
|
||||
sampleCount: number
|
||||
boxCount: number
|
||||
modelAvailable: boolean
|
||||
modelInfo?: TrainingModelInfo
|
||||
confidence?: TrainingConfidence
|
||||
labels: {
|
||||
people: TrainingLabelStat[]
|
||||
@ -252,6 +280,54 @@ function confidenceLabel(confidence?: TrainingConfidence | null) {
|
||||
return confidence?.label || 'Keine'
|
||||
}
|
||||
|
||||
function formatModelTrainedAt(info?: TrainingModelInfo): string {
|
||||
if (!info) return ''
|
||||
|
||||
const ms = Number(info.trainedAtMs)
|
||||
const date =
|
||||
Number.isFinite(ms) && ms > 0
|
||||
? new Date(ms)
|
||||
: info.trainedAt
|
||||
? new Date(info.trainedAt)
|
||||
: null
|
||||
|
||||
if (!date || Number.isNaN(date.getTime())) return ''
|
||||
|
||||
return date.toLocaleString('de-DE', {
|
||||
day: '2-digit',
|
||||
month: '2-digit',
|
||||
year: 'numeric',
|
||||
hour: '2-digit',
|
||||
minute: '2-digit',
|
||||
})
|
||||
}
|
||||
|
||||
function formatMapPercent(value?: number | null): string {
|
||||
const n = Number(value)
|
||||
if (!Number.isFinite(n) || n <= 0) return ''
|
||||
return `${(n * 100).toFixed(1)}%`
|
||||
}
|
||||
|
||||
function formatHistoryDate(entry: TrainingHistoryEntry): string {
|
||||
const ms = Number(entry.trainedAtMs)
|
||||
const date =
|
||||
Number.isFinite(ms) && ms > 0
|
||||
? new Date(ms)
|
||||
: entry.trainedAt
|
||||
? new Date(entry.trainedAt)
|
||||
: null
|
||||
|
||||
if (!date || Number.isNaN(date.getTime())) return '—'
|
||||
|
||||
return date.toLocaleString('de-DE', {
|
||||
day: '2-digit',
|
||||
month: '2-digit',
|
||||
year: '2-digit',
|
||||
hour: '2-digit',
|
||||
minute: '2-digit',
|
||||
})
|
||||
}
|
||||
|
||||
function confidencePillClass(confidence?: TrainingConfidence | null) {
|
||||
switch (confidence?.level) {
|
||||
case 'high':
|
||||
@ -780,6 +856,7 @@ function TrainingStageOverlay(props: {
|
||||
progress?: number
|
||||
backgroundUrl?: string
|
||||
visible?: boolean
|
||||
instantBackground?: boolean
|
||||
}) {
|
||||
const progress = clampPercent(props.progress ?? 0)
|
||||
const isTraining = props.mode === 'training'
|
||||
@ -819,15 +896,21 @@ function TrainingStageOverlay(props: {
|
||||
<div className="absolute inset-1 overflow-hidden rounded-md sm:inset-2">
|
||||
{hasBackground ? (
|
||||
<img
|
||||
key={props.backgroundUrl}
|
||||
// Beim schnellen Durchlaufen (Training) dasselbe Element wiederverwenden,
|
||||
// damit nur die Bildquelle wechselt und kein Ein-/Ausblenden je Bild läuft.
|
||||
key={props.instantBackground ? undefined : props.backgroundUrl}
|
||||
src={props.backgroundUrl}
|
||||
alt=""
|
||||
aria-hidden="true"
|
||||
draggable={false}
|
||||
className={[
|
||||
'absolute inset-0 z-0 h-full w-full object-contain blur-[1px]',
|
||||
'transition-opacity duration-500 ease-out will-change-opacity motion-reduce:transition-none',
|
||||
backgroundVisible ? 'opacity-80' : 'opacity-0',
|
||||
props.instantBackground
|
||||
? 'opacity-80'
|
||||
: [
|
||||
'transition-opacity duration-500 ease-out will-change-opacity motion-reduce:transition-none',
|
||||
backgroundVisible ? 'opacity-80' : 'opacity-0',
|
||||
].join(' '),
|
||||
].join(' ')}
|
||||
/>
|
||||
) : null}
|
||||
@ -1591,6 +1674,7 @@ function TrainingStatsModal(props: {
|
||||
open: boolean
|
||||
onClose: () => void
|
||||
stats: TrainingStats | null
|
||||
history?: TrainingHistoryEntry[]
|
||||
loading: boolean
|
||||
error: string | null
|
||||
feedbackCount: number
|
||||
@ -1607,6 +1691,24 @@ function TrainingStatsModal(props: {
|
||||
const sampleCount = stats?.sampleCount ?? 0
|
||||
const overallConfidence = stats?.confidence
|
||||
|
||||
const modelTrainedAtLabel = formatModelTrainedAt(stats?.modelInfo)
|
||||
const modelMap50Label = formatMapPercent(stats?.modelInfo?.map50)
|
||||
const modelMap5095Label = formatMapPercent(stats?.modelInfo?.map5095)
|
||||
const modelInfoDetails = (() => {
|
||||
const info = stats?.modelInfo
|
||||
if (!info) return ''
|
||||
|
||||
const parts: string[] = []
|
||||
if (Number(info.epochs) > 0) parts.push(`${info.epochs} Epochen`)
|
||||
if (Number(info.trainSamples) > 0) parts.push(`${info.trainSamples} Train`)
|
||||
if (Number(info.valSamples) > 0) parts.push(`${info.valSamples} Val`)
|
||||
if (String(info.device || '').trim()) parts.push(String(info.device).trim())
|
||||
|
||||
return parts.join(' · ')
|
||||
})()
|
||||
|
||||
const history = props.history ?? []
|
||||
|
||||
const tabItems: Array<{
|
||||
key: TrainingStatsTabKey
|
||||
title: string
|
||||
@ -1858,11 +1960,42 @@ function TrainingStatsModal(props: {
|
||||
: 'Noch kein trainiertes Modell verfügbar'}
|
||||
</div>
|
||||
|
||||
<div className="mt-1 text-xs leading-relaxed text-indigo-800/80 dark:text-indigo-100/70">
|
||||
{stats?.modelAvailable
|
||||
? 'Die aktuellen Trainingsdaten können bereits von einem Modell genutzt werden.'
|
||||
: 'Sammle weiter Feedback und starte anschließend das Training.'}
|
||||
</div>
|
||||
{stats?.modelAvailable && modelTrainedAtLabel ? (
|
||||
<div className="mt-2 space-y-1">
|
||||
<div className="flex items-center justify-between gap-2 text-xs">
|
||||
<span className="font-medium text-indigo-800/80 dark:text-indigo-100/70">
|
||||
Version vom
|
||||
</span>
|
||||
<span className="font-semibold tabular-nums text-indigo-950 dark:text-indigo-50">
|
||||
{modelTrainedAtLabel}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
{modelMap50Label ? (
|
||||
<div className="flex items-center justify-between gap-2 text-xs">
|
||||
<span className="font-medium text-indigo-800/80 dark:text-indigo-100/70">
|
||||
Qualität (mAP50{modelMap5095Label ? ' / 50-95' : ''})
|
||||
</span>
|
||||
<span className="font-semibold tabular-nums text-indigo-950 dark:text-indigo-50">
|
||||
{modelMap50Label}
|
||||
{modelMap5095Label ? ` / ${modelMap5095Label}` : ''}
|
||||
</span>
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
{modelInfoDetails ? (
|
||||
<div className="text-[11px] text-indigo-800/70 dark:text-indigo-100/60">
|
||||
{modelInfoDetails}
|
||||
</div>
|
||||
) : null}
|
||||
</div>
|
||||
) : (
|
||||
<div className="mt-1 text-xs leading-relaxed text-indigo-800/80 dark:text-indigo-100/70">
|
||||
{stats?.modelAvailable
|
||||
? 'Die aktuellen Trainingsdaten können bereits von einem Modell genutzt werden.'
|
||||
: 'Sammle weiter Feedback und starte anschließend das Training.'}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div className="rounded-xl border border-gray-200 bg-white p-4 shadow-sm dark:border-white/10 dark:bg-gray-900/70">
|
||||
@ -1900,6 +2033,77 @@ function TrainingStatsModal(props: {
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{history.length > 0 ? (
|
||||
<div className="overflow-hidden rounded-xl border border-gray-200 bg-white shadow-sm dark:border-white/10 dark:bg-gray-900/70">
|
||||
<div className="border-b border-gray-200 bg-gray-50/80 px-4 py-3 dark:border-white/10 dark:bg-white/[0.03]">
|
||||
<div className="text-sm font-semibold text-gray-900 dark:text-white">
|
||||
Trainings-Verlauf
|
||||
</div>
|
||||
<div className="mt-0.5 text-xs text-gray-500 dark:text-gray-400">
|
||||
Modellqualität (mAP) über die letzten Trainingsläufe
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="max-h-56 divide-y divide-gray-100 overflow-y-auto dark:divide-white/10">
|
||||
{history.map((entry, idx) => {
|
||||
const map50 = formatMapPercent(entry.map50)
|
||||
const map5095 = formatMapPercent(entry.map5095)
|
||||
const duration =
|
||||
Number(entry.durationMs) > 0
|
||||
? formatDuration(Number(entry.durationMs))
|
||||
: ''
|
||||
const barPct = Math.max(
|
||||
0,
|
||||
Math.min(100, Math.round(Number(entry.map50 ?? 0) * 100))
|
||||
)
|
||||
|
||||
const meta: string[] = []
|
||||
if (Number(entry.epochs) > 0) meta.push(`${entry.epochs} Ep.`)
|
||||
if (Number(entry.trainSamples) > 0) meta.push(`${entry.trainSamples} Train`)
|
||||
if (duration) meta.push(duration)
|
||||
|
||||
return (
|
||||
<div key={`${entry.trainedAtMs}-${idx}`} className="px-4 py-2.5">
|
||||
<div className="flex items-center justify-between gap-3">
|
||||
<div className="min-w-0">
|
||||
<div className="truncate text-sm font-semibold text-gray-900 dark:text-white">
|
||||
{formatHistoryDate(entry)}
|
||||
{idx === 0 ? (
|
||||
<span className="ml-2 align-middle rounded-full bg-indigo-100 px-1.5 py-0.5 text-[9px] font-bold text-indigo-700 ring-1 ring-indigo-200 dark:bg-indigo-500/20 dark:text-indigo-100 dark:ring-indigo-300/30">
|
||||
aktuell
|
||||
</span>
|
||||
) : null}
|
||||
</div>
|
||||
{meta.length > 0 ? (
|
||||
<div className="mt-0.5 truncate text-[11px] text-gray-500 dark:text-gray-400">
|
||||
{meta.join(' · ')}
|
||||
</div>
|
||||
) : null}
|
||||
</div>
|
||||
|
||||
<div className="shrink-0 text-right">
|
||||
<div className="text-sm font-bold tabular-nums text-gray-900 dark:text-white">
|
||||
{map50 || '—'}
|
||||
</div>
|
||||
<div className="text-[10px] tabular-nums text-gray-500 dark:text-gray-400">
|
||||
{map5095 ? `50-95: ${map5095}` : 'mAP50'}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="mt-2 h-1.5 overflow-hidden rounded-full bg-gray-200 dark:bg-white/10">
|
||||
<div
|
||||
className="h-full rounded-full bg-indigo-500 transition-all"
|
||||
style={{ width: `${barPct}%` }}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
{/* Mobile: Tabs kompakter, direkt nach Summary */}
|
||||
<div className="overflow-hidden rounded-xl border border-gray-200 bg-gray-50/70 p-1.5 dark:border-white/10 dark:bg-white/[0.03] sm:p-2">
|
||||
<div className="grid grid-cols-2 gap-1 sm:grid-cols-5">
|
||||
@ -2060,6 +2264,7 @@ export default function TrainingTab(props: {
|
||||
const [trainingStats, setTrainingStats] = useState<TrainingStats | null>(null)
|
||||
const [trainingStatsLoading, setTrainingStatsLoading] = useState(false)
|
||||
const [trainingStatsError, setTrainingStatsError] = useState<string | null>(null)
|
||||
const [trainingHistory, setTrainingHistory] = useState<TrainingHistoryEntry[]>([])
|
||||
const wasTrainingRunningRef = useRef(false)
|
||||
const shownTrainingCompletionRef = useRef<string | null>(null)
|
||||
const [dismissedTrainingInfoKey, setDismissedTrainingInfoKey] = useState(() => {
|
||||
@ -2116,6 +2321,14 @@ export default function TrainingTab(props: {
|
||||
const [loadingPreviewLoaded, setLoadingPreviewLoaded] = useState(false)
|
||||
const [loadingPreviewFailed, setLoadingPreviewFailed] = useState(false)
|
||||
|
||||
// Während des Trainings sendet das Backend zum gerade trainierten Batch eine
|
||||
// Vorschau. Wir zeigen immer das zuletzt eingegangene Bild (live) und merken es
|
||||
// in einem Ref, damit die Anzeige unabhängig von den (gedrosselten) Status-
|
||||
// Updates bleibt und die Render-Rate begrenzt ist.
|
||||
const [trainingPreviewUrl, setTrainingPreviewUrl] = useState('')
|
||||
const latestTrainingPreviewRef = useRef('')
|
||||
const lastTrainingStatusApplyRef = useRef(0)
|
||||
|
||||
const [stageOverlayMounted, setStageOverlayMounted] = useState(false)
|
||||
const [stageOverlayVisible, setStageOverlayVisible] = useState(false)
|
||||
|
||||
@ -2717,6 +2930,8 @@ export default function TrainingTab(props: {
|
||||
epoch: Number(job.epoch ?? 0),
|
||||
epochs: Number(job.epochs ?? 0),
|
||||
previewUrl: String(job.previewUrl ?? ''),
|
||||
map50: Number(job.map50 ?? 0),
|
||||
map5095: Number(job.map5095 ?? 0),
|
||||
}
|
||||
: prev?.training,
|
||||
}))
|
||||
@ -3068,6 +3283,19 @@ export default function TrainingTab(props: {
|
||||
sampleCount: Number(data?.sampleCount ?? 0),
|
||||
boxCount: Number(data?.boxCount ?? 0),
|
||||
modelAvailable: Boolean(data?.modelAvailable),
|
||||
modelInfo: data?.modelInfo
|
||||
? {
|
||||
trainedAt: data.modelInfo.trainedAt,
|
||||
trainedAtMs: Number(data.modelInfo.trainedAtMs ?? 0),
|
||||
epochs: Number(data.modelInfo.epochs ?? 0),
|
||||
trainSamples: Number(data.modelInfo.trainSamples ?? 0),
|
||||
valSamples: Number(data.modelInfo.valSamples ?? 0),
|
||||
imgsz: Number(data.modelInfo.imgsz ?? 0),
|
||||
device: data.modelInfo.device,
|
||||
map50: Number(data.modelInfo.map50 ?? 0),
|
||||
map5095: Number(data.modelInfo.map5095 ?? 0),
|
||||
}
|
||||
: undefined,
|
||||
confidence: data?.confidence,
|
||||
labels: {
|
||||
people: Array.isArray(data?.labels?.people) ? data.labels.people : [],
|
||||
@ -3084,6 +3312,34 @@ export default function TrainingTab(props: {
|
||||
}
|
||||
}, [])
|
||||
|
||||
const loadTrainingHistory = useCallback(async () => {
|
||||
try {
|
||||
const res = await fetch('/api/training/history', { cache: 'no-store' })
|
||||
const data = await res.json().catch(() => null)
|
||||
|
||||
if (!res.ok || !data) return
|
||||
|
||||
setTrainingHistory(
|
||||
Array.isArray(data?.entries)
|
||||
? data.entries.map((e: any) => ({
|
||||
trainedAt: e?.trainedAt,
|
||||
trainedAtMs: Number(e?.trainedAtMs ?? 0),
|
||||
durationMs: Number(e?.durationMs ?? 0),
|
||||
epochs: Number(e?.epochs ?? 0),
|
||||
trainSamples: Number(e?.trainSamples ?? 0),
|
||||
valSamples: Number(e?.valSamples ?? 0),
|
||||
imgsz: Number(e?.imgsz ?? 0),
|
||||
device: e?.device,
|
||||
map50: Number(e?.map50 ?? 0),
|
||||
map5095: Number(e?.map5095 ?? 0),
|
||||
}))
|
||||
: []
|
||||
)
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
}, [])
|
||||
|
||||
useEffect(() => {
|
||||
updateImageLayerStyle()
|
||||
|
||||
@ -3125,9 +3381,23 @@ export default function TrainingTab(props: {
|
||||
|
||||
if (data?.type !== 'training_status') return
|
||||
|
||||
applyTrainingStatus({
|
||||
training: data.training,
|
||||
})
|
||||
const job = data.training || null
|
||||
const running = Boolean(job?.running)
|
||||
|
||||
// Immer das zuletzt gesendete Bild merken (live, ältere werden übersprungen).
|
||||
const previewUrl = String(job?.previewUrl ?? '').trim()
|
||||
if (running && previewUrl) {
|
||||
latestTrainingPreviewRef.current = previewUrl
|
||||
}
|
||||
|
||||
// Status (Progress/Epoche) nur gedrosselt anwenden, damit pro Bild
|
||||
// kein Re-Render des gesamten Tabs ausgelöst wird. Das Abschluss-Event
|
||||
// (running=false) wird immer angewendet.
|
||||
const now = Date.now()
|
||||
if (!running || now - lastTrainingStatusApplyRef.current >= 200) {
|
||||
lastTrainingStatusApplyRef.current = now
|
||||
applyTrainingStatus({ training: job })
|
||||
}
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
@ -3140,6 +3410,25 @@ export default function TrainingTab(props: {
|
||||
}
|
||||
}, [applyTrainingStatus])
|
||||
|
||||
// Im festen Takt immer das zuletzt eingegangene Bild anzeigen.
|
||||
// So bleibt die Anzeige live (max. ~110 ms Versatz) und die Render-Rate begrenzt.
|
||||
useEffect(() => {
|
||||
if (!trainingRunning) {
|
||||
latestTrainingPreviewRef.current = ''
|
||||
setTrainingPreviewUrl('')
|
||||
return
|
||||
}
|
||||
|
||||
const timer = window.setInterval(() => {
|
||||
const latest = latestTrainingPreviewRef.current
|
||||
if (!latest) return
|
||||
|
||||
setTrainingPreviewUrl((cur) => (cur === latest ? cur : latest))
|
||||
}, 110)
|
||||
|
||||
return () => window.clearInterval(timer)
|
||||
}, [trainingRunning])
|
||||
|
||||
useEffect(() => {
|
||||
const onAnalysis = (event: Event) => {
|
||||
try {
|
||||
@ -3266,7 +3555,8 @@ export default function TrainingTab(props: {
|
||||
if (!statsModalOpen) return
|
||||
|
||||
void loadTrainingStats()
|
||||
}, [statsModalOpen, loadTrainingStats])
|
||||
void loadTrainingHistory()
|
||||
}, [statsModalOpen, loadTrainingStats, loadTrainingHistory])
|
||||
|
||||
const onTrainingRunningChange = props.onTrainingRunningChange
|
||||
|
||||
@ -5800,9 +6090,10 @@ export default function TrainingTab(props: {
|
||||
text={stageOverlayText}
|
||||
progress={stageOverlayProgress}
|
||||
visible={stageOverlayIsVisible}
|
||||
instantBackground={stageOverlayMode === 'training'}
|
||||
backgroundUrl={
|
||||
stageOverlayMode === 'training'
|
||||
? trainingStatus?.training?.previewUrl || imageSrc
|
||||
? trainingPreviewUrl || trainingStatus?.training?.previewUrl || imageSrc
|
||||
: loadingPreviewBackgroundUrl
|
||||
}
|
||||
/>
|
||||
@ -5933,7 +6224,7 @@ export default function TrainingTab(props: {
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-4 gap-2 text-[11px]">
|
||||
<div className="grid grid-cols-5 gap-2 text-[11px]">
|
||||
<div className="rounded-lg bg-indigo-50 px-2 py-1.5 text-center text-indigo-900 ring-1 ring-indigo-100 dark:bg-indigo-500/10 dark:text-indigo-100 dark:ring-indigo-400/20">
|
||||
<div className="text-[9px] font-semibold uppercase tracking-wide opacity-70">
|
||||
Laufzeit
|
||||
@ -5971,6 +6262,15 @@ export default function TrainingTab(props: {
|
||||
{estimatedEpochMs > 0 ? formatDuration(estimatedEpochMs) : '—'}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="rounded-lg bg-emerald-50 px-2 py-1.5 text-center text-emerald-900 ring-1 ring-emerald-100 dark:bg-emerald-500/10 dark:text-emerald-100 dark:ring-emerald-400/20">
|
||||
<div className="text-[9px] font-semibold uppercase tracking-wide opacity-70">
|
||||
mAP50
|
||||
</div>
|
||||
<div className="mt-0.5 font-bold tabular-nums">
|
||||
{formatMapPercent(trainingStatus?.training?.map50) || '—'}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
) : null}
|
||||
@ -6378,6 +6678,7 @@ export default function TrainingTab(props: {
|
||||
open={statsModalOpen}
|
||||
onClose={() => setStatsModalOpen(false)}
|
||||
stats={trainingStats}
|
||||
history={trainingHistory}
|
||||
loading={trainingStatsLoading}
|
||||
error={trainingStatsError}
|
||||
feedbackCount={feedbackCount}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user