added real training progress + bugfixes
This commit is contained in:
Linrador
parent
34df62aa13
commit
a1000e9085
@ -52,6 +52,7 @@ def main():
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device = 0 if torch.cuda.is_available() else "cpu"
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try:
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results = model.predict(
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source=str(image_path),
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conf=float(args.conf),
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@ -59,6 +60,16 @@ def main():
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verbose=False,
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device=device,
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)
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except Exception as e:
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print(json.dumps({
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"available": False,
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"source": "detector_predict_failed",
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"modelPath": str(model_path),
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"image": str(image_path),
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"error": repr(e),
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"boxes": [],
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}, ensure_ascii=False))
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return
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boxes = []
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model_names = {}
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@ -56,11 +56,13 @@ def main():
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parser.add_argument("--base", default="yolo11n.pt")
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parser.add_argument("--epochs", default="80")
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parser.add_argument("--imgsz", default="640")
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parser.add_argument("--device", default="cpu")
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parser.add_argument("--device", default="auto")
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parser.add_argument("--workers", default="2")
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parser.add_argument("--patience", default="20")
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args = parser.parse_args()
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import torch
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root = Path(args.root).resolve()
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dataset_root = root / "detector" / "dataset"
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yaml_path = dataset_root / "dataset.yaml"
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@ -72,6 +74,11 @@ def main():
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workers = max(0, safe_int(args.workers, 2))
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patience = max(0, safe_int(args.patience, 20))
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if str(args.device).lower() == "auto":
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train_device = 0 if torch.cuda.is_available() else "cpu"
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else:
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train_device = args.device
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if not yaml_path.exists():
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raise SystemExit(f"dataset.yaml not found: {yaml_path}")
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@ -86,7 +93,7 @@ def main():
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valSamples=val_count,
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epochs=epochs,
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imgsz=imgsz,
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device=args.device,
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device=str(train_device),
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)
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if train_count <= 0:
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@ -100,6 +107,7 @@ def main():
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0.03,
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"YOLO-Basismodell wird geladen…",
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base=args.base,
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device=str(train_device),
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)
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model = YOLO(args.base)
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@ -119,6 +127,7 @@ def main():
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epochs=total,
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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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def on_train_epoch_end(trainer):
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@ -136,12 +145,14 @@ def main():
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epochs=total,
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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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def on_fit_epoch_end(trainer):
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nonlocal best_epoch
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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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metrics = getattr(trainer, "metrics", None) or {}
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# Ultralytics nutzt je nach Version unterschiedliche Keys.
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@ -161,12 +172,13 @@ def main():
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emit_progress(
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"detector",
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0.04 + 0.90 * (epoch / max(1, epochs)),
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f"Object Detector validiert… Epoche {epoch}/{epochs}",
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0.04 + 0.90 * (epoch / max(1, total)),
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f"Object Detector validiert… Epoche {epoch}/{total}",
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epoch=epoch,
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epochs=epochs,
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epochs=total,
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mAP50=map50,
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mAP5095=map5095,
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device=str(train_device),
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)
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model.add_callback("on_train_epoch_start", on_train_epoch_start)
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@ -180,6 +192,8 @@ def main():
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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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)
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result = model.train(
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@ -189,7 +203,7 @@ def main():
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project=str(runs_dir),
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name="detect",
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exist_ok=True,
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device=args.device,
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device=train_device,
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workers=workers,
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patience=patience,
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)
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@ -200,6 +214,7 @@ def main():
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"Bestes YOLO-Modell wird übernommen…",
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lastEpoch=last_epoch,
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bestEpoch=best_epoch,
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device=str(train_device),
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)
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best = runs_dir / "detect" / "weights" / "best.pt"
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@ -226,7 +241,7 @@ def main():
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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(args.device),
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"device": str(train_device),
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}
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with (out_dir / "status.json").open("w", encoding="utf-8") as f:
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@ -205,8 +205,9 @@ def main():
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continue
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label = target_from_annotation(row)
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if not label:
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label = "unknown"
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if not label or label == "unknown":
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skipped += 1
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continue
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emb = embed_image(clip_model, processor, device, image_path)
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113
backend/sse.go
113
backend/sse.go
@ -82,6 +82,119 @@ type taskStateEvent struct {
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TS int64 `json:"ts"`
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}
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type analysisProgressEvent struct {
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Type string `json:"type"` // "analysis_progress"
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Running bool `json:"running"`
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Phase string `json:"phase,omitempty"`
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Progress float64 `json:"progress"` // 0..1
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Current int `json:"current,omitempty"`
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Total int `json:"total,omitempty"`
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File string `json:"file,omitempty"`
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Message string `json:"message,omitempty"`
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Error string `json:"error,omitempty"`
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StartedAtMs int64 `json:"startedAtMs,omitempty"`
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FinishedAtMs int64 `json:"finishedAtMs,omitempty"`
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DurationMs int64 `json:"durationMs,omitempty"`
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TS int64 `json:"ts"`
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}
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func publishAnalysisProgress(ev analysisProgressEvent) {
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ev.Type = "analysis_progress"
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ev.TS = time.Now().UnixMilli()
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if ev.Progress < 0 {
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ev.Progress = 0
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}
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if ev.Progress > 1 {
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ev.Progress = 1
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}
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b, err := json.Marshal(ev)
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if err != nil {
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return
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}
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publishSSE("analysisProgress", b)
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}
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func publishAnalysisStarted(total int, message string) int64 {
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startedAtMs := time.Now().UnixMilli()
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publishAnalysisProgress(analysisProgressEvent{
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Running: true,
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Phase: "starting",
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Progress: 0,
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Current: 0,
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Total: total,
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Message: message,
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StartedAtMs: startedAtMs,
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})
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return startedAtMs
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}
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func publishAnalysisStep(startedAtMs int64, current int, total int, file string, message string) {
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progress := 0.0
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if total > 0 {
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progress = float64(current) / float64(total)
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}
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publishAnalysisProgress(analysisProgressEvent{
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Running: true,
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Phase: "running",
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Progress: progress,
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Current: current,
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Total: total,
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File: file,
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Message: message,
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StartedAtMs: startedAtMs,
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})
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}
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func publishAnalysisFinished(startedAtMs int64, total int, message string) {
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finishedAtMs := time.Now().UnixMilli()
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durationMs := finishedAtMs - startedAtMs
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if durationMs < 0 {
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durationMs = 0
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}
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publishAnalysisProgress(analysisProgressEvent{
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Running: false,
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Phase: "done",
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Progress: 1,
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Current: total,
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Total: total,
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Message: message,
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StartedAtMs: startedAtMs,
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FinishedAtMs: finishedAtMs,
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DurationMs: durationMs,
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})
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}
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func publishAnalysisError(startedAtMs int64, message string, err error) {
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finishedAtMs := time.Now().UnixMilli()
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durationMs := finishedAtMs - startedAtMs
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if durationMs < 0 {
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durationMs = 0
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}
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errText := ""
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if err != nil {
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errText = err.Error()
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}
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publishAnalysisProgress(analysisProgressEvent{
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Running: false,
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Phase: "error",
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Progress: 0,
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Message: message,
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Error: errText,
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StartedAtMs: startedAtMs,
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FinishedAtMs: finishedAtMs,
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DurationMs: durationMs,
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})
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}
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func publishFinishedPostworkStateForJob(
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j *RecordJob,
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queue string,
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@ -391,25 +391,44 @@ func trainingNextHandler(w http.ResponseWriter, r *http.Request) {
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strings.EqualFold(r.URL.Query().Get("refresh"), "true")
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if !forceNew {
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if sample, ok, err := trainingLatestOpenSample(root, refreshPrediction); err != nil {
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var startedAtMs int64
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if refreshPrediction {
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startedAtMs = publishAnalysisStarted(2, "Aktuelles Bild wird neu analysiert…")
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}
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if sample, ok, err := trainingLatestOpenSample(root, refreshPrediction, startedAtMs); err != nil {
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if refreshPrediction {
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publishAnalysisError(startedAtMs, "Aktuelles Bild konnte nicht neu analysiert werden.", err)
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}
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trainingWriteError(w, http.StatusInternalServerError, err.Error())
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return
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} else if ok {
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if refreshPrediction {
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publishAnalysisFinished(startedAtMs, 2, "Analyse abgeschlossen.")
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}
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trainingWriteJSON(w, http.StatusOK, sample)
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return
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}
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}
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sample, err := trainingCreateNextSample()
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startedAtMs := publishAnalysisStarted(4, "Neues Trainingsbild wird vorbereitet…")
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sample, err := trainingCreateNextSampleWithProgress(startedAtMs)
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if err != nil {
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publishAnalysisError(startedAtMs, "Trainingsbild konnte nicht erstellt werden.", err)
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trainingWriteError(w, http.StatusInternalServerError, err.Error())
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return
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}
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publishAnalysisFinished(startedAtMs, 4, "Analyse abgeschlossen.")
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trainingWriteJSON(w, http.StatusOK, sample)
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}
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func trainingLatestOpenSample(root string, refreshPrediction bool) (*TrainingSample, bool, error) {
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func trainingLatestOpenSample(root string, refreshPrediction bool, startedAtMs int64) (*TrainingSample, bool, error) {
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answered, err := trainingAnsweredSampleIDs(root)
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if err != nil {
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return nil, false, err
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@ -475,11 +494,6 @@ func trainingLatestOpenSample(root string, refreshPrediction bool) (*TrainingSam
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continue
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}
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if refreshPrediction {
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sample.Prediction = trainingPredictFrame(framePath)
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_ = trainingWriteSample(root, sample)
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}
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return sample, true, nil
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}
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@ -603,8 +617,16 @@ func trainingFeedbackHandler(w http.ResponseWriter, r *http.Request) {
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return
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}
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if req.Correction != nil && len(req.Correction.Boxes) > 0 {
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if err := trainingWriteDetectorSample(root, sample, req.Correction.Boxes); err != nil {
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detectorBoxes := []TrainingBox{}
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if req.Correction != nil {
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detectorBoxes = req.Correction.Boxes
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} else if req.Accepted {
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detectorBoxes = sample.Prediction.Boxes
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}
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if len(detectorBoxes) > 0 {
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if err := trainingWriteDetectorSample(root, sample, detectorBoxes); err != nil {
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fmt.Println("⚠️ detector sample write failed:", err)
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}
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}
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@ -1014,7 +1036,7 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
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// Pipeline:
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// - YOLO erkennt Personen/Gender für die Counts.
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// - Automatisch erkannte Personenboxen werden nicht an das Frontend als sichtbare Boxen zurückgegeben.
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// - Personenboxen werden jetzt auch sichtbar zurückgegeben.
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// - Manuell gezeichnete Personenboxen werden trotzdem als Trainingsdaten gespeichert.
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trainingWriteJSON(w, http.StatusOK, map[string]any{
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"ok": true,
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@ -1742,6 +1764,84 @@ func trainingCreateNextSample() (*TrainingSample, error) {
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return sample, nil
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}
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func trainingCreateNextSampleWithProgress(startedAtMs int64) (*TrainingSample, error) {
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publishAnalysisStep(startedAtMs, 1, 4, "", "Video wird ausgewählt…")
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settings := getSettings()
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doneDir := strings.TrimSpace(settings.DoneDir)
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if doneDir == "" {
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return nil, errors.New("doneDir ist leer")
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}
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videoPath, err := trainingPickRandomVideo(doneDir)
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if err != nil {
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return nil, err
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}
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publishAnalysisStep(startedAtMs, 2, 4, filepath.Base(videoPath), "Frame wird extrahiert…")
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duration := trainingProbeDurationSeconds(videoPath)
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second := trainingRandomSecond(duration)
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root, err := trainingRootDir()
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if err != nil {
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return nil, err
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}
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if err := trainingEnsureDetectorDirs(root); err != nil {
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return nil, err
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}
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if err := os.MkdirAll(filepath.Join(root, "frames"), 0755); err != nil {
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return nil, err
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}
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if err := os.MkdirAll(filepath.Join(root, "samples"), 0755); err != nil {
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return nil, err
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}
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id := trainingMakeSampleID(videoPath, second)
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framePath := filepath.Join(root, "frames", id+".jpg")
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if err := trainingExtractFrame(videoPath, framePath, second); err != nil {
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second = 0
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id = trainingMakeSampleID(videoPath, second)
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framePath = filepath.Join(root, "frames", id+".jpg")
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if err2 := trainingExtractFrame(videoPath, framePath, second); err2 != nil {
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return nil, fmt.Errorf("frame extraction failed: %v / fallback: %w", err, err2)
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}
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}
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publishAnalysisStep(startedAtMs, 3, 4, filepath.Base(videoPath), "Frame wird analysiert…")
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prediction := trainingPredictFrame(framePath)
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var sourceSizeBytes int64
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if st, err := os.Stat(videoPath); err == nil && st != nil && !st.IsDir() {
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sourceSizeBytes = st.Size()
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}
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sample := &TrainingSample{
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SampleID: id,
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FrameURL: "/api/training/frame?id=" + id,
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SourceFile: filepath.Base(videoPath),
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SourcePath: videoPath,
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SourceSizeBytes: sourceSizeBytes,
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Second: second,
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CreatedAt: time.Now().UTC().Format(time.RFC3339),
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Prediction: prediction,
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}
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publishAnalysisStep(startedAtMs, 4, 4, filepath.Base(videoPath), "Analyse-Ergebnis wird gespeichert…")
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if err := trainingWriteSample(root, sample); err != nil {
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return nil, err
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}
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return sample, nil
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}
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func trainingPickRandomVideo(doneDir string) (string, error) {
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extOK := map[string]bool{
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".mp4": true,
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@ -14,6 +14,29 @@ type ScoredLabel = {
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score: number
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}
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type AnalysisProgressEvent = {
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type?: 'analysis_progress'
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running?: boolean
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phase?: string
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progress?: number
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current?: number
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total?: number
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file?: string
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message?: string
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error?: string
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startedAtMs?: number
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finishedAtMs?: number
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durationMs?: number
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ts?: number
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}
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type TrainingStatus = {
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feedbackCount: number
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requiredCount: number
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canTrain: boolean
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training?: TrainingJobStatus
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}
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type TrainingJobStatus = {
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running: boolean
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progress: number
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@ -28,13 +51,6 @@ type TrainingJobStatus = {
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epochs?: number
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}
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type TrainingStatus = {
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feedbackCount: number
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requiredCount: number
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canTrain: boolean
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training?: TrainingJobStatus
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}
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type TrainingPrediction = {
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modelAvailable: boolean
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source?: string
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@ -551,6 +567,15 @@ function clamp01(v: number) {
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return Math.max(0, Math.min(1, v))
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}
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function snap01(v: number, epsilon = 0.006) {
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const n = clamp01(v)
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if (n <= epsilon) return 0
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if (n >= 1 - epsilon) return 1
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return n
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}
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function normalizeBox(box: TrainingBox): TrainingBox {
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const x = clamp01(box.x)
|
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const y = clamp01(box.y)
|
||||
@ -601,12 +626,13 @@ function predictionToCorrection(sample: TrainingSample | null): CorrectionState
|
||||
|
||||
const maleCount = safeCount(p?.maleCount)
|
||||
const femaleCount = safeCount(p?.femaleCount)
|
||||
const unknownCount = safeCount(p?.unknownCount)
|
||||
|
||||
return {
|
||||
peopleCount: maleCount + femaleCount,
|
||||
peopleCount: maleCount + femaleCount + unknownCount,
|
||||
maleCount,
|
||||
femaleCount,
|
||||
unknownCount: 0,
|
||||
unknownCount,
|
||||
sexPosition: p?.sexPosition || 'unknown',
|
||||
bodyPartsPresent: (p?.bodyPartsPresent ?? []).map((x) => x.label),
|
||||
objectsPresent: (p?.objectsPresent ?? []).map((x) => x.label),
|
||||
@ -1099,8 +1125,12 @@ function DetectorBoxLabelSelect(props: {
|
||||
const rect = button.getBoundingClientRect()
|
||||
const viewportH = window.innerHeight
|
||||
const viewportW = window.innerWidth
|
||||
const maxHeight = Math.min(260, Math.max(140, viewportH - rect.bottom - 12))
|
||||
const openUp = viewportH - rect.bottom < 180 && rect.top > rect.bottom
|
||||
const spaceBelow = viewportH - rect.bottom
|
||||
const spaceAbove = rect.top
|
||||
const openUp = spaceBelow < 180 && spaceAbove > spaceBelow
|
||||
const maxHeight = openUp
|
||||
? Math.min(260, Math.max(140, spaceAbove - 12))
|
||||
: Math.min(260, Math.max(140, spaceBelow - 12))
|
||||
|
||||
setMenuStyle({
|
||||
position: 'fixed',
|
||||
@ -2149,19 +2179,6 @@ export default function TrainingTab(props: {
|
||||
: 'Trainingsbild wird geladen…'
|
||||
)
|
||||
|
||||
let progressTimer: number | undefined
|
||||
|
||||
progressTimer = window.setInterval(() => {
|
||||
setAnalysisProgress((value) => {
|
||||
if (value < 35) return Math.min(35, value + 4)
|
||||
if (value < 65) return Math.min(65, value + 2)
|
||||
if (value < 95) return Math.min(95, value + 1)
|
||||
|
||||
setAnalysisStep('Analyse läuft noch… Ergebnis wird erwartet.')
|
||||
return value
|
||||
})
|
||||
}, 350)
|
||||
|
||||
if (!opts?.preserveNotice) {
|
||||
setError(null)
|
||||
setMessage(null)
|
||||
@ -2219,12 +2236,8 @@ export default function TrainingTab(props: {
|
||||
} catch (e) {
|
||||
setError(e instanceof Error ? e.message : String(e))
|
||||
} finally {
|
||||
if (progressTimer !== undefined) {
|
||||
window.clearInterval(progressTimer)
|
||||
}
|
||||
|
||||
setAnalysisProgress(100)
|
||||
setAnalysisStep('Analyse abgeschlossen.')
|
||||
setAnalysisProgress((value) => Math.max(value, 100))
|
||||
setAnalysisStep((value) => value || 'Analyse abgeschlossen.')
|
||||
|
||||
window.setTimeout(() => {
|
||||
setLoading(false)
|
||||
@ -2305,7 +2318,46 @@ export default function TrainingTab(props: {
|
||||
}
|
||||
}
|
||||
|
||||
const onAnalysisProgress = (ev: MessageEvent) => {
|
||||
try {
|
||||
const data = JSON.parse(String(ev.data ?? 'null')) as AnalysisProgressEvent
|
||||
|
||||
if (data?.type !== 'analysis_progress') return
|
||||
|
||||
const progress = clampPercent(Number(data.progress ?? 0) * 100)
|
||||
const message = String(data.message || '').trim()
|
||||
|
||||
setAnalysisProgress(progress)
|
||||
|
||||
if (message) {
|
||||
setAnalysisStep(message)
|
||||
}
|
||||
|
||||
if (data.running) {
|
||||
setLoading(true)
|
||||
}
|
||||
|
||||
if (!data.running && data.phase === 'error') {
|
||||
setLoading(false)
|
||||
setAnalysisProgress(0)
|
||||
setAnalysisStep('')
|
||||
|
||||
if (data.error || data.message) {
|
||||
setError(String(data.error || data.message))
|
||||
}
|
||||
}
|
||||
|
||||
if (!data.running && data.phase === 'done') {
|
||||
setAnalysisProgress(100)
|
||||
setAnalysisStep(message || 'Analyse abgeschlossen.')
|
||||
}
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
|
||||
es.addEventListener('training', onTraining as EventListener)
|
||||
es.addEventListener('analysisProgress', onAnalysisProgress as EventListener)
|
||||
|
||||
es.onerror = () => {
|
||||
// Optional: Polling-Fallback bleibt separat bestehen.
|
||||
@ -2313,6 +2365,7 @@ export default function TrainingTab(props: {
|
||||
|
||||
return () => {
|
||||
es.removeEventListener('training', onTraining as EventListener)
|
||||
es.removeEventListener('analysisProgress', onAnalysisProgress as EventListener)
|
||||
es.close()
|
||||
}
|
||||
}, [applyTrainingStatus])
|
||||
@ -2516,12 +2569,16 @@ export default function TrainingTab(props: {
|
||||
setMessage(null)
|
||||
|
||||
try {
|
||||
const maleCount = safeCount(correction.maleCount)
|
||||
const femaleCount = safeCount(correction.femaleCount)
|
||||
const unknownCount = safeCount(correction.unknownCount)
|
||||
|
||||
const correctionPayload: CorrectionState = {
|
||||
...correction,
|
||||
peopleCount:
|
||||
safeCount(correction.maleCount) +
|
||||
safeCount(correction.femaleCount),
|
||||
unknownCount: 0,
|
||||
maleCount,
|
||||
femaleCount,
|
||||
unknownCount,
|
||||
peopleCount: maleCount + femaleCount + unknownCount,
|
||||
boxes: (correction.boxes ?? [])
|
||||
.map(normalizeBox)
|
||||
.filter((box) => box.label && box.w > 0 && box.h > 0),
|
||||
@ -2741,10 +2798,17 @@ export default function TrainingTab(props: {
|
||||
let x2 = original.x + original.w
|
||||
let y2 = original.y + original.h
|
||||
|
||||
if (boxInteraction.handle.includes('n')) y1 = clamp01(y1 + dy)
|
||||
if (boxInteraction.handle.includes('s')) y2 = clamp01(y2 + dy)
|
||||
if (boxInteraction.handle.includes('w')) x1 = clamp01(x1 + dx)
|
||||
if (boxInteraction.handle.includes('e')) x2 = clamp01(x2 + dx)
|
||||
const pointerX = snap01(clampedPos.x)
|
||||
const pointerY = snap01(clampedPos.y)
|
||||
|
||||
// Wichtig:
|
||||
// Beim Resize folgt die gezogene Ecke direkt dem Pointer.
|
||||
// Dadurch bleibt kein Grab-Offset übrig, wenn der Handle nicht exakt
|
||||
// auf der mathematischen Box-Ecke getroffen wurde.
|
||||
if (boxInteraction.handle.includes('n')) y1 = pointerY
|
||||
if (boxInteraction.handle.includes('s')) y2 = pointerY
|
||||
if (boxInteraction.handle.includes('w')) x1 = pointerX
|
||||
if (boxInteraction.handle.includes('e')) x2 = pointerX
|
||||
|
||||
const left = Math.min(x1, x2)
|
||||
const top = Math.min(y1, y2)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user