bugfixes

backend/ai_server.py

@@ -123,18 +123,10 @@ CLOTHING_LABELS = LABEL_GROUPS["clothing"]
 POSITION_LABELS = set()
 
 PERSON_LABELS = {
-    "person",
     "person_male",
     "person_female",
-    "person_unknown",
-    "male_person",
-    "female_person",
 }
 
-MALE_LABELS = {"person_male", "male_person"}
-FEMALE_LABELS = {"person_female", "female_person"}
-UNKNOWN_PERSON_LABELS = {"person", "person_unknown"}
-
 _MODEL_PATH = ""
 _MODEL_ERROR = ""
 _LABEL_ERROR = ""
@@ -161,12 +153,9 @@ def empty_prediction(source: str = "model_missing") -> dict:
     return {
         "modelAvailable": False,
         "source": source,
-        "peopleCount": 0,
-        "maleCount": 0,
-        "femaleCount": 0,
-        "unknownCount": 0,
         "sexPosition": "unknown",
         "sexPositionScore": 0.0,
+        "peoplePresent": [],
         "bodyPartsPresent": [],
         "objectsPresent": [],
         "clothingPresent": [],
@@ -248,13 +237,6 @@ def load_label_groups_safe() -> None:
     PERSON_LABELS = {
         label for label in LABEL_GROUPS["people"]
         if label
-    } | {
-        "person",
-        "person_male",
-        "person_female",
-        "person_unknown",
-        "male_person",
-        "female_person",
     }
 
 
@@ -307,6 +289,7 @@ def prediction_from_result(result) -> dict:
     names = result.names or {}
 
     boxes_out = []
+    people_present = []
     body_parts = []
     objects = []
     clothing = []
@@ -333,6 +316,21 @@ def prediction_from_result(result) -> dict:
             w = max(0.0, min(1.0 - x, w))
             h = max(0.0, min(1.0 - y, h))
 
+            is_person = label in PERSON_LABELS
+            is_body = label in BODY_LABELS
+            is_object = label in OBJECT_LABELS
+            is_clothing = label in CLOTHING_LABELS
+            is_position = label in POSITION_LABELS
+
+            if is_position:
+                if score > sex_position_score:
+                    sex_position = label
+                    sex_position_score = score
+                continue
+
+            if not (is_person or is_body or is_object or is_clothing):
+                continue
+
             boxes_out.append({
                 "label": label,
                 "score": score,
@@ -342,33 +340,24 @@ def prediction_from_result(result) -> dict:
                 "h": h,
             })
 
-            if label in BODY_LABELS:
+            if is_person:
+                best_score(people_present, label, score)
+
+            if is_body:
                 best_score(body_parts, label, score)
 
-            if label in OBJECT_LABELS:
+            if is_object:
                 best_score(objects, label, score)
 
-            if label in CLOTHING_LABELS:
+            if is_clothing:
                 best_score(clothing, label, score)
 
-            if label in POSITION_LABELS and score > sex_position_score:
-                sex_position = label
-                sex_position_score = score
-
-    people_count = sum(1 for box in boxes_out if box["label"] in PERSON_LABELS)
-    male_count = sum(1 for box in boxes_out if box["label"] in MALE_LABELS)
-    female_count = sum(1 for box in boxes_out if box["label"] in FEMALE_LABELS)
-    unknown_count = sum(1 for box in boxes_out if box["label"] in UNKNOWN_PERSON_LABELS)
-
     return {
         "modelAvailable": True,
         "source": f"yolo-server:{Path(_MODEL_PATH).name}",
-        "peopleCount": people_count,
-        "maleCount": male_count,
-        "femaleCount": female_count,
-        "unknownCount": unknown_count,
         "sexPosition": sex_position,
         "sexPositionScore": sex_position_score,
+        "peoplePresent": people_present,
         "bodyPartsPresent": body_parts,
         "objectsPresent": objects,
         "clothingPresent": clothing,

backend/analyze.go

@@ -54,8 +54,13 @@ type videoFrameSample struct {
 
 const (
 	analyzeSegmentMergeGapSeconds = 8.0
-	nsfwThresholdModerate         = 0.35
-	nsfwThresholdStrong           = 0.60
+
+	// Ein Label darf kurz verschwinden, ohne dass ein neues Segment entsteht.
+	// Bei 3s Frame-Intervall heißt das: ein fehlender Frame wird überbrückt.
+	analyzeLabelInvisibleGraceSeconds = 3.0
+
+	nsfwThresholdModerate = 0.35
+	nsfwThresholdStrong   = 0.60
 
 	// Video-Modus: extrahiert 1 Frame alle N Sekunden.
 	// 1 = jeder Sekunde, 3 = alle 3 Sekunden, 5 = alle 5 Sekunden.
@@ -109,26 +114,15 @@ func shouldAutoSelectAnalyzeHit(label string) bool {
 		return false
 	}
 
-	autoSelectedAILabelsOnce.Do(func() {
-		autoSelectedAILabelsCache = autoSelectedAILabelSet()
-	})
-
-	_, ok := autoSelectedAILabelsCache[label]
+	labels := autoSelectedAILabelSet()
+	_, ok := labels[label]
 	return ok
 }
 
 var nsfwIgnoredLabels = map[string]struct{}{
 	// Personen sollen nicht als interessante Segmente auftauchen.
-	"person":         {},
-	"person_male":    {},
-	"person_female":  {},
-	"person_unknown": {},
-	"male_person":    {},
-	"female_person":  {},
-
-	// Falls dein Detector irgendwann diese Varianten liefert:
-	"people_male":   {},
-	"people_female": {},
+	"person_male":   {},
+	"person_female": {},
 }
 
 func isIgnoredNSFWLabel(label string) bool {
@@ -1084,26 +1078,43 @@ func appendNSFWHitFromPrediction(
 	t float64,
 ) []analyzeHit {
 	if !pred.ModelAvailable {
+		appLogln("⚠️ nsfw: modelAvailable=false bei", t)
 		return hits
 	}
 
 	nsfwResults := trainingPredictionToNSFWResults(pred)
-	bestLabel, bestScore := pickBestNSFWResult(nsfwResults)
-	if bestLabel == "" {
+	if len(nsfwResults) == 0 {
 		return hits
 	}
 
-	if bestScore < nsfwThresholdForLabel(bestLabel) {
-		return hits
+	for _, r := range nsfwResults {
+		label := strings.ToLower(strings.TrimSpace(r.Label))
+		if label == "" || label == "unknown" {
+			continue
+		}
+		if isIgnoredNSFWLabel(label) || isPersonSegmentLabel(label) {
+			continue
+		}
+
+		score := r.Score
+		if score <= 0 {
+			score = 1
+		}
+
+		if score < nsfwThresholdForLabel(label) {
+			continue
+		}
+
+		hits = append(hits, analyzeHit{
+			Time:  t,
+			Label: label,
+			Score: score,
+			Start: t,
+			End:   t,
+		})
 	}
 
-	return append(hits, analyzeHit{
-		Time:  t,
-		Label: bestLabel,
-		Score: bestScore,
-		Start: t,
-		End:   t,
-	})
+	return hits
 }
 
 type highlightSignal struct {
@@ -1351,15 +1362,14 @@ func buildCombinedHighlightHitFromPrediction(pred TrainingPrediction, t float64)
 		addHighlightSignal(best, "detector:"+label, box.Score)
 	}
 
-	if len(best) < 2 {
-		return analyzeHit{}, false
-	}
-
 	signals := make([]highlightSignal, 0, len(best))
 	groupSeen := map[string]bool{}
 	nonPositionCount := 0
 	hasPosition := false
 
+	var bestSingle highlightSignal
+	var bestSingleQuality float64
+
 	for _, sig := range best {
 		if sig.Label == "" {
 			continue
@@ -1369,27 +1379,62 @@ func buildCombinedHighlightHitFromPrediction(pred TrainingPrediction, t float64)
 			hasPosition = true
 		} else {
 			nonPositionCount++
+
+			sev := segmentSeverityWeight(sig.Label)
+			quality := sig.Score * sev
+
+			if quality > bestSingleQuality {
+				bestSingle = sig
+				bestSingleQuality = quality
+			}
 		}
 
 		groupSeen[sig.Group] = true
 		signals = append(signals, sig)
 	}
 
-	// Nur echte interessante Kombis:
-	// - Position + mindestens ein weiteres Signal
-	// - oder mindestens zwei Nicht-Positions-Signale
-	// - oder mindestens zwei unterschiedliche Signalgruppen
+	// Fallback: starke Einzel-Treffer sollen auch Highlights werden.
+	// Sonst verschwinden viele explizite Stellen, wenn sie nicht zufällig
+	// im selben Frame mit Position/Object/Clothing kombiniert werden.
+	returnSingleIfGoodEnough := func() (analyzeHit, bool) {
+		if bestSingle.Label == "" {
+			return analyzeHit{}, false
+		}
+
+		sev := segmentSeverityWeight(bestSingle.Label)
+
+		// Nur wirklich relevante Einzel-Signale übernehmen.
+		if sev < 0.65 {
+			return analyzeHit{}, false
+		}
+		if bestSingle.Score < 0.35 {
+			return analyzeHit{}, false
+		}
+		if bestSingleQuality < 0.32 {
+			return analyzeHit{}, false
+		}
+
+		return analyzeHit{
+			Time:  t,
+			Label: bestSingle.Label,
+			Score: bestSingle.Score,
+			Start: t,
+			End:   t,
+		}, true
+	}
+
+	// Combo nur, wenn wirklich genug Kontext da ist.
 	if len(signals) < 2 {
-		return analyzeHit{}, false
+		return returnSingleIfGoodEnough()
 	}
 	if hasPosition && nonPositionCount < 1 {
-		return analyzeHit{}, false
+		return returnSingleIfGoodEnough()
 	}
 	if !hasPosition && nonPositionCount < 2 {
-		return analyzeHit{}, false
+		return returnSingleIfGoodEnough()
 	}
 	if len(groupSeen) < 2 && len(signals) < 3 {
-		return analyzeHit{}, false
+		return returnSingleIfGoodEnough()
 	}
 
 	sort.SliceStable(signals, func(i, j int) bool {
@@ -1464,17 +1509,84 @@ func buildCombinedHighlightHitFromPrediction(pred TrainingPrediction, t float64)
 	}, true
 }
 
+func buildHighlightHitsFromPrediction(pred TrainingPrediction, t float64) []analyzeHit {
+	if !pred.ModelAvailable {
+		return nil
+	}
+
+	best := map[string]highlightSignal{}
+
+	sexPosition := strings.ToLower(strings.TrimSpace(pred.SexPosition))
+	if sexPosition != "" && sexPosition != "unknown" && isKnownPositionLabel(sexPosition) {
+		addHighlightSignal(best, "position:"+sexPosition, pred.SexPositionScore)
+	}
+
+	addHighlightSignalsFromScoredLabels(best, "body", pred.BodyPartsPresent)
+	addHighlightSignalsFromScoredLabels(best, "object", pred.ObjectsPresent)
+	addHighlightSignalsFromScoredLabels(best, "clothing", pred.ClothingPresent)
+
+	for _, box := range pred.Boxes {
+		label := strings.ToLower(strings.TrimSpace(box.Label))
+		if label == "" || label == "unknown" {
+			continue
+		}
+		if isIgnoredNSFWLabel(label) {
+			continue
+		}
+		if !shouldAutoSelectAnalyzeHit(label) {
+			continue
+		}
+
+		addHighlightSignal(best, "detector:"+label, box.Score)
+	}
+
+	out := make([]analyzeHit, 0, len(best))
+
+	for _, sig := range best {
+		label := strings.ToLower(strings.TrimSpace(sig.Label))
+		if label == "" {
+			continue
+		}
+
+		// Schwache Positions-Kontexte wie standing/sitting nicht alleine als Segment anzeigen.
+		if sig.Group == "position" && segmentSeverityWeight(label) < 0.70 {
+			continue
+		}
+
+		out = append(out, analyzeHit{
+			Time:  t,
+			Label: label,
+			Score: sig.Score,
+			Start: t,
+			End:   t,
+		})
+	}
+
+	sort.SliceStable(out, func(i, j int) bool {
+		wi := segmentSeverityWeight(out[i].Label) * out[i].Score
+		wj := segmentSeverityWeight(out[j].Label) * out[j].Score
+
+		if wi != wj {
+			return wi > wj
+		}
+
+		return out[i].Label < out[j].Label
+	})
+
+	return out
+}
+
 func appendHighlightHitsFromPrediction(
 	hits []analyzeHit,
 	pred TrainingPrediction,
 	t float64,
 ) []analyzeHit {
-	hit, ok := buildCombinedHighlightHitFromPrediction(pred, t)
-	if !ok {
+	next := buildHighlightHitsFromPrediction(pred, t)
+	if len(next) == 0 {
 		return hits
 	}
 
-	return append(hits, hit)
+	return append(hits, next...)
 }
 
 func analyzeVideoFromFrames(ctx context.Context, outPath, goal string) ([]analyzeHit, error) {
@@ -2191,18 +2303,27 @@ func segmentTagsFromAnalyzeLabel(label string) []string {
 	return out
 }
 
+func analyzeHitContinuationGapSeconds() float64 {
+	// Treffer bei 0s und 6s sollen bei 3s Sampling noch zusammengehören:
+	// 0s erkannt, 3s nicht erkannt, 6s wieder erkannt.
+	return float64(analyzeVideoFrameIntervalSeconds) + analyzeLabelInvisibleGraceSeconds + 0.25
+}
+
 func mergeAnalyzeHits(in []analyzeHit) []analyzeHit {
 	if len(in) == 0 {
 		return []analyzeHit{}
 	}
 
-	cp := make([]analyzeHit, 0, len(in))
+	maxGap := analyzeHitContinuationGapSeconds()
+
+	byLabel := map[string][]analyzeHit{}
+
 	for _, h := range in {
 		label := strings.ToLower(strings.TrimSpace(h.Label))
-		if label == "" {
+		if label == "" || label == "unknown" {
 			continue
 		}
-		if isIgnoredNSFWLabel(label) {
+		if isIgnoredNSFWLabel(label) || isPersonSegmentLabel(label) {
 			continue
 		}
 
@@ -2221,62 +2342,84 @@ func mergeAnalyzeHits(in []analyzeHit) []analyzeHit {
 			}
 		}
 
+		if start > end {
+			start, end = end, start
+		}
+
 		h.Label = label
 		h.Start = start
 		h.End = end
-		cp = append(cp, h)
-	}
 
-	if len(cp) == 0 {
-		return []analyzeHit{}
-	}
-
-	sort.Slice(cp, func(i, j int) bool {
-		if cp[i].Start != cp[j].Start {
-			return cp[i].Start < cp[j].Start
-		}
-		if cp[i].End != cp[j].End {
-			return cp[i].End < cp[j].End
-		}
-		return cp[i].Label < cp[j].Label
-	})
-
-	out := make([]analyzeHit, 0, len(cp))
-	cur := cp[0]
-
-	for i := 1; i < len(cp); i++ {
-		n := cp[i]
-
-		// Direkt aufeinanderfolgende Treffer mit gleichem Label immer zusammenfassen.
-		// Sobald ein anderes Label dazwischen liegt, wird automatisch nicht gemergt.
-		sameLabel := sameAnalyzeSegmentLabel(cur.Label, n.Label)
-		gap := n.Start - cur.End
-
-		if sameLabel && gap >= -0.25 && gap <= analyzeSegmentMergeGapSeconds {
-			cur.Label = preferAnalyzeSegmentLabel(cur.Label, n.Label)
-			if n.Start < cur.Start {
-				cur.Start = n.Start
-			}
-			if n.End > cur.End {
-				cur.End = n.End
-			}
-			if n.Score > cur.Score {
-				cur.Score = n.Score
-			}
-			cur.Time = (cur.Start + cur.End) / 2
+		key := normalizeSegmentLabel(label)
+		if key == "" {
 			continue
 		}
 
-		out = append(out, cur)
-		cur = n
+		byLabel[key] = append(byLabel[key], h)
 	}
 
-	out = append(out, cur)
+	out := make([]analyzeHit, 0, len(in))
+
+	for _, items := range byLabel {
+		if len(items) == 0 {
+			continue
+		}
+
+		sort.SliceStable(items, func(i, j int) bool {
+			if items[i].Start != items[j].Start {
+				return items[i].Start < items[j].Start
+			}
+			if items[i].End != items[j].End {
+				return items[i].End < items[j].End
+			}
+			return items[i].Label < items[j].Label
+		})
+
+		cur := items[0]
+
+		for i := 1; i < len(items); i++ {
+			n := items[i]
+			gap := n.Start - cur.End
+
+			if gap >= -0.25 && gap <= maxGap {
+				cur.Label = preferAnalyzeSegmentLabel(cur.Label, n.Label)
+
+				if n.Start < cur.Start {
+					cur.Start = n.Start
+				}
+				if n.End > cur.End {
+					cur.End = n.End
+				}
+				if n.Score > cur.Score {
+					cur.Score = n.Score
+				}
+
+				cur.Time = (cur.Start + cur.End) / 2
+				continue
+			}
+
+			out = append(out, cur)
+			cur = n
+		}
+
+		out = append(out, cur)
+	}
+
+	sort.SliceStable(out, func(i, j int) bool {
+		if out[i].Start != out[j].Start {
+			return out[i].Start < out[j].Start
+		}
+		if out[i].End != out[j].End {
+			return out[i].End < out[j].End
+		}
+		return normalizeSegmentLabel(out[i].Label) < normalizeSegmentLabel(out[j].Label)
+	})
+
 	return out
 }
 
 func inferAnalyzePointSpanSeconds(hits []analyzeHit, duration float64) float64 {
-	const fallback = 3.0
+	const fallback = 10.0
 
 	if len(hits) < 2 {
 		return fallback
@@ -2338,8 +2481,8 @@ func inferAnalyzePointSpanSeconds(hits []analyzeHit, duration float64) float64 {
 	// aber wir deckeln, damit Sparse-Hits nicht riesig werden.
 	span := median * 0.90
 
-	if span < 2 {
-		span = 2
+	if span < 6 {
+		span = 6
 	}
 	if span > 12 {
 		span = 12
@@ -2387,17 +2530,56 @@ func expandAnalyzePointToSpan(t, span, duration float64) (float64, float64) {
 	return start, end
 }
 
-func buildSegmentsFromAnalyzeHits(hits []analyzeHit, duration float64) []aiSegmentMeta {
+type analyzeLabelSegmentPoint struct {
+	Label string
+	Start float64
+	End   float64
+	Score float64
+}
+
+func isAllowedAnalyzeSegmentLabel(label string) bool {
+	label = strings.ToLower(strings.TrimSpace(label))
+	if label == "" || label == "unknown" {
+		return false
+	}
+	if isIgnoredNSFWLabel(label) || isPersonSegmentLabel(label) {
+		return false
+	}
+
+	if strings.HasPrefix(label, "combo:") {
+		for _, part := range segmentLabelParts(label) {
+			if isAllowedAnalyzeSegmentLabel(part) {
+				return true
+			}
+		}
+		return false
+	}
+
+	raw := normalizeSegmentLabel(label)
+	if raw == "" || raw == "unknown" {
+		return false
+	}
+
+	return shouldAutoSelectAnalyzeHit(raw) || isKnownPositionLabel(raw)
+}
+
+func buildLabelContinuitySegmentsFromAnalyzeHits(
+	hits []analyzeHit,
+	duration float64,
+) []aiSegmentMeta {
 	if len(hits) == 0 || duration <= 0 {
 		return []aiSegmentMeta{}
 	}
 
-	pointSpan := inferAnalyzePointSpanSeconds(hits, duration)
+	sampleSpan := math.Max(1.0, float64(analyzeVideoFrameIntervalSeconds))
+	halfSample := sampleSpan / 2.0
+	maxGap := analyzeHitContinuationGapSeconds()
 
-	out := make([]aiSegmentMeta, 0, len(hits))
+	byLabel := map[string][]analyzeLabelSegmentPoint{}
 
 	for _, hit := range hits {
-		if !shouldAutoSelectAnalyzeHit(hit.Label) {
+		label := strings.ToLower(strings.TrimSpace(hit.Label))
+		if !isAllowedAnalyzeSegmentLabel(label) {
 			continue
 		}
 
@@ -2420,201 +2602,174 @@ func buildSegmentsFromAnalyzeHits(hits []analyzeHit, duration float64) []aiSegme
 			start, end = end, start
 		}
 
+		if start < 0 {
+			start = hit.Time
+		}
+		if end < 0 {
+			end = start
+		}
+
 		start = math.Max(0, math.Min(start, duration))
 		end = math.Max(0, math.Min(end, duration))
 
-		// Wichtig:
-		// Einzelne AI-Treffer sind oft Punkt-Treffer: Start == End.
-		// Für Segmente und Rating brauchen sie aber eine kleine Dauer.
-		if end <= start {
-			marker := hit.Time
-			if marker < 0 {
-				marker = start
-			}
-
-			start, end = expandAnalyzePointToSpan(marker, pointSpan, duration)
-		}
-
-		if end <= start {
+		key := normalizeSegmentLabel(label)
+		if key == "" {
 			continue
 		}
 
-		label := strings.ToLower(strings.TrimSpace(hit.Label))
+		score := hit.Score
+		if score <= 0 {
+			score = 1
+		}
 
-		out = append(out, aiSegmentMeta{
-			Label:           label,
-			StartSeconds:    start,
-			EndSeconds:      end,
-			DurationSeconds: end - start,
-			Score:           hit.Score,
-			AutoSelected:    true,
-			Position:        segmentPositionFromAnalyzeLabel(label),
-			Tags:            segmentTagsFromAnalyzeLabel(label),
+		byLabel[key] = append(byLabel[key], analyzeLabelSegmentPoint{
+			Label: label,
+			Start: start,
+			End:   end,
+			Score: score,
 		})
 	}
 
-	if len(out) == 0 {
-		return []aiSegmentMeta{}
+	out := make([]aiSegmentMeta, 0)
+
+	for _, points := range byLabel {
+		if len(points) == 0 {
+			continue
+		}
+
+		sort.SliceStable(points, func(i, j int) bool {
+			if points[i].Start != points[j].Start {
+				return points[i].Start < points[j].Start
+			}
+			if points[i].End != points[j].End {
+				return points[i].End < points[j].End
+			}
+			return points[i].Label < points[j].Label
+		})
+
+		curLabel := points[0].Label
+		curStartMarker := points[0].Start
+		curEndMarker := points[0].End
+		curScoreSum := points[0].Score
+		curScoreCount := 1
+
+		for i := 1; i < len(points); i++ {
+			n := points[i]
+
+			gap := n.Start - curEndMarker
+
+			if gap >= -0.25 && gap <= maxGap {
+				curLabel = preferAnalyzeSegmentLabel(curLabel, n.Label)
+
+				if n.Start < curStartMarker {
+					curStartMarker = n.Start
+				}
+				if n.End > curEndMarker {
+					curEndMarker = n.End
+				}
+
+				curScoreSum += n.Score
+				curScoreCount++
+				continue
+			}
+
+			segment := makeLabelContinuitySegment(
+				curLabel,
+				curStartMarker,
+				curEndMarker,
+				curScoreSum,
+				curScoreCount,
+				halfSample,
+				duration,
+			)
+
+			if segment.DurationSeconds > 0 {
+				out = append(out, segment)
+			}
+
+			curLabel = n.Label
+			curStartMarker = n.Start
+			curEndMarker = n.End
+			curScoreSum = n.Score
+			curScoreCount = 1
+		}
+
+		segment := makeLabelContinuitySegment(
+			curLabel,
+			curStartMarker,
+			curEndMarker,
+			curScoreSum,
+			curScoreCount,
+			halfSample,
+			duration,
+		)
+
+		if segment.DurationSeconds > 0 {
+			out = append(out, segment)
+		}
 	}
 
-	sort.Slice(out, func(i, j int) bool {
+	sort.SliceStable(out, func(i, j int) bool {
 		if out[i].StartSeconds != out[j].StartSeconds {
 			return out[i].StartSeconds < out[j].StartSeconds
 		}
 		if out[i].EndSeconds != out[j].EndSeconds {
 			return out[i].EndSeconds < out[j].EndSeconds
 		}
-		return out[i].Label < out[j].Label
+		return normalizeSegmentLabel(out[i].Label) < normalizeSegmentLabel(out[j].Label)
 	})
 
-	merged := make([]aiSegmentMeta, 0, len(out))
-	cur := out[0]
+	return out
+}
 
-	for i := 1; i < len(out); i++ {
-		n := out[i]
+func makeLabelContinuitySegment(
+	label string,
+	startMarker float64,
+	endMarker float64,
+	scoreSum float64,
+	scoreCount int,
+	halfSample float64,
+	duration float64,
+) aiSegmentMeta {
+	label = strings.ToLower(strings.TrimSpace(label))
 
-		gap := n.StartSeconds - cur.EndSeconds
-		if gap < 0 {
-			gap = 0
-		}
+	start := startMarker - halfSample
+	end := endMarker + halfSample
 
-		if sameAnalyzeSegmentLabel(cur.Label, n.Label) && gap <= analyzeSegmentMergeGapSeconds {
-			cur.Label = preferAnalyzeSegmentLabel(cur.Label, n.Label)
-			if n.StartSeconds < cur.StartSeconds {
-				cur.StartSeconds = n.StartSeconds
-			}
-			if n.EndSeconds > cur.EndSeconds {
-				cur.EndSeconds = n.EndSeconds
-			}
-			cur.DurationSeconds = cur.EndSeconds - cur.StartSeconds
-			if n.Score > cur.Score {
-				cur.Score = n.Score
-			}
-			cur.AutoSelected = cur.AutoSelected || n.AutoSelected
-			continue
-		}
-
-		merged = append(merged, cur)
-		cur = n
+	if start < 0 {
+		start = 0
+	}
+	if duration > 0 && end > duration {
+		end = duration
 	}
 
-	merged = append(merged, cur)
-	return mergeAdjacentAISegments(merged, analyzeSegmentMergeGapSeconds)
+	if end <= start {
+		end = math.Min(duration, start+math.Max(1, halfSample*2))
+	}
+
+	score := 0.0
+	if scoreCount > 0 {
+		score = scoreSum / float64(scoreCount)
+	}
+
+	return aiSegmentMeta{
+		Label:           label,
+		StartSeconds:    start,
+		EndSeconds:      end,
+		DurationSeconds: math.Max(0, end-start),
+		Score:           score,
+		AutoSelected:    true,
+		Position:        segmentPositionFromAnalyzeLabel(label),
+		Tags:            segmentTagsFromAnalyzeLabel(label),
+	}
+}
+
+func buildSegmentsFromAnalyzeHits(hits []analyzeHit, duration float64) []aiSegmentMeta {
+	return buildLabelContinuitySegmentsFromAnalyzeHits(hits, duration)
 }
 
 func buildHighlightSegmentsFromAnalyzeHits(hits []analyzeHit, duration float64) []aiSegmentMeta {
-	if len(hits) == 0 || duration <= 0 {
-		return []aiSegmentMeta{}
-	}
-
-	pointSpan := inferAnalyzePointSpanSeconds(hits, duration)
-
-	out := make([]aiSegmentMeta, 0, len(hits))
-
-	for _, hit := range hits {
-		label := strings.ToLower(strings.TrimSpace(hit.Label))
-		if label == "" || label == "unknown" {
-			continue
-		}
-
-		if isIgnoredNSFWLabel(label) {
-			continue
-		}
-
-		start := hit.Start
-		end := hit.End
-
-		if start < 0 && end < 0 {
-			start = hit.Time
-			end = hit.Time
-		} else {
-			if start < 0 {
-				start = hit.Time
-			}
-			if end < 0 {
-				end = hit.Time
-			}
-		}
-
-		if start > end {
-			start, end = end, start
-		}
-
-		start = math.Max(0, math.Min(start, duration))
-		end = math.Max(0, math.Min(end, duration))
-
-		if end <= start {
-			marker := hit.Time
-			if marker < 0 {
-				marker = start
-			}
-
-			start, end = expandAnalyzePointToSpan(marker, pointSpan, duration)
-		}
-
-		if end <= start {
-			continue
-		}
-
-		out = append(out, aiSegmentMeta{
-			Label:           label,
-			StartSeconds:    start,
-			EndSeconds:      end,
-			DurationSeconds: end - start,
-			Score:           hit.Score,
-			AutoSelected:    true,
-			Position:        segmentPositionFromAnalyzeLabel(label),
-			Tags:            segmentTagsFromAnalyzeLabel(label),
-		})
-	}
-
-	if len(out) == 0 {
-		return []aiSegmentMeta{}
-	}
-
-	sort.Slice(out, func(i, j int) bool {
-		if out[i].StartSeconds != out[j].StartSeconds {
-			return out[i].StartSeconds < out[j].StartSeconds
-		}
-		if out[i].EndSeconds != out[j].EndSeconds {
-			return out[i].EndSeconds < out[j].EndSeconds
-		}
-		return out[i].Label < out[j].Label
-	})
-
-	merged := make([]aiSegmentMeta, 0, len(out))
-	cur := out[0]
-
-	for i := 1; i < len(out); i++ {
-		n := out[i]
-
-		gap := n.StartSeconds - cur.EndSeconds
-		if gap < 0 {
-			gap = 0
-		}
-
-		if sameAnalyzeSegmentLabel(cur.Label, n.Label) && gap <= analyzeSegmentMergeGapSeconds {
-			cur.Label = preferAnalyzeSegmentLabel(cur.Label, n.Label)
-			if n.StartSeconds < cur.StartSeconds {
-				cur.StartSeconds = n.StartSeconds
-			}
-			if n.EndSeconds > cur.EndSeconds {
-				cur.EndSeconds = n.EndSeconds
-			}
-			cur.DurationSeconds = cur.EndSeconds - cur.StartSeconds
-			if n.Score > cur.Score {
-				cur.Score = n.Score
-			}
-			cur.AutoSelected = cur.AutoSelected || n.AutoSelected
-			continue
-		}
-
-		merged = append(merged, cur)
-		cur = n
-	}
-
-	merged = append(merged, cur)
-	return mergeAdjacentAISegments(merged, analyzeSegmentMergeGapSeconds)
+	return buildLabelContinuitySegmentsFromAnalyzeHits(hits, duration)
 }
 
 func buildAnalyzeSegmentsForGoal(

backend/assets_generate.go

@@ -1433,29 +1433,13 @@ func hasAIAnalysisForOutputGoal(outPath string, goal string) bool {
 		return false
 	}
 
+	rawHits, hasHits := aiMap["hits"].([]any)
+	rawSegs, hasSegs := aiMap["segments"].([]any)
+
 	// Wichtig:
-	// Auch eine Analyse ohne Treffer ist fertig, solange sie gespeichert wurde.
-	if rawGoal, ok := aiMap["goal"].(string); ok && normalizeAIGoal(rawGoal) == goal {
-		return true
-	}
-
-	if rawMode, ok := aiMap["mode"].(string); ok && strings.TrimSpace(rawMode) != "" {
-		return true
-	}
-
-	if _, ok := aiMap["hits"]; ok {
-		return true
-	}
-
-	if _, ok := aiMap["segments"]; ok {
-		return true
-	}
-
-	if _, ok := aiMap["analyzedAtUnix"]; ok {
-		return true
-	}
-
-	return false
+	// Eine leere Analyse zählt NICHT mehr als fertig.
+	// Sonst bleibt ein kaputter 0-Treffer-Run für immer gecached.
+	return (hasHits && len(rawHits) > 0) || (hasSegs && len(rawSegs) > 0)
 }
 
 func hasAIAnalysisForAllOutputGoals(outPath string, goals []string) bool {

backend/rating.go

@@ -847,9 +847,9 @@ func computeNSFWRating(segments []aiSegmentMeta, durationSec float64) *aiRatingM
 			0.04*confNorm
 
 	// Sicherheits-Caps:
-	// Ohne Positionssignal soll Kontext alleine nicht auf 4–5 Sterne kippen.
+	// Ohne Positionssignal soll Kontext alleine nicht auf 5 Sterne kippen.
 	if positionEffectiveWeighted <= 0 {
-		raw = math.Min(raw, 0.58)
+		raw = math.Min(raw, 0.72)
 	}
 
 	// Sehr kurze/spärliche Treffer nicht überbewerten.

backend/training.go

@@ -50,12 +50,9 @@ type TrainingPrediction struct {
 	ModelAvailable bool   `json:"modelAvailable"`
 	Source         string `json:"source,omitempty"`
 
-	PeopleCount      int                   `json:"peopleCount"`
-	MaleCount        int                   `json:"maleCount"`
-	FemaleCount      int                   `json:"femaleCount"`
-	UnknownCount     int                   `json:"unknownCount"`
 	SexPosition      string                `json:"sexPosition"`
 	SexPositionScore float64               `json:"sexPositionScore"`
+	PeoplePresent    []TrainingScoredLabel `json:"peoplePresent"`
 	BodyPartsPresent []TrainingScoredLabel `json:"bodyPartsPresent"`
 	ObjectsPresent   []TrainingScoredLabel `json:"objectsPresent"`
 	ClothingPresent  []TrainingScoredLabel `json:"clothingPresent"`
@@ -63,11 +60,8 @@ type TrainingPrediction struct {
 }
 
 type TrainingCorrection struct {
-	PeopleCount      int           `json:"peopleCount"`
-	MaleCount        int           `json:"maleCount"`
-	FemaleCount      int           `json:"femaleCount"`
-	UnknownCount     int           `json:"unknownCount"`
 	SexPosition      string        `json:"sexPosition"`
+	PeoplePresent    []string      `json:"peoplePresent"`
 	BodyPartsPresent []string      `json:"bodyPartsPresent"`
 	ObjectsPresent   []string      `json:"objectsPresent"`
 	ClothingPresent  []string      `json:"clothingPresent"`
@@ -1271,11 +1265,8 @@ func trainingEffectiveCorrection(annotation TrainingAnnotation) TrainingCorrecti
 	p := annotation.Prediction
 
 	return TrainingCorrection{
-		PeopleCount:      p.PeopleCount,
-		MaleCount:        p.MaleCount,
-		FemaleCount:      p.FemaleCount,
-		UnknownCount:     p.UnknownCount,
 		SexPosition:      p.SexPosition,
+		PeoplePresent:    trainingScoredLabelsToStrings(p.PeoplePresent),
 		BodyPartsPresent: trainingScoredLabelsToStrings(p.BodyPartsPresent),
 		ObjectsPresent:   trainingScoredLabelsToStrings(p.ObjectsPresent),
 		ClothingPresent:  trainingScoredLabelsToStrings(p.ClothingPresent),
@@ -2034,12 +2025,9 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
 	pred := TrainingPrediction{
 		ModelAvailable:   det.Available,
 		Source:           det.Source,
-		PeopleCount:      0,
-		MaleCount:        0,
-		FemaleCount:      0,
-		UnknownCount:     0,
 		SexPosition:      "unknown",
 		SexPositionScore: 0,
+		PeoplePresent:    []TrainingScoredLabel{},
 		BodyPartsPresent: []TrainingScoredLabel{},
 		ObjectsPresent:   []TrainingScoredLabel{},
 		ClothingPresent:  []TrainingScoredLabel{},
@@ -2133,17 +2121,6 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
 		}
 
 		if peopleSet[label] {
-			switch label {
-			case "person_male", "male_person":
-				pred.MaleCount++
-
-			case "person_female", "female_person":
-				pred.FemaleCount++
-
-			case "person", "person_unknown":
-				pred.UnknownCount++
-			}
-
 			visibleBoxes = append(visibleBoxes, box)
 			continue
 		}
@@ -2158,9 +2135,9 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
 		pred.SexPositionScore = bestSexPositionScore
 	}
 
-	pred.PeopleCount = pred.MaleCount + pred.FemaleCount + pred.UnknownCount
 	pred.Boxes = visibleBoxes
 
+	pred.PeoplePresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.People)
 	pred.BodyPartsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.BodyParts)
 	pred.ObjectsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Objects)
 	pred.ClothingPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Clothing)
@@ -2526,12 +2503,9 @@ func trainingEmptyPrediction(source string) TrainingPrediction {
 	return TrainingPrediction{
 		ModelAvailable:   false,
 		Source:           source,
-		PeopleCount:      0,
-		MaleCount:        0,
-		FemaleCount:      0,
-		UnknownCount:     0,
 		SexPosition:      "unknown",
 		SexPositionScore: 0,
+		PeoplePresent:    []TrainingScoredLabel{},
 		BodyPartsPresent: []TrainingScoredLabel{},
 		ObjectsPresent:   []TrainingScoredLabel{},
 		ClothingPresent:  []TrainingScoredLabel{},

frontend/src/App.tsx

@@ -2627,23 +2627,23 @@ export default function App() {
   const onlineLikedCount = headerStats.onlineLikedCount
 
   const runningTabCount = useMemo(() => {
-    const activeDownloads = jobs.filter((j) => {
+    return jobs.filter((j) => {
+      const status = String((j as any)?.status ?? '').trim().toLowerCase()
+      const phase = String((j as any)?.phase ?? '').trim().toLowerCase()
+
+      // Nacharbeit / Postwork nicht mitzählen
       if (isPostworkJobForCount(j)) return false
-      if (isTerminalJobStatusForCount((j as any)?.status)) return false
+
+      // Fertige/abgebrochene/fehlgeschlagene Jobs nicht mitzählen
+      if (isTerminalJobStatusForCount(status)) return false
+
+      // Sobald endedAt gesetzt ist, ist die aktive Aufnahme vorbei
       if (Boolean((j as any)?.endedAt)) return false
-      return true
+
+      // Nur wirklich laufende Downloads zählen
+      return status === 'running' && (phase === '' || phase === 'recording')
     }).length
-
-    const activePostwork = jobs.filter((j) => {
-      if (!isPostworkJobForCount(j)) return false
-      if (isTerminalJobStatusForCount((j as any)?.status)) return false
-      return true
-    }).length
-
-    const pendingCount = pendingWatchedRooms.length
-
-    return activeDownloads + activePostwork + pendingCount
-  }, [jobs, pendingWatchedRooms])
+  }, [jobs])
 
   const tabs: TabItem[] = [
     {

frontend/src/components/ui/RecorderSettings.tsx

@@ -299,6 +299,7 @@ export default function RecorderSettings({ onAssetsGenerated }: Props) {
   const [saving, setSaving] = useState(false)
   const [saveSuccessUntilMs, setSaveSuccessUntilMs] = useState<number>(0)
   const saveSuccessTimerRef = useRef<number | null>(null)
+  const appLogPreRef = useRef<HTMLPreElement | null>(null)
   const [browsing, setBrowsing] = useState<'record' | 'done' | 'ffmpeg' | null>(null)
   const [msg, setMsg] = useState<string | null>(null)
   const [err, setErr] = useState<string | null>(null)
@@ -317,6 +318,15 @@ export default function RecorderSettings({ onAssetsGenerated }: Props) {
   type SaveUiState = 'idle' | 'saving' | 'success' | 'error'
   const saveUiState: SaveUiState = saving ? 'saving' : saveSucceeded ? 'success' : saveFailed ? 'error' : 'idle'
 
+  function scrollAppLogToBottom() {
+    window.requestAnimationFrame(() => {
+      const el = appLogPreRef.current
+      if (!el) return
+
+      el.scrollTop = el.scrollHeight
+    })
+  }
+
   const saveButton = (() => {
     if (saveUiState === 'saving') {
       return {
@@ -749,6 +759,7 @@ export default function RecorderSettings({ onAssetsGenerated }: Props) {
       })
     } finally {
       setAppLogLoading(false)
+      scrollAppLogToBottom()
     }
   }
 
@@ -757,6 +768,12 @@ export default function RecorderSettings({ onAssetsGenerated }: Props) {
     void loadAppLog()
   }, [appLogOpen])
 
+  useEffect(() => {
+    if (!appLogOpen) return
+
+    scrollAppLogToBottom()
+  }, [appLogOpen, appLog?.log, appLogLoading])
+
   async function browse(target: 'record' | 'done' | 'ffmpeg') {
     setErr(null)
     setMsg(null)
@@ -1743,7 +1760,10 @@ export default function RecorderSettings({ onAssetsGenerated }: Props) {
                     </div>
                   ) : null}
 
-                  <pre className="max-h-96 overflow-auto whitespace-pre-wrap rounded-xl bg-gray-950 p-3 font-mono text-[11px] leading-relaxed text-gray-100 ring-1 ring-black/10 dark:ring-white/10">
+                  <pre
+                    ref={appLogPreRef}
+                    className="max-h-96 overflow-auto whitespace-pre-wrap rounded-xl bg-gray-950 p-3 font-mono text-[11px] leading-relaxed text-gray-100 ring-1 ring-black/10 dark:ring-white/10"
+                  >
                     {appLogLoading
                       ? 'Log wird geladen…'
                       : appLog?.log?.trim()

frontend/src/components/ui/TrainingTab.tsx

@@ -65,12 +65,9 @@ type TrainingPrediction = {
   modelAvailable: boolean
   source?: string
 
-  peopleCount: number
-  maleCount: number
-  femaleCount: number
-  unknownCount: number
   sexPosition: string
   sexPositionScore: number
+  peoplePresent: ScoredLabel[]
   bodyPartsPresent: ScoredLabel[]
   objectsPresent: ScoredLabel[]
   clothingPresent: ScoredLabel[]
@@ -97,11 +94,8 @@ type TrainingLabels = {
 }
 
 type CorrectionState = {
-  peopleCount: number
-  maleCount: number
-  femaleCount: number
-  unknownCount: number
   sexPosition: string
+  peoplePresent: string[]
   bodyPartsPresent: string[]
   objectsPresent: string[]
   clothingPresent: string[]
@@ -604,11 +598,6 @@ function toggleArrayValue(arr: string[], value: string) {
     : [...arr, value]
 }
 
-function safeCount(value: unknown) {
-  const n = Number(value)
-  return Number.isFinite(n) && n > 0 ? Math.floor(n) : 0
-}
-
 function clamp01(v: number) {
   if (!Number.isFinite(v)) return 0
   return Math.max(0, Math.min(1, v))
@@ -668,32 +657,35 @@ function uniqStrings(values: string[]) {
   return out
 }
 
+function peopleLabelsFromBoxes(boxes: TrainingBox[], labels: TrainingLabels) {
+  return uniqStrings(
+    boxes
+      .map((box) => String(box.label || '').trim())
+      .filter((label) => labels.people.includes(label))
+  )
+}
+
 function predictionToCorrection(sample: TrainingSample | null): CorrectionState {
   const p = sample?.prediction
 
-  const maleCount = safeCount(p?.maleCount)
-  const femaleCount = safeCount(p?.femaleCount)
-  const unknownCount = safeCount(p?.unknownCount)
+  const boxes = (p?.boxes ?? [])
+    .map((box) => ({
+      label: String(box.label || '').trim(),
+      score: box.score,
+      x: clamp01(Number(box.x)),
+      y: clamp01(Number(box.y)),
+      w: clamp01(Number(box.w)),
+      h: clamp01(Number(box.h)),
+    }))
+    .filter((box) => box.label && box.w > 0 && box.h > 0)
 
   return {
-    peopleCount: maleCount + femaleCount + unknownCount,
-    maleCount,
-    femaleCount,
-    unknownCount,
     sexPosition: p?.sexPosition || 'unknown',
+    peoplePresent: (p?.peoplePresent ?? []).map((x) => x.label),
     bodyPartsPresent: (p?.bodyPartsPresent ?? []).map((x) => x.label),
     objectsPresent: (p?.objectsPresent ?? []).map((x) => x.label),
     clothingPresent: (p?.clothingPresent ?? []).map((x) => x.label),
-    boxes: (p?.boxes ?? [])
-      .map((box) => ({
-        label: String(box.label || '').trim(),
-        score: box.score,
-        x: clamp01(Number(box.x)),
-        y: clamp01(Number(box.y)),
-        w: clamp01(Number(box.w)),
-        h: clamp01(Number(box.h)),
-      }))
-      .filter((box) => box.label && box.w > 0 && box.h > 0),
+    boxes,
   }
 }
 
@@ -706,39 +698,12 @@ function applyBoxLabelToCorrection(
   if (!clean) return state
 
   if (labels.people.includes(clean)) {
-    if (clean === 'person_male' || clean === 'male_person') {
-      return {
-        ...state,
-        maleCount: safeCount(state.maleCount) + 1,
-        unknownCount: 0,
-        peopleCount:
-          safeCount(state.maleCount) + 1 + safeCount(state.femaleCount),
-      }
+    return {
+      ...state,
+      peoplePresent: state.peoplePresent.includes(clean)
+        ? state.peoplePresent
+        : [...state.peoplePresent, clean],
     }
-
-    if (clean === 'person_female' || clean === 'female_person') {
-      return {
-        ...state,
-        femaleCount: safeCount(state.femaleCount) + 1,
-        unknownCount: 0,
-        peopleCount:
-          safeCount(state.maleCount) + safeCount(state.femaleCount) + 1,
-      }
-    }
-
-    if (clean === 'person' || clean === 'person_unknown') {
-      return {
-        ...state,
-        unknownCount: safeCount(state.unknownCount) + 1,
-        peopleCount:
-          safeCount(state.maleCount) +
-          safeCount(state.femaleCount) +
-          safeCount(state.unknownCount) +
-          1,
-      }
-    }
-
-    return state
   }
 
   if (labels.bodyParts.includes(clean)) {
@@ -786,6 +751,13 @@ function removeBoxLabelFromCorrection(
   // Badge nur abwählen, wenn keine weitere Box mit diesem Label existiert.
   if (remainingBoxesWithSameLabel) return state
 
+  if (labels.people.includes(clean)) {
+    return {
+      ...state,
+      peoplePresent: state.peoplePresent.filter((x) => x !== clean),
+    }
+  }
+
   if (labels.bodyParts.includes(clean)) {
     return {
       ...state,
@@ -822,42 +794,11 @@ function removeBoxFromCorrection(
 
   const removedLabel = String(removed.label || '').trim()
 
-  let next: CorrectionState = {
+  const next: CorrectionState = {
     ...state,
     boxes: boxes.filter((_, i) => i !== index),
   }
 
-  if (removedLabel === 'person_male' || removedLabel === 'male_person') {
-    next = {
-      ...next,
-      maleCount: Math.max(0, safeCount(next.maleCount) - 1),
-      unknownCount: 0,
-    }
-  }
-
-  if (removedLabel === 'person_female' || removedLabel === 'female_person') {
-    next = {
-      ...next,
-      femaleCount: Math.max(0, safeCount(next.femaleCount) - 1),
-      unknownCount: 0,
-    }
-  }
-
-  if (removedLabel === 'person' || removedLabel === 'person_unknown') {
-    next = {
-      ...next,
-      unknownCount: Math.max(0, safeCount(next.unknownCount) - 1),
-    }
-  }
-
-  next = {
-    ...next,
-    peopleCount:
-      safeCount(next.maleCount) +
-      safeCount(next.femaleCount) +
-      safeCount(next.unknownCount),
-  }
-
   return removeBoxLabelFromCorrection(next, removedLabel, labels)
 }
 
@@ -890,52 +831,9 @@ function changeBoxLabelInCorrection(
     }),
   }
 
-  // Alte Personen-Zählung entfernen.
-  if (oldLabel === 'person_male' || oldLabel === 'male_person') {
-    next = {
-      ...next,
-      maleCount: Math.max(0, safeCount(next.maleCount) - 1),
-      unknownCount: 0,
-    }
-  }
-
-  if (oldLabel === 'person_female' || oldLabel === 'female_person') {
-    next = {
-      ...next,
-      femaleCount: Math.max(0, safeCount(next.femaleCount) - 1),
-      unknownCount: 0,
-    }
-  }
-
-  if (oldLabel === 'person' || oldLabel === 'person_unknown') {
-    next = {
-      ...next,
-      unknownCount: Math.max(0, safeCount(next.unknownCount) - 1),
-    }
-  }
-
-  next = {
-    ...next,
-    peopleCount:
-      safeCount(next.maleCount) +
-      safeCount(next.femaleCount) +
-      safeCount(next.unknownCount),
-  }
-
-  // Alte Bodypart/Object/Clothing-Badges entfernen, falls keine weitere Box damit existiert.
   next = removeBoxLabelFromCorrection(next, oldLabel, labels)
-
-  // Neues Label anwenden.
   next = applyBoxLabelToCorrection(next, cleanNextLabel, labels)
 
-  next = {
-    ...next,
-    peopleCount:
-      safeCount(next.maleCount) +
-      safeCount(next.femaleCount) +
-      safeCount(next.unknownCount),
-  }
-
   return next
 }
 
@@ -1503,8 +1401,12 @@ function CollapsibleLabelSection(props: {
   singleDrawMode?: boolean
   gridClassName?: string
 }) {
+  const cleanDrawLabel = String(props.drawLabel || '').trim()
+  const hasDrawLabelInSection =
+    cleanDrawLabel !== '' && props.values.includes(cleanDrawLabel)
+
   const activeCount = props.singleDrawMode
-    ? Math.max(props.selected.length, props.drawLabel ? 1 : 0)
+    ? Math.max(props.selected.length, hasDrawLabelInSection ? 1 : 0)
     : props.selected.length
 
   const hasActiveItems = activeCount > 0
@@ -2262,12 +2164,13 @@ export default function TrainingTab(props: {
   }, [sample?.prediction.boxes, labels.people])
 
   const selectedPeopleLabels = useMemo(() => {
-    return uniqStrings(
-      correctionBoxes
-        .map((box) => String(box.label || '').trim())
-        .filter((label) => labels.people.includes(label))
-    )
-  }, [correctionBoxes, labels.people])
+    return correction.peoplePresent
+  }, [correction.peoplePresent])
+
+  const drawLabelForSection = useCallback((values: string[]) => {
+    const clean = String(boxLabel || '').trim()
+    return values.includes(clean) ? clean : ''
+  }, [boxLabel])
 
   const imageSrc = useMemo(() => {
     if (!sample?.frameUrl) return ''
@@ -2445,18 +2348,11 @@ export default function TrainingTab(props: {
       setCorrection(nextCorrection)
       setHasManualCorrection(false)
 
-      const currentLabels = labelsRef.current
-      const personLabels = new Set(currentLabels.people)
-
-      const hasPersonBoxes = nextCorrection.boxes.some((box) =>
-        personLabels.has(box.label)
-      )
-
       setExpandedCorrectionSections({
         sexPosition:
           Boolean(nextCorrection.sexPosition) &&
           nextCorrection.sexPosition !== 'unknown',
-        people: hasPersonBoxes || nextCorrection.peopleCount > 0,
+        people: nextCorrection.peoplePresent.length > 0,
         bodyParts: nextCorrection.bodyPartsPresent.length > 0,
         objects: nextCorrection.objectsPresent.length > 0,
         clothing: nextCorrection.clothingPresent.length > 0,
@@ -2805,19 +2701,14 @@ export default function TrainingTab(props: {
       setMessage(null)
 
       try {
-        const maleCount = safeCount(correction.maleCount)
-        const femaleCount = safeCount(correction.femaleCount)
-        const unknownCount = safeCount(correction.unknownCount)
+        const normalizedBoxes = (correction.boxes ?? [])
+          .map(normalizeBox)
+          .filter((box) => box.label && box.w > 0 && box.h > 0)
 
         const correctionPayload: CorrectionState = {
           ...correction,
-          maleCount,
-          femaleCount,
-          unknownCount,
-          peopleCount: maleCount + femaleCount + unknownCount,
-          boxes: (correction.boxes ?? [])
-            .map(normalizeBox)
-            .filter((box) => box.label && box.w > 0 && box.h > 0),
+          peoplePresent: peopleLabelsFromBoxes(normalizedBoxes, labelsRef.current),
+          boxes: normalizedBoxes,
         }
 
         const payload = {
@@ -3230,10 +3121,7 @@ export default function TrainingTab(props: {
 
     setCorrection((prev) => ({
       ...prev,
-      maleCount: 0,
-      femaleCount: 0,
-      unknownCount: 0,
-      peopleCount: 0,
+      peoplePresent: [],
       bodyPartsPresent: [],
       objectsPresent: [],
       clothingPresent: [],
@@ -4647,7 +4535,7 @@ export default function TrainingTab(props: {
                       toggleMobileCorrectionSection('people', expanded)
                     }
                     onToggle={() => {}}
-                    drawLabel={boxLabel}
+                    drawLabel={drawLabelForSection(labels.people)}
                     onDrawLabelChange={setBoxLabel}
                     disabled={uiLocked}
                     singleDrawMode
@@ -4680,7 +4568,7 @@ export default function TrainingTab(props: {
                         }
                       })
                     }
-                    drawLabel={boxLabel}
+                    drawLabel={drawLabelForSection(labels.bodyParts)}
                     onDrawLabelChange={setBoxLabel}
                     disabled={uiLocked}
                   />
@@ -4711,7 +4599,7 @@ export default function TrainingTab(props: {
                         }
                       })
                     }
-                    drawLabel={boxLabel}
+                    drawLabel={drawLabelForSection(labels.objects)}
                     onDrawLabelChange={setBoxLabel}
                     disabled={uiLocked}
                   />
@@ -4742,7 +4630,7 @@ export default function TrainingTab(props: {
                         }
                       })
                     }
-                    drawLabel={boxLabel}
+                    drawLabel={drawLabelForSection(labels.clothing)}
                     onDrawLabelChange={setBoxLabel}
                     disabled={uiLocked}
                   />
@@ -4838,7 +4726,7 @@ export default function TrainingTab(props: {
                 }))
               }
               onToggle={() => {}}
-              drawLabel={boxLabel}
+              drawLabel={drawLabelForSection(labels.people)}
               onDrawLabelChange={setBoxLabel}
               disabled={uiLocked}
               singleDrawMode
@@ -4868,7 +4756,7 @@ export default function TrainingTab(props: {
                   }
                 })
               }
-              drawLabel={boxLabel}
+              drawLabel={drawLabelForSection(labels.bodyParts)}
               onDrawLabelChange={setBoxLabel}
               disabled={uiLocked}
             />
@@ -4896,7 +4784,7 @@ export default function TrainingTab(props: {
                   }
                 })
               }
-              drawLabel={boxLabel}
+              drawLabel={drawLabelForSection(labels.objects)}
               onDrawLabelChange={setBoxLabel}
               disabled={uiLocked}
             />
@@ -4924,7 +4812,7 @@ export default function TrainingTab(props: {
                   }
                 })
               }
-              drawLabel={boxLabel}
+              drawLabel={drawLabelForSection(labels.clothing)}
               onDrawLabelChange={setBoxLabel}
               disabled={uiLocked}
             />