updated

backend/rating.go

@@ -11,6 +11,17 @@ import (
 type aiRatingMeta struct {
 	Score                  float64 `json:"score"`
 	ActionScore            float64 `json:"actionScore"`
+	BestPosition           string  `json:"bestPosition,omitempty"`
+	BestPositionRank       int     `json:"bestPositionRank,omitempty"`
+	BestClothing           string  `json:"bestClothing,omitempty"`
+	BestClothingRank       int     `json:"bestClothingRank,omitempty"`
+	PositionScore          float64 `json:"positionScore"`
+	ClothingScore          float64 `json:"clothingScore"`
+	DurationScore          float64 `json:"durationScore"`
+	StrengthScore          float64 `json:"strengthScore"`
+	CoverageScore          float64 `json:"coverageScore"`
+	ContextScore           float64 `json:"contextScore"`
+	VarietyScore           float64 `json:"varietyScore"`
 	Stars                  int     `json:"stars"`
 	Segments               int     `json:"segments"`
 	SegmentsPerMinute      float64 `json:"segmentsPerMinute"`
@@ -136,31 +147,46 @@ func isKnownPositionLabel(label string) bool {
 	}
 }
 
-func positionSeverityWeight(label string) float64 {
+type explicitRatingRank struct {
+	Rank   int
+	Weight float64
+}
+
+func positionExplicitRatingRank(label string) explicitRatingRank {
 	label = strings.ToLower(strings.TrimSpace(label))
 
 	switch label {
+	case "cowgirl":
+		return explicitRatingRank{Rank: 1, Weight: 1.00}
+	case "reverse_cowgirl":
+		return explicitRatingRank{Rank: 2, Weight: 0.99}
 	case "doggy", "doggystyle", "standing_doggy":
-		return 1.00
+		return explicitRatingRank{Rank: 3, Weight: 0.98}
-	case "cowgirl", "reverse_cowgirl":
-		return 0.98
-	case "missionary", "prone_bone":
-		return 0.95
-	case "blowjob", "cunnilingus", "69", "facesitting", "boobjob":
-		return 0.94
 	case "toy_play":
-		return 0.88
+		return explicitRatingRank{Rank: 4, Weight: 0.96}
-	case "handjob", "fingering":
+	case "fingering":
-		return 0.84
+		return explicitRatingRank{Rank: 5, Weight: 0.94}
+	case "missionary":
+		return explicitRatingRank{Rank: 6, Weight: 0.92}
+	case "prone_bone":
+		return explicitRatingRank{Rank: 7, Weight: 0.90}
 	case "spooning":
-		return 0.78
+		return explicitRatingRank{Rank: 8, Weight: 0.86}
 	case "standing":
-		return 0.42
+		return explicitRatingRank{Rank: 9, Weight: 0.56}
+	case "69", "facesitting":
+		return explicitRatingRank{Rank: 10, Weight: 0.54}
+	case "blowjob", "handjob", "cunnilingus", "boobjob":
+		return explicitRatingRank{Rank: 11, Weight: 0.52}
 	default:
-		return 0.00
+		return explicitRatingRank{}
 	}
 }
 
+func positionSeverityWeight(label string) float64 {
+	return positionExplicitRatingRank(label).Weight
+}
+
 func bodyPartSeverityWeight(label string) float64 {
 	label = strings.ToLower(strings.TrimSpace(label))
 
@@ -199,25 +225,39 @@ func objectSeverityWeight(label string) float64 {
 	}
 }
 
-func clothingSeverityWeight(label string) float64 {
+func clothingExplicitRatingRank(label string) explicitRatingRank {
 	label = strings.ToLower(strings.TrimSpace(label))
 
 	switch label {
 	case "lingerie":
-		return 0.50
+		return explicitRatingRank{Rank: 1, Weight: 0.62}
-	case "panties", "bra":
+	case "stockings":
-		return 0.44
+		return explicitRatingRank{Rank: 2, Weight: 0.57}
+	case "skirt":
+		return explicitRatingRank{Rank: 3, Weight: 0.52}
+	case "hotpants":
+		return explicitRatingRank{Rank: 4, Weight: 0.47}
 	case "bikini":
-		return 0.30
+		return explicitRatingRank{Rank: 5, Weight: 0.44}
-	case "stockings", "heels":
+	case "bra":
-		return 0.28
+		return explicitRatingRank{Rank: 6, Weight: 0.41}
-	case "skirt", "dress", "hotpants", "croptop":
+	case "panties":
-		return 0.22
+		return explicitRatingRank{Rank: 7, Weight: 0.36}
+	case "croptop":
+		return explicitRatingRank{Rank: 8, Weight: 0.31}
+	case "heels":
+		return explicitRatingRank{Rank: 9, Weight: 0.26}
+	case "dress":
+		return explicitRatingRank{Rank: 10, Weight: 0.22}
 	default:
-		return 0.00
+		return explicitRatingRank{}
 	}
 }
 
+func clothingSeverityWeight(label string) float64 {
+	return clothingExplicitRatingRank(label).Weight
+}
+
 func personContextWeight(label string) float64 {
 	if !isPersonSegmentLabel(label) {
 		return 0
@@ -437,26 +477,6 @@ func contextualSegmentSeverityWeightFromSet(set ratingSignalSet) float64 {
 		return 0
 	}
 
-	var labelParts []string
-
-	if set.HasPosition {
-		labelParts = append(labelParts, "position:x")
-	}
-	if set.HasBody {
-		labelParts = append(labelParts, "body:x")
-	}
-	if set.HasObject {
-		labelParts = append(labelParts, "object:x")
-	}
-	if set.HasClothing {
-		labelParts = append(labelParts, "clothing:x")
-	}
-	if set.HasPerson {
-		labelParts = append(labelParts, "person:x")
-	}
-
-	_ = labelParts
-
 	var score float64
 
 	if set.HasPosition {
@@ -484,7 +504,9 @@ func contextualSegmentSeverityWeightFromSet(set ratingSignalSet) float64 {
 
 		// Standing ohne echten Kontext klar schwächer halten.
 		if set.Position < 0.60 && !set.HasBody && !set.HasObject && !set.HasClothing {
-			score = math.Min(score, 0.32)
+			// Den Rang innerhalb der schwachen Positionen erhalten, statt
+			// Standing, 69 und orale Positionen auf denselben Wert zu kappen.
+			score = math.Min(score, 0.10+0.40*set.Position)
 		}
 
 		return ratingClamp01(score)
@@ -655,6 +677,37 @@ func addRatingLabelsFromSegment(labels map[string]bool, label string) {
 	labels[normalized] = true
 }
 
+func updateBestExplicitRatingRanks(
+	label string,
+	bestPosition *string,
+	bestPositionRank *int,
+	bestClothing *string,
+	bestClothingRank *int,
+) {
+	labels := map[string]bool{}
+	addRatingLabelsFromSegment(labels, label)
+
+	for normalized := range labels {
+		switch {
+		case strings.HasPrefix(normalized, "position:"):
+			raw := strings.TrimPrefix(normalized, "position:")
+			ranked := positionExplicitRatingRank(raw)
+			if ranked.Rank > 0 && (*bestPositionRank == 0 || ranked.Rank < *bestPositionRank) {
+				*bestPosition = raw
+				*bestPositionRank = ranked.Rank
+			}
+
+		case strings.HasPrefix(normalized, "clothing:"):
+			raw := strings.TrimPrefix(normalized, "clothing:")
+			ranked := clothingExplicitRatingRank(raw)
+			if ranked.Rank > 0 && (*bestClothingRank == 0 || ranked.Rank < *bestClothingRank) {
+				*bestClothing = raw
+				*bestClothingRank = ranked.Rank
+			}
+		}
+	}
+}
+
 func ratingSignalSetFromLabels(labels map[string]bool) ratingSignalSet {
 	var set ratingSignalSet
 
@@ -1256,119 +1309,103 @@ func ratingLinearGate(value float64, start float64, full float64) float64 {
 	return ratingClamp01((value - start) / (full - start))
 }
 
-func ratingExplicitContextBonus(
+type ratingTimeRange struct {
-	bodyEffectiveWeighted float64,
+	Start float64
-	clothingEffectiveWeighted float64,
+	End   float64
-	objectEffectiveWeighted float64,
+}
-	videoMinutes float64,
+
-) float64 {
+func ratingCoveredSeconds(ranges []ratingTimeRange, durationSec float64) float64 {
-	if videoMinutes <= 0 {
+	if len(ranges) == 0 || durationSec <= 0 {
 		return 0
 	}
 
-	bodyDensity := bodyEffectiveWeighted / videoMinutes
+	valid := make([]ratingTimeRange, 0, len(ranges))
-	clothingDensity := clothingEffectiveWeighted / videoMinutes
+	for _, item := range ranges {
-	objectDensity := objectEffectiveWeighted / videoMinutes
+		start := math.Max(0, math.Min(item.Start, durationSec))
+		end := math.Max(0, math.Min(item.End, durationSec))
+		if end > start {
+			valid = append(valid, ratingTimeRange{Start: start, End: end})
+		}
+	}
+	if len(valid) == 0 {
+		return 0
+	}
 
-	bonus :=
+	sort.SliceStable(valid, func(i, j int) bool {
-		0.060*ratingSaturatingEvidence(bodyDensity, 1.8) +
+		if valid[i].Start != valid[j].Start {
-			0.035*ratingSaturatingEvidence(clothingDensity, 1.5) +
+			return valid[i].Start < valid[j].Start
-			0.015*ratingSaturatingEvidence(objectDensity, 2.2)
+		}
+		return valid[i].End < valid[j].End
+	})
 
-	return math.Min(bonus, 0.10)
+	total := 0.0
+	current := valid[0]
+
+	for _, item := range valid[1:] {
+		if item.Start <= current.End {
+			if item.End > current.End {
+				current.End = item.End
+			}
+			continue
+		}
+
+		total += current.End - current.Start
+		current = item
+	}
+
+	return total + current.End - current.Start
 }
 
-func ratingBlendWithAction(base, action, actionWeight float64) float64 {
+func ratingStrengthComponent(avgConfidence, peakConfidence float64) float64 {
-	actionWeight = ratingClamp01(actionWeight)
+	avg := ratingSmoothStep(ratingLinearGate(avgConfidence, 0.35, 0.90))
-	return ratingClamp01((1-actionWeight)*base + actionWeight*action)
+	peak := ratingSmoothStep(ratingLinearGate(peakConfidence, 0.50, 0.95))
+	return ratingClamp01(0.75*avg + 0.25*peak)
 }
 
-func ratingPositionActionScore(
+func ratingDurationComponent(totalSeconds, longestSeconds float64) float64 {
-	durationNorm float64,
+	total := ratingSaturatingEvidence(totalSeconds, 55.0)
-	longestNorm float64,
+	longest := ratingSaturatingEvidence(longestSeconds, 30.0)
-	densityNorm float64,
+	return ratingClamp01(0.62*total + 0.38*longest)
-	coverageNorm float64,
-	peakNorm float64,
-) float64 {
-	return ratingClamp01(
-		0.38*durationNorm +
-			0.24*longestNorm +
-			0.18*densityNorm +
-			0.12*coverageNorm +
-			0.08*peakNorm,
-	)
 }
 
-func ratingContextActionScore(
+func ratingCoverageComponent(coveredSeconds, durationSec float64) float64 {
-	durationNorm float64,
+	if coveredSeconds <= 0 || durationSec <= 0 {
-	longestNorm float64,
+		return 0
-	coverageNorm float64,
+	}
-	densityNorm float64,
+	return ratingSaturatingEvidence(ratingClamp01(coveredSeconds/durationSec), 0.12)
-	peakNorm float64,
-) float64 {
-	return ratingClamp01(
-		0.40*durationNorm +
-			0.25*longestNorm +
-			0.20*coverageNorm +
-			0.10*densityNorm +
-			0.05*peakNorm,
-	)
 }
 
-func ratingExcellenceBonus(
+func ratingActionComponent(duration, coverage, strength float64) float64 {
-	peakQuality float64,
+	return ratingClamp01(0.50*duration + 0.30*coverage + 0.20*strength)
-	positionEffectiveWeighted float64,
+}
-	positionDensity float64,
+
-	weightedCoverageRatio float64,
+func ratingApplyPositionEvidenceCaps(
-	totalFlagged float64,
+	raw float64,
-	longest float64,
+	positionSeconds float64,
+	longestSeconds float64,
 	avgConfidence float64,
-	segmentsPerMinute float64,
+	positionScore float64,
-	n int,
 ) float64 {
-	// Kein pauschaler Score-Shift:
+	switch {
-	// Der Bonus darf nur greifen, wenn wirklich mehrere starke Signale vorhanden sind.
+	case positionSeconds < 5:
-	if n < 2 {
+		raw = math.Min(raw, 0.21)
-		return 0
+	case positionSeconds < 12:
-	}
+		raw = math.Min(raw, 0.44)
-	if positionEffectiveWeighted <= 0 {
+	case positionSeconds < 30:
-		return 0
+		raw = math.Min(raw, 0.67)
-	}
-	if totalFlagged < 12.0 {
-		return 0
-	}
-	if peakQuality < 0.68 {
-		return 0
-	}
-	if avgConfidence < 0.42 {
-		return 0
 	}
 
-	peakGate := ratingLinearGate(peakQuality, 0.68, 0.90)
+	if positionSeconds < 75 ||
-	positionGate := ratingLinearGate(positionDensity, 2.20, 7.00)
+		longestSeconds < 18 ||
-	coverageGate := ratingLinearGate(weightedCoverageRatio, 0.045, 0.160)
+		avgConfidence < 0.55 ||
-	durationGate := ratingLinearGate(totalFlagged, 12.0, 90.0)
+		positionScore < 0.65 {
-	longestGate := ratingLinearGate(longest, 8.0, 45.0)
+		raw = math.Min(raw, 0.84)
-	confGate := ratingLinearGate(avgConfidence, 0.42, 0.78)
+	}
-	frequencyGate := ratingLinearGate(segmentsPerMinute, 0.25, 1.00)
+	if avgConfidence < 0.50 && positionScore < 0.65 {
-
+		raw = math.Min(raw, 0.21)
-	bonus :=
-		0.030*peakGate +
-			0.022*positionGate +
-			0.016*coverageGate +
-			0.012*durationGate +
-			0.012*longestGate +
-			0.010*confGate +
-			0.006*frequencyGate
-
-	// Maximal +8.5 Scorepunkte.
-	// Dadurch werden starke 72-79 Scores 5-Sterne-fähig,
-	// aber mittelmäßige Scores springen nicht einfach pauschal hoch.
-	if bonus > 0.085 {
-		bonus = 0.085
 	}
 
-	return bonus
+	return ratingClamp01(raw)
 }
 
 func starsFromHighlightScore(score float64) int {
@@ -1434,34 +1471,39 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 
 	videoMinutes := math.Max(durationSec/60.0, 0.25)
 
-	var totalFlagged float64
+	var positionRanges []ratingTimeRange
-	var totalWeighted float64
+	var positionWeighted float64
+	var positionExplicitSum float64
+	var positionEvidenceWeight float64
+	var peakPositionExplicitness float64
+	var peakPositionConfidence float64
 
-	var positionEffectiveWeighted float64
+	var contextRanges []ratingTimeRange
-	var positionFlagged float64
-	var peakQuality float64
-	var qualityDurationSum float64
-	var qualityDurationWeight float64
-
-	var contextFlagged float64
 	var contextWeighted float64
-	var contextEffectiveWeighted float64
+	var contextExplicitSum float64
-	var contextPeakQuality float64
+	var contextEvidenceWeight float64
-	var contextQualityDurationSum float64
+	var peakContextExplicitness float64
-	var contextQualityDurationWeight float64
+	var peakContextConfidence float64
 	var contextLongest float64
 	var contextConfSum float64
 	var contextConfWeightSum float64
 	var contextN int
+
 	var bodyEffectiveWeighted float64
 	var clothingEffectiveWeighted float64
 	var objectEffectiveWeighted float64
+	var clothingExplicitSum float64
+	var clothingEvidenceWeight float64
 
 	var longest float64
 	var confSum float64
 	var confWeightSum float64
 	var n int
 	uniquePositions := map[string]bool{}
+	bestPosition := ""
+	bestPositionRank := 0
+	bestClothing := ""
+	bestClothingRank := 0
 
 	for _, s := range segments {
 		segDur := s.DurationSeconds
@@ -1492,25 +1534,41 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 
 		effectiveDur := ratingEffectiveDurationSeconds(segDur)
 		weightedDur := segDur * quality
-		effectiveWeightedDur := effectiveDur * quality
+		segmentRange := ratingTimeRange{
+			Start: s.StartSeconds,
+			End:   s.StartSeconds + segDur,
+		}
 
 		set := ratingSignalSetFromLabel(s.Label)
+		updateBestExplicitRatingRanks(
+			s.Label,
+			&bestPosition,
+			&bestPositionRank,
+			&bestClothing,
+			&bestClothingRank,
+		)
 		bodyEffectiveWeighted += effectiveDur * set.Body * confWeight
 		clothingEffectiveWeighted += effectiveDur * set.Clothing * confWeight
 		objectEffectiveWeighted += effectiveDur * set.Object * confWeight
+		if set.HasClothing {
+			clothingExplicitSum += (set.Clothing / clothingExplicitRatingRank("lingerie").Weight) * effectiveDur
+			clothingEvidenceWeight += effectiveDur
+		}
 
 		if !set.HasPosition {
-			contextFlagged += segDur
+			contextRanges = append(contextRanges, segmentRange)
 			contextWeighted += weightedDur
-			contextEffectiveWeighted += effectiveWeightedDur
+			contextExplicitSum += sev * effectiveDur
-			contextQualityDurationSum += quality * effectiveDur
+			contextEvidenceWeight += effectiveDur
-			contextQualityDurationWeight += effectiveDur
 			contextConfSum += conf * effectiveDur
 			contextConfWeightSum += effectiveDur
 			contextN++
 
-			if quality > contextPeakQuality {
+			if sev > peakContextExplicitness {
-				contextPeakQuality = quality
+				peakContextExplicitness = sev
+			}
+			if conf > peakContextConfidence {
+				peakContextConfidence = conf
 			}
 			if segDur > contextLongest {
 				contextLongest = segDur
@@ -1519,16 +1577,16 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 			continue
 		}
 
-		totalFlagged += segDur
+		positionRanges = append(positionRanges, segmentRange)
-		totalWeighted += weightedDur
+		positionWeighted += weightedDur
+		positionExplicitSum += set.Position * effectiveDur
+		positionEvidenceWeight += effectiveDur
 
-		positionEffectiveWeighted += effectiveWeightedDur
+		if set.Position > peakPositionExplicitness {
-		positionFlagged += segDur
+			peakPositionExplicitness = set.Position
-		qualityDurationSum += quality * effectiveDur
+		}
-		qualityDurationWeight += effectiveDur
+		if conf > peakPositionConfidence {
-
+			peakPositionConfidence = conf
-		if quality > peakQuality {
-			peakQuality = quality
 		}
 
 		confSum += conf * effectiveDur
@@ -1546,58 +1604,71 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 		}
 	}
 
+	clothingScore := 0.0
+	if clothingEvidenceWeight > 0 {
+		avgClothingExplicitness := ratingClamp01(clothingExplicitSum / clothingEvidenceWeight)
+		clothingDensity := clothingEffectiveWeighted / videoMinutes
+		clothingScore = ratingClamp01(
+			0.70*avgClothingExplicitness +
+				0.30*ratingSaturatingEvidence(clothingDensity, 1.8),
+		)
+	}
+
+	bodyContext := ratingSaturatingEvidence(bodyEffectiveWeighted/videoMinutes, 1.8)
+	objectContext := ratingSaturatingEvidence(objectEffectiveWeighted/videoMinutes, 2.2)
+	contextScore := ratingClamp01(0.78*bodyContext + 0.22*objectContext)
+
 	if n == 0 {
 		if contextN == 0 {
 			appLogf("⚠️ %s rating result is zero because all segments were skipped", ratingLogSubject(username))
 			return r
 		}
 
+		contextFlagged := ratingCoveredSeconds(contextRanges, durationSec)
 		contextCoverageRatio := ratingClamp01(contextFlagged / durationSec)
 		contextWeightedCoverageRatio := ratingClamp01(contextWeighted / durationSec)
 		contextSegmentsPerMinute := float64(contextN) / videoMinutes
 		contextAvgConfidence := contextConfSum / contextConfWeightSum
-		contextAvgQuality := contextQualityDurationSum / contextQualityDurationWeight
+		avgContextExplicitness := contextExplicitSum / contextEvidenceWeight
-		contextDensity := contextEffectiveWeighted / videoMinutes
+		contextQuality := ratingClamp01(
-
+			(0.75*avgContextExplicitness + 0.25*peakContextExplicitness) / 0.72,
-		qualityNorm := ratingSmoothStep(
-			ratingLinearGate(contextAvgQuality, 0.08, 0.55),
 		)
-		peakNorm := ratingSmoothStep(
+		durationScore := ratingDurationComponent(contextFlagged, contextLongest)
-			ratingLinearGate(contextPeakQuality, 0.12, 0.62),
+		strengthScore := ratingStrengthComponent(contextAvgConfidence, peakContextConfidence)
-		)
+		coverageScore := ratingCoverageComponent(contextFlagged, durationSec)
-		durationNorm := ratingSaturatingEvidence(contextFlagged, 150.0)
-		longestNorm := ratingSaturatingEvidence(contextLongest, 60.0)
-		densityNorm := ratingSaturatingEvidence(contextDensity, 2.5)
-		coverageNorm := ratingSaturatingEvidence(contextWeightedCoverageRatio, 0.08)
 
 		raw :=
-			0.28*qualityNorm +
+			0.30*contextQuality +
-				0.12*peakNorm +
+				0.25*durationScore +
-				0.25*durationNorm +
+				0.15*strengthScore +
-				0.15*longestNorm +
+				0.12*coverageScore +
-				0.15*coverageNorm +
+				0.12*clothingScore +
-				0.05*densityNorm
+				0.06*contextScore
 
-		actionRaw := ratingContextActionScore(
+		actionRaw := ratingActionComponent(durationScore, coverageScore, strengthScore)
-			durationNorm,
-			longestNorm,
-			coverageNorm,
-			densityNorm,
-			peakNorm,
-		)
-		raw = ratingBlendWithAction(raw, actionRaw, 0.35)
 
-		// Context can produce useful 1-3 star ratings, but never the 4-5 star
-		// range reserved for sustained position detections.
 		if contextFlagged < 20.0 {
 			raw = math.Min(raw, 0.44)
 		}
-		raw = math.Min(raw, 0.64)
+		semanticCeiling := 0.52 + 0.12*math.Max(
+			contextQuality,
+			math.Max(clothingScore, contextScore),
+		)
+		raw = math.Min(raw, semanticCeiling)
 
 		score := ratingRound(ratingClamp01(raw)*100, 1)
 
 		r.Score = score
 		r.ActionScore = ratingRound(actionRaw*100, 1)
+		r.BestPosition = bestPosition
+		r.BestPositionRank = bestPositionRank
+		r.BestClothing = bestClothing
+		r.BestClothingRank = bestClothingRank
+		r.ClothingScore = ratingRound(clothingScore*100, 1)
+		r.DurationScore = ratingRound(durationScore*100, 1)
+		r.StrengthScore = ratingRound(strengthScore*100, 1)
+		r.CoverageScore = ratingRound(coverageScore*100, 1)
+		r.ContextScore = ratingRound(contextScore*100, 1)
 		r.Stars = starsFromHighlightScore(score)
 		r.Segments = contextN
 		r.SegmentsPerMinute = ratingRound(contextSegmentsPerMinute, 2)
@@ -1609,13 +1680,13 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 		r.AvgConfidence = ratingRound(contextAvgConfidence, 3)
 
 		appLogf(
-			"✅ %s context rating score=%.1f stars=%d segs=%d flagged=%.1f density=%.2f coverage=%.4f longest=%.1f avgConf=%.3f",
+			"rating %s context score=%.1f stars=%d segs=%d flagged=%.1f quality=%.2f coverage=%.4f longest=%.1f avgConf=%.3f",
 			ratingLogSubject(username),
 			r.Score,
 			r.Stars,
 			r.Segments,
 			r.FlaggedSeconds,
-			contextDensity,
+			contextQuality,
 			r.WeightedCoverageRatio,
 			r.LongestSegmentSeconds,
 			r.AvgConfidence,
@@ -1624,97 +1695,76 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 		return r
 	}
 
-	coverageRatio := ratingClamp01(totalFlagged / durationSec)
+	positionFlagged := ratingCoveredSeconds(positionRanges, durationSec)
-	weightedCoverageRatio := ratingClamp01(totalWeighted / durationSec)
+	coverageRatio := ratingClamp01(positionFlagged / durationSec)
+	weightedCoverageRatio := ratingClamp01(positionWeighted / durationSec)
 	segmentsPerMinute := float64(n) / videoMinutes
 	avgConfidence := confSum / confWeightSum
-	avgPositionQuality := qualityDurationSum / qualityDurationWeight
+	avgPositionExplicitness := positionExplicitSum / positionEvidenceWeight
-
+	positionScore := ratingClamp01(
-	positionDensity := positionEffectiveWeighted / videoMinutes
+		0.75*avgPositionExplicitness + 0.25*peakPositionExplicitness,
-
-	avgQualityNorm := ratingSmoothStep(
-		ratingLinearGate(avgPositionQuality, 0.45, 0.95),
 	)
-	peakNorm := ratingSmoothStep(
+	durationScore := ratingDurationComponent(positionFlagged, longest)
-		ratingLinearGate(peakQuality, 0.42, 0.95),
+	strengthScore := ratingStrengthComponent(avgConfidence, peakPositionConfidence)
-	)
+	coverageScore := ratingCoverageComponent(positionFlagged, durationSec)
-	positionDurationNorm := ratingSaturatingEvidence(positionFlagged, 65.0)
+	varietyScore := ratingLinearGate(float64(len(uniquePositions)), 1, 4)
-	longestNorm := ratingSaturatingEvidence(longest, 35.0)
-	positionDensityNorm := ratingSaturatingEvidence(positionDensity, 3.2)
-	coverageNorm := ratingSaturatingEvidence(weightedCoverageRatio, 0.075)
-	positionVarietyNorm := ratingLinearGate(float64(len(uniquePositions)), 1, 4)
 
-	// Position quality is the primary signal. Actual position duration and the
-	// longest continuous segment decide whether a detection is brief or strong.
 	raw :=
-		0.30*avgQualityNorm +
+		0.36*positionScore +
-			0.10*peakNorm +
+			0.27*durationScore +
-			0.26*positionDurationNorm +
+			0.14*strengthScore +
-			0.16*longestNorm +
+			0.10*coverageScore +
-			0.10*positionDensityNorm +
+			0.06*contextScore +
-			0.06*coverageNorm +
+			0.04*clothingScore +
-			0.02*positionVarietyNorm
+			0.03*varietyScore
 
-	actionRaw := ratingPositionActionScore(
+	actionRaw := ratingActionComponent(durationScore, coverageScore, strengthScore)
-		positionDurationNorm,
-		longestNorm,
-		positionDensityNorm,
-		coverageNorm,
-		peakNorm,
-	)
-	raw = ratingBlendWithAction(raw, actionRaw, 0.30)
 
-	explicitContextBonus := ratingExplicitContextBonus(
+	// Short or weak detections cannot reach high ratings on rank alone.
-		bodyEffectiveWeighted,
+	raw = ratingApplyPositionEvidenceCaps(
-		clothingEffectiveWeighted,
+		raw,
-		objectEffectiveWeighted,
+		positionFlagged,
-		videoMinutes,
-	)
-
-	// Sehr wenig Material nicht überbewerten.
-	if totalFlagged < 5.0 && n <= 1 {
-		raw = math.Min(raw, 0.44)
-	}
-
-	excellenceBonus := ratingExcellenceBonus(
-		peakQuality,
-		positionEffectiveWeighted,
-		positionDensity,
-		weightedCoverageRatio,
-		totalFlagged,
 		longest,
 		avgConfidence,
-		segmentsPerMinute,
+		positionScore,
-		n,
 	)
 
-	raw = ratingClamp01(raw + explicitContextBonus + excellenceBonus)
-
 	score := ratingRound(raw*100, 1)
 
 	r.Score = score
 	r.ActionScore = ratingRound(actionRaw*100, 1)
+	r.BestPosition = bestPosition
+	r.BestPositionRank = bestPositionRank
+	r.BestClothing = bestClothing
+	r.BestClothingRank = bestClothingRank
+	r.PositionScore = ratingRound(positionScore*100, 1)
+	r.ClothingScore = ratingRound(clothingScore*100, 1)
+	r.DurationScore = ratingRound(durationScore*100, 1)
+	r.StrengthScore = ratingRound(strengthScore*100, 1)
+	r.CoverageScore = ratingRound(coverageScore*100, 1)
+	r.ContextScore = ratingRound(contextScore*100, 1)
+	r.VarietyScore = ratingRound(varietyScore*100, 1)
 	r.Stars = starsFromHighlightScore(score)
 	r.Segments = n
 	r.SegmentsPerMinute = ratingRound(segmentsPerMinute, 2)
-	r.FlaggedSeconds = ratingRound(totalFlagged, 2)
+	r.FlaggedSeconds = ratingRound(positionFlagged, 2)
-	r.WeightedFlaggedSeconds = ratingRound(totalWeighted, 2)
+	r.WeightedFlaggedSeconds = ratingRound(positionWeighted, 2)
 	r.CoverageRatio = ratingRound(coverageRatio, 4)
 	r.WeightedCoverageRatio = ratingRound(weightedCoverageRatio, 4)
 	r.LongestSegmentSeconds = ratingRound(longest, 2)
 	r.AvgConfidence = ratingRound(avgConfidence, 3)
 
 	appLogf(
-		"✅ %s rating score=%.1f stars=%d contextBonus=%.1f excellenceBonus=%.1f segs=%d flagged=%.1f posFlagged=%.1f posDensity=%.2f coverage=%.4f longest=%.1f avgConf=%.3f",
+		"rating %s score=%.1f stars=%d position=%.1f clothing=%.1f segs=%d flagged=%.1f duration=%.1f strength=%.1f coverage=%.4f longest=%.1f avgConf=%.3f",
 		ratingLogSubject(username),
 		r.Score,
 		r.Stars,
-		ratingRound(explicitContextBonus*100, 1),
+		r.PositionScore,
-		ratingRound(excellenceBonus*100, 1),
+		r.ClothingScore,
 		r.Segments,
 		r.FlaggedSeconds,
-		ratingRound(positionFlagged, 1),
+		r.DurationScore,
-		positionDensity,
+		r.StrengthScore,
 		r.WeightedCoverageRatio,
 		r.LongestSegmentSeconds,
 		r.AvgConfidence,

backend/rating_test.go

@@ -46,22 +46,22 @@ func TestComputeHighlightRatingSpansAllStars(t *testing.T) {
 		{
 			name: "several long high quality positions",
 			segments: []aiSegmentMeta{
-				ratingTestSegment("position:doggy", 20, 25, 0.82),
+				ratingTestSegment("position:cowgirl", 20, 25, 0.82),
-				ratingTestSegment("position:cowgirl", 120, 25, 0.82),
+				ratingTestSegment("position:reverse_cowgirl", 120, 25, 0.82),
-				ratingTestSegment("position:missionary", 220, 25, 0.82),
+				ratingTestSegment("position:doggy", 220, 25, 0.82),
-				ratingTestSegment("position:blowjob", 320, 25, 0.82),
+				ratingTestSegment("position:toy_play", 320, 25, 0.82),
 			},
 			want: 4,
 		},
 		{
 			name: "exceptional sustained and varied positions",
 			segments: []aiSegmentMeta{
-				ratingTestSegment("position:doggy", 10, 40, 0.90),
+				ratingTestSegment("position:cowgirl", 10, 40, 0.90),
-				ratingTestSegment("position:cowgirl", 100, 40, 0.90),
+				ratingTestSegment("position:reverse_cowgirl", 100, 40, 0.90),
-				ratingTestSegment("position:missionary", 190, 40, 0.90),
+				ratingTestSegment("position:doggy", 190, 40, 0.90),
-				ratingTestSegment("position:blowjob", 280, 40, 0.90),
+				ratingTestSegment("position:toy_play", 280, 40, 0.90),
-				ratingTestSegment("position:cunnilingus", 370, 40, 0.90),
+				ratingTestSegment("position:fingering", 370, 40, 0.90),
-				ratingTestSegment("position:reverse_cowgirl", 460, 40, 0.90),
+				ratingTestSegment("position:missionary", 460, 40, 0.90),
 			},
 			want: 5,
 		},
@@ -77,6 +77,141 @@ func TestComputeHighlightRatingSpansAllStars(t *testing.T) {
 	}
 }
 
+func TestExplicitPositionRanking(t *testing.T) {
+	labels := []string{
+		"cowgirl",
+		"reverse_cowgirl",
+		"doggy",
+		"toy_play",
+		"fingering",
+		"missionary",
+		"prone_bone",
+		"spooning",
+		"standing",
+		"69",
+		"blowjob",
+	}
+
+	previousWeight := 2.0
+	for index, label := range labels {
+		ranked := positionExplicitRatingRank(label)
+		wantRank := index + 1
+
+		if ranked.Rank != wantRank {
+			t.Fatalf("%s rank = %d, want %d", label, ranked.Rank, wantRank)
+		}
+		if ranked.Weight <= 0 || ranked.Weight >= previousWeight {
+			t.Fatalf(
+				"%s weight = %.2f, want a positive value below %.2f",
+				label,
+				ranked.Weight,
+				previousWeight,
+			)
+		}
+
+		previousWeight = ranked.Weight
+	}
+
+	for _, alias := range []string{"doggystyle", "standing_doggy"} {
+		if ranked := positionExplicitRatingRank(alias); ranked.Rank != 3 {
+			t.Fatalf("%s rank = %d, want alias rank 3", alias, ranked.Rank)
+		}
+	}
+	for _, grouped := range []string{"facesitting", "handjob", "cunnilingus", "boobjob"} {
+		if ranked := positionExplicitRatingRank(grouped); ranked.Rank != positionExplicitRatingRank("69").Rank &&
+			ranked.Rank != positionExplicitRatingRank("blowjob").Rank {
+			t.Fatalf("%s has unexpected grouped rank %d", grouped, ranked.Rank)
+		}
+	}
+}
+
+func TestExplicitClothingRanking(t *testing.T) {
+	labels := []string{
+		"lingerie",
+		"stockings",
+		"skirt",
+		"hotpants",
+		"bikini",
+		"bra",
+		"panties",
+		"croptop",
+		"heels",
+		"dress",
+	}
+
+	previousWeight := 2.0
+	for index, label := range labels {
+		ranked := clothingExplicitRatingRank(label)
+		wantRank := index + 1
+
+		if ranked.Rank != wantRank {
+			t.Fatalf("%s rank = %d, want %d", label, ranked.Rank, wantRank)
+		}
+		if ranked.Weight <= 0 || ranked.Weight >= previousWeight {
+			t.Fatalf(
+				"%s weight = %.2f, want a positive value below %.2f",
+				label,
+				ranked.Weight,
+				previousWeight,
+			)
+		}
+
+		previousWeight = ranked.Weight
+	}
+}
+
+func TestComputeHighlightRatingReportsBestExplicitRanks(t *testing.T) {
+	got := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("combo:position:standing+clothing:dress", 10, 20, 0.90),
+		ratingTestSegment("combo:position:cowgirl+clothing:panties", 80, 20, 0.90),
+		ratingTestSegment("combo:position:doggy+clothing:lingerie", 150, 20, 0.90),
+	}, 300)
+
+	if got.BestPosition != "cowgirl" || got.BestPositionRank != 1 {
+		t.Fatalf(
+			"best position = %q rank %d, want cowgirl rank 1",
+			got.BestPosition,
+			got.BestPositionRank,
+		)
+	}
+	if got.BestClothing != "lingerie" || got.BestClothingRank != 1 {
+		t.Fatalf(
+			"best clothing = %q rank %d, want lingerie rank 1",
+			got.BestClothing,
+			got.BestClothingRank,
+		)
+	}
+}
+
+func TestComputeHighlightRatingFollowsExplicitRank(t *testing.T) {
+	positionScore := func(label string) float64 {
+		return computeHighlightRating([]aiSegmentMeta{
+			ratingTestSegment("position:"+label, 10, 30, 0.85),
+		}, 300).Score
+	}
+	clothingScore := func(label string) float64 {
+		return computeHighlightRating([]aiSegmentMeta{
+			ratingTestSegment("clothing:"+label, 10, 90, 0.90),
+		}, 300).Score
+	}
+
+	if !(positionScore("cowgirl") > positionScore("doggy") &&
+		positionScore("doggy") > positionScore("toy_play") &&
+		positionScore("toy_play") > positionScore("missionary") &&
+		positionScore("missionary") > positionScore("standing") &&
+		positionScore("standing") > positionScore("blowjob")) {
+		t.Fatal("position score does not follow the explicit ranking")
+	}
+
+	if !(clothingScore("lingerie") > clothingScore("stockings") &&
+		clothingScore("stockings") > clothingScore("hotpants") &&
+		clothingScore("hotpants") > clothingScore("bikini") &&
+		clothingScore("bikini") > clothingScore("panties") &&
+		clothingScore("panties") > clothingScore("dress")) {
+		t.Fatal("clothing score does not follow the explicit ranking")
+	}
+}
+
 func TestComputeHighlightRatingRewardsPositionDuration(t *testing.T) {
 	short := computeHighlightRating([]aiSegmentMeta{
 		ratingTestSegment("position:doggy", 10, 8, 0.80),
@@ -122,6 +257,67 @@ func TestComputeHighlightRatingActionPrefersSustainedActivity(t *testing.T) {
 	}
 }
 
+func TestComputeHighlightRatingDoesNotDoubleCountOverlaps(t *testing.T) {
+	got := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("position:cowgirl", 0, 30, 0.90),
+		ratingTestSegment("position:doggy", 10, 30, 0.90),
+	}, 100)
+
+	if got.FlaggedSeconds != 40 {
+		t.Fatalf("flagged seconds = %.1f, want 40.0", got.FlaggedSeconds)
+	}
+	if got.CoverageRatio != 0.4 {
+		t.Fatalf("coverage ratio = %.4f, want 0.4000", got.CoverageRatio)
+	}
+}
+
+func TestComputeHighlightRatingReportsIndependentComponents(t *testing.T) {
+	got := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("combo:position:cowgirl+body:pussy+clothing:lingerie", 10, 40, 0.90),
+		ratingTestSegment("combo:position:doggy+object:dildo+clothing:stockings", 100, 40, 0.90),
+	}, 300)
+
+	components := map[string]float64{
+		"position": got.PositionScore,
+		"clothing": got.ClothingScore,
+		"duration": got.DurationScore,
+		"strength": got.StrengthScore,
+		"coverage": got.CoverageScore,
+		"context":  got.ContextScore,
+		"variety":  got.VarietyScore,
+		"action":   got.ActionScore,
+	}
+	for name, value := range components {
+		if value <= 0 || value > 100 {
+			t.Fatalf("%s component = %.1f, want value in (0, 100]", name, value)
+		}
+	}
+}
+
+func TestComputeHighlightRatingStrengthFollowsConfidence(t *testing.T) {
+	weak := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("position:doggy", 10, 45, 0.45),
+	}, 300)
+	strong := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("position:doggy", 10, 45, 0.90),
+	}, 300)
+
+	if strong.StrengthScore <= weak.StrengthScore {
+		t.Fatalf(
+			"strength should follow confidence: strong=%.1f weak=%.1f",
+			strong.StrengthScore,
+			weak.StrengthScore,
+		)
+	}
+	if strong.Score <= weak.Score {
+		t.Fatalf(
+			"rating should reward stronger evidence: strong=%.1f weak=%.1f",
+			strong.Score,
+			weak.Score,
+		)
+	}
+}
+
 func TestComputeHighlightRatingRatesContextWithoutPosition(t *testing.T) {
 	clothingOnly := computeHighlightRating([]aiSegmentMeta{
 		ratingTestSegment("clothing:lingerie", 10, 60, 0.90),
@@ -134,9 +330,9 @@ func TestComputeHighlightRatingRatesContextWithoutPosition(t *testing.T) {
 		ratingTestSegment("combo:body:penis+object:dildo", 240, 45, 0.90),
 	}, 600)
 
-	if clothingOnly.Stars != 2 {
+	if clothingOnly.Stars != 3 {
 		t.Fatalf(
-			"clothing-only stars = %d, want 2 (score %.1f)",
+			"clothing-only stars = %d, want 3 (score %.1f)",
 			clothingOnly.Stars,
 			clothingOnly.Score,
 		)
@@ -210,10 +406,10 @@ func TestComputeHighlightRatingExplicitSignalsBoostPositions(t *testing.T) {
 
 func TestComputeHighlightRatingExplicitContextCanElevateStrongPositions(t *testing.T) {
 	positions := []aiSegmentMeta{
-		ratingTestSegment("position:doggy", 20, 25, 0.82),
+		ratingTestSegment("position:cowgirl", 20, 25, 0.82),
-		ratingTestSegment("position:cowgirl", 120, 25, 0.82),
+		ratingTestSegment("position:reverse_cowgirl", 120, 25, 0.82),
-		ratingTestSegment("position:missionary", 220, 25, 0.82),
+		ratingTestSegment("position:doggy", 220, 25, 0.82),
-		ratingTestSegment("position:blowjob", 320, 25, 0.82),
+		ratingTestSegment("position:toy_play", 320, 25, 0.82),
 	}
 	positionOnly := computeHighlightRating(positions, 600)
 	withExplicitContext := computeHighlightRating(append(

backend/training.go

@@ -1481,11 +1481,8 @@ func trainingImportVideoHandler(w http.ResponseWriter, r *http.Request) {
 			continue
 		}
 
-		// Ab hier existiert das erste echte extrahierte Bild.
+		// Nach jeder erfolgreichen Extraktion das aktuell verarbeitete Frame zeigen.
-		// Dieses bleibt als Overlay-Background für Analyse/Speichern/weitere Frames.
+		previewURL = "/api/training/frame?id=" + url.QueryEscape(id)
-		if previewURL == "" {
-			previewURL = "/api/training/frame?id=" + url.QueryEscape(id)
-		}
 
 		trainingPublishAnalysisStepWithPreview(
 			requestID,

frontend/src/aiLabels.ts

@@ -138,6 +138,17 @@ export const RATING_VALUE_LABELS: Record<string, string> = {
   stars: 'Sterne',
   score: 'Score',
   actionScore: 'Action',
+  bestPosition: 'Top-Position',
+  bestPositionRank: 'Positionsrang',
+  bestClothing: 'Top-Kleidung',
+  bestClothingRank: 'Kleidungsrang',
+  positionScore: 'Position',
+  clothingScore: 'Kleidung',
+  durationScore: 'Dauer',
+  strengthScore: 'Stärke',
+  coverageScore: 'Abdeckung',
+  contextScore: 'Kontext',
+  varietyScore: 'Vielfalt',
   confidence: 'Konfidenz',
   probability: 'Wahrscheinlichkeit',
   nsfw: 'NSFW',

frontend/src/components/ui/TrainingTab.tsx

@@ -767,13 +767,13 @@ function TrainingStageOverlay(props: {
 }) {
   const progress = clampPercent(props.progress ?? 0)
   const isTraining = props.mode === 'training'
-  const hasBackground = !isTraining && Boolean(props.backgroundUrl)
+  const hasBackground = Boolean(props.backgroundUrl)
   const visible = props.visible ?? true
 
   const [backgroundVisible, setBackgroundVisible] = useState(false)
 
   useEffect(() => {
-    if (!props.backgroundUrl || isTraining) {
+    if (!props.backgroundUrl) {
       setBackgroundVisible(false)
       return
     }
@@ -785,7 +785,7 @@ function TrainingStageOverlay(props: {
     })
 
     return () => window.cancelAnimationFrame(frame)
-  }, [props.backgroundUrl, isTraining])
+  }, [props.backgroundUrl])
 
   const title = isTraining ? 'Training läuft…' : 'Analyse läuft…'
   const fallbackText = isTraining
@@ -5685,9 +5685,9 @@ export default function TrainingTab(props: {
                   progress={stageOverlayProgress}
                   visible={stageOverlayIsVisible}
                   backgroundUrl={
-                    stageOverlayMode === 'analysis'
+                    stageOverlayMode === 'training'
-                      ? loadingPreviewBackgroundUrl
+                      ? imageSrc
-                      : undefined
+                      : loadingPreviewBackgroundUrl
                   }
                 />
               ) : null}