updated rating

backend/meta.go

@@ -107,6 +107,7 @@ type aiSegmentMeta struct {
 	EndSeconds      float64  `json:"endSeconds"`
 	DurationSeconds float64  `json:"durationSeconds"`
 	Score           float64  `json:"score,omitempty"`
+	RatingIntensity float64  `json:"ratingIntensity,omitempty"`
 	AutoSelected    bool     `json:"autoSelected,omitempty"`
 	Position        string   `json:"position,omitempty"`
 	Tags            []string `json:"tags,omitempty"`

backend/rating.go

@@ -10,6 +10,7 @@ import (
 
 type aiRatingMeta struct {
 	Score                  float64 `json:"score"`
+	ActionScore            float64 `json:"actionScore"`
 	Stars                  int     `json:"stars"`
 	Segments               int     `json:"segments"`
 	SegmentsPerMinute      float64 `json:"segmentsPerMinute"`
@@ -1130,6 +1131,7 @@ func mergeRatingActivitySegments(
 		return aiSegmentMeta{
 			Label:           label,
 			Score:           ratingClamp01(score),
+			RatingIntensity: ratingClamp01(sev * ratingConfidenceWeight(score)),
 			StartSeconds:    start,
 			EndSeconds:      end,
 			DurationSeconds: ratingDuration,
@@ -1276,6 +1278,43 @@ func ratingExplicitContextBonus(
 	return math.Min(bonus, 0.10)
 }
 
+func ratingBlendWithAction(base, action, actionWeight float64) float64 {
+	actionWeight = ratingClamp01(actionWeight)
+	return ratingClamp01((1-actionWeight)*base + actionWeight*action)
+}
+
+func ratingPositionActionScore(
+	durationNorm float64,
+	longestNorm float64,
+	densityNorm float64,
+	coverageNorm float64,
+	peakNorm float64,
+) float64 {
+	return ratingClamp01(
+		0.38*durationNorm +
+			0.24*longestNorm +
+			0.18*densityNorm +
+			0.12*coverageNorm +
+			0.08*peakNorm,
+	)
+}
+
+func ratingContextActionScore(
+	durationNorm float64,
+	longestNorm float64,
+	coverageNorm float64,
+	densityNorm float64,
+	peakNorm float64,
+) float64 {
+	return ratingClamp01(
+		0.40*durationNorm +
+			0.25*longestNorm +
+			0.20*coverageNorm +
+			0.10*densityNorm +
+			0.05*peakNorm,
+	)
+}
+
 func ratingExcellenceBonus(
 	peakQuality float64,
 	positionEffectiveWeighted float64,
@@ -1539,6 +1578,15 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 				0.15*coverageNorm +
 				0.05*densityNorm
 
+		actionRaw := ratingContextActionScore(
+			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 {
@@ -1549,6 +1597,7 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 		score := ratingRound(ratingClamp01(raw)*100, 1)
 
 		r.Score = score
+		r.ActionScore = ratingRound(actionRaw*100, 1)
 		r.Stars = starsFromHighlightScore(score)
 		r.Segments = contextN
 		r.SegmentsPerMinute = ratingRound(contextSegmentsPerMinute, 2)
@@ -1606,6 +1655,15 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 			0.06*coverageNorm +
 			0.02*positionVarietyNorm
 
+	actionRaw := ratingPositionActionScore(
+		positionDurationNorm,
+		longestNorm,
+		positionDensityNorm,
+		coverageNorm,
+		peakNorm,
+	)
+	raw = ratingBlendWithAction(raw, actionRaw, 0.30)
+
 	explicitContextBonus := ratingExplicitContextBonus(
 		bodyEffectiveWeighted,
 		clothingEffectiveWeighted,
@@ -1635,6 +1693,7 @@ func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec fl
 	score := ratingRound(raw*100, 1)
 
 	r.Score = score
+	r.ActionScore = ratingRound(actionRaw*100, 1)
 	r.Stars = starsFromHighlightScore(score)
 	r.Segments = n
 	r.SegmentsPerMinute = ratingRound(segmentsPerMinute, 2)

backend/rating_test.go

@@ -98,6 +98,30 @@ func TestComputeHighlightRatingRewardsPositionDuration(t *testing.T) {
 	}
 }
 
+func TestComputeHighlightRatingActionPrefersSustainedActivity(t *testing.T) {
+	briefPeak := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("position:doggy", 10, 8, 0.95),
+	}, 600)
+	sustained := computeHighlightRating([]aiSegmentMeta{
+		ratingTestSegment("position:doggy", 10, 45, 0.55),
+	}, 600)
+
+	if sustained.ActionScore <= briefPeak.ActionScore {
+		t.Fatalf(
+			"sustained action should outrank a brief peak: sustained=%.1f brief=%.1f",
+			sustained.ActionScore,
+			briefPeak.ActionScore,
+		)
+	}
+	if sustained.Score <= briefPeak.Score {
+		t.Fatalf(
+			"rating should follow sustained action: sustained=%.1f brief=%.1f",
+			sustained.Score,
+			briefPeak.Score,
+		)
+	}
+}
+
 func TestComputeHighlightRatingRatesContextWithoutPosition(t *testing.T) {
 	clothingOnly := computeHighlightRating([]aiSegmentMeta{
 		ratingTestSegment("clothing:lingerie", 10, 60, 0.90),
@@ -268,4 +292,19 @@ func TestPrepareAIRatingSegmentsMergesSamePositionAcrossShortGap(t *testing.T) {
 			segments[0].DurationSeconds,
 		)
 	}
+	if segments[0].RatingIntensity <= 0 {
+		t.Fatalf("rating intensity = %.3f, want a positive heatcurve value", segments[0].RatingIntensity)
+	}
+
+	wantIntensity := ratingClamp01(
+		ratingSegmentSeverity(segments[0].Label) *
+			ratingConfidenceWeight(segments[0].Score),
+	)
+	if diff := segments[0].RatingIntensity - wantIntensity; diff < -0.0001 || diff > 0.0001 {
+		t.Fatalf(
+			"rating intensity = %.4f, want %.4f",
+			segments[0].RatingIntensity,
+			wantIntensity,
+		)
+	}
 }

frontend/src/aiLabels.ts

@@ -137,6 +137,7 @@ export const AI_LABEL_FALLBACKS: Record<string, string> = {
 export const RATING_VALUE_LABELS: Record<string, string> = {
   stars: 'Sterne',
   score: 'Score',
+  actionScore: 'Action',
   confidence: 'Konfidenz',
   probability: 'Wahrscheinlichkeit',
   nsfw: 'NSFW',

frontend/src/components/ui/videoHeatmap.ts

@@ -77,6 +77,18 @@ function readScore(value: unknown): number {
   return 0.35
 }
 
+function readOptionalScore(value: unknown): number | null {
+  if (value == null || value === '') return null
+
+  const n = Number(value)
+  if (!Number.isFinite(n)) return null
+
+  if (n >= 0 && n <= 1) return clamp(n, 0, 1)
+  if (n > 1 && n <= 100) return clamp(n / 100, 0, 1)
+
+  return null
+}
+
 type WeightedDetectedLabel = {
   label: string
   scoreWeight: number
@@ -115,16 +127,34 @@ function weightedLabelFromValue(value: unknown): WeightedDetectedLabel | null {
   if (group === 'position') {
     return {
       label: prettyAiLabel(normalized),
-      scoreWeight: 1.0,
-      priority: 2,
+      scoreWeight: 0.82,
+      priority: 4,
       source: 'position',
     }
   }
 
+  if (group === 'body') {
+    return {
+      label: prettyAiLabel(normalized),
+      scoreWeight: 0.50,
+      priority: 3,
+      source: 'other',
+    }
+  }
+
+  if (group === 'object') {
+    return {
+      label: prettyAiLabel(normalized),
+      scoreWeight: 0.15,
+      priority: 2,
+      source: 'other',
+    }
+  }
+
   if (group === 'clothing') {
     return {
       label: prettyAiLabel(normalized),
-      scoreWeight: 0.58,
+      scoreWeight: 0.28,
       priority: 1,
       source: 'clothing',
     }
@@ -237,17 +267,22 @@ export function buildVideoHeatmapSegments(
     ? (ai?.segments ?? ai?.Segments)
     : []
 
-  for (const item of segments) {
+  const appendItems = (items: unknown[]) => {
+    for (const item of items) {
       if (!isPlainObject(item)) continue
 
       const detected = readWeightedLabel(item)
       if (!detected) continue
 
+      const ratingIntensity = readOptionalScore(
+        item.ratingIntensity ?? item.RatingIntensity
+      )
       const rawScore = readScore(
           item.score ?? item.Score ?? item.confidence
       )
-
-    const weightedScore = clamp(rawScore * detected.scoreWeight, 0, 1)
+      const intensity =
+        ratingIntensity ??
+        clamp(rawScore * detected.scoreWeight, 0, 1)
 
       addSegment(
         out,
@@ -256,37 +291,21 @@ export function buildVideoHeatmapSegments(
         item.time ?? item.Time ?? item.at ?? item.timestamp,
         durationSec,
         detected.label,
-        weightedScore,
+        intensity,
         detected.source
       )
     }
+  }
 
+  appendItems(segments)
+
+  // Raw hits are the source of the condensed segments and would otherwise
+  // paint the same activity twice. Keep them only for legacy metadata.
+  if (out.length === 0) {
     const hits = Array.isArray(ai?.hits ?? ai?.Hits)
       ? (ai?.hits ?? ai?.Hits)
       : []
-
-  for (const item of hits) {
-    if (!isPlainObject(item)) continue
-
-    const detected = readWeightedLabel(item)
-    if (!detected) continue
-
-    const rawScore = readScore(
-        item.score ?? item.Score ?? item.confidence
-    )
-
-    const weightedScore = clamp(rawScore * detected.scoreWeight, 0, 1)
-
-    addSegment(
-        out,
-        item.startSeconds ?? item.StartSeconds ?? item.start ?? item.Start,
-        item.endSeconds ?? item.EndSeconds ?? item.end ?? item.End,
-        item.time ?? item.Time ?? item.at ?? item.timestamp,
-        durationSec,
-        detected.label,
-        weightedScore,
-        detected.source
-    )
+    appendItems(hits)
   }
 
   return mergeHeatmapSegments(out, durationSec)