bugfixes

backend/training.go

@@ -784,9 +784,9 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
 	canTrain := feedbackCount >= minTrainingFeedbackCount
 
 	// Pipeline:
-	// - YOLO trainiert nur BodyParts, Objects, Clothing und deren Boxen.
+	// - YOLO erkennt Personen/Gender für die Counts.
-	// - CLIP + Logistic Regression/KNN trainiert die Sexposition.
+	// - Automatisch erkannte Personenboxen werden nicht an das Frontend als sichtbare Boxen zurückgegeben.
-	// - Personen/Gender werden manuell korrigiert und nicht per YOLO erkannt.
+	// - Manuell gezeichnete Personenboxen werden trotzdem als Trainingsdaten gespeichert.
 	trainingWriteJSON(w, http.StatusOK, map[string]any{
 		"ok":            true,
 		"feedbackCount": feedbackCount,
@@ -800,8 +800,8 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
 			"usesSceneKNN":    false,
 			"usesResNet18KNN": false,
 
-			"detectsPeople":    false,
+			"detectsPeople":    true,
-			"detectsGender":    false,
+			"detectsGender":    true,
 			"detectsBodyParts": true,
 			"detectsObjects":   true,
 			"detectsClothing":  true,
@@ -856,8 +856,8 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
 		"pipeline": map[string]any{
 			"variant": "B",
 
-			"peopleSource":    "manual",
+			"peopleSource":    "yolo_detector",
-			"genderSource":    "manual",
+			"genderSource":    "yolo_detector",
 			"bodyPartsSource": "yolo_detector",
 			"objectsSource":   "yolo_detector",
 			"clothingSource":  "yolo_detector",
@@ -1304,9 +1304,9 @@ func trainingApplyScenePosition(
 }
 
 func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPrediction {
-	boxes := det.Boxes
+	rawBoxes := det.Boxes
-	if boxes == nil {
+	if rawBoxes == nil {
-		boxes = []TrainingBox{}
+		rawBoxes = []TrainingBox{}
 	}
 
 	pred := TrainingPrediction{
@@ -1321,7 +1321,7 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
 		BodyPartsPresent: []TrainingScoredLabel{},
 		ObjectsPresent:   []TrainingScoredLabel{},
 		ClothingPresent:  []TrainingScoredLabel{},
-		Boxes:            boxes,
+		Boxes:            []TrainingBox{},
 	}
 
 	if pred.Source == "" {
@@ -1338,36 +1338,78 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
 		return pred
 	}
 
-	allowed := map[string]bool{}
+	peopleSet := map[string]bool{}
-	for _, label := range grouped.BodyParts {
+	for _, label := range grouped.People {
-		allowed[strings.TrimSpace(label)] = true
+		clean := strings.TrimSpace(label)
-	}
+		if clean != "" {
-	for _, label := range grouped.Objects {
+			peopleSet[clean] = true
-		allowed[strings.TrimSpace(label)] = true
-	}
-	for _, label := range grouped.Clothing {
-		allowed[strings.TrimSpace(label)] = true
-	}
-
-	filteredBoxes := []TrainingBox{}
-	for _, box := range boxes {
-		label := strings.TrimSpace(box.Label)
-		if allowed[label] {
-			filteredBoxes = append(filteredBoxes, box)
 		}
 	}
 
-	boxes = filteredBoxes
+	detectionSet := map[string]bool{}
-	pred.Boxes = boxes
 
-	pred.BodyPartsPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.BodyParts)
+	for _, label := range grouped.BodyParts {
-	pred.ObjectsPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.Objects)
+		clean := strings.TrimSpace(label)
-	pred.ClothingPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.Clothing)
+		if clean != "" {
+			detectionSet[clean] = true
+		}
+	}
 
-	pred.UnknownCount = 0
+	for _, label := range grouped.Objects {
-	pred.PeopleCount = 0
+		clean := strings.TrimSpace(label)
-	pred.MaleCount = 0
+		if clean != "" {
-	pred.FemaleCount = 0
+			detectionSet[clean] = true
+		}
+	}
+
+	for _, label := range grouped.Clothing {
+		clean := strings.TrimSpace(label)
+		if clean != "" {
+			detectionSet[clean] = true
+		}
+	}
+
+	visibleBoxes := []TrainingBox{}
+
+	for _, box := range rawBoxes {
+		label := strings.TrimSpace(box.Label)
+		if label == "" {
+			continue
+		}
+
+		box.Label = label
+
+		// Personen erkennen und zählen.
+		// Personenboxen werden jetzt auch sichtbar zurückgegeben,
+		// damit sie im Frontend gezeichnet, verschoben, gelöscht und trainiert werden können.
+		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
+		}
+
+		// Nur Bodyparts/Objects/Clothing bleiben als Boxen sichtbar.
+		if detectionSet[label] {
+			visibleBoxes = append(visibleBoxes, box)
+		}
+	}
+
+	pred.PeopleCount = pred.MaleCount + pred.FemaleCount + pred.UnknownCount
+	pred.Boxes = visibleBoxes
+
+	pred.BodyPartsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.BodyParts)
+	pred.ObjectsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Objects)
+	pred.ClothingPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Clothing)
 
 	return pred
 }

backend/training_label_loader.go

@@ -87,7 +87,10 @@ func trainingDetectorLabels() ([]string, error) {
 
 	labels := []string{}
 
-	// Bestehende Reihenfolge beibehalten, damit alte Class-IDs stabil bleiben.
+	// Wichtig:
+	// People zuerst oder zuletzt ist egal, aber die Reihenfolge bestimmt YOLO-Class-IDs.
+	// Wenn du schon ein bestehendes Detector-Modell hast, musst du danach neu trainieren.
+	labels = append(labels, grouped.People...)
 	labels = append(labels, grouped.BodyParts...)
 	labels = append(labels, grouped.Objects...)
 	labels = append(labels, grouped.Clothing...)

frontend/src/components/ui/TrainingTab.tsx

@@ -124,6 +124,88 @@ function percent(v: number) {
   return `${Math.round(v * 100)}%`
 }
 
+function scoreLevel(score?: number | null): 'none' | 'low' | 'mid' | 'high' {
+  const n = Number(score)
+
+  if (!Number.isFinite(n)) return 'none'
+  if (n < 0.5) return 'low'
+  if (n < 0.75) return 'mid'
+  return 'high'
+}
+
+function scoreBorderClass(score?: number | null, opts?: { draft?: boolean }) {
+  if (opts?.draft) return 'border-amber-400'
+
+  switch (scoreLevel(score)) {
+    case 'low':
+      return 'border-red-500'
+    case 'mid':
+      return 'border-yellow-400'
+    case 'high':
+      return 'border-emerald-400'
+    default:
+      return 'border-gray-300'
+  }
+}
+
+function scoreBgClass(score?: number | null, opts?: { draft?: boolean }) {
+  if (opts?.draft) return 'bg-amber-400'
+
+  switch (scoreLevel(score)) {
+    case 'low':
+      return 'bg-red-500'
+    case 'mid':
+      return 'bg-yellow-400'
+    case 'high':
+      return 'bg-emerald-400'
+    default:
+      return 'bg-gray-300'
+  }
+}
+
+function scoreHoverClass(score?: number | null, opts?: { draft?: boolean }) {
+  if (opts?.draft) return ''
+
+  switch (scoreLevel(score)) {
+    case 'low':
+      return 'hover:bg-red-400'
+    case 'mid':
+      return 'hover:bg-yellow-300'
+    case 'high':
+      return 'hover:bg-emerald-300'
+    default:
+      return 'hover:bg-gray-200'
+  }
+}
+
+function scoreRingClass(score?: number | null, opts?: { draft?: boolean }) {
+  if (opts?.draft) return 'ring-amber-500'
+
+  switch (scoreLevel(score)) {
+    case 'low':
+      return 'ring-red-500'
+    case 'mid':
+      return 'ring-yellow-400'
+    case 'high':
+      return 'ring-emerald-500'
+    default:
+      return 'ring-gray-400'
+  }
+}
+
+function scoreDetectionPillClass(score?: number | null) {
+  switch (scoreLevel(score)) {
+    case 'low':
+      return 'bg-red-50 text-red-800 ring-red-200 dark:bg-red-500/15 dark:text-red-100 dark:ring-red-400/30'
+    case 'mid':
+      return 'bg-yellow-50 text-yellow-900 ring-yellow-200 dark:bg-yellow-500/15 dark:text-yellow-100 dark:ring-yellow-400/30'
+    case 'high':
+      return 'bg-emerald-50 text-emerald-800 ring-emerald-200 dark:bg-emerald-500/15 dark:text-emerald-100 dark:ring-emerald-400/30'
+    default:
+      return 'bg-gray-50 text-gray-700 ring-gray-200 dark:bg-white/5 dark:text-gray-200 dark:ring-white/10'
+  }
+}
+
 function normalizeMovedBox(box: TrainingBox): TrainingBox {
   const w = clamp01(box.w)
   const h = clamp01(box.h)
@@ -137,22 +219,6 @@ function normalizeMovedBox(box: TrainingBox): TrainingBox {
   }
 }
 
-function scoredLabelsText(items?: ScoredLabel[] | null) {
-  const list = Array.isArray(items) ? items : []
-
-  if (list.length === 0) return '—'
-
-  return list
-    .map((x) => {
-      const label = String(x?.label ?? '').trim()
-      if (!label) return null
-
-      return `${label} (${percent(Number(x?.score ?? 0))})`
-    })
-    .filter(Boolean)
-    .join(', ')
-}
-
 function clampPercent(v: number) {
   if (!Number.isFinite(v)) return 0
   return Math.max(0, Math.min(100, v))
@@ -209,19 +275,6 @@ function uniqStrings(values: string[]) {
   return out
 }
 
-function isPersonBoxLabel(label?: string) {
-  const normalized = String(label || '').trim().toLowerCase()
-
-  return (
-    normalized === 'person' ||
-    normalized === 'person_male' ||
-    normalized === 'person_female' ||
-    normalized === 'person_unknown' ||
-    normalized === 'male_person' ||
-    normalized === 'female_person'
-  )
-}
-
 function predictionToCorrection(sample: TrainingSample | null): CorrectionState {
   const p = sample?.prediction
 
@@ -246,8 +299,7 @@ function predictionToCorrection(sample: TrainingSample | null): CorrectionState
         w: clamp01(Number(box.w)),
         h: clamp01(Number(box.h)),
       }))
-      .filter((box) => box.label && box.w > 0 && box.h > 0)
+      .filter((box) => box.label && box.w > 0 && box.h > 0),
-      .filter((box) => !isPersonBoxLabel(box.label)),
   }
 }
 
@@ -532,6 +584,52 @@ function LabelToggleGrid(props: {
   )
 }
 
+function ScoredLabelChips(props: {
+  items?: ScoredLabel[] | null
+}) {
+  const list = Array.isArray(props.items) ? props.items : []
+
+  if (list.length === 0) {
+    return <span className="text-gray-500 dark:text-gray-400">—</span>
+  }
+
+  return (
+    <div className="mt-1 flex flex-wrap gap-1">
+      {list.map((entry, index) => {
+        const label = String(entry?.label ?? '').trim()
+        if (!label) return null
+
+        const score = Number(entry?.score ?? NaN)
+        const item = getSegmentLabelItem(label)
+        const Icon = item.icon
+
+        return (
+          <span
+            key={`${label}-${index}`}
+            title={`${label} ${Number.isFinite(score) ? percent(score) : ''}`}
+            className={[
+              'inline-flex max-w-full items-center gap-1 rounded-full px-1.5 py-0.5 text-[10px] font-semibold ring-1',
+              scoreDetectionPillClass(score),
+            ].join(' ')}
+          >
+            <Icon className="h-3 w-3 shrink-0" aria-hidden="true" />
+
+            <span className="max-w-[92px] truncate">
+              {item.text}
+            </span>
+
+            {Number.isFinite(score) ? (
+              <span className="shrink-0 opacity-80">
+                {percent(score)}
+              </span>
+            ) : null}
+          </span>
+        )
+      })}
+    </div>
+  )
+}
+
 function DetectorBoxLabelSelect(props: {
   values: string[]
   value: string
@@ -1388,20 +1486,48 @@ export default function TrainingTab() {
             <div>Weiblich: {sample?.prediction.femaleCount ?? '—'}</div>
 
             <div>
-              Position: {sample?.prediction.sexPosition ?? '—'}{' '}
+              <div className="font-medium text-gray-700 dark:text-gray-200">
-              {sample ? `(${percent(sample.prediction.sexPositionScore)})` : ''}
+                Position
+              </div>
+
+              {sample ? (
+                <span
+                  className={[
+                    'mt-1 inline-flex max-w-full items-center rounded-full px-1.5 py-0.5 text-[10px] font-semibold ring-1',
+                    scoreDetectionPillClass(sample.prediction.sexPositionScore),
+                  ].join(' ')}
+                >
+                  <span className="truncate">
+                    {sample.prediction.sexPosition || '—'}
+                  </span>
+                  <span className="ml-1 shrink-0 opacity-80">
+                    {percent(sample.prediction.sexPositionScore)}
+                  </span>
+                </span>
+              ) : (
+                <span>—</span>
+              )}
             </div>
 
             <div>
-              Körperteile: {scoredLabelsText(sample?.prediction.bodyPartsPresent)}
+              <div className="font-medium text-gray-700 dark:text-gray-200">
+                Körperteile
+              </div>
+              <ScoredLabelChips items={sample?.prediction.bodyPartsPresent} />
             </div>
 
             <div>
-              Gegenstände: {scoredLabelsText(sample?.prediction.objectsPresent)}
+              <div className="font-medium text-gray-700 dark:text-gray-200">
+                Gegenstände
+              </div>
+              <ScoredLabelChips items={sample?.prediction.objectsPresent} />
             </div>
 
             <div>
-              Kleidung: {scoredLabelsText(sample?.prediction.clothingPresent)}
+              <div className="font-medium text-gray-700 dark:text-gray-200">
+                Kleidung
+              </div>
+              <ScoredLabelChips items={sample?.prediction.clothingPresent} />
             </div>
           </div>
 
@@ -1441,7 +1567,10 @@ export default function TrainingTab() {
                   return (
                     <div
                       key={`${box.label}-${index}`}
-                      className="flex items-center justify-between gap-2 rounded-md bg-white px-2 py-1 text-[11px] ring-1 ring-gray-200 dark:bg-gray-950 dark:ring-white/10"
+                      className={[
+                        'flex items-center justify-between gap-2 rounded-md px-2 py-1 text-[11px] ring-1',
+                        scoreDetectionPillClass(box.score),
+                      ].join(' ')}
                     >
                       <div className="flex min-w-0 flex-1 items-center justify-between gap-2 text-gray-700 dark:text-gray-200">
                         <span className="flex min-w-0 items-center gap-1.5">
@@ -1583,9 +1712,7 @@ export default function TrainingTab() {
                           key={`${box.label}-${index}-${isDraft ? 'draft' : 'box'}`}
                           className={[
                             'pointer-events-none absolute rounded border-2 shadow-[0_0_0_1px_rgba(0,0,0,0.75)]',
-                            isDraft
+                            scoreBorderClass(box.score, { draft: isDraft }),
-                              ? 'border-amber-400'
-                              : 'border-emerald-400',
                           ].join(' ')}
                           style={{
                             left: `${left}%`,
@@ -1600,9 +1727,7 @@ export default function TrainingTab() {
                             data-box-control="true"
                             className={[
                               'pointer-events-auto absolute left-0 top-0 flex h-5 max-w-[min(180px,calc(100vw-24px))] -translate-y-full touch-none items-stretch overflow-hidden rounded-t-md text-[10px] font-semibold leading-none text-black shadow',
-                              isDraft
+                              scoreBgClass(box.score, { draft: isDraft }),
-                                ? 'bg-amber-400'
-                                : 'bg-emerald-400',
                             ].join(' ')}
                             title={isDraft ? box.label : `${item.text} verschieben`}
                           >
@@ -1612,7 +1737,7 @@ export default function TrainingTab() {
                                 'flex min-w-0 touch-none items-center gap-1 px-1.5 text-left',
                                 isDraft
                                   ? 'cursor-default'
-                                  : 'cursor-move hover:bg-emerald-300',
+                                  : ['cursor-move', scoreHoverClass(box.score)].join(' '),
                               ].join(' ')}
                               disabled={Boolean(isDraft) || uiLocked}
                               onPointerDown={(e) => {
@@ -1741,7 +1866,12 @@ export default function TrainingTab() {
                                     })
                                   }}
                                 >
-                                  <span className="block h-2 w-2 rounded-full bg-white shadow-[0_0_0_1px_rgba(0,0,0,0.75)] ring-2 ring-emerald-500 sm:h-2 sm:w-2" />
+                                  <span
+                                    className={[
+                                      'block h-2 w-2 rounded-full bg-white shadow-[0_0_0_1px_rgba(0,0,0,0.75)] ring-2 sm:h-2 sm:w-2',
+                                      scoreRingClass(box.score),
+                                    ].join(' ')}
+                                  />
                                 </button>
                               ))}
                             </>
@@ -1848,7 +1978,10 @@ export default function TrainingTab() {
 
                       {hasUsableBox ? (
                         <div
-                          className="absolute rounded border-2 border-amber-400 bg-amber-400/10 shadow-[0_0_0_1px_rgba(0,0,0,0.85)]"
+                          className={[
+                            'absolute rounded border-2 bg-black/0 shadow-[0_0_0_1px_rgba(0,0,0,0.85)]',
+                            scoreBorderClass(activeBox.score, { draft: Boolean(drawingBox) }),
+                          ].join(' ')}
                           style={{
                             left: boxLeft,
                             top: boxTop,