bugfixes
This commit is contained in:
Linrador
parent
03ff0029f2
commit
a598ac025f
@ -784,9 +784,9 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
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canTrain := feedbackCount >= minTrainingFeedbackCount
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// Pipeline:
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// - YOLO trainiert nur BodyParts, Objects, Clothing und deren Boxen.
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// - CLIP + Logistic Regression/KNN trainiert die Sexposition.
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// - Personen/Gender werden manuell korrigiert und nicht per YOLO erkannt.
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// - YOLO erkennt Personen/Gender für die Counts.
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// - Automatisch erkannte Personenboxen werden nicht an das Frontend als sichtbare Boxen zurückgegeben.
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// - Manuell gezeichnete Personenboxen werden trotzdem als Trainingsdaten gespeichert.
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trainingWriteJSON(w, http.StatusOK, map[string]any{
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"ok": true,
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"feedbackCount": feedbackCount,
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@ -800,8 +800,8 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
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"usesSceneKNN": false,
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"usesResNet18KNN": false,
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"detectsPeople": false,
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"detectsGender": false,
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"detectsPeople": true,
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"detectsGender": true,
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"detectsBodyParts": true,
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"detectsObjects": true,
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"detectsClothing": true,
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@ -856,8 +856,8 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
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"pipeline": map[string]any{
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"variant": "B",
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"peopleSource": "manual",
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"genderSource": "manual",
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"peopleSource": "yolo_detector",
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"genderSource": "yolo_detector",
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"bodyPartsSource": "yolo_detector",
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"objectsSource": "yolo_detector",
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"clothingSource": "yolo_detector",
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@ -1304,9 +1304,9 @@ func trainingApplyScenePosition(
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}
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func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPrediction {
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boxes := det.Boxes
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if boxes == nil {
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boxes = []TrainingBox{}
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rawBoxes := det.Boxes
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if rawBoxes == nil {
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rawBoxes = []TrainingBox{}
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}
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pred := TrainingPrediction{
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@ -1321,7 +1321,7 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
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BodyPartsPresent: []TrainingScoredLabel{},
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ObjectsPresent: []TrainingScoredLabel{},
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ClothingPresent: []TrainingScoredLabel{},
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Boxes: boxes,
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Boxes: []TrainingBox{},
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}
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if pred.Source == "" {
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@ -1338,36 +1338,78 @@ func trainingPredictionFromDetector(det TrainingDetectorPrediction) TrainingPred
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return pred
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}
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allowed := map[string]bool{}
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for _, label := range grouped.BodyParts {
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allowed[strings.TrimSpace(label)] = true
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}
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for _, label := range grouped.Objects {
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allowed[strings.TrimSpace(label)] = true
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}
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for _, label := range grouped.Clothing {
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allowed[strings.TrimSpace(label)] = true
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}
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filteredBoxes := []TrainingBox{}
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for _, box := range boxes {
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label := strings.TrimSpace(box.Label)
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if allowed[label] {
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filteredBoxes = append(filteredBoxes, box)
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peopleSet := map[string]bool{}
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for _, label := range grouped.People {
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clean := strings.TrimSpace(label)
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if clean != "" {
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peopleSet[clean] = true
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}
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}
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boxes = filteredBoxes
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pred.Boxes = boxes
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detectionSet := map[string]bool{}
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pred.BodyPartsPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.BodyParts)
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pred.ObjectsPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.Objects)
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pred.ClothingPresent = trainingScoredLabelsFromDetectorBoxes(boxes, grouped.Clothing)
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for _, label := range grouped.BodyParts {
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clean := strings.TrimSpace(label)
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if clean != "" {
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detectionSet[clean] = true
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}
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}
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pred.UnknownCount = 0
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pred.PeopleCount = 0
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pred.MaleCount = 0
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pred.FemaleCount = 0
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for _, label := range grouped.Objects {
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clean := strings.TrimSpace(label)
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if clean != "" {
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detectionSet[clean] = true
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}
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}
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for _, label := range grouped.Clothing {
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clean := strings.TrimSpace(label)
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if clean != "" {
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detectionSet[clean] = true
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}
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}
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visibleBoxes := []TrainingBox{}
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for _, box := range rawBoxes {
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label := strings.TrimSpace(box.Label)
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if label == "" {
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continue
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}
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box.Label = label
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// Personen erkennen und zählen.
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// Personenboxen werden jetzt auch sichtbar zurückgegeben,
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// damit sie im Frontend gezeichnet, verschoben, gelöscht und trainiert werden können.
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if peopleSet[label] {
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switch label {
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case "person_male", "male_person":
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pred.MaleCount++
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case "person_female", "female_person":
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pred.FemaleCount++
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case "person", "person_unknown":
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pred.UnknownCount++
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}
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visibleBoxes = append(visibleBoxes, box)
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continue
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}
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// Nur Bodyparts/Objects/Clothing bleiben als Boxen sichtbar.
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if detectionSet[label] {
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visibleBoxes = append(visibleBoxes, box)
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}
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}
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pred.PeopleCount = pred.MaleCount + pred.FemaleCount + pred.UnknownCount
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pred.Boxes = visibleBoxes
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pred.BodyPartsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.BodyParts)
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pred.ObjectsPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Objects)
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pred.ClothingPresent = trainingScoredLabelsFromDetectorBoxes(visibleBoxes, grouped.Clothing)
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return pred
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}
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@ -87,7 +87,10 @@ func trainingDetectorLabels() ([]string, error) {
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labels := []string{}
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// Bestehende Reihenfolge beibehalten, damit alte Class-IDs stabil bleiben.
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// Wichtig:
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// People zuerst oder zuletzt ist egal, aber die Reihenfolge bestimmt YOLO-Class-IDs.
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// Wenn du schon ein bestehendes Detector-Modell hast, musst du danach neu trainieren.
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labels = append(labels, grouped.People...)
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labels = append(labels, grouped.BodyParts...)
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labels = append(labels, grouped.Objects...)
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labels = append(labels, grouped.Clothing...)
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@ -124,6 +124,88 @@ function percent(v: number) {
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return `${Math.round(v * 100)}%`
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}
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function scoreLevel(score?: number | null): 'none' | 'low' | 'mid' | 'high' {
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const n = Number(score)
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if (!Number.isFinite(n)) return 'none'
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if (n < 0.5) return 'low'
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if (n < 0.75) return 'mid'
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return 'high'
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}
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function scoreBorderClass(score?: number | null, opts?: { draft?: boolean }) {
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if (opts?.draft) return 'border-amber-400'
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switch (scoreLevel(score)) {
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case 'low':
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return 'border-red-500'
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case 'mid':
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return 'border-yellow-400'
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case 'high':
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return 'border-emerald-400'
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default:
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return 'border-gray-300'
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}
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}
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function scoreBgClass(score?: number | null, opts?: { draft?: boolean }) {
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if (opts?.draft) return 'bg-amber-400'
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switch (scoreLevel(score)) {
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case 'low':
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return 'bg-red-500'
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case 'mid':
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return 'bg-yellow-400'
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case 'high':
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return 'bg-emerald-400'
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default:
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return 'bg-gray-300'
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}
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}
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function scoreHoverClass(score?: number | null, opts?: { draft?: boolean }) {
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if (opts?.draft) return ''
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switch (scoreLevel(score)) {
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case 'low':
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return 'hover:bg-red-400'
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case 'mid':
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return 'hover:bg-yellow-300'
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case 'high':
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return 'hover:bg-emerald-300'
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default:
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return 'hover:bg-gray-200'
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}
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}
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function scoreRingClass(score?: number | null, opts?: { draft?: boolean }) {
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if (opts?.draft) return 'ring-amber-500'
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switch (scoreLevel(score)) {
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case 'low':
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return 'ring-red-500'
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case 'mid':
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return 'ring-yellow-400'
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case 'high':
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return 'ring-emerald-500'
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default:
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return 'ring-gray-400'
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}
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}
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function scoreDetectionPillClass(score?: number | null) {
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switch (scoreLevel(score)) {
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case 'low':
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return 'bg-red-50 text-red-800 ring-red-200 dark:bg-red-500/15 dark:text-red-100 dark:ring-red-400/30'
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case 'mid':
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return 'bg-yellow-50 text-yellow-900 ring-yellow-200 dark:bg-yellow-500/15 dark:text-yellow-100 dark:ring-yellow-400/30'
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case 'high':
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return 'bg-emerald-50 text-emerald-800 ring-emerald-200 dark:bg-emerald-500/15 dark:text-emerald-100 dark:ring-emerald-400/30'
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default:
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return 'bg-gray-50 text-gray-700 ring-gray-200 dark:bg-white/5 dark:text-gray-200 dark:ring-white/10'
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}
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}
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function normalizeMovedBox(box: TrainingBox): TrainingBox {
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const w = clamp01(box.w)
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const h = clamp01(box.h)
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@ -137,22 +219,6 @@ function normalizeMovedBox(box: TrainingBox): TrainingBox {
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}
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}
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function scoredLabelsText(items?: ScoredLabel[] | null) {
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const list = Array.isArray(items) ? items : []
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if (list.length === 0) return '—'
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return list
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.map((x) => {
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const label = String(x?.label ?? '').trim()
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if (!label) return null
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return `${label} (${percent(Number(x?.score ?? 0))})`
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})
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.filter(Boolean)
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.join(', ')
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}
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function clampPercent(v: number) {
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if (!Number.isFinite(v)) return 0
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return Math.max(0, Math.min(100, v))
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@ -209,19 +275,6 @@ function uniqStrings(values: string[]) {
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return out
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}
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function isPersonBoxLabel(label?: string) {
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const normalized = String(label || '').trim().toLowerCase()
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return (
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normalized === 'person' ||
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normalized === 'person_male' ||
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normalized === 'person_female' ||
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normalized === 'person_unknown' ||
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normalized === 'male_person' ||
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normalized === 'female_person'
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)
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}
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function predictionToCorrection(sample: TrainingSample | null): CorrectionState {
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const p = sample?.prediction
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@ -246,8 +299,7 @@ function predictionToCorrection(sample: TrainingSample | null): CorrectionState
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w: clamp01(Number(box.w)),
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h: clamp01(Number(box.h)),
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}))
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.filter((box) => box.label && box.w > 0 && box.h > 0)
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.filter((box) => !isPersonBoxLabel(box.label)),
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.filter((box) => box.label && box.w > 0 && box.h > 0),
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}
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}
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@ -532,6 +584,52 @@ function LabelToggleGrid(props: {
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)
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}
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function ScoredLabelChips(props: {
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items?: ScoredLabel[] | null
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}) {
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const list = Array.isArray(props.items) ? props.items : []
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if (list.length === 0) {
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return <span className="text-gray-500 dark:text-gray-400">—</span>
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}
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return (
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<div className="mt-1 flex flex-wrap gap-1">
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{list.map((entry, index) => {
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const label = String(entry?.label ?? '').trim()
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if (!label) return null
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const score = Number(entry?.score ?? NaN)
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const item = getSegmentLabelItem(label)
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const Icon = item.icon
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return (
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<span
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key={`${label}-${index}`}
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title={`${label} ${Number.isFinite(score) ? percent(score) : ''}`}
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className={[
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'inline-flex max-w-full items-center gap-1 rounded-full px-1.5 py-0.5 text-[10px] font-semibold ring-1',
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scoreDetectionPillClass(score),
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].join(' ')}
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>
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<Icon className="h-3 w-3 shrink-0" aria-hidden="true" />
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<span className="max-w-[92px] truncate">
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{item.text}
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</span>
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{Number.isFinite(score) ? (
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<span className="shrink-0 opacity-80">
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{percent(score)}
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</span>
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) : null}
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</span>
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)
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})}
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</div>
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)
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}
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function DetectorBoxLabelSelect(props: {
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values: string[]
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value: string
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@ -1388,20 +1486,48 @@ export default function TrainingTab() {
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<div>Weiblich: {sample?.prediction.femaleCount ?? '—'}</div>
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<div>
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Position: {sample?.prediction.sexPosition ?? '—'}{' '}
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{sample ? `(${percent(sample.prediction.sexPositionScore)})` : ''}
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<div className="font-medium text-gray-700 dark:text-gray-200">
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Position
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</div>
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{sample ? (
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<span
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className={[
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'mt-1 inline-flex max-w-full items-center rounded-full px-1.5 py-0.5 text-[10px] font-semibold ring-1',
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scoreDetectionPillClass(sample.prediction.sexPositionScore),
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].join(' ')}
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>
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<span className="truncate">
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{sample.prediction.sexPosition || '—'}
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</span>
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<span className="ml-1 shrink-0 opacity-80">
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{percent(sample.prediction.sexPositionScore)}
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</span>
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</span>
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) : (
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<span>—</span>
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)}
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</div>
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<div>
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Körperteile: {scoredLabelsText(sample?.prediction.bodyPartsPresent)}
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<div className="font-medium text-gray-700 dark:text-gray-200">
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Körperteile
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</div>
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<ScoredLabelChips items={sample?.prediction.bodyPartsPresent} />
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</div>
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<div>
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Gegenstände: {scoredLabelsText(sample?.prediction.objectsPresent)}
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<div className="font-medium text-gray-700 dark:text-gray-200">
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Gegenstände
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</div>
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<ScoredLabelChips items={sample?.prediction.objectsPresent} />
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</div>
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<div>
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Kleidung: {scoredLabelsText(sample?.prediction.clothingPresent)}
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<div className="font-medium text-gray-700 dark:text-gray-200">
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Kleidung
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</div>
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<ScoredLabelChips items={sample?.prediction.clothingPresent} />
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</div>
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</div>
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@ -1441,7 +1567,10 @@ export default function TrainingTab() {
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return (
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<div
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key={`${box.label}-${index}`}
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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"
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className={[
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'flex items-center justify-between gap-2 rounded-md px-2 py-1 text-[11px] ring-1',
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scoreDetectionPillClass(box.score),
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].join(' ')}
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>
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<div className="flex min-w-0 flex-1 items-center justify-between gap-2 text-gray-700 dark:text-gray-200">
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<span className="flex min-w-0 items-center gap-1.5">
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@ -1583,9 +1712,7 @@ export default function TrainingTab() {
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key={`${box.label}-${index}-${isDraft ? 'draft' : 'box'}`}
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className={[
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'pointer-events-none absolute rounded border-2 shadow-[0_0_0_1px_rgba(0,0,0,0.75)]',
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isDraft
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? 'border-amber-400'
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: 'border-emerald-400',
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scoreBorderClass(box.score, { draft: isDraft }),
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].join(' ')}
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style={{
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left: `${left}%`,
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@ -1600,9 +1727,7 @@ export default function TrainingTab() {
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data-box-control="true"
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className={[
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'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',
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isDraft
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? 'bg-amber-400'
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: 'bg-emerald-400',
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scoreBgClass(box.score, { draft: isDraft }),
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].join(' ')}
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title={isDraft ? box.label : `${item.text} verschieben`}
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>
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@ -1612,7 +1737,7 @@ export default function TrainingTab() {
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'flex min-w-0 touch-none items-center gap-1 px-1.5 text-left',
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isDraft
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? 'cursor-default'
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: 'cursor-move hover:bg-emerald-300',
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: ['cursor-move', scoreHoverClass(box.score)].join(' '),
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].join(' ')}
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disabled={Boolean(isDraft) || uiLocked}
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onPointerDown={(e) => {
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@ -1741,7 +1866,12 @@ export default function TrainingTab() {
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})
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}}
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>
|
||||
<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,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user