Claude: Lens-Distortions

2026-06-02 11:37:29 +02:00
parent ac81c2e0cb
commit 5ad956be81
91 changed files with 31707 additions and 23702 deletions
--- a/data/evaluations/Scene6/aruco_marker_poses.json
+++ b/data/evaluations/Scene6/aruco_marker_poses.json
@@ -1,7 +1,7 @@
 {
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--- a/data/evaluations/Scene6/aruco_positions_initial.json
+++ b/data/evaluations/Scene6/aruco_positions_initial.json
@@ -1,7 +1,7 @@
 {
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--- a/data/evaluations/Scene6/aruco_positions_optimized.json
+++ b/data/evaluations/Scene6/aruco_positions_optimized.json
@@ -1,6 +1,6 @@
 {
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+  "created_utc": "2026-06-02T06:13:00Z",
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--- a/data/evaluations/Scene6/render_a_aruco_detection.json
+++ b/data/evaluations/Scene6/render_a_aruco_detection.json
@@ -1,6 +1,6 @@
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--- a/data/evaluations/Scene6/render_a_camera_pose.json
+++ b/data/evaluations/Scene6/render_a_camera_pose.json
@@ -1,6 +1,6 @@
 {
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--- a/data/evaluations/Scene6/render_b_aruco_detection.json
+++ b/data/evaluations/Scene6/render_b_aruco_detection.json
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--- a/data/evaluations/Scene6/render_b_camera_pose.json
+++ b/data/evaluations/Scene6/render_b_camera_pose.json
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--- a/data/evaluations/Scene6/render_f_camera_pose.json
+++ b/data/evaluations/Scene6/render_f_camera_pose.json
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 {
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--- a/data/evaluations/Scene6/render_g_camera_pose.json
+++ b/data/evaluations/Scene6/render_g_camera_pose.json
@@ -1,6 +1,6 @@
 {
  "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:04:52Z",
+  "created_utc": "2026-06-02T06:12:47Z",
  "source": {
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    "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
--- a/data/evaluations/Scene6/robot_state.json
+++ b/data/evaluations/Scene6/robot_state.json
@@ -1,6 +1,6 @@
 {
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-  "created_utc": "2026-06-02T05:05:09Z",
+  "created_utc": "2026-06-02T06:13:02Z",
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--- a/data/evaluations/Scene8/aruco_marker_poses.json
+++ b/data/evaluations/Scene8/aruco_marker_poses.json
--- a/data/evaluations/Scene8/aruco_positions_initial.json
+++ b/data/evaluations/Scene8/aruco_positions_initial.json
--- a/data/evaluations/Scene8/aruco_positions_optimized.json
+++ b/data/evaluations/Scene8/aruco_positions_optimized.json
--- a/data/evaluations/Scene8/render_a_aruco_detection.json
+++ b/data/evaluations/Scene8/render_a_aruco_detection.json
--- a/data/evaluations/Scene8/render_a_camera_pose.json
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 {
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--- a/data/evaluations/Scene8/render_b_aruco_detection.json
+++ b/data/evaluations/Scene8/render_b_aruco_detection.json
--- a/data/evaluations/Scene8/render_b_camera_pose.json
+++ b/data/evaluations/Scene8/render_b_camera_pose.json
--- a/data/evaluations/Scene8/render_c_aruco_detection.json
+++ b/data/evaluations/Scene8/render_c_aruco_detection.json
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 {
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--- a/data/evaluations/Scene8/render_d_aruco_detection.json
+++ b/data/evaluations/Scene8/render_d_aruco_detection.json
--- a/data/evaluations/Scene8/render_d_camera_pose.json
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@@ -1,6 +1,6 @@
 {
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--- a/data/evaluations/Scene8/render_e_aruco_detection.json
+++ b/data/evaluations/Scene8/render_e_aruco_detection.json
--- a/data/evaluations/Scene8/render_e_camera_pose.json
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 {
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--- a/data/evaluations/Scene8/render_f_aruco_detection.json
+++ b/data/evaluations/Scene8/render_f_aruco_detection.json
--- a/data/evaluations/Scene8/render_f_camera_pose.json
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 {
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--- a/data/evaluations/Scene8/robot_state.json
+++ b/data/evaluations/Scene8/robot_state.json
@@ -1,58 +1,58 @@
 {
  "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:07:09Z",
+  "created_utc": "2026-06-02T09:26:33Z",
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  "movements": {
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      "confidence": "medium",
      "n_markers": 3
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      "observable": true,
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 }
--- a/data/robot/robot.json
+++ b/data/robot/robot.json
@@ -99,6 +99,12 @@
    "markerRotationMaxDeg": 3,
    "motionBlur": true,
    "motionBlurMaxPx": 5.5,
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@@ -209,13 +215,13 @@
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--- a/data/simulation/Scene6/pose.json
+++ b/data/simulation/Scene6/pose.json
@@ -10,8 +10,8 @@
    "e": 3
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-    "height": 720,
+    "width": 1440,
+    "height": 1080,
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--- a/data/simulation/Scene6/render_a.json
+++ b/data/simulation/Scene6/render_a.json
--- a/data/simulation/Scene6/render_a.npz
+++ b/data/simulation/Scene6/render_a.npz
--- a/data/simulation/Scene6/render_a.png
+++ b/data/simulation/Scene6/render_a.png
--- a/data/simulation/Scene6/render_b.json
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--- a/data/simulation/Scene6/render_b.npz
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--- a/data/simulation/Scene6/render_b.png
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--- a/data/simulation/Scene6/render_c.json
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--- a/data/simulation/Scene6/render_c.npz
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--- a/data/simulation/Scene6/render_c.png
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--- a/data/simulation/Scene6/render_d.png
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--- a/data/simulation/Scene6/render_g.png
+++ b/data/simulation/Scene6/render_g.png
--- a/data/simulation/Scene8/pose.json
+++ b/data/simulation/Scene8/pose.json
@@ -10,8 +10,8 @@
    "e": 3
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-    "height": 720,
+    "width": 1440,
+    "height": 1080,
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--- a/data/simulation/Scene8/render_a.json
+++ b/data/simulation/Scene8/render_a.json
--- a/data/simulation/Scene8/render_a.npz
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--- a/data/simulation/Scene8/render_a.png
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--- a/data/simulation/Scene8/render_c.json
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--- a/data/simulation/debug/render.npz
+++ b/data/simulation/debug/render.npz
--- a/data/simulation/debug/render.png
+++ b/data/simulation/debug/render.png
--- a/doc/camera_number_roadmap.md
+++ b/doc/camera_number_roadmap.md
@@ -0,0 +1,275 @@
+# Roadmap: Wie viele Kameras braucht die Pose-Rekonstruktion wirklich?
+
+## Ziel
+
+Untersuchen, ob die vollständige Roboter-Pose mit **3 Kameras** ausreichend präzise rekonstruiert werden kann, verglichen mit der aktuellen Konfiguration mit **6–8 Kameras**.
+
+Die Kernfrage ist:
+
+**X-Achse:** Anzahl verwendeter Kameras
+**Y-Achse:** Genauigkeit der rekonstruierten Pose, gemessen als **Fingerspitzen-Abweichung in mm**
+
+---
+
+## 1. Was dafür bekannt sein muss
+
+Bevor die Auswertung sinnvoll ist, sollten diese Punkte klar dokumentiert sein:
+
+### 1.1 Datenbasis
+
+* Wie viele Szenen existieren insgesamt?
+* Welche Posen decken die Szenen ab?
+* Sind die Szenen gleichmäßig über den Arbeitsraum verteilt oder nur ein Teilbereich?
+* Gibt es Wiederholungen derselben Pose unter leicht anderen Bedingungen?
+
+### 1.2 Kamera-Setup
+
+* Welche Kameras sind in jeder Szene aktiv?
+* Sind alle Kameras synchronisiert?
+* Sind alle Kameras kalibriert?
+* Sind intrinsische und extrinsische Parameter pro Kamera vorhanden?
+* Gibt es Ausfälle, verdeckte Sicht oder variable Bildqualität?
+
+### 1.3 Ground Truth
+
+* Wie werden die „echten“ Roboter-Posen gespeichert?
+* Ist die Ground Truth in einem konsistenten Koordinatensystem verfügbar?
+* Ist die Fingerspitzenposition direkt ableitbar oder muss sie aus der Pose berechnet werden?
+* Welche Referenz gilt als Wahrheit: Robotermodell, Encoder-Daten, Simulation oder beides?
+
+### 1.4 Rekonstruktionspipeline
+
+* Welche Zwischenresultate liefert die Pipeline?
+
+  * Detektion im Bild
+  * Modellanpassung
+  * 2D/3D-Keypoints
+  * vollständige Pose
+* Ist die Pipeline deterministisch oder stochastisch?
+* Welche Fehlerquellen sind bereits bekannt?
+
+### 1.5 Bewertungsmetrik
+
+* Wird nur die Fingerspitze bewertet oder auch weitere Gelenkpunkte?
+* Ist die Abweichung als **mittlere Distanz**, **Median**, **RMSE** oder **95%-Quantil** interessant?
+* Soll die Auswertung pro Szene, pro Kamera-Subset und aggregiert über alle Szenen erfolgen?
+
+---
+
+## 2. Was umgesetzt werden muss
+
+### 2.1 Dateninventur und Datenformat
+
+Zuerst sollte eine saubere Übersicht aller Szenen entstehen:
+
+* Szenen-ID
+* verfügbare Kameras
+* Zeitstempel / Synchronisationsstatus
+* Ground-Truth-Pose
+* rekonstruierte Pose
+* Bildqualität oder Sichtbarkeitsstatus
+
+Empfehlung: ein tabellarisches Metadatenformat, z. B. CSV, JSON oder eine kleine Datenbank.
+
+### 2.2 Subset-Definition für Kameras
+
+Für den Vergleich muss festgelegt werden, **welche 3 Kameras** verwendet werden.
+
+Mögliche Varianten:
+
+* feste Auswahl der besten 3 Kameras
+* alle Kombinationen aus 3 Kameras
+* Auswahl nach Sichtbarkeit / Geometrie / Robustheit
+
+Wichtig: Die Wahl der 3 Kameras beeinflusst das Ergebnis stark. Deshalb sollte nicht nur eine Kombination getestet werden, sondern möglichst mehrere.
+
+### 2.3 Rekonstruktion pro Kameraset
+
+Die Pipeline muss für verschiedene Kamerakombinationen erneut laufen:
+
+* 3 Kameras
+* 4 Kameras
+* 5 Kameras
+* 6 Kameras
+* 7 Kameras
+* 8 Kameras
+
+Optional zusätzlich:
+
+* jede einzelne Kamera weglassen, um die Sensitivität zu messen
+* nur die geometrisch günstigsten Kameras verwenden
+
+### 2.4 Fehlerberechnung
+
+Für jede Szene und jedes Kameraset:
+
+1. Rekonstruierte Pose erzeugen
+2. Fingerspitze aus rekonstruierter Pose bestimmen
+3. Ground-Truth-Fingerspitze bestimmen
+4. Abstand in Millimetern berechnen
+5. Ergebnis speichern
+
+Empfohlenes Ergebnisformat pro Versuch:
+
+* Szene
+* Kameraset-ID
+* Anzahl Kameras
+* Fingerpunkt-Fehler [mm]
+* weitere optionale Metriken, z. B. Gelenkfehler, Sichtbarkeitsrate, Rekonstruktionsqualität
+
+### 2.5 Aggregation und Auswertung
+
+Am Ende sollten Kennzahlen über alle Szenen berechnet werden:
+
+* Mittelwert des Fehlers
+* Median des Fehlers
+* Standardabweichung
+* 95%-Konfidenzintervall oder Bootstrap-Intervall
+* Fehlerverteilung pro Kamerazahl
+
+Zusätzlich hilfreich:
+
+* Boxplots je Kamerazahl
+* Fehlerbalken mit Konfidenzintervallen
+* Plot der besten / schlechtesten Kamerakombinationen
+
+---
+
+## 3. Empfohlene Auswertungslogik
+
+### 3.1 Vergleich nach Kamerazahl
+
+Für jede Kamerazahl k:
+
+* alle relevanten Kamerakombinationen testen oder eine definierte Auswahl bilden
+* Fehler pro Szene berechnen
+* Ergebnisse aggregieren
+
+So entsteht die Kurve:
+
+* 3 Kameras → mittlere Abweichung
+* 4 Kameras → mittlere Abweichung
+* ...
+* 8 Kameras → mittlere Abweichung
+
+### 3.2 Vergleich nach Kamerakombination
+
+Nicht nur die Anzahl zählt, sondern auch die Anordnung.
+
+Deshalb sollte zusätzlich ausgewertet werden:
+
+* welche 3-Kamera-Kombination am besten ist
+* ob bestimmte Kameras besonders wichtig sind
+* ob eine gute Geometrie wichtiger ist als reine Anzahl
+
+### 3.3 Robustheit über Szenen
+
+Die Frage ist nicht nur „Was ist im Mittel gut?“, sondern auch:
+
+* Gibt es Szenen, in denen 3 Kameras deutlich scheitern?
+* Gibt es Posen, bei denen schon 3 Kameras reichen?
+* Ist der Fehler bei bestimmten Roboterausrichtungen systematisch höher?
+
+---
+
+## 4. Praktische Umsetzungsschritte
+
+### Phase A: Datengrundlage sichern
+
+* Alle Szenen inventarisieren
+* Kamerazustand pro Szene prüfen
+* Ground Truth und Rekonstruktionen in ein konsistentes Format bringen
+* Ein eindeutiges Schema für Szenen- und Kameraset-IDs definieren
+
+### Phase B: Vergleichsdesign festlegen
+
+* Festlegen, ob alle 3er-Kombinationen getestet werden oder nur ausgewählte Sets
+* Definieren, welche Kameraauswahl als Referenz dient
+* Fehlermaß final festlegen
+
+### Phase C: Batch-Auswertung bauen
+
+* Pipeline über mehrere Kamerasets automatisieren
+* Ergebnisse versionieren und speichern
+* Laufzeit und Fehler robust protokollieren
+
+### Phase D: Statistische Analyse
+
+* Pro Kamerazahl Mittelwert, Median und Streuung berechnen
+* Signifikanztests oder Bootstrap-Vergleiche zwischen Kamerazahlen durchführen
+* Ausreißer identifizieren
+
+### Phase E: Visualisierung und Entscheidung
+
+* Plot „Anzahl Kameras vs. Fehler in mm“
+* Plot pro Szene oder pro Posegruppe
+* Entscheidungsregel ableiten, ab wann zusätzliche Kameras kaum noch Verbesserungen bringen
+
+---
+
+## 5. Wichtige Fragen, die vorab beantwortet sein sollten
+
+1. Welche 3 Kameras sind gemeint: beliebige, beste, feste oder geometrisch ausgewählte?
+2. Sind alle Kameras pro Szene vorhanden oder gibt es Lücken?
+3. Wie genau wird die Fingerspitze aus der Robotermodell-Pose berechnet?
+4. Soll der Fehler nur an einem Punkt oder über mehrere Posepunkte bewertet werden?
+5. Wie wird mit Szenen umgegangen, in denen eine Rekonstruktion scheitert?
+6. Ist die Messung in Simulationsdaten, Realwelt oder gemischt?
+7. Soll die Auswertung pro Szene oder über alle Szenen gepoolt erfolgen?
+
+---
+
+## 6. Empfohlenes Ergebnis der Analyse
+
+Am Ende sollte die Auswertung mindestens diese Ergebnisse liefern:
+
+* eine Tabelle mit Fehlern pro Szene und Kameraset
+* ein Diagramm „Kameraszahl vs. mittlere Fingerspitzen-Abweichung“
+* eine Aussage, ob 3 Kameras praktisch ausreichend sind
+* eine Aussage, welche Kameras oder Geometrien besonders wichtig sind
+* eine Empfehlung für ein Minimal-Setup mit akzeptabler Genauigkeit
+
+---
+
+## 7. Mögliche Entscheidungslogik
+
+Eine einfache Entscheidungsregel könnte sein:
+
+* 3 Kameras sind ausreichend, wenn der mittlere Fehler nur wenig schlechter ist als bei 6–8 Kameras
+* die Verteilung der Fehler bei 3 Kameras darf nicht zu viele Ausreißer enthalten
+* das System muss für die meisten Szenen stabil bleiben
+
+Beispiel für eine praktische Schwelle:
+
+* maximal zulässige mittlere Abweichung
+* maximal zulässiger Fehler in 95% der Fälle
+* maximaler Verlust gegenüber der Vollkonfiguration
+
+Diese Schwelle sollte fachlich mit der Anwendung abgestimmt werden.
+
+---
+
+## 8. Nächste konkrete Arbeitspakete
+
+1. Metadaten aller Szenen konsolidieren
+2. Ground Truth und Rekonstruktionsausgabe vereinheitlichen
+3. Kameraset-Strategie festlegen
+4. Batch-Runner für mehrere Kamerakonfigurationen bauen
+5. Fingerpunkt-Fehler je Szene berechnen
+6. Aggregation und Plots erstellen
+7. Ergebnis interpretieren und Empfehlung ableiten
+
+---
+
+## 9. Kurzfassung
+
+Die Kernaufgabe ist nicht nur ein einfacher Plot, sondern ein **systematischer Vergleich verschiedener Kamerasets**. Dafür braucht es:
+
+* saubere Ground Truth
+* konsistente Kamerakalibrierung
+* definierte Kamerakombinationen
+* automatisierte Rekonstruktion
+* robuste Fehlerberechnung in mm
+* aggregierte Analyse über alle Szenen
+
+Erst dann lässt sich belastbar sagen, ob **3 Kameras genügen** oder ob die zusätzlichen Kameras einen messbaren Mehrwert bringen.
--- a/doc/docker_containerization_roadmap.md
+++ b/doc/docker_containerization_roadmap.md
@@ -0,0 +1,420 @@
+# Docker Containerization Roadmap
+
+## Ziel
+
+Die Blender-Rendering-Pipeline soll vollständig containerisiert und später in eine verteilte Job-Infrastruktur integriert werden.
+
+Wichtige Randbedingungen:
+
+* Blender-Container sollen nur laufen, wenn tatsächlich Renderaufträge vorhanden sind.
+* Verschiedene Jobtypen sollen unterschiedliche Container verwenden können.
+* Die Infrastruktur soll zukünftig nicht nur Blender-Rendering, sondern auch andere Workloads (AI-Training, STL-Verarbeitung, Datensatz-Generierung, Batch-Konvertierungen usw.) unterstützen.
+* Die Ausführung soll auf mehreren PCs/Worker-Nodes möglich sein.
+* Die bestehende Python-Logik soll möglichst unverändert bleiben.
+
+---
+
+# Review / Bewertung
+
+**Gesamteinschätzung:** schlüssig und machbar. Die Zielarchitektur (Redis-Queue +
+Nomad-Scheduler, Container nur bei Bedarf, mehrere Workload-Typen, horizontale Skalierung)
+ist tragfähig und zukunftssicher. Es fehlen einige technische Punkte, die unten ergänzt sind.
+
+## Wichtigste Korrektur: Renderer und Pipeline sind ZWEI Container, nicht einer
+
+Phase 1 spricht von „Rendering-Pipeline **+** Analyse-Pipeline" in *einem* Container.
+Das sollte getrennt werden — die beiden haben grundverschiedene Profile:
+
+| | Render-Container | Pipeline-Container |
+|---|---|---|
+| Image | `approbot/blender-renderer` | `approbot/pose-pipeline` |
+| Basis | Blender 4.5 + bpy + cv2 + numpy (~1–2 GB) | `python:3.11-slim` + numpy/scipy/opencv (~200 MB) |
+| GPU | ja (Cycles, CUDA/OPTIX) | nein |
+| Zweck | **Test-Datengenerator** (nur Sim/Dev) | **Produkt** (läuft auch mit echten Webcam-Fotos!) |
+| Änderungsrate | selten | häufig |
+| CI-tauglich | nein (schwer, GPU) | ja (schnell, headless) |
+
+Das ist keine Kosmetik: Die **Pipeline ist das eigentliche Deliverable** (Roboter-Pose
+aus Fotos) und wird real deployed; der Renderer ist nur Werkzeug zur Testdaten-Erzeugung.
+Getrennt heißt: die Pipeline bleibt schlank, schnell CI-testbar und ohne Blender-Ballast
+deploybar; der Renderer wird nur dort gestartet, wo eine GPU steht.
+
+**Datenaustausch** zwischen beiden: gemeinsames Volume (Phase 1–3), später Object-Storage
+(MinIO/S3) im verteilten Betrieb.
+
+## Technische Schlüsselpunkte (bisher nicht in der Roadmap)
+
+1. **cv2 in Blenders Python:** `render_robot.py` importiert `cv2` (ArUco-Generierung).
+   Blender bringt sein *eigenes* gebündeltes Python mit — `opencv-python` muss **dort**
+   installiert werden (`<blender>/python/bin/python -m pip install opencv-python`),
+   nicht ins System-Python. Häufige Stolperfalle.
+2. **ArUco = opencv-contrib:** Die Pipeline nutzt `cv2.aruco`. Im Pipeline-Container
+   `opencv-contrib-python-headless` verwenden (nicht Basis-`opencv-python`), Version pinnen.
+3. **robot.json-Mutation ist im Parallelbetrieb ein Race:** `render_Loop.py` schreibt
+   pro Render in die *gemeinsame* `robot.json` (Kamera, Pose, Auflösung). Laufen mehrere
+   Render-Jobs parallel auf einem Node, überschreiben sie sich gegenseitig. Im Job-Modell
+   muss jeder Job seine Parameter aus dem **Job-Payload** bekommen und in eine **eigene
+   temporäre Config** schreiben — nicht in die geteilte robot.json. (Architektur-Fix,
+   gehört *vor* Phase 4.)
+4. **GPU im Container & Scheduler:** `--gpus all` + NVIDIA Container Toolkit; Nomad braucht
+   das GPU-Device-Plugin, damit Render-Jobs nur auf GPU-Nodes landen. CPU-Fallback (Cycles
+   `device='CPU'`) als Sicherheitsnetz behalten.
+5. **Versionen pinnen:** Blender 4.5.x exakt, `requirements.txt` mit festen Versionen
+   (numpy/scipy/opencv) — sonst driften die Mess-Ergebnisse (vgl. der fy-Intrinsik-Bug).
+6. **Job-Granularität:** `{"jobType":"blender-render","pose":8}` rendert 7 Kameras pro Job.
+   Feinkörniger (1 Job = 1 Kamera) parallelisiert besser über mehrere Nodes — abwägen.
+
+## Empfehlung: MVP vor Nomad
+
+Redis + Nomad ist die richtige End-Vision, aber als Zwischenstufe genügt **docker-compose
+mit zwei Images + Profiles** (Renderer on-demand via `docker compose run`, Pipeline als
+schneller Service / CI-Step). Damit sind getrennte Container + Phase 1–3 produktiv nutzbar,
+bevor die verteilte Orchestrierung (Phase 5–8) aufgebaut wird.
+
+---
+
+# Phase 1 – Dockerisierung der bestehenden Rendering-Pipeline
+
+## Ziel
+
+Die aktuelle Blender-Pipeline läuft vollständig innerhalb eines Docker-Containers.
+
+### Aufgaben
+
+* [x] Dockerfile erstellen → **in zwei aufteilen:** `Dockerfile.renderer` (Blender) + `Dockerfile.pipeline` (slim Python)
+* [x] docker-compose.yaml erstellen
+* [ ] cv2 in Blenders gebündeltem Python installieren (für ArUco-Generierung)
+* [ ] requirements.txt für den Pipeline-Container (numpy, scipy, opencv-contrib-python-headless), Versionen gepinnt
+* [ ] Blender 4.5 Container erfolgreich starten
+* [ ] render_loop.py innerhalb des Containers ausführen
+* [ ] render_robot.py innerhalb des Containers ausführen
+* [ ] STL-Import validieren
+* [ ] PNG-Ausgabe validieren
+* [ ] NPZ-Ausgabe validieren
+* [ ] Pipeline auf PNG-Ausgaben ermöglichen
+* [ ] markers.json validieren
+
+### Ergebnis
+
+Zwei Container: `blender-renderer` erzeugt Bilder/npz/Ground-Truth, `pose-pipeline`
+wertet sie aus — beide reproduzierbar, über ein gemeinsames Daten-Volume verbunden.
+
+---
+
+# Phase 2 – Pfad-Unabhängigkeit
+
+## Ziel
+
+Alle absoluten Benutzerpfade entfernen.
+
+### Aktuelle Situation
+
+Beispiel:
+
+```python
+Path.home() / "SynologyDrive" / ...
+```
+
+### Ziel
+
+Container-interne Pfade verwenden:
+
+```text
+/workspace/data
+/workspace/setup
+/workspace/output
+```
+
+### Aufgaben
+
+* [ ] ROBOT_JSON_FILE auf Containerpfade umstellen
+* [ ] OUTPUT_FILE auf Containerpfade umstellen
+* [ ] STL-Suche auf Containerpfade umstellen
+* [ ] Konfiguration über Environment-Variablen ermöglichen
+* [ ] **render_robot.py: Config-Pfad per env (`ROBOT_JSON`)** statt hardcodiert; und
+  **render_Loop.py: pro Render eine *temporäre* Config schreiben** statt die geteilte
+  robot.json zu mutieren (Voraussetzung für parallele Jobs, siehe Schlüsselpunkt 3)
+* [ ] BLENDER_EXE per env/PATH (aktuell hardcodierter Windows-Pfad)
+
+### Ergebnis
+
+Die Pipeline ist vollständig unabhängig vom lokalen Benutzerprofil und parallel-tauglich.
+
+---
+
+# Phase 3 – Repository-Struktur bereinigen
+
+## Ziel
+
+Klare Trennung zwischen Code, Daten und Ergebnissen.
+
+### Zielstruktur
+
+```text
+appRobotRendering/
+
+├── data/
+│   ├── robot/
+│   ├── simulation/
+│   └── surfaces/
+│
+├── setup/
+│   └── generateSets/
+│       ├── render_loop.py
+│       ├── render_robot.py
+│       ├── Dockerfile
+│       └── docker-compose.yaml
+│
+├── jobs/
+│
+└── output/
+```
+
+### Aufgaben
+
+* [ ] Verzeichnisstruktur vereinheitlichen
+* [ ] Dokumentation ergänzen
+* [ ] Mountpoints definieren
+
+---
+
+# Phase 4 – Job-Modell einführen
+
+## Ziel
+
+Rendering-Aufträge werden als eigenständige Jobs beschrieben.
+
+### Beispiel
+
+```json
+{
+  "jobType": "blender-render",
+  "pose": 8
+}
+```
+
+### Spätere Erweiterungen
+
+```json
+{
+  "jobType": "dataset-generation"
+}
+```
+
+```json
+{
+  "jobType": "stl-conversion"
+}
+```
+
+```json
+{
+  "jobType": "ai-training"
+}
+```
+
+### Aufgaben
+
+* [ ] Job-Format definieren
+* [ ] Job-Metadaten definieren
+* [ ] Retry-Konzept definieren
+* [ ] Statusmodell definieren
+
+### Status
+
+```text
+queued
+running
+completed
+failed
+timeout
+```
+
+---
+
+# Phase 5 – Redis als Queue
+
+## Ziel
+
+Entkopplung zwischen Auftragserzeugung und Ausführung.
+
+### Aufgaben
+
+* [ ] Redis bereitstellen
+* [ ] Job-Queue definieren
+* [ ] Job-Status speichern
+* [ ] Retry-Mechanismus definieren
+
+### Hinweis
+
+Redis startet keine Worker.
+
+Redis dient ausschließlich als:
+
+* Queue
+* Statusspeicher
+* Kommunikationsschicht
+
+---
+
+# Phase 6 – Nomad Scheduler
+
+## Ziel
+
+Container werden nur bei tatsächlichem Bedarf gestartet.
+
+### Architektur
+
+```text
+Job Producer
+      │
+      ▼
+
+    Redis
+      │
+      ▼
+
+    Nomad
+      │
+      ▼
+
+ Docker Container
+```
+
+### Aufgaben
+
+* [ ] Nomad Server aufsetzen
+* [ ] Worker-Nodes registrieren
+* [ ] Docker Driver aktivieren
+* [ ] Batch-Jobs definieren
+
+### Ergebnis
+
+Nomad entscheidet:
+
+* welcher Node frei ist
+* welcher Container gestartet wird
+* wann ein Container beendet wird
+
+---
+
+# Phase 7 – Blender als Nomad Batch Job
+
+## Ziel
+
+Blender läuft nur während der Bearbeitung eines Auftrags.
+
+### Beispiel
+
+```text
+Job:
+blender-render
+
+Container:
+approbot/blender-renderer:latest
+```
+
+Ablauf:
+
+1. Job wird eingereicht
+2. Nomad startet Blender-Container
+3. Rendering läuft
+4. Ergebnisse werden gespeichert
+5. Container beendet sich
+6. Ressourcen werden freigegeben
+
+### Ergebnis
+
+Keine dauerhaft laufenden Blender-Worker.
+
+---
+
+# Phase 8 – Multi-Container Plattform
+
+## Ziel
+
+Beliebige Jobtypen auf derselben Infrastruktur ausführen.
+
+### Beispiele
+
+#### Blender Rendering
+
+```text
+Container:
+approbot/blender-renderer
+```
+
+#### AI Training
+
+```text
+Container:
+approbot/trainer
+```
+
+#### STL Processing
+
+```text
+Container:
+approbot/stl-worker
+```
+
+#### Dataset Generation
+
+```text
+Container:
+approbot/dataset-worker
+```
+
+### Ergebnis
+
+Der verwendete Container wird durch den Jobtyp bestimmt.
+
+---
+
+# Zielarchitektur
+
+```text
+                    Nomad
+                       │
+
+      ┌────────────────┼────────────────┐
+      │                │                │
+
+ Blender Job      AI Job         Dataset Job
+      │                │                │
+
+ Blender       Training      Dataset Worker
+ Container      Container      Container
+
+      └────────────────┼────────────────┘
+                       │
+
+                    Docker
+
+                       │
+
+              Worker Nodes (PCs)
+```
+
+Eigenschaften:
+
+* keine dauerhaft laufenden Blender-Container
+* unterschiedliche Container pro Jobtyp
+* horizontale Skalierung auf mehrere PCs
+* saubere Trennung von Scheduling und Ausführung
+* zukünftige Erweiterbarkeit für beliebige Workloads
+
+---
+
+# Offene Punkte / nicht vergessen
+
+* **Cluster-Storage:** geteiltes NFS/Volume (einfach) vs. Object-Storage MinIO/S3
+  (entkoppelt Nodes, skaliert). Ab >1 Worker-Node bevorzugt Object-Storage.
+* **robot.json als geteilter Mutable-State auflösen** (Schlüsselpunkt 3) — Voraussetzung
+  für parallele Render-Jobs auf einem Node.
+* **Reproduzierbarkeit:** Blender- und Python-Versionen pinnen; `requirements.txt` einchecken.
+* **GPU-Headless:** prüfen, ob Cycles im Container EGL braucht; sonst CPU-Fallback verifizieren.
+* **CI-Hook:** Pipeline-Container in CI → `benchmark/run_benchmark.py` gegen Ground-Truth
+  bei jedem Commit (schnell, ohne Blender) — fängt Regressionen wie den fy-Bug automatisch.
+* **Monitoring/Logs:** Job-Logs zentral (stdout → Nomad/Loki), Render-Zeiten messen.
+* **Image-Registry:** private Registry (Harbor o. Ä.) für die `approbot/*`-Images.
--- a/doc/improvements_roadmap.md
+++ b/doc/improvements_roadmap.md
@@ -0,0 +1,30 @@
+# Nötige Anpassungen #
+
+## Marker ##
+
+In der Render-Pipeline werden die Marker zufällit "falsch aufgeklebt" 
+das passiert momentan in alle Raumrichtungen falsch (soweit ich weiss).
+Realistisch ist nur ein Verschieben senkrecht zur Normalen. Der Aufkleber
+der falsch angebracht wird. Die Normale, der Untergrund verändert sich am 
+Arm nicht.
+
+Anders sieht es auf der Base aus. Hier skann es passieren, dass die "unteren"
+Marker sich auf dem Papier verwerfen. Die Marker sind bei -20mm angebracht,
+auf einem Papier. und das kann sich falten oder verwerfen. Hier muss noch überlegt 
+werden, wie das gut simuliert werden kann.
+
+## Gewichtung ## 
+
+Die Marker-Gewichtung wurde glaub ich noch nicht angemessen umgesetzt, oder?
+
+
+
+## Versionierung ##
+
+Es muss klar sein, mit welcher Version der Ausgabe, mit welcher Version des Robot.json gearbeitet
+wurde. Dazu braucht es
+
+1) Label in allen Python Programmen, dass klar ist, welches Python Programm hier grad aktiv ist
+2) Label im robot.json
+3) Jeweils die Nachvollziehbarkeit in den erzeugten Auswertungs.json
+
--- a/doc/lens_errors_roadmap.md
+++ b/doc/lens_errors_roadmap.md
@@ -0,0 +1,103 @@
+# Roadmap: Kamera-/Linsenfehler simulieren (billige WebCams)
+
+**Status:** ✅ umgesetzt (A–D) in `setup/generateSets/render_robot.py`, Parameter in
+`robot.json → renderingInfo`. **A ist isoliert getestet** (deterministisches npz-Rauschen).
+**B–D sind Compositor-Effekte und müssen einmal in Blender 4.5 visuell geprüft werden** —
+jeder Effekt ist in `try/except` gekapselt: ein API-Problem überspringt nur den betroffenen
+Effekt mit Log-Warnung `[render][WARN] <effekt> skipped: ...`, der Render läuft weiter.
+
+### Parameter (robot.json → renderingInfo)
+
+| Effekt | Parameter | Beispiel |
+|---|---|---|
+| A Brennweiten-Restfehler | `focalErrorPct` | 0.5 (±%) |
+| A Hauptpunkt-Versatz | `principalErrorPx` | 3.0 (±px) |
+| A Rest-Verzeichnung | `residualDistortion` | [0.02, -0.01] (k1,k2) |
+| B Linsenstaub | `lensDirt` / `lensDirtStrength` | true / 0.08 |
+| C Vignette | `vignette` / `vignetteStrength` | true / 0.08 |
+| C Rand-Unschärfe | `localizedBlur` / `localizedBlurStrength` | false / 0.15 |
+| D Sensor-Rauschen | `sensorNoise` / `sensorNoiseStrength` | true / 0.01 |
+| D chromat. Aberration | `lensDistortion` / `lensDistortionStrength` | true / 0.002 |
+
+**Wichtig:** A verfälscht nur die **npz-Kalibrierung** (Bild bleibt ideal) → echter
+geometrischer Härtetest gegen unvollständige Kalibrierung; deterministisch pro Kamera
+(Seed aus `cameraPosition`), damit über alle Posen konsistent. B–D ändern nur das **Bild**
+(Detektion erschwert), nicht die Ground-Truth.
+
+---
+
+(Ursprüngliche Detailplanung als Referenz:)
+
+**Ziel:** Realistische WebCam-Fehler in den Blender-Renderer (`setup/generateSets/render_robot.py`)
+einbauen, um die Pose-Pipeline gegen reale Kamera-Imperfektionen zu härten — analog
+zu den bereits umgesetzten Marker-Störungen (`markerOffsetMaxMm`, `motionBlur`, `arucoDust`).
+
+## Kernkonzept (wichtig!)
+
+Es gibt zwei Fehlerklassen:
+
+1. **Geometrische Fehler** (Brennweite, Hauptpunkt, Verzeichnung): verschieben die
+   Marker-Pixel. **Entscheidend:** Kennt die Kalibrierung (`render_*.npz`) den Fehler
+   exakt, korrigiert die `undistort`-Kette ihn vollständig → *kein* Pose-Fehler. Den
+   Fehler erzeugt nur die **unvollständige Kalibrierung**. Daher: nicht „Bild verzeichnen",
+   sondern die **npz-Intrinsik leicht von der Wahrheit abweichen lassen** (Kalibrier-Restfehler).
+2. **Photometrische Fehler** (Staub, Vignette, Rand-Unschärfe, Rauschen): ändern die npz
+   nicht, erschweren nur die Detektion (verrauschte Ecken, fehlende Marker).
+
+## Hinweis: tote Parameter
+
+In `robot.json → renderingInfo` existieren bereits, werden aber von `render_robot.py`
+**nicht angewandt**: `lensDirt`, `lensDirtStrength`, `vignette`, `vignetteStrength`,
+`sensorNoise`, `sensorNoiseStrength`, `lensDistortion`, `lensDistortionStrength`,
+`localizedBlur`, `localizedBlurStrength`. Diese zum Leben erwecken.
+
+## A — Kalibrier-Restfehler (geometrisch) ⭐ zuerst
+
+Höchster Testwert, deckt eine Lücke der bisherigen Störungen ab. Modell-konsistent
+(kein Blender↔OpenCV-Mismatch), weil das Bild ideal bleibt und nur die *angenommene*
+Kalibrierung in der npz falsch ist.
+
+**Implementierung:** im npz-Schreibblock von `render_robot.py` (~Zeile 460–510, wo
+`camera_matrix`/`dist_coeffs` gebaut werden) die Werte gezielt verrauschen —
+**deterministisch pro Kamera** (Seed aus Kameraposition/-id), damit über alle Posen konsistent.
+
+Neue robot.json-Parameter (Vorschlag):
+- `focalErrorPct` (z. B. 0.5) → `fx,fy *= 1 ± rnd·focalErrorPct/100`
+- `principalErrorPx` (z. B. 3) → `cx,cy += rnd·principalErrorPx`
+- `residualDistortion` (z. B. [0.02, -0.01]) → kleine `k1,k2` in `dist_coeffs`,
+  die die Pipeline „korrigiert", obwohl das Bild ideal ist (Rest-Verzeichnung)
+
+## B — Staub auf der Linse (photometrisch)
+
+Halbtransparentes Schmutz-Overlay über dem *ganzen* Bild (Compositor), verdeckt zufällig
+Marker teilweise. Parameter: `lensDirt`, `lensDirtStrength`.
+
+**Implementierung:** Compositor — RenderLayers → Mix (über eine prozedurale Fleckentextur,
+Voronoi/Noise mit hoher Schwelle) → Composite. Wenige, weiche, leicht abdunkelnde Flecken.
+
+## C — Vignette + Rand-Unschärfe (Feldkrümmung)
+
+Randabdunklung und radial nach außen zunehmende Unschärfe (billige Linsen sind am Rand
+schlechter). Parameter: `vignette`/`vignetteStrength`, `localizedBlur`/`localizedBlurStrength`.
+
+**Implementierung:** Compositor — Vignette via Ellipse-Maske × Bild; Rand-Unschärfe via
+`CompositorNodeBlur` gemischt über eine radiale Maske (Lens Distortion „Distort" auf einer
+Maske oder Ellipse-Maske als Mix-Faktor zwischen scharf und Blur).
+
+## D — Sensor-Rauschen + chromatische Aberration
+
+Leichtes Pixelrauschen und Farbsäume an Kanten. Parameter: `sensorNoise`/`sensorNoiseStrength`,
+`lensDistortion`/`lensDistortionStrength` (Dispersion).
+
+**Implementierung:** Compositor — additives Noise (klein) auf das Bild; chromatische
+Aberration via `CompositorNodeLensdist` mit kleinem `dispersion`-Wert.
+
+## Reihenfolge & Test
+
+1. **A** (geometrisch, einfach, konsistent) — danach Benchmark *mit* vs. *ohne* laufen lassen.
+2. **B + C** (Detektions-Robustheit).
+3. **D** (Feinschliff).
+
+Wichtig: Compositor-Node-API variiert leicht je Blender-Version — Code im echten Blender
+(4.5) gegenchecken. Determinismus pro Kamera nur bei A nötig (geometrische Konsistenz);
+B–D dürfen pro Aufnahme zufällig sein.
--- a/setup/generateSets/Dockerfile
+++ b/setup/generateSets/Dockerfile
@@ -0,0 +1,9 @@
+FROM linuxserver/blender:latest
+
+USER root
+
+RUN pip3 install --no-cache-dir \
+    numpy \
+    opencv-python
+
+WORKDIR /workspace
--- a/setup/generateSets/pycache/render_robot.cpython-311.pyc
+++ b/setup/generateSets/pycache/render_robot.cpython-311.pyc
--- a/setup/generateSets/docker-compose.yaml
+++ b/setup/generateSets/docker-compose.yaml
--- a/setup/generateSets/render_Loop.py
+++ b/setup/generateSets/render_Loop.py
@@ -96,11 +96,13 @@ def split_pose(pose_entry):

 def main():
    USER_HOME = Path.home()
-    BASE = USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering"
+    BASE = Path(__file__).resolve().parents[2]
+
+
+    BLENDER_EXE = str("C:/Program Files/Blender Foundation/Blender 4.5/blender.exe")

    ROBOT_JSON_FILE = str(BASE / "data" / "robot" / "robot.json")
    OUTPUT_DIR = str(BASE / "data" / "simulation" / "debug")
-    BLENDER_EXE = str("C:/Program Files/Blender Foundation/Blender 4.5/blender.exe")
    RENDER_PY = str(BASE / "setup" / "generateSets" / "render_robot.py")
    RENDER_PNG = str(BASE / "data" / "simulation" / "debug" / "render.png")
    OUTPUT_SET = str(BASE / "data" / "simulation")
--- a/setup/generateSets/render_robot.py
+++ b/setup/generateSets/render_robot.py
@@ -18,9 +18,11 @@ from mathutils import Matrix
 # Holt dynamisch den Pfad zum aktuellen Benutzerverzeichnis (z.B. C:\Users\Name)
 USER_HOME = Path.home()

+BASE = Path(__file__).resolve().parents[2]
+
 # Kombiniert den Benutzerpfad mit dem spezifischen Ordnerpfad und konvertiert direkt zu str
-ROBOT_JSON_FILE = str(USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering" / "data" / "robot" / "robot.json")
-OUTPUT_FILE = str(USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering" / "data" / "simulation" / "debug" / "render.png")
+ROBOT_JSON_FILE = str(BASE / "data" / "robot" / "robot.json")
+OUTPUT_FILE = str(BASE / "data" / "simulation" / "debug" / "render.png")

 print("Using robot JSON file:", ROBOT_JSON_FILE)
 print("Using output file:", OUTPUT_FILE)
@@ -88,6 +90,23 @@ marker_rot_max_deg   = float(rendering_info.get("markerRotationMaxDeg", 0.0))
 motion_blur          = as_bool(rendering_info.get("motionBlur", False))
 motion_blur_max_px   = float(rendering_info.get("motionBlurMaxPx", 1.5))

+# ── Linsen-/Kamerafehler ───────────────────────────────────────────
+# (A) Kalibrier-Restfehler: die npz-Intrinsik leicht von der Wahrheit abweichen lassen
+#     (deterministisch pro Kamera -> über alle Posen konsistent). Das Bild bleibt ideal,
+#     nur die *angenommene* Kalibrierung ist falsch — wie bei realer Webcam-Kalibrierung.
+intr_focal_err_pct = float(rendering_info.get("focalErrorPct", 0.0))      # ±% auf fx, fy
+intr_principal_px  = float(rendering_info.get("principalErrorPx", 0.0))   # ±px auf cx, cy
+intr_residual_dist = rendering_info.get("residualDistortion", None)        # [k1, k2] in dist_coeffs
+# (B-D) photometrische Effekte (Compositor)
+vignette                 = as_bool(rendering_info.get("vignette", False))            # C
+vignette_strength        = float(rendering_info.get("vignetteStrength", 0.25))
+localized_blur           = as_bool(rendering_info.get("localizedBlur", False))       # C
+localized_blur_strength  = float(rendering_info.get("localizedBlurStrength", 0.15))
+sensor_noise             = as_bool(rendering_info.get("sensorNoise", False))         # D
+sensor_noise_strength    = float(rendering_info.get("sensorNoiseStrength", 0.02))
+lens_distortion          = as_bool(rendering_info.get("lensDistortion", False))      # D (chromat. Aberration)
+lens_distortion_strength = float(rendering_info.get("lensDistortionStrength", 0.01))
+
 state: Dict[str, float] = {k: 0.0 for k in STATE_KEYS}
 for source_name in ("defaultPosition", "recognized", "movements"):
    source = robot.get(source_name, {}) or {}
@@ -518,6 +537,28 @@ camera_matrix = np.array([
 # ideal synthetic camera
 dist_coeffs = np.zeros((5, 1), dtype=np.float32)

+# ── (A) Kalibrier-Restfehler: Intrinsik gezielt verfälschen ──
+# Seed deterministisch aus der Kameraposition (NICHT hash() -> PYTHONHASHSEED), damit
+# dieselbe Kamera über alle Posen denselben Kalibrierfehler trägt.
+if intr_focal_err_pct > 0.0 or intr_principal_px > 0.0 or intr_residual_dist:
+    _cp = rendering_info.get("cameraPosition", [0, 0, 0])
+    _seed = abs(int(round(float(_cp[0]) * 1000 + float(_cp[1]) * 100 + float(_cp[2]) * 10))) + 17
+    _rng = random.Random(_seed)
+    if intr_focal_err_pct > 0.0:
+        fx *= 1.0 + _rng.uniform(-1.0, 1.0) * intr_focal_err_pct / 100.0
+        fy *= 1.0 + _rng.uniform(-1.0, 1.0) * intr_focal_err_pct / 100.0
+    if intr_principal_px > 0.0:
+        cx += _rng.uniform(-1.0, 1.0) * intr_principal_px
+        cy += _rng.uniform(-1.0, 1.0) * intr_principal_px
+    camera_matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
+    if intr_residual_dist:
+        _k = [float(v) for v in intr_residual_dist]
+        dist_coeffs = np.array([[_k[0] if len(_k) > 0 else 0.0],
+                                [_k[1] if len(_k) > 1 else 0.0],
+                                [0.0], [0.0], [0.0]], dtype=np.float32)
+    print(f"[render] (A) intrinsics jitter -> fx={fx:.1f} fy={fy:.1f} cx={cx:.1f} cy={cy:.1f} "
+          f"dist={dist_coeffs.ravel()[:2]}")
+
 np.savez(
    CALIBRATION_OUTPUT,

@@ -1045,27 +1086,127 @@ enable_best_device(scene)
 # RENDER
 # ============================================================

-# ── leichtes Verwackeln: pro Aufnahme zufällig gerichteter kleiner Blur ──
-if motion_blur and motion_blur_max_px > 0.0:
+# ── Post-Processing: Verwackeln + Linsen-/Sensor-Effekte (B-D) ──
+# Eine Compositor-Kette; jeder Effekt einzeln gekapselt, damit ein API-Problem den
+# Render nicht abbricht (der betroffene Effekt wird dann nur übersprungen + gewarnt).
+_post_active = ((motion_blur and motion_blur_max_px > 0.0) or lens_distortion or
+                localized_blur or vignette or lens_dirt or sensor_noise)
+if _post_active:
    scene.use_nodes = True
    tree = scene.node_tree
    for _n in list(tree.nodes):
        tree.nodes.remove(_n)
    rl = tree.nodes.new("CompositorNodeRLayers")
-    blur = tree.nodes.new("CompositorNodeBlur")
-    comp = tree.nodes.new("CompositorNodeComposite")
-    blur.filter_type = "GAUSS"
-    blur.use_relative = False
-    _ang = random.uniform(0.0, math.pi)               # zufällige Verwackel-Richtung
+    cur = rl.outputs["Image"]
+
+    # (D) chromatische Aberration (Dispersion) — KEINE geometrische Verzeichnung (-> A/npz)
+    if lens_distortion and lens_distortion_strength > 0.0:
+        try:
+            ld = tree.nodes.new("CompositorNodeLensdist")
+            ld.use_projector = False
+            ld.inputs["Distort"].default_value = 0.0
+            ld.inputs["Dispersion"].default_value = min(1.0, lens_distortion_strength)
+            tree.links.new(cur, ld.inputs["Image"]); cur = ld.outputs["Image"]
+            print("[render] (D) chromatic aberration")
+        except Exception as _e:
+            print("[render][WARN] lens_distortion skipped:", _e)
+
+    # Verwackeln (Motion Blur), pro Aufnahme zufällig gerichtet
+    if motion_blur and motion_blur_max_px > 0.0:
+        try:
+            mb = tree.nodes.new("CompositorNodeBlur")
+            mb.filter_type = "GAUSS"; mb.use_relative = False
+            _ang = random.uniform(0.0, math.pi)
            _amp = random.uniform(0.35, 1.0) * motion_blur_max_px
-    blur.size_x = max(0, int(round(abs(_amp * math.cos(_ang)))))
-    blur.size_y = max(0, int(round(abs(_amp * math.sin(_ang)))))
-    rl.location   = (-300, 0)
-    blur.location = (0, 0)
-    comp.location = (300, 0)
-    tree.links.new(rl.outputs["Image"], blur.inputs["Image"])
-    tree.links.new(blur.outputs["Image"], comp.inputs["Image"])
-    print(f"[render] motion blur size=({blur.size_x},{blur.size_y}) px")
+            mb.size_x = max(0, int(round(abs(_amp * math.cos(_ang)))))
+            mb.size_y = max(0, int(round(abs(_amp * math.sin(_ang)))))
+            tree.links.new(cur, mb.inputs["Image"]); cur = mb.outputs["Image"]
+            print(f"[render] motion blur size=({mb.size_x},{mb.size_y})")
+        except Exception as _e:
+            print("[render][WARN] motion_blur skipped:", _e)
+
+    # (C) Rand-Unschärfe (Feldkrümmung): scharf in der Mitte, unscharf am Rand
+    if localized_blur and localized_blur_strength > 0.0:
+        try:
+            eb = tree.nodes.new("CompositorNodeBlur")
+            eb.filter_type = "GAUSS"; eb.use_relative = True
+            eb.factor_x = min(0.5, localized_blur_strength)
+            eb.factor_y = min(0.5, localized_blur_strength)
+            em = tree.nodes.new("CompositorNodeEllipseMask")
+            em.width = 0.7; em.height = 0.7
+            ex = tree.nodes.new("CompositorNodeMixRGB")
+            tree.links.new(cur, eb.inputs["Image"])
+            tree.links.new(em.outputs["Mask"], ex.inputs["Fac"])
+            tree.links.new(eb.outputs["Image"], ex.inputs[1])   # Fac=0 (Rand) -> Blur
+            tree.links.new(cur, ex.inputs[2])                   # Fac=1 (Mitte) -> scharf
+            cur = ex.outputs["Image"]
+            print("[render] (C) edge blur")
+        except Exception as _e:
+            print("[render][WARN] localized_blur skipped:", _e)
+
+    # (C) Vignette: Randabdunklung über weiche Ellipse-Maske
+    if vignette and vignette_strength > 0.0:
+        try:
+            vm = tree.nodes.new("CompositorNodeEllipseMask")
+            vm.width = 1.0; vm.height = 1.0
+            vs = tree.nodes.new("CompositorNodeBlur")
+            vs.filter_type = "GAUSS"; vs.use_relative = True
+            vs.factor_x = 0.3; vs.factor_y = 0.3
+            vr = tree.nodes.new("CompositorNodeMapRange")
+            vr.inputs["From Min"].default_value = 0.0
+            vr.inputs["From Max"].default_value = 1.0
+            vr.inputs["To Min"].default_value = 1.0 - min(0.9, vignette_strength)
+            vr.inputs["To Max"].default_value = 1.0
+            vmul = tree.nodes.new("CompositorNodeMixRGB")
+            vmul.blend_type = "MULTIPLY"; vmul.inputs["Fac"].default_value = 1.0
+            tree.links.new(vm.outputs["Mask"], vs.inputs["Image"])
+            tree.links.new(vs.outputs["Image"], vr.inputs["Value"])
+            tree.links.new(cur, vmul.inputs[1])
+            tree.links.new(vr.outputs["Value"], vmul.inputs[2])
+            cur = vmul.outputs["Image"]
+            print("[render] (C) vignette")
+        except Exception as _e:
+            print("[render][WARN] vignette skipped:", _e)
+
+    # (B) Staub auf der Linse: wenige dunkle Flecken über dem ganzen Bild
+    if lens_dirt and lens_dirt_strength > 0.0:
+        try:
+            dtex = bpy.data.textures.new("lensDirtTex", type="VORONOI")
+            dt = tree.nodes.new("CompositorNodeTexture")
+            dt.texture = dtex
+            dramp = tree.nodes.new("CompositorNodeValToRGB")
+            dramp.color_ramp.elements[0].position = 0.0
+            dramp.color_ramp.elements[1].position = 0.12   # nur wenige Flecken
+            dramp.color_ramp.elements[0].color = (1, 1, 1, 1)
+            dramp.color_ramp.elements[1].color = (1.0 - min(0.9, lens_dirt_strength),) * 3 + (1.0,)
+            dmul = tree.nodes.new("CompositorNodeMixRGB")
+            dmul.blend_type = "MULTIPLY"; dmul.inputs["Fac"].default_value = 1.0
+            tree.links.new(dt.outputs["Value"], dramp.inputs["Fac"])
+            tree.links.new(cur, dmul.inputs[1])
+            tree.links.new(dramp.outputs["Image"], dmul.inputs[2])
+            cur = dmul.outputs["Image"]
+            print("[render] (B) lens dirt")
+        except Exception as _e:
+            print("[render][WARN] lens_dirt skipped:", _e)
+
+    # (D) Sensor-Rauschen: feines additives Rauschen
+    if sensor_noise and sensor_noise_strength > 0.0:
+        try:
+            ntex = bpy.data.textures.new("sensorNoiseTex", type="NOISE")
+            nt = tree.nodes.new("CompositorNodeTexture")
+            nt.texture = ntex
+            nmix = tree.nodes.new("CompositorNodeMixRGB")
+            nmix.blend_type = "ADD"
+            nmix.inputs["Fac"].default_value = min(0.3, sensor_noise_strength)
+            tree.links.new(cur, nmix.inputs[1])
+            tree.links.new(nt.outputs["Image"], nmix.inputs[2])
+            cur = nmix.outputs["Image"]
+            print("[render] (D) sensor noise")
+        except Exception as _e:
+            print("[render][WARN] sensor_noise skipped:", _e)
+
+    comp = tree.nodes.new("CompositorNodeComposite")
+    tree.links.new(cur, comp.inputs["Image"])

 bpy.ops.render.render(write_still=True)
 print("Finished rendering:", OUTPUT_FILE)
--- a/setup/generateSets/render_robot_5_1.py
+++ b/setup/generateSets/render_robot_5_1.py