API mit .npz
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chk
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setup/README.md
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setup/README.md
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# Kamera-Kalibrierung einrichten
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Jede Kamera braucht eine Kalibrierungsdatei (`calibration.npz`), die
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Kameramatrix und Verzerrungskoeffizienten enthält.
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Die Datei wird beim Serverstart einmalig in den RAM geladen und über
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`GET /api/cameras/{id}/calibration` ausgeliefert.
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---
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## Voraussetzungen
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```
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pip install opencv-python numpy requests
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```
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---
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## Schritt 1 – Schachbrettmuster drucken
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Datei: [`checkerboard_11x8_25mm.pdf`](checkerboard_11x8_25mm.pdf)
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- **A4 Querformat, 100 % drucken** (keine Skalierung, kein „An Seite anpassen")
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- Muster: 11 × 8 Felder → **10 × 7 innere Ecken**, 25 mm pro Feld
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- Auf festes Papier oder Karton drucken; flach und verwindungssteif halten
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---
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## Schritt 2 – Fotos aufnehmen
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Server läuft auf `thinkcentre.local:8444`. Skript schießt für alle Kameras
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gleichzeitig Hires-Aufnahmen und legt sie in `test/files/{camId}/` ab:
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```bash
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python test/grabSnapShot.py
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```
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Das Schachbrettmuster dabei aus **verschiedenen Winkeln und Positionen** halten
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(mind. 15–20 Aufnahmen pro Kamera).
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Bilder landen unter `test/files/cam0/`, `test/files/cam1/`, `test/files/cam2/`.
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---
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## Schritt 3 – Kalibrierung berechnen
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Für jede Kamera einmal ausführen:
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```bash
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python setup/calibrate.py test/files/cam0 data/calibration/cam0/calibration.npz
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python setup/calibrate.py test/files/cam1 data/calibration/cam1/calibration.npz
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python setup/calibrate.py test/files/cam2 data/calibration/cam2/calibration.npz
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```
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Ein guter RMS-Wert liegt unter **0.5 px**. Wenn der Wert zu hoch ist:
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- Bilder mit schlechter Beleuchtung oder Bewegungsunschärfe löschen
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- Schachbrettmuster flacher halten
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- Mehr Aufnahmen aus verschiedenen Winkeln machen
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---
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## Schritt 4 – Dateien committen
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Die `.npz`-Dateien sind Deployment-Konfiguration und gehören ins Repository:
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```bash
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git add data/calibration/
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git commit -m "Kalibrierungsdaten für cam0–cam2"
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```
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---
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## Ergebnis prüfen
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```bash
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curl http://thinkcentre.local:8444/api/cameras
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# → jede Kamera mit "calibrationUrl": "/api/cameras/cam0/calibration"
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curl -o cam0.npz http://thinkcentre.local:8444/api/cameras/cam0/calibration
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python -c "import numpy as np; d=np.load('cam0.npz'); print(d['camera_matrix'])"
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```
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77
setup/calibrate.py
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setup/calibrate.py
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"""
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Berechnet eine Kamera-Kalibrierung aus Schachbrett-Fotos und speichert das
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Ergebnis als .npz.
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Aufruf:
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python calibrate.py <Bildordner> <Ausgabedatei.npz>
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Beispiel:
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python setup/calibrate.py test/files/cam0 data/calibration/cam0/calibration.npz
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Erwartet *.jpg-Dateien im Bildordner, die ein 10x7-Eck-Schachbrettmuster
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(= 11x8 Felder, 25 mm/Feld) zeigen.
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"""
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import sys
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import glob
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import pathlib
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import numpy as np
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import cv2
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CHECKERBOARD = (10, 7) # innere Ecken
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SQUARE_MM = 25.0 / 1000 # 25 mm in Metern
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def calibrate(image_dir: str, out_path: str) -> None:
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images = sorted(glob.glob(str(pathlib.Path(image_dir) / "*.jpg")))
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if not images:
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sys.exit(f"Keine .jpg-Dateien in {image_dir!r}")
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print(f"Gefundene Bilder: {len(images)}")
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objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
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objp[:, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
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objp *= SQUARE_MM
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objpoints, imgpoints = [], []
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img_size = None
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for fname in images:
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img = cv2.imread(fname)
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if img is None:
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print(f" WARNUNG: konnte nicht lesen: {fname}")
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continue
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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if img_size is None:
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img_size = gray.shape[::-1]
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ret, corners = cv2.findChessboardCorners(
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gray, CHECKERBOARD,
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cv2.CALIB_CB_ADAPTIVE_THRESH | cv2.CALIB_CB_NORMALIZE_IMAGE | cv2.CALIB_CB_FAST_CHECK,
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)
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if ret:
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corners2 = cv2.cornerSubPix(
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gray, corners, (11, 11), (-1, -1),
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(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6),
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)
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objpoints.append(objp)
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imgpoints.append(corners2)
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print(f" OK {pathlib.Path(fname).name}")
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else:
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print(f" -- {pathlib.Path(fname).name} (keine Ecken gefunden)")
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print(f"\nGültige Bilder: {len(objpoints)} / {len(images)}")
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if not objpoints:
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sys.exit("Kalibrierung fehlgeschlagen: keine Ecken in keinem Bild gefunden.")
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rms, K, D, _rvecs, _tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
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print(f"RMS-Fehler: {rms:.4f} px")
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print(f"Kameramatrix:\n{K}")
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print(f"Verzerrungskoeff: {D}")
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pathlib.Path(out_path).parent.mkdir(parents=True, exist_ok=True)
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np.savez(out_path, camera_matrix=K, dist_coeffs=D)
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print(f"\nGespeichert: {out_path}")
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if __name__ == "__main__":
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if len(sys.argv) != 3:
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sys.exit(__doc__)
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calibrate(sys.argv[1], sys.argv[2])
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95
setup/checkerboard_11x8_25mm.pdf
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setup/checkerboard_11x8_25mm.pdf
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Q
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0.5 w
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0 G
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31.18 14.18 779.53 566.93 re
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S
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Q
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BT
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/F1 7.5 Tf
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31.18 3.18 Td
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(Kalibrierungsmuster | 11x8 Felder | 10x7 innere Ecken | 25 mm/Feld | A4 Querformat, 100% drucken ohne Skalierung) Tj
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