- Tiled YOLO (3x2, 20% overlap): plate confidence 0.307 -> 0.838 on test frame
- Aspect ratio filter (2.0-6.5:1): rejects non-plate shapes
- Confidence threshold 0.03 -> 0.04: removes fence/noise false positives
- Exclude top 15% (sky) and bottom 8% (timestamp overlay) from detection zone
- Add Tesseract as primary OCR (better for Latin plates), PaddleOCR as fallback
- Enhance plate crop before OCR: CLAHE + sharpening + min 80px upscale
- Save plate_crop.jpg (4x upscaled) to event dir for manual review
- Show plate crop in event detail page
- Add manual plate edit form in event detail (POST /event/{id}/plate)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remplace OpenCV VideoCapture par ffmpeg pour la capture RTSP (plus fiable)
- Capture 15s → extrait ~75 frames à 5fps via ffmpeg
- Le LPR analyse toutes les frames et garde la meilleure (plaque la plus grande/confiante)
- Ajoute CAPTURE_FPS env var
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remplace la source Frigate par l'API GetAiState de la caméra Reolink
- Capture RTSP en temps réel (~20s) quand véhicule détecté, garde la meilleure frame
- LPR avec YOLOv9 (détection plaque) + PaddleOCR v4 (lecture texte) via ONNX
- Modèles partagés avec Frigate (volume local ./models/)
- Cooldown 60s entre événements pour éviter les doublons
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Polls Frigate events API, extracts dominant vehicle color (KMeans),
reads LPR plate from Frigate sub_label. Responsive dark UI with
filter by plate/camera/date and auto-refresh. FastAPI + SQLite.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
