Passe de Frigate polling à GetAiState Reolink + LPR ONNX local
- 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>
This commit is contained in:
perco
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
f6422b0e7e
commit
65b74ce46d
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import os
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import cv2
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import numpy as np
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import logging
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log = logging.getLogger("lpr")
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MODEL_CACHE = os.environ.get("MODEL_CACHE", "/models")
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# Output format: [N, 7] where each row = [batch_idx, x1, y1, x2, y2, class_id, confidence]
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_YOLO_CONF_THRESHOLD = 0.03
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class PlateAnalyzer:
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def __init__(self):
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self._yolo = None
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self._rec = None
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self._keys: list[str] = []
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self._load()
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def _load(self):
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try:
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import onnxruntime as ort
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providers = ["CPUExecutionProvider"]
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yolo_path = os.path.join(MODEL_CACHE, "yolov9_license_plate", "yolov9-256-license-plates.onnx")
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rec_path = os.path.join(MODEL_CACHE, "paddleocr-onnx", "recognition_v4.onnx")
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keys_path = os.path.join(MODEL_CACHE, "paddleocr-onnx", "ppocr_keys_v1.txt")
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if os.path.exists(yolo_path):
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self._yolo = ort.InferenceSession(yolo_path, providers=providers)
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log.info("YOLOv9 plate detector loaded")
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else:
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log.warning(f"YOLOv9 model not found at {yolo_path}")
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if os.path.exists(rec_path):
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self._rec = ort.InferenceSession(rec_path, providers=providers)
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log.info("PaddleOCR recognition model loaded")
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if os.path.exists(keys_path):
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with open(keys_path) as f:
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self._keys = f.read().splitlines()
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log.info(f"Loaded {len(self._keys)} OCR characters")
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except ImportError:
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log.warning("onnxruntime not installed — LPR disabled")
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except Exception as e:
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log.error(f"LPR model load error: {e}")
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def detect_plates(self, frame: np.ndarray) -> list[tuple]:
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"""Returns [(x1, y1, x2, y2, conf), ...] in original frame coordinates."""
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if self._yolo is None:
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return []
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h, w = frame.shape[:2]
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inp = cv2.resize(frame, (256, 256))
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inp = cv2.cvtColor(inp, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
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inp = inp.transpose(2, 0, 1)[np.newaxis]
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out = self._yolo.run(None, {"images": inp})[0] # [N, 7]
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boxes = []
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for row in out:
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# row: [batch_idx, x1, y1, x2, y2, class_id, confidence]
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if len(row) >= 7:
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_, x1, y1, x2, y2, _, conf = row
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if conf < _YOLO_CONF_THRESHOLD:
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continue
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# Scale from 256x256 to original
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x1 = max(0.0, float(x1) / 256.0 * w)
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y1 = max(0.0, float(y1) / 256.0 * h)
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x2 = min(float(w), float(x2) / 256.0 * w)
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y2 = min(float(h), float(y2) / 256.0 * h)
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if x2 > x1 + 4 and y2 > y1 + 4:
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boxes.append((x1, y1, x2, y2, float(conf)))
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return sorted(boxes, key=lambda b: -b[4])
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def _ocr_crop(self, crop: np.ndarray) -> tuple[str, float]:
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"""Run PaddleOCR recognition on a crop. Returns (text, confidence)."""
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if self._rec is None or not self._keys or crop.size == 0:
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return "", 0.0
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h, w = crop.shape[:2]
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if h == 0 or w == 0:
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return "", 0.0
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inp_h = 48
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inp_w = max(10, int(inp_h * w / h))
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resized = cv2.resize(crop, (inp_w, inp_h))
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rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32)
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normalized = (rgb / 127.5) - 1.0
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inp = normalized.transpose(2, 0, 1)[np.newaxis]
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out = self._rec.run(None, {"x": inp})[0][0] # [seq, 6625]
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chars: list[str] = []
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confs: list[float] = []
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prev_idx = 0
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for step in out:
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idx = int(np.argmax(step))
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conf = float(step[idx])
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if idx != prev_idx and idx != 0 and idx <= len(self._keys):
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chars.append(self._keys[idx - 1])
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confs.append(conf)
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prev_idx = idx
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text = "".join(chars)
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avg_conf = float(np.mean(confs)) if confs else 0.0
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return text, avg_conf
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def read_plate(self, frame: np.ndarray) -> tuple[str, float]:
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"""Detect plate in frame, read text. Returns (plate_text, confidence)."""
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plate_boxes = self.detect_plates(frame)
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if not plate_boxes:
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return "", 0.0
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x1, y1, x2, y2, plate_conf = plate_boxes[0]
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pad = 8
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cx1 = max(0, int(x1) - pad)
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cy1 = max(0, int(y1) - pad)
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cx2 = min(frame.shape[1], int(x2) + pad)
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cy2 = min(frame.shape[0], int(y2) + pad)
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crop = frame[cy1:cy2, cx1:cx2]
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text, ocr_conf = self._ocr_crop(crop)
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# Filter noise: plate must have at least 4 alphanumeric chars
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alnum = "".join(c for c in text if c.isalnum())
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if len(alnum) < 4:
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return "", 0.0
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return text, (plate_conf + ocr_conf) / 2.0
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