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
@@ -1,3 +1,4 @@
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data/
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__pycache__/
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*.pyc
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models/
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+1
-1
@@ -1,7 +1,7 @@
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FROM python:3.11-slim
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libgl1 libglib2.0-0 \
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libgl1 libglib2.0-0 libgomp1 \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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+12
-1
@@ -3,11 +3,22 @@ import numpy as np
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import colorsys
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def extract_dominant_color_from_frame(frame: np.ndarray, bbox: dict | None = None) -> tuple[str, str]:
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"""Returns (hex_color, color_name) from a BGR numpy frame."""
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if frame is None:
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return "#808080", "Inconnu"
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return _dominant_color(frame, bbox)
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def extract_dominant_color(image_path: str, bbox: dict | None = None) -> tuple[str, str]:
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"""Returns (hex_color, color_name) from image, optionally cropped to bbox."""
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"""Returns (hex_color, color_name) from image path, optionally cropped to bbox."""
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img = cv2.imread(image_path)
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if img is None:
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return "#808080", "Inconnu"
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return _dominant_color(img, bbox)
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def _dominant_color(img: np.ndarray, bbox: dict | None = None) -> tuple[str, str]:
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if bbox:
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h, w = img.shape[:2]
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+135
@@ -0,0 +1,135 @@
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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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@@ -5,3 +5,4 @@ python-multipart==0.0.9
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requests==2.32.3
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opencv-python-headless==4.10.0.84
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numpy==1.26.4
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onnxruntime==1.20.0
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@@ -54,7 +54,7 @@
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<div class="text-center py-20 text-slate-500">
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<div class="text-5xl mb-4">📷</div>
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<p class="text-lg">Aucun passage enregistré</p>
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<p class="text-sm mt-1">En attente de détections Frigate…</p>
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<p class="text-sm mt-1">En attente de détections véhicules…</p>
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</div>
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{% endif %}
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+130
-60
@@ -1,94 +1,164 @@
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import os
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import time
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import uuid
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import logging
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import requests
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import shutil
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from database import event_exists, insert_event
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from analyzer import extract_dominant_color
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import cv2
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import numpy as np
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from urllib.parse import quote
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from database import insert_event
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from analyzer import extract_dominant_color_from_frame
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from lpr import PlateAnalyzer
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log = logging.getLogger("watcher")
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FRIGATE_URL = os.environ.get("FRIGATE_URL", "http://frigate:5000")
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CAMERA_URL = os.environ.get("CAMERA_URL", "http://192.168.1.44")
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CAMERA_USER = os.environ.get("CAMERA_USER", "admin")
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CAMERA_PASS = os.environ.get("CAMERA_PASS", "")
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CAMERA_RTSP = os.environ.get("CAMERA_RTSP", "")
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CAMERA_NAME = os.environ.get("CAMERA_NAME", "portail")
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SNAPSHOTS_DIR = os.environ.get("SNAPSHOTS_DIR", "/data/snapshots")
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POLL_INTERVAL = int(os.environ.get("POLL_INTERVAL", "30"))
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POLL_INTERVAL = float(os.environ.get("POLL_INTERVAL", "2"))
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CAPTURE_DURATION = int(os.environ.get("CAPTURE_DURATION", "20"))
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COOLDOWN = int(os.environ.get("COOLDOWN", "60"))
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_token: str | None = None
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_token_time = 0.0
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_last_event_time = 0.0
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_analyzer: PlateAnalyzer | None = None
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def fetch_new_events() -> list[dict]:
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def _rtsp_url() -> str:
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if CAMERA_RTSP:
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return CAMERA_RTSP
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host = CAMERA_URL.replace("http://", "").replace("https://", "").split(":")[0]
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return f"rtsp://{quote(CAMERA_USER, safe='')}:{quote(CAMERA_PASS, safe='')}@{host}/h264Preview_01_main"
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def _login() -> str | None:
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global _token, _token_time
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if _token and (time.time() - _token_time) < 3000:
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return _token
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try:
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resp = requests.get(
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f"{FRIGATE_URL}/api/events",
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params={"labels": "car", "has_snapshot": "1", "limit": "50"},
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timeout=10
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resp = requests.post(
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f"{CAMERA_URL}/api.cgi?cmd=Login",
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json=[{"cmd": "Login", "param": {"User": {"userName": CAMERA_USER, "password": CAMERA_PASS}}}],
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timeout=5,
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)
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resp.raise_for_status()
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return resp.json()
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data = resp.json()
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if data[0]["code"] == 0:
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_token = data[0]["value"]["Token"]["name"]
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_token_time = time.time()
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log.debug("Camera login OK")
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return _token
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except Exception as e:
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log.warning(f"Frigate fetch error: {e}")
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return []
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log.warning(f"Login error: {e}")
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return None
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def download_snapshot(event_id: str, dest_path: str) -> bool:
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def _get_ai_state() -> dict | None:
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token = _login()
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if not token:
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return None
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try:
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resp = requests.get(
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f"{FRIGATE_URL}/api/events/{event_id}/snapshot.jpg",
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params={"bbox": "1", "crop": "1", "quality": "95"},
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timeout=15, stream=True
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resp = requests.post(
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f"{CAMERA_URL}/api.cgi?cmd=GetAiState&token={token}",
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json=[{"cmd": "GetAiState", "action": 0, "param": {"channel": 0}}],
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timeout=5,
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)
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resp.raise_for_status()
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with open(dest_path, "wb") as f:
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shutil.copyfileobj(resp.raw, f)
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return True
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data = resp.json()
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if data[0]["code"] == 0:
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return data[0]["value"]
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except Exception as e:
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log.warning(f"Snapshot download error for {event_id}: {e}")
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return False
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log.warning(f"GetAiState error: {e}")
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return None
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def process_event(ev: dict):
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event_id = ev.get("id", "")
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if not event_id or event_exists(event_id):
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def _frame_score(frame: np.ndarray, plates: list) -> float:
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"""Score a frame: prefer large, high-confidence plates. Fallback to sharpness."""
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if plates:
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x1, y1, x2, y2, conf = plates[0]
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return (x2 - x1) * (y2 - y1) * conf
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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return cv2.Laplacian(gray, cv2.CV_64F).var() * 0.001
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def _capture_best_frame() -> np.ndarray | None:
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url = _rtsp_url()
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log.info(f"Capturing RTSP for {CAPTURE_DURATION}s...")
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cap = cv2.VideoCapture(url)
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if not cap.isOpened():
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log.error("Cannot open RTSP stream")
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return None
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best_frame: np.ndarray | None = None
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best_score = -1.0
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start = time.time()
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try:
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while time.time() - start < CAPTURE_DURATION:
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ret, frame = cap.read()
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if not ret:
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log.warning("RTSP read failed, retrying...")
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time.sleep(0.5)
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continue
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plates = _analyzer.detect_plates(frame) if _analyzer else []
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score = _frame_score(frame, plates)
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if score > best_score:
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best_score = score
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best_frame = frame.copy()
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time.sleep(0.8)
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finally:
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cap.release()
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log.info(f"Capture done. Best frame score: {best_score:.2f}")
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return best_frame
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def _process_event():
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frame = _capture_best_frame()
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if frame is None:
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log.warning("No frame captured")
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return
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camera = ev.get("camera", "unknown")
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start_time = int(ev.get("start_time", time.time()))
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# Plate from Frigate LPR
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plate = None
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data = ev.get("data", {})
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if data.get("sub_label"):
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plate = data["sub_label"]
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if isinstance(plate, list):
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plate = plate[0] if plate else None
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# Download snapshot
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event_id = str(uuid.uuid4())
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snapshot_file = f"{event_id}.jpg"
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snapshot_path = os.path.join(SNAPSHOTS_DIR, snapshot_file)
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if not download_snapshot(event_id, snapshot_path):
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return
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cv2.imwrite(snapshot_path, frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
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# Extract color from vehicle bounding box
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bbox = None
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if ev.get("box"):
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b = ev["box"]
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bbox = {"x": b[0], "y": b[1], "width": b[2] - b[0], "height": b[3] - b[1]}
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elif data.get("box"):
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b = data["box"]
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if len(b) == 4:
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bbox = {"x": b[0], "y": b[1], "width": b[2] - b[0], "height": b[3] - b[1]}
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plate, conf = "", 0.0
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if _analyzer:
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plate, conf = _analyzer.read_plate(frame)
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hex_color, color_name = extract_dominant_color(snapshot_path, bbox)
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log.info(f"LPR: plate={plate!r} conf={conf:.2f}")
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insert_event(event_id, camera, start_time, f"snapshots/{snapshot_file}",
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plate, hex_color, color_name)
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log.info(f"Processed event {event_id} | plate={plate} | color={color_name} | camera={camera}")
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hex_color, color_name = extract_dominant_color_from_frame(frame)
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insert_event(
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event_id, CAMERA_NAME, int(time.time()),
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f"snapshots/{snapshot_file}", plate or None, hex_color, color_name,
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)
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log.info(f"Stored: {event_id} | plate={plate} | color={color_name}")
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def run_watcher():
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log.info(f"Watcher started — polling Frigate every {POLL_INTERVAL}s")
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global _last_event_time, _analyzer
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log.info("Loading plate analyzer...")
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_analyzer = PlateAnalyzer()
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log.info(f"Watcher started — polling {CAMERA_URL} every {POLL_INTERVAL}s")
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while True:
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events = fetch_new_events()
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for ev in events:
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try:
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process_event(ev)
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state = _get_ai_state()
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if state:
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vehicle = state.get("vehicle", {})
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if vehicle.get("alarm_state") == 1:
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now = time.time()
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if now - _last_event_time > COOLDOWN:
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_last_event_time = now
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log.info("Vehicle detected! Triggering capture...")
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_process_event()
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except Exception as e:
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log.error(f"Error processing event: {e}")
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log.error(f"Watcher loop error: {e}", exc_info=True)
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time.sleep(POLL_INTERVAL)
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+9
-2
@@ -5,11 +5,18 @@ services:
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restart: unless-stopped
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volumes:
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- ./data:/data
|
||||
- ./models:/models:ro
|
||||
environment:
|
||||
- FRIGATE_URL=http://frigate:5000
|
||||
- CAMERA_URL=http://192.168.1.44
|
||||
- CAMERA_USER=admin
|
||||
- CAMERA_PASS=Wxcvbn99--$$$$
|
||||
- CAMERA_NAME=portail
|
||||
- MODEL_CACHE=/models
|
||||
- DB_PATH=/data/camwatch.db
|
||||
- SNAPSHOTS_DIR=/data/snapshots
|
||||
- POLL_INTERVAL=30
|
||||
- POLL_INTERVAL=2
|
||||
- CAPTURE_DURATION=20
|
||||
- COOLDOWN=60
|
||||
labels:
|
||||
- traefik.enable=true
|
||||
- traefik.http.routers.camwatch.rule=Host(`camwatch.nas.percolouco.com`)
|
||||
|
||||
Reference in New Issue
Block a user