percoandClaude Sonnet 4.6
ea58a860b8
Whitelist: plates added to /whitelist are silently ignored on detection — no clip, no frames, no DB entry. Manageable via /whitelist page or from the event detail page per-event. Stats: /stats shows all known plates ranked by passage count, with first/last seen dates and one-click whitelist toggle. Navigation links added to the index header. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
334 lines
13 KiB
Python
334 lines
13 KiB
Python
import os
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import time
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import uuid
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import shutil
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import logging
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import subprocess
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import glob
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import requests
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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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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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EVENTS_DIR = os.environ.get("EVENTS_DIR", "/data/events")
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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", "15"))
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CAPTURE_FPS = int(os.environ.get("CAPTURE_FPS", "5"))
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COOLDOWN = int(os.environ.get("COOLDOWN", "60"))
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PLATERECOGNIZER_KEY = os.environ.get("PLATERECOGNIZER_API_KEY", "")
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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 _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.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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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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return _token
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except Exception as e:
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log.warning(f"Login error: {e}")
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return None
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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.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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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"GetAiState error: {e}")
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return None
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def _vehicle_color(frame: np.ndarray, plate_bbox: tuple | None) -> tuple[str, str]:
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"""Extract dominant color from the vehicle body (above the plate, or center frame)."""
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h, w = frame.shape[:2]
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if plate_bbox:
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px1, py1, px2, py2 = plate_bbox
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pw = px2 - px1
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ph = py2 - py1
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crop_x1 = max(0, px1 - pw * 2)
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crop_x2 = min(w, px2 + pw * 2)
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crop_y2 = max(0, py1 - 5)
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crop_y1 = max(0, py1 - ph * 10)
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if crop_y2 > crop_y1 and crop_x2 > crop_x1:
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crop = frame[int(crop_y1):int(crop_y2), int(crop_x1):int(crop_x2)]
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return extract_dominant_color_from_frame(crop)
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cy1 = h // 4
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cy2 = 3 * h // 4
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cx1 = w // 4
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cx2 = 3 * w // 4
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return extract_dominant_color_from_frame(frame[cy1:cy2, cx1:cx2])
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def _frame_score(frame: np.ndarray, plates: list) -> float:
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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 _process_clip(event_id: str, event_dir: str, clip_path: str, camera_name: str = None):
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"""Process an existing clip: extract frames at full res, transcode for browser, LPR, store in DB."""
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if camera_name is None:
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camera_name = CAMERA_NAME
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frames_pattern = os.path.join(event_dir, "frame_%04d.jpg")
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# Extract frames at full original resolution BEFORE any transcode
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subprocess.run([
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"ffmpeg", "-y", "-i", clip_path,
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"-vf", f"fps={CAPTURE_FPS}", "-q:v", "2", frames_pattern,
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], capture_output=True, timeout=60)
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# Transcode to H.264 baseline 720p for browser (after frame extraction)
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web_clip = os.path.join(event_dir, "clip_web.mp4")
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subprocess.run([
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"ffmpeg", "-y", "-i", clip_path,
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"-c:v", "libx264", "-profile:v", "baseline", "-level", "3.1",
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"-preset", "fast", "-crf", "28",
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"-vf", "scale=-2:720",
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"-an",
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"-movflags", "+faststart",
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web_clip,
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], capture_output=True, timeout=120)
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if os.path.exists(web_clip):
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os.replace(web_clip, clip_path)
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else:
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log.warning("Transcode failed, keeping raw clip")
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frame_files = sorted(glob.glob(os.path.join(event_dir, "frame_*.jpg")))
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log.info(f"Extracted {len(frame_files)} frames")
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if not frame_files:
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shutil.rmtree(event_dir, ignore_errors=True)
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return None
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# Pass 1: load all frames, compute sharpness + inter-frame motion
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# Motion detection uses a downscaled gray image (fast) to find frames where
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# the vehicle is present — a moving car causes high frame diff even when blurry.
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_MOTION_W = 320
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loaded: list[tuple[np.ndarray, str, float, float]] = [] # frame, path, sharpness, motion
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prev_small: np.ndarray | None = None
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for path in frame_files:
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frame = cv2.imread(path)
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if frame is None:
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continue
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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lap = cv2.Laplacian(gray, cv2.CV_64F).var()
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h, w = gray.shape
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small = cv2.resize(gray, (_MOTION_W, _MOTION_W * h // w))
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motion = float(np.mean(np.abs(small.astype(np.float32) - prev_small.astype(np.float32)))) if prev_small is not None else 0.0
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prev_small = small
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loaded.append((frame, path, lap, motion))
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# Frame 0 has no predecessor — inherit frame 1's motion so car entering on first frame isn't missed
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if len(loaded) > 1:
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loaded[0] = (loaded[0][0], loaded[0][1], loaded[0][2], loaded[1][3])
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max_sharp = max((x[2] for x in loaded), default=1.0) or 1.0
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max_motion = max((x[3] for x in loaded), default=1.0) or 1.0
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# Combined score: motion weighted higher to prioritise car-present frames,
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# sharpness as tiebreaker within those frames.
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combined: list[tuple[float, np.ndarray, str]] = sorted(
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[(0.65 * m / max_motion + 0.35 * s / max_sharp, frame, path)
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for frame, path, s, m in loaded],
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key=lambda x: -x[0],
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)
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# Pass 2: YOLO on top 15 by combined score → plate-area × conf ranking
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top15 = combined[:15]
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scored: list[tuple[float, np.ndarray, str]] = []
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for _, frame, path in top15:
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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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scored.append((score, frame, path))
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scored.sort(key=lambda x: -x[0])
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# Merge: YOLO-scored top15 first, then remaining frames by combined score
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scored_paths = {path for _, _, path in scored}
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rest = [(score, frame, path) for score, frame, path in combined if path not in scored_paths]
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full_sorted = scored + rest
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for i, (_, _, old_path) in enumerate(full_sorted):
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os.rename(old_path, old_path + ".tmp")
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for i, (_, _, old_path) in enumerate(full_sorted):
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os.rename(old_path + ".tmp", os.path.join(event_dir, f"frame_{i+1:04d}.jpg"))
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best_frame = full_sorted[0][1] if full_sorted else None
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if best_frame is None:
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shutil.rmtree(event_dir, ignore_errors=True)
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return None
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# LPR: PlateRecognizer API (top 3 frames) → fallback to local
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plate, conf, plate_bbox, plate_corrected = ("", 0.0, None, None)
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if PLATERECOGNIZER_KEY:
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from lpr import call_platerecognizer
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for _, frame, _ in scored[:3]:
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p, c = call_platerecognizer(frame, PLATERECOGNIZER_KEY)
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if p and c > conf:
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plate, conf = p, c
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if conf >= 0.7:
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break
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if plate:
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# Get local perspective-corrected crop for display
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if _analyzer:
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plates = _analyzer.detect_plates(best_frame)
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if plates:
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x1, y1, x2, y2, _ = plates[0]
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quad = _analyzer._find_plate_quad(best_frame, int(x1), int(y1), int(x2), int(y2))
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plate_bbox = (int(x1), int(y1), int(x2), int(y2))
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plate_corrected = _analyzer._perspective_correct(best_frame, quad) if quad is not None else None
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else:
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log.info("PlateRecognizer returned no result, falling back to local LPR")
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if _analyzer:
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plate, conf, plate_bbox, plate_corrected = _analyzer.read_plate(best_frame)
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elif _analyzer:
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plate, conf, plate_bbox, plate_corrected = _analyzer.read_plate(best_frame)
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log.info(f"LPR: plate={plate!r} conf={conf:.2f} bbox={plate_bbox}")
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# Save raw plate crop (4× upscale) and the perspective-corrected OCR crop
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if plate_bbox:
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px1, py1, px2, py2 = plate_bbox
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fh, fw = best_frame.shape[:2]
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pad = max(8, int((py2 - py1) * 0.5))
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cx1, cy1 = max(0, px1 - pad), max(0, py1 - pad)
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cx2, cy2 = min(fw, px2 + pad), min(fh, py2 + pad)
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plate_crop = best_frame[cy1:cy2, cx1:cx2]
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if plate_crop.size > 0:
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ph, pw = plate_crop.shape[:2]
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big = cv2.resize(plate_crop, (pw * 4, ph * 4), interpolation=cv2.INTER_CUBIC)
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cv2.imwrite(os.path.join(event_dir, "plate_crop.jpg"), big, [cv2.IMWRITE_JPEG_QUALITY, 95])
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if plate_corrected is not None and plate_corrected.size > 0:
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enhanced = _analyzer._enhance_crop(plate_corrected)
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ch, cw = enhanced.shape[:2]
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if cw < 300: # upscale small crops for display
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enhanced = cv2.resize(enhanced, (300, int(300 * ch / cw)), interpolation=cv2.INTER_CUBIC)
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cv2.imwrite(os.path.join(event_dir, "plate_ocr.jpg"), enhanced, [cv2.IMWRITE_JPEG_QUALITY, 95])
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# Save thumbnail
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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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cv2.imwrite(snapshot_path, best_frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
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from database import is_whitelisted
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if plate and is_whitelisted(plate):
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log.info(f"Skipped (whitelist): {plate} — event {event_id[:8]}")
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shutil.rmtree(event_dir, ignore_errors=True)
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return None
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hex_color, color_name = _vehicle_color(best_frame, plate_bbox)
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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}",
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f"events/{event_id}/clip.mp4",
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plate or None, hex_color, color_name,
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)
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log.info(f"Stored: {event_id[:8]} | plate={plate} | color={color_name} | frames={len(frame_files)}")
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return event_id
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def _process_event():
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event_id = str(uuid.uuid4())
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event_dir = os.path.join(EVENTS_DIR, event_id)
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os.makedirs(event_dir, exist_ok=True)
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url = _rtsp_url()
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clip_path = os.path.join(event_dir, "clip.mp4")
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log.info(f"Capturing {CAPTURE_DURATION}s at {CAPTURE_FPS}fps — event {event_id[:8]}")
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ret = subprocess.run([
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"ffmpeg", "-y", "-rtsp_transport", "tcp",
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"-i", url, "-t", str(CAPTURE_DURATION), "-c", "copy", clip_path,
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], capture_output=True, timeout=CAPTURE_DURATION + 10)
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if not os.path.exists(clip_path) or os.path.getsize(clip_path) < 1000:
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log.error(f"ffmpeg capture failed: {ret.stderr[-200:].decode(errors='ignore')}")
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shutil.rmtree(event_dir, ignore_errors=True)
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return
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_process_clip(event_id, event_dir, clip_path)
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def process_uploaded_clip(src_path: str) -> str:
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"""Create a new event from an uploaded MP4. Returns event_id."""
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event_id = str(uuid.uuid4())
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event_dir = os.path.join(EVENTS_DIR, event_id)
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os.makedirs(event_dir, exist_ok=True)
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clip_path = os.path.join(event_dir, "clip.mp4")
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shutil.copy2(src_path, clip_path)
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log.info(f"Processing uploaded clip — event {event_id[:8]}")
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result = _process_clip(event_id, event_dir, clip_path, camera_name="upload")
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if result is None:
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raise RuntimeError("Processing failed: no frames extracted")
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return event_id
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def run_watcher():
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global _last_event_time, _analyzer
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os.makedirs(EVENTS_DIR, exist_ok=True)
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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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try:
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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!")
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_process_event()
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except Exception as 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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