최초 커밋으로 내려가니 그래도 조금만 손대서 돌아간다. 휴
| $git checkout cfef9771 $ python main_stfpm.py --train --eval --model_name mobilenet_v2 --categories bottle --ad_layers 3 4 5 --boot_layer 2 --results_dirpath debug_outputs/metrics --checkpoint_dir debug_outputs/checkpoints --seeds 0 --epochs 1000 --input_size 224 224 --device cuda:1 |
[링크 : https://github.com/AMCO-UniPD/moviad]
아래 사진을 모니터에 띄우고
예제 클로드(무료)로 작성하라고 했는데
처음에는 모니터 없어서 안된다고(아니.. ssh -X로 해서 떠야 하는데?)
그래서 웹으로 뚝딱 만들어 줌.. 오.. 나보다 100배 낫네
cudnn 비활성화 하고 (빨간색 부분)
웹캠으로 노트북 모니터 비추도록 해서 테스트. 음.. 잘 되는건지 안되는건지 미묘하다
| """ MoViAD STFPM + 웹캠 실시간 이상 감지 (브라우저 스트리밍 버전) ============================================================= cv2.imshow 없이 HTTP MJPEG 스트리밍으로 브라우저에서 확인합니다. → http://localhost:8080 에 접속하면 실시간 영상이 표시됩니다. [사전 준비] cd moviad && pip install -e ./ pip install opencv-python-headless torch torchvision [모델 학습] python main_scripts/main_stfpm.py \ --train \ --model_name mobilenet_v2 \ --ad_layers 4 7 10 \ --categories bottle \ --dataset_path /path/to/mvtec \ --checkpoint_dir ./checkpoints/stfpm \ --device cuda:0 \ --epochs 100 [실행] python webcam_stfpm_anomaly.py \ --checkpoint ./checkpoints/stfpm/bottle/mobilenet_v2_100ep_IMAGENET1K_V2_4_7_10_s0.pth.tar [REST 제어 API] (curl 또는 브라우저로 호출) GET /threshold/up 임계값 +0.02 GET /threshold/down 임계값 -0.02 GET /threshold/auto 자동 보정 (현재 점수 기준) GET /heatmap/toggle 히트맵 ON/OFF GET /reset 점수 히스토리 초기화 GET /status 현재 상태 JSON """ import argparse import sys import time import threading import json from datetime import datetime from pathlib import Path from http.server import HTTPServer, BaseHTTPRequestHandler from io import BytesIO import cv2 import numpy as np import torch from torchvision import transforms torch.backends.cudnn.enabled = False torch.backends.cudnn.benchmark = False try: from moviad.models import Stfpm except ImportError: sys.exit( "\n[ERROR] moviad 패키지를 찾을 수 없습니다.\n" " moviad 레포 루트에서 'pip install -e ./' 를 실행하세요.\n" ) # ────────────────────────────────────────────────────────────────── # 전역 공유 상태 (스레드 간 공유) # ────────────────────────────────────────────────────────────────── class AppState: def __init__(self): self.lock = threading.Lock() self.jpeg_frame = b"" # MJPEG 브라우저 전송용 프레임 self.threshold = 0.5 self.show_heat = True self.norm_score = 0.0 self.raw_score = 0.0 self.is_anomaly = False self.fps = 0.0 self.score_hist: list[float] = [] self.running = True STATE = AppState() # ────────────────────────────────────────────────────────────────── # 전처리 # ────────────────────────────────────────────────────────────────── IMG_SIZE = 224 RESIZE = 256 def make_preprocess(img_size=224): resize = int(img_size * 256 / 224) return transforms.Compose([ transforms.ToPILImage(), transforms.Resize(resize), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std =[0.229, 0.224, 0.225]), ]) # ────────────────────────────────────────────────────────────────── # 모델 로드 # ────────────────────────────────────────────────────────────────── def load_stfpm(checkpoint_path: str, device: torch.device) -> Stfpm: print(f"[INFO] 체크포인트 로드: {checkpoint_path}") state = torch.load(checkpoint_path, map_location=device) model = Stfpm() model.load_state_dict(state, strict=False) model.to(device) model.eval() print(f"[INFO] 백본={model.backbone_model_name} " f"AD레이어={model.ad_layers} 입력크기={model.input_size}") return model # ────────────────────────────────────────────────────────────────── # 시각화 헬퍼 # ────────────────────────────────────────────────────────────────── C_NORMAL = (50, 205, 50) C_ANOMALY = (30, 30, 220) C_PANEL = (15, 15, 15) def score_to_heatmap(score_map: torch.Tensor, wh: tuple) -> np.ndarray: arr = score_map.squeeze().cpu().numpy() arr = cv2.normalize(arr, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) heat = cv2.applyColorMap(arr, cv2.COLORMAP_JET) return cv2.resize(heat, wh) def draw_ui(frame, norm_score, raw_score, threshold, is_anomaly, fps, heatmap, show_heat) -> np.ndarray: out = frame.copy() h, w = out.shape[:2] if show_heat and heatmap is not None: out = cv2.addWeighted(out, 0.5, heatmap, 0.5, 0) color = C_ANOMALY if is_anomaly else C_NORMAL cv2.rectangle(out, (0, 0), (w-1, h-1), color, 6) # 반투명 상단 패널 panel_h = 100 overlay = out.copy() cv2.rectangle(overlay, (0, 0), (w, panel_h), C_PANEL, -1) cv2.addWeighted(overlay, 0.70, out, 0.30, 0, out) label = "[ ANOMALY ]" if is_anomaly else "[ NORMAL ]" cv2.putText(out, label, (10, 36), cv2.FONT_HERSHEY_DUPLEX, 1.1, color, 2, cv2.LINE_AA) info = (f"Score(norm): {norm_score:.4f} Raw: {raw_score:.4f} " f"Thr: {threshold:.4f} FPS: {fps:.1f}") cv2.putText(out, info, (10, 64), cv2.FONT_HERSHEY_SIMPLEX, 0.52, (210, 210, 210), 1, cv2.LINE_AA) cv2.putText(out, "http://localhost:8080 | /status /threshold/up /threshold/down /threshold/auto /heatmap/toggle /reset", (10, 88), cv2.FONT_HERSHEY_SIMPLEX, 0.38, (160, 160, 160), 1, cv2.LINE_AA) # 점수 바 bx, by0, by1 = 12, 74, 88 bw = w - 24 fill = int(bw * min(norm_score, 1.0)) tx = bx + int(bw * min(threshold, 1.0)) cv2.rectangle(out, (bx, by0), (bx+bw, by1), (55,55,55), -1) cv2.rectangle(out, (bx, by0), (bx+fill, by1), color, -1) cv2.line(out, (tx, by0-3), (tx, by1+3), (0, 255, 255), 2) return out # ────────────────────────────────────────────────────────────────── # 추론 스레드 # ────────────────────────────────────────────────────────────────── def inference_loop(model, preprocess, device, camera_id, width, height): cap = cv2.VideoCapture(camera_id) if not cap.isOpened(): print(f"[ERROR] 카메라(id={camera_id})를 열 수 없습니다.") STATE.running = False return cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) t_prev = time.time() fps = 0.0 save_dir = Path("captures") print(f"[INFO] 카메라 시작 (id={camera_id}, {width}x{height})") print("[INFO] 브라우저에서 http://localhost:8080 접속하세요\n") while STATE.running: ret, frame = cap.read() if not ret: time.sleep(0.03) continue # 추론 rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) tensor = preprocess(rgb).unsqueeze(0).to(device) with torch.no_grad(): score_maps, anomaly_scores = model(tensor) raw_score = float(anomaly_scores[0].cpu()) # 정규화 (min-max, 최근 500프레임) with STATE.lock: STATE.score_hist.append(raw_score) if len(STATE.score_hist) > 500: STATE.score_hist.pop(0) s_min = min(STATE.score_hist) s_max = max(STATE.score_hist) norm = (raw_score - s_min) / (s_max - s_min + 1e-8) threshold = STATE.threshold show_heat = STATE.show_heat is_anomaly = norm > threshold heatmap = score_to_heatmap(score_maps, (frame.shape[1], frame.shape[0])) # FPS t_now = time.time() fps = 0.9 * fps + 0.1 / max(t_now - t_prev, 1e-6) t_prev = t_now # 화면 렌더링 display = draw_ui(frame, norm, raw_score, threshold, is_anomaly, fps, heatmap, show_heat) # JPEG 인코딩 → 공유 버퍼 ok, buf = cv2.imencode('.jpg', display, [cv2.IMWRITE_JPEG_QUALITY, 85]) if ok: with STATE.lock: STATE.jpeg_frame = buf.tobytes() STATE.norm_score = norm STATE.raw_score = raw_score STATE.is_anomaly = is_anomaly STATE.fps = fps # 콘솔 출력 tag = "ANOMALY ⚠" if is_anomaly else "normal ✓ " print(f"\r[{tag}] norm={norm:.4f} raw={raw_score:.4f} " f"thr={threshold:.4f} fps={fps:.1f} ", end="", flush=True) cap.release() print("\n[INFO] 카메라 종료") # ────────────────────────────────────────────────────────────────── # HTTP 서버 (MJPEG + REST API) # ────────────────────────────────────────────────────────────────── HTML_PAGE = """\ <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>MoViAD STFPM - Bottle Anomaly Detection</title> <style> body {{ background:#111; color:#eee; font-family:monospace; display:flex; flex-direction:column; align-items:center; margin:0; padding:16px; }} h2 {{ color:#4fc3f7; margin:8px 0; }} img {{ max-width:100%; border:3px solid #333; border-radius:6px; }} .controls {{ display:flex; gap:10px; flex-wrap:wrap; justify-content:center; margin:12px 0; }} button {{ padding:8px 18px; border:none; border-radius:5px; cursor:pointer; background:#1e88e5; color:#fff; font-size:14px; font-weight:bold; }} button:hover {{ background:#1565c0; }} button.danger {{ background:#e53935; }} button.success {{ background:#43a047; }} #status {{ background:#1e1e1e; border-radius:6px; padding:12px 20px; font-size:15px; min-width:340px; text-align:center; margin-top:6px; }} .normal {{ color:#69f0ae; font-weight:bold; }} .anomaly {{ color:#ff5252; font-weight:bold; }} </style> </head> <body> <h2>🔍 MoViAD STFPM — Bottle Anomaly Detection</h2> <img src="/stream" alt="webcam stream"> <div class="controls"> <button onclick="api('/threshold/up')" >Threshold ▲ (+0.02)</button> <button onclick="api('/threshold/down')">Threshold ▼ (−0.02)</button> <button class="success" onclick="api('/threshold/auto')">Auto Calibrate (t)</button> <button onclick="api('/heatmap/toggle')">Heatmap Toggle (h)</button> <button class="danger" onclick="api('/reset')" >Reset History (r)</button> </div> <div id="status">로딩 중...</div> <script> function api(path) {{ fetch(path).then(r=>r.json()).then(updateStatus); }} function updateStatus(d) {{ const cls = d.is_anomaly ? 'anomaly' : 'normal'; const lab = d.is_anomaly ? '⚠ ANOMALY' : '✓ NORMAL'; document.getElementById('status').innerHTML = `<span class="${{cls}}">${{lab}}</span> | `+ `norm: <b>${{d.norm_score.toFixed(4)}}</b> `+ `raw: ${{d.raw_score.toFixed(4)}} `+ `thr: <b>${{d.threshold.toFixed(4)}}</b> `+ `fps: ${{d.fps.toFixed(1)}} `+ `heatmap: ${{d.show_heat ? 'ON' : 'OFF'}}`; }} setInterval(() => fetch('/status').then(r=>r.json()).then(updateStatus), 500); </script> </body> </html> """ class StreamHandler(BaseHTTPRequestHandler): def log_message(self, *args): pass # 콘솔 노이즈 억제 def do_GET(self): p = self.path.split('?')[0] # ── MJPEG 스트림 ───────────────────────────── if p == '/stream': self.send_response(200) self.send_header('Content-Type', 'multipart/x-mixed-replace; boundary=frame') self.end_headers() try: while STATE.running: with STATE.lock: frame = STATE.jpeg_frame if frame: self.wfile.write( b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n" ) time.sleep(0.03) except (BrokenPipeError, ConnectionResetError): pass return # ── REST API ────────────────────────────────── with STATE.lock: if p == '/threshold/up': STATE.threshold = min(STATE.threshold + 0.02, 0.99) elif p == '/threshold/down': STATE.threshold = max(STATE.threshold - 0.02, 0.01) elif p == '/threshold/auto': STATE.threshold = float( np.clip(STATE.norm_score + 0.05, 0.01, 0.99) ) elif p == '/heatmap/toggle': STATE.show_heat = not STATE.show_heat elif p == '/reset': STATE.score_hist.clear() resp = { "threshold" : STATE.threshold, "norm_score": STATE.norm_score, "raw_score" : STATE.raw_score, "is_anomaly": STATE.is_anomaly, "fps" : STATE.fps, "show_heat" : STATE.show_heat, } # ── HTML 홈 ─────────────────────────────────── if p == '/': body = HTML_PAGE.encode() self.send_response(200) self.send_header('Content-Type', 'text/html; charset=utf-8') self.send_header('Content-Length', str(len(body))) self.end_headers() self.wfile.write(body) return body = json.dumps(resp).encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.send_header('Content-Length', str(len(body))) self.end_headers() self.wfile.write(body) # ────────────────────────────────────────────────────────────────── # 메인 # ────────────────────────────────────────────────────────────────── def run(args): # 디바이스 dev_str = args.device if "cuda" in dev_str and not torch.cuda.is_available(): print("[WARN] CUDA 불가 — CPU로 전환합니다.") dev_str = "cpu" device = torch.device(dev_str) # 모델 로드 model = load_stfpm(args.checkpoint, device) # 입력 크기 동기화 img_size = model.input_size[0] if model.input_size else 224 preprocess = make_preprocess(img_size) STATE.threshold = args.threshold # 추론 스레드 시작 t = threading.Thread( target=inference_loop, args=(model, preprocess, device, args.camera_id, args.width, args.height), daemon=True, ) t.start() # HTTP 서버 시작 server = HTTPServer(("0.0.0.0", args.port), StreamHandler) print(f"[INFO] HTTP 서버 시작: http://localhost:{args.port}") print("[INFO] Ctrl+C 로 종료\n") try: server.serve_forever() except KeyboardInterrupt: print("\n[INFO] 서버 종료") finally: STATE.running = False server.server_close() # ────────────────────────────────────────────────────────────────── # CLI # ────────────────────────────────────────────────────────────────── def parse_args(): p = argparse.ArgumentParser( description="MoViAD STFPM bottle — 웹캠 이상 감지 (브라우저 스트리밍)" ) p.add_argument("--checkpoint", type=str, required=True, help=".pth.tar 체크포인트 경로") p.add_argument("--camera_id", type=int, default=0) p.add_argument("--device", type=str, default="cuda:0") p.add_argument("--threshold", type=float, default=0.5) p.add_argument("--width", type=int, default=640) p.add_argument("--height", type=int, default=480) p.add_argument("--port", type=int, default=8080) return p.parse_args() if __name__ == "__main__": run(parse_args()) |
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