Ajoute vue détail par passage (clip + galerie frames)

- Sauvegarde le clip MP4 et les top 10 frames par événement dans /data/events/{id}/
- Nouvelle route GET /event/{id} avec page détail : info, vignette interactive, lecteur vidéo, galerie frames
- Les cartes de l'index sont maintenant cliquables
- Migration DB : ajout colonne clip_path sur les BDD existantes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/database.py

@@ -16,12 +16,18 @@ def init_db():
             camera TEXT,
             start_time INTEGER,
             snapshot_path TEXT,
+            clip_path TEXT,
             plate TEXT,
             color_hex TEXT,
             color_name TEXT,
             processed_at INTEGER
         )
     """)
+    # Migrate existing DBs that lack clip_path
+    try:
+        conn.execute("ALTER TABLE events ADD COLUMN clip_path TEXT")
+    except Exception:
+        pass
     conn.commit()
     conn.close()
 
@@ -31,14 +37,14 @@ def event_exists(event_id: str) -> bool:
     conn.close()
     return row is not None
 
-def insert_event(event_id, camera, start_time, snapshot_path, plate, color_hex, color_name):
+def insert_event(event_id, camera, start_time, snapshot_path, clip_path, plate, color_hex, color_name):
     import time
     conn = get_db()
     conn.execute("""
         INSERT OR IGNORE INTO events
-        (id, camera, start_time, snapshot_path, plate, color_hex, color_name, processed_at)
-        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-    """, (event_id, camera, start_time, snapshot_path, plate, color_hex, color_name, int(time.time())))
+        (id, camera, start_time, snapshot_path, clip_path, plate, color_hex, color_name, processed_at)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+    """, (event_id, camera, start_time, snapshot_path, clip_path, plate, color_hex, color_name, int(time.time())))
     conn.commit()
     conn.close()
 
@@ -94,6 +100,13 @@ def count_events(plate_filter=None, camera_filter=None, date_filter=None):
     conn.close()
     return count
 
+def get_event(event_id: str) -> dict | None:
+    conn = get_db()
+    row = conn.execute("SELECT * FROM events WHERE id = ?", (event_id,)).fetchone()
+    conn.close()
+    return dict(row) if row else None
+
+
 def get_cameras():
     conn = get_db()
     rows = conn.execute("SELECT DISTINCT camera FROM events ORDER BY camera").fetchall()

app/main.py

@@ -1,9 +1,10 @@
 import os
+import glob
 import logging
 import threading
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, Request, Query
-from fastapi.responses import HTMLResponse, FileResponse
+from fastapi import FastAPI, Request, Query, HTTPException
+from fastapi.responses import HTMLResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 from datetime import datetime
@@ -13,8 +14,10 @@ import watcher
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s")
 
-DATA_DIR = os.environ.get("SNAPSHOTS_DIR", "/data/snapshots")
-os.makedirs(DATA_DIR, exist_ok=True)
+SNAPSHOTS_DIR = os.environ.get("SNAPSHOTS_DIR", "/data/snapshots")
+EVENTS_DIR = os.environ.get("EVENTS_DIR", "/data/events")
+os.makedirs(SNAPSHOTS_DIR, exist_ok=True)
+os.makedirs(EVENTS_DIR, exist_ok=True)
 
 
 @asynccontextmanager
@@ -26,7 +29,8 @@ async def lifespan(app: FastAPI):
 
 
 app = FastAPI(lifespan=lifespan)
-app.mount("/snapshots", StaticFiles(directory="/data/snapshots"), name="snapshots")
+app.mount("/snapshots", StaticFiles(directory=SNAPSHOTS_DIR), name="snapshots")
+app.mount("/events", StaticFiles(directory=EVENTS_DIR), name="events")
 
 templates = Jinja2Templates(directory="/app/templates")
 
@@ -74,6 +78,29 @@ async def index(
     })
 
 
+@app.get("/event/{event_id}", response_class=HTMLResponse)
+async def event_detail(request: Request, event_id: str):
+    ev = database.get_event(event_id)
+    if not ev:
+        raise HTTPException(status_code=404, detail="Événement introuvable")
+
+    ev["time_str"] = ts_to_str(ev["start_time"])
+
+    # List frames for this event
+    event_dir = os.path.join(EVENTS_DIR, event_id)
+    frame_paths = sorted(glob.glob(os.path.join(event_dir, "frame_*.jpg")))
+    frames = [f"events/{event_id}/frame_{i+1:04d}.jpg" for i in range(len(frame_paths))]
+
+    has_clip = ev.get("clip_path") and os.path.exists(os.path.join("/data", ev["clip_path"]))
+
+    return templates.TemplateResponse("event_detail.html", {
+        "request": request,
+        "ev": ev,
+        "frames": frames,
+        "has_clip": has_clip,
+    })
+
+
 @app.get("/api/events")
 async def api_events(
     page: int = Query(1, ge=1),

app/templates/event_detail.html

@@ -0,0 +1,132 @@
+<!DOCTYPE html>
+<html lang="fr">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>CamWatch — Passage {{ ev.time_str }}</title>
+  <script src="https://cdn.tailwindcss.com"></script>
+  <script>tailwind.config = { darkMode: 'class' }</script>
+  <style>
+    body { background: #0f172a; color: #e2e8f0; }
+    .card { background: #1e293b; border: 1px solid #334155; }
+    .plate { font-family: monospace; letter-spacing: 0.15em; }
+    .btn { background: #2563eb; color: #fff; border-radius: 6px; padding: 7px 16px; font-size: 0.85rem; cursor: pointer; text-decoration: none; display: inline-block; }
+    .btn:hover { background: #1d4ed8; }
+    .btn-ghost { background: #1e293b; border: 1px solid #475569; color: #94a3b8; }
+    .btn-ghost:hover { background: #334155; color: #e2e8f0; }
+    .frame-thumb { cursor: pointer; transition: transform 0.1s; }
+    .frame-thumb:hover { transform: scale(1.03); border-color: #3b82f6 !important; }
+    .frame-thumb.active { border-color: #3b82f6 !important; }
+  </style>
+</head>
+<body class="min-h-screen">
+
+  <!-- Header -->
+  <header class="sticky top-0 z-10 px-4 py-3 flex items-center justify-between" style="background:#0f172a;border-bottom:1px solid #1e293b;">
+    <div class="flex items-center gap-3">
+      <a href="/" class="btn btn-ghost text-sm px-3 py-1.5">← Retour</a>
+      <span class="text-xl">🚗</span>
+      <span class="font-bold">Passage — {{ ev.time_str }}</span>
+    </div>
+    <span class="text-slate-500 text-sm hidden sm:inline">{{ ev.camera }}</span>
+  </header>
+
+  <main class="max-w-5xl mx-auto px-3 py-5 space-y-5">
+
+    <!-- Top row: info + main image -->
+    <div class="grid grid-cols-1 md:grid-cols-3 gap-4">
+
+      <!-- Info card -->
+      <div class="card rounded-xl p-4 space-y-3">
+        <h2 class="text-slate-400 text-xs uppercase font-semibold tracking-wide">Informations</h2>
+
+        <div>
+          <div class="text-xs text-slate-500 mb-0.5">Date / Heure</div>
+          <div class="font-medium">{{ ev.time_str }}</div>
+        </div>
+
+        <div>
+          <div class="text-xs text-slate-500 mb-0.5">Caméra</div>
+          <div class="font-medium">{{ ev.camera }}</div>
+        </div>
+
+        <div>
+          <div class="text-xs text-slate-500 mb-1">Plaque détectée</div>
+          {% if ev.plate %}
+          <span class="plate text-lg font-bold px-4 py-1.5 rounded"
+                style="background:#1e3a5f;color:#60a5fa;border:1px solid #2563eb;">
+            {{ ev.plate }}
+          </span>
+          {% else %}
+          <span class="text-slate-500 italic text-sm">Non lue</span>
+          {% endif %}
+        </div>
+
+        <div>
+          <div class="text-xs text-slate-500 mb-1">Couleur véhicule</div>
+          <div class="flex items-center gap-2">
+            <div class="w-6 h-6 rounded-full border border-slate-600"
+                 style="background:{{ ev.color_hex or '#808080' }};"></div>
+            <span class="font-medium">{{ ev.color_name or '—' }}</span>
+            <span class="text-slate-600 text-xs">{{ ev.color_hex or '' }}</span>
+          </div>
+        </div>
+      </div>
+
+      <!-- Main image viewer -->
+      <div class="md:col-span-2 card rounded-xl overflow-hidden">
+        <div class="relative bg-black" style="aspect-ratio:16/9;">
+          <img id="main-img"
+               src="{% if ev.snapshot_path %}/{{ ev.snapshot_path }}{% endif %}"
+               alt="Meilleure frame"
+               class="w-full h-full object-contain">
+        </div>
+        <div class="px-3 py-2 text-xs text-slate-500 text-center" id="img-label">Meilleure frame (score LPR)</div>
+      </div>
+    </div>
+
+    <!-- Video clip -->
+    {% if has_clip %}
+    <div class="card rounded-xl p-4">
+      <h2 class="text-slate-400 text-xs uppercase font-semibold tracking-wide mb-3">Clip vidéo ({{ ev.clip_path.split('/')[-1] }})</h2>
+      <video controls class="w-full rounded-lg" style="max-height:400px;background:#000;"
+             preload="metadata">
+        <source src="/{{ ev.clip_path }}" type="video/mp4">
+        Votre navigateur ne supporte pas la vidéo HTML5.
+      </video>
+    </div>
+    {% endif %}
+
+    <!-- Frames gallery -->
+    {% if frames %}
+    <div class="card rounded-xl p-4">
+      <h2 class="text-slate-400 text-xs uppercase font-semibold tracking-wide mb-3">
+        Frames extraites ({{ frames|length }} meilleures sur {{ (15 * 5)|int }} capturées)
+      </h2>
+      <div class="grid grid-cols-2 sm:grid-cols-3 md:grid-cols-5 gap-2">
+        {% for frame_path in frames %}
+        <div class="frame-thumb rounded overflow-hidden border border-slate-700 {% if loop.first %}active{% endif %}"
+             onclick="selectFrame(this, '/{{ frame_path }}', 'Frame {{ loop.index }} / {{ frames|length }}')">
+          <img src="/{{ frame_path }}" alt="Frame {{ loop.index }}"
+               class="w-full object-cover" style="aspect-ratio:16/9;">
+          <div class="text-center text-xs text-slate-500 py-0.5">
+            #{{ loop.index }}{% if loop.first %} ★{% endif %}
+          </div>
+        </div>
+        {% endfor %}
+      </div>
+    </div>
+    {% endif %}
+
+  </main>
+
+  <script>
+    function selectFrame(el, src, label) {
+      document.getElementById('main-img').src = src;
+      document.getElementById('img-label').textContent = label;
+      document.querySelectorAll('.frame-thumb').forEach(t => t.classList.remove('active'));
+      el.classList.add('active');
+    }
+  </script>
+</body>
+</html>

app/templates/index.html

@@ -61,7 +61,7 @@
     <!-- Events grid -->
     <div class="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-3">
       {% for ev in events %}
-      <div class="card rounded-xl overflow-hidden">
+      <a href="/event/{{ ev.id }}" class="card rounded-xl overflow-hidden block hover:border-blue-500 transition-colors" style="text-decoration:none;color:inherit;">
         <!-- Snapshot -->
         <div class="relative bg-black" style="aspect-ratio:16/9;">
           {% if ev.snapshot_path %}
@@ -103,7 +103,7 @@
             {% endif %}
           </div>
         </div>
-      </div>
+      </a>
       {% endfor %}
     </div>
 

app/watcher.py

@@ -1,9 +1,9 @@
 import os
 import time
 import uuid
+import shutil
 import logging
 import subprocess
-import tempfile
 import glob
 import requests
 import cv2
@@ -22,9 +22,11 @@ CAMERA_PASS = os.environ.get("CAMERA_PASS", "")
 CAMERA_RTSP = os.environ.get("CAMERA_RTSP", "")
 CAMERA_NAME = os.environ.get("CAMERA_NAME", "portail")
 SNAPSHOTS_DIR = os.environ.get("SNAPSHOTS_DIR", "/data/snapshots")
+EVENTS_DIR = os.environ.get("EVENTS_DIR", "/data/events")
 POLL_INTERVAL = float(os.environ.get("POLL_INTERVAL", "2"))
 CAPTURE_DURATION = int(os.environ.get("CAPTURE_DURATION", "15"))
 CAPTURE_FPS = int(os.environ.get("CAPTURE_FPS", "5"))
+TOP_FRAMES = int(os.environ.get("TOP_FRAMES", "10"))
 COOLDOWN = int(os.environ.get("COOLDOWN", "60"))
 
 _token: str | None = None
@@ -54,7 +56,6 @@ def _login() -> str | None:
         if data[0]["code"] == 0:
             _token = data[0]["value"]["Token"]["name"]
             _token_time = time.time()
-            log.debug("Camera login OK")
             return _token
     except Exception as e:
         log.warning(f"Login error: {e}")
@@ -80,7 +81,6 @@ def _get_ai_state() -> dict | None:
 
 
 def _frame_score(frame: np.ndarray, plates: list) -> float:
-    """Score a frame: prefer large, high-confidence plates. Fallback to sharpness."""
     if plates:
         x1, y1, x2, y2, conf = plates[0]
         return (x2 - x1) * (y2 - y1) * conf
@@ -88,89 +88,94 @@ def _frame_score(frame: np.ndarray, plates: list) -> float:
     return cv2.Laplacian(gray, cv2.CV_64F).var() * 0.001
 
 
-def _capture_best_frame() -> np.ndarray | None:
-    url = _rtsp_url()
-    log.info(f"Capturing {CAPTURE_DURATION}s at {CAPTURE_FPS}fps via ffmpeg...")
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        clip_path = os.path.join(tmpdir, "clip.mp4")
-        frames_pattern = os.path.join(tmpdir, "frame_%04d.jpg")
-
-        # Step 1: capture clip with ffmpeg (TCP for reliability)
-        ret = subprocess.run([
-            "ffmpeg", "-y",
-            "-rtsp_transport", "tcp",
-            "-i", url,
-            "-t", str(CAPTURE_DURATION),
-            "-c", "copy",
-            clip_path,
-        ], capture_output=True, timeout=CAPTURE_DURATION + 10)
-
-        if not os.path.exists(clip_path) or os.path.getsize(clip_path) < 1000:
-            log.error(f"ffmpeg capture failed: {ret.stderr[-200:].decode(errors='ignore')}")
-            return None
-
-        # Step 2: extract frames at CAPTURE_FPS
-        subprocess.run([
-            "ffmpeg", "-y",
-            "-i", clip_path,
-            "-vf", f"fps={CAPTURE_FPS}",
-            "-q:v", "2",
-            frames_pattern,
-        ], capture_output=True, timeout=30)
-
-        frame_files = sorted(glob.glob(os.path.join(tmpdir, "frame_*.jpg")))
-        log.info(f"Extracted {len(frame_files)} frames")
-
-        if not frame_files:
-            return None
-
-        # Step 3: score each frame, keep the best
-        best_frame: np.ndarray | None = None
-        best_score = -1.0
-
-        for path in frame_files:
-            frame = cv2.imread(path)
-            if frame is None:
-                continue
-            plates = _analyzer.detect_plates(frame) if _analyzer else []
-            score = _frame_score(frame, plates)
-            if score > best_score:
-                best_score = score
-                best_frame = frame.copy()
-
-        log.info(f"Best frame score: {best_score:.2f}")
-        return best_frame
-
-
 def _process_event():
-    frame = _capture_best_frame()
-    if frame is None:
-        log.warning("No frame captured")
+    event_id = str(uuid.uuid4())
+    event_dir = os.path.join(EVENTS_DIR, event_id)
+    os.makedirs(event_dir, exist_ok=True)
+
+    url = _rtsp_url()
+    clip_path = os.path.join(event_dir, "clip.mp4")
+    frames_pattern = os.path.join(event_dir, "frame_%04d.jpg")
+
+    log.info(f"Capturing {CAPTURE_DURATION}s at {CAPTURE_FPS}fps — event {event_id[:8]}")
+
+    # Step 1: capture clip
+    ret = subprocess.run([
+        "ffmpeg", "-y", "-rtsp_transport", "tcp",
+        "-i", url, "-t", str(CAPTURE_DURATION), "-c", "copy", clip_path,
+    ], capture_output=True, timeout=CAPTURE_DURATION + 10)
+
+    if not os.path.exists(clip_path) or os.path.getsize(clip_path) < 1000:
+        log.error(f"ffmpeg capture failed: {ret.stderr[-200:].decode(errors='ignore')}")
+        shutil.rmtree(event_dir, ignore_errors=True)
         return
 
-    event_id = str(uuid.uuid4())
-    snapshot_file = f"{event_id}.jpg"
-    snapshot_path = os.path.join(SNAPSHOTS_DIR, snapshot_file)
-    cv2.imwrite(snapshot_path, frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
+    # Step 2: extract frames
+    subprocess.run([
+        "ffmpeg", "-y", "-i", clip_path,
+        "-vf", f"fps={CAPTURE_FPS}", "-q:v", "2", frames_pattern,
+    ], capture_output=True, timeout=30)
 
-    plate, conf = "", 0.0
+    frame_files = sorted(glob.glob(os.path.join(event_dir, "frame_*.jpg")))
+    log.info(f"Extracted {len(frame_files)} frames")
+
+    if not frame_files:
+        shutil.rmtree(event_dir, ignore_errors=True)
+        return
+
+    # Step 3: score and select top frames
+    scored: list[tuple[float, np.ndarray, str]] = []
+    for path in frame_files:
+        frame = cv2.imread(path)
+        if frame is None:
+            continue
+        plates = _analyzer.detect_plates(frame) if _analyzer else []
+        score = _frame_score(frame, plates)
+        scored.append((score, frame, path))
+
+    scored.sort(key=lambda x: -x[0])
+
+    # Delete frames below TOP_FRAMES
+    for _, _, path in scored[TOP_FRAMES:]:
+        os.remove(path)
+
+    # Rename kept frames to sorted order (best first = frame_0001)
+    kept = scored[:TOP_FRAMES]
+    for i, (_, _, old_path) in enumerate(kept):
+        new_path = os.path.join(event_dir, f"frame_{i+1:04d}.jpg")
+        if old_path != new_path:
+            os.rename(old_path, new_path)
+
+    best_frame = kept[0][1] if kept else None
+    if best_frame is None:
+        shutil.rmtree(event_dir, ignore_errors=True)
+        return
+
+    # Step 4: run LPR on best frame
+    plate, conf = ("", 0.0)
     if _analyzer:
-        plate, conf = _analyzer.read_plate(frame)
-
+        plate, conf = _analyzer.read_plate(best_frame)
     log.info(f"LPR: plate={plate!r} conf={conf:.2f}")
 
-    hex_color, color_name = extract_dominant_color_from_frame(frame)
+    # Step 5: save thumbnail (best frame)
+    snapshot_file = f"{event_id}.jpg"
+    snapshot_path = os.path.join(SNAPSHOTS_DIR, snapshot_file)
+    cv2.imwrite(snapshot_path, best_frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
+
+    hex_color, color_name = extract_dominant_color_from_frame(best_frame)
     insert_event(
         event_id, CAMERA_NAME, int(time.time()),
-        f"snapshots/{snapshot_file}", plate or None, hex_color, color_name,
+        f"snapshots/{snapshot_file}",
+        f"events/{event_id}/clip.mp4",
+        plate or None, hex_color, color_name,
     )
-    log.info(f"Stored: {event_id} | plate={plate} | color={color_name}")
+    log.info(f"Stored: {event_id[:8]} | plate={plate} | color={color_name} | frames={len(kept)}")
 
 
 def run_watcher():
     global _last_event_time, _analyzer
 
+    os.makedirs(EVENTS_DIR, exist_ok=True)
     log.info("Loading plate analyzer...")
     _analyzer = PlateAnalyzer()
 
@@ -184,7 +189,7 @@ def run_watcher():
                     now = time.time()
                     if now - _last_event_time > COOLDOWN:
                         _last_event_time = now
-                        log.info("Vehicle detected! Triggering capture...")
+                        log.info("Vehicle detected!")
                         _process_event()
         except Exception as e:
             log.error(f"Watcher loop error: {e}", exc_info=True)

docker-compose.yml

@@ -14,6 +14,7 @@ services:
       - MODEL_CACHE=/models
       - DB_PATH=/data/camwatch.db
       - SNAPSHOTS_DIR=/data/snapshots
+      - EVENTS_DIR=/data/events
       - POLL_INTERVAL=2
       - CAPTURE_DURATION=15
       - CAPTURE_FPS=5