Fotos: Fotowand je Verband + Gesichtssuche (lokales SCRFD/MobileFaceNet via onnxruntime)
This commit is contained in:
Nicolas Gruber
@@ -4,3 +4,4 @@ __pycache__/
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.venv/
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venv/
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*.log
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data/
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@@ -6,6 +6,9 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
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WORKDIR /app
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RUN apt-get update && apt-get install -y --no-install-recommends libgomp1 \
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&& rm -rf /var/lib/apt/lists/*
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COPY requirements.txt .
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RUN pip install -r requirements.txt
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@@ -5,6 +5,7 @@ Bessere Ansichten für das FH-Burgenland-CIS:
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- **Studentenplan** - eigener oder fremder Plan (Matrikelnummer), Wochenansicht + Semesterübersicht, iCal/CSV-Export
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- **Studiengangs-Pläne** - nach Studiengang, Studien-Semester und Verband (Vollzeit/Berufsbegleitend), inkl. Gruppen-Filter
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- **Namenslisten** - je Studiengang/Semester/Verband mit Suche und CSV-Export
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- **Fotos** - Fotowand je Verband + **Gesichtssuche**: eigenes Foto hochladen, die App findet die passenden Profile (lokaler SCRFD/MobileFaceNet, läuft ohne Internet-API)
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- **Studiennummer-Decoder** - Matrikelnummer wird in Startsemester, Studiengang und Nummer zerlegt
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## Lokal starten
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@@ -31,6 +32,15 @@ OWN_UID=<eigene matrikelnummer>
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`.env` wird nicht in das Docker-Image kopiert (`.dockerignore`), sondern zur Laufzeit übergeben.
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### Datenverzeichnis `data/`
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Das Gesichtsmatching lädt beim ersten Start die Modelle (~16 MB,
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`insightface/buffalo_sc`) nach `data/models` und speichert Caches in
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`data/` (Profilbilder, Person-IDs, Gesichtsembeddings). Alles wird
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automatisch angelegt. Im Docker-Setup ist `data/` als Named Volume
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(`cis-data`) gemountet, damit Modelle und Caches beim Rebuild erhalten
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bleiben.
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## Docker
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```bash
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+65
@@ -1,14 +1,20 @@
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"""CIS API client for FH-Burgenland (basic auth, HTML/iCal scraping)."""
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import json
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import re
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import time
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone
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from functools import lru_cache
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from pathlib import Path
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from typing import Optional
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import requests
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from bs4 import BeautifulSoup
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DATA_DIR = Path(__file__).resolve().parent.parent / "data"
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PERSON_ID_CACHE = DATA_DIR / "person_ids.json"
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PHOTO_DIR = DATA_DIR / "photos"
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TZ = timezone(timedelta(hours=1)) # Europe/Vienna winter; events carry their own tz, we just parse naive
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SLOTS = [
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@@ -206,6 +212,65 @@ class CISClient:
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return Person(uid=u, name=name.strip(), stg=stg.strip(),
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semester=sem.strip(), verband=ver.strip(), gruppe="")
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# ---------- person_id + photos ----------
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def person_id_for(self, uid: str) -> str:
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"""Matrikel/username → CIS person_id (persistent JSON cache)."""
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uid = (uid or "").strip()
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if not uid:
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return ""
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cache: dict = {}
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if PERSON_ID_CACHE.exists():
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try:
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cache = json.loads(PERSON_ID_CACHE.read_text())
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except Exception:
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cache = {}
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if uid in cache:
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return cache[uid]
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try:
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html = self._get(f"{self.base}/private/profile/index.php?uid={uid}")
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except requests.HTTPError:
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return ""
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m = re.search(r"bild\.php\?src=person&person_id=(\d+)", html)
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if not m:
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return ""
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pid = m.group(1)
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cache[uid] = pid
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try:
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PERSON_ID_CACHE.parent.mkdir(parents=True, exist_ok=True)
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PERSON_ID_CACHE.write_text(json.dumps(cache, indent=0))
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except OSError:
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pass
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return pid
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def photo_bytes(self, person_id: str, max_age: int = 7 * 86400) -> Optional[tuple[bytes, str]]:
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"""Profilfoto holen (7-Tage-Disk-Cache). (bytes, content_type) oder None."""
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person_id = (person_id or "").strip()
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if not person_id.isdigit():
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return None
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f = PHOTO_DIR / f"{person_id}.bin"
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if f.exists() and time.time() - f.stat().st_mtime < max_age:
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try:
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return f.read_bytes(), "image/jpeg"
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except OSError:
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pass
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try:
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r = self.s.get(f"{self.base}/public/bild.php?src=person&person_id={person_id}", timeout=30)
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r.raise_for_status()
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except requests.RequestException:
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return None
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data = r.content
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if not data:
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return None
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ct = (r.headers.get("Content-Type") or "image/jpeg").split(";")[0].strip()
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if ct not in ("image/jpeg", "image/png", "image/gif"):
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return None
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try:
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PHOTO_DIR.mkdir(parents=True, exist_ok=True)
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f.write_bytes(data)
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except OSError:
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pass
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return data, ct
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# ---------- Namensliste ----------
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def namensliste(self, kz: str, sem: str, ver: str) -> list[Student]:
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html = self._get(f"{self.base}/private/stud_in_grp.php?kz={kz}&sem={sem}&verband={ver}")
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+209
@@ -0,0 +1,209 @@
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"""Face matching: SCRFD detection + MobileFaceNet embeddings (onnxruntime, insightface buffalo_sc)."""
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import io
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import zipfile
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from pathlib import Path
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import numpy as np
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import onnxruntime as ort
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from PIL import Image
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DATA_DIR = Path(__file__).resolve().parent.parent / "data"
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MODEL_DIR = DATA_DIR / "models"
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ZIP_URL = "https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_sc.zip"
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DET_FILE = MODEL_DIR / "det_500m.onnx"
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REC_FILE = MODEL_DIR / "w600k_mbf.onnx"
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DET_SIZE = 640
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DET_STRIDES = (8, 16, 32)
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NUM_ANCHORS = 2
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DET_SCORE_THRESH = 0.40
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DET_NMS_THRESH = 0.40
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DET_INPUT_MEAN = 127.5
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DET_INPUT_STD = 128.0
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REC_SIZE = 112
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REC_INPUT_MEAN = 127.5
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REC_INPUT_STD = 127.5
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# ArcFace 5-point reference (le, re, nose, lm, rm) in 112x112
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REF5 = np.array([
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[38.2946, 51.6963],
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[73.5318, 51.5014],
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[56.0252, 71.7366],
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[41.5493, 92.3655],
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[70.7299, 92.2041],
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], dtype=np.float64)
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def _nms(boxes, scores, thresh):
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x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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rest = order[1:]
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if rest.size == 0:
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break
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xx1 = np.maximum(x1[i], x1[rest])
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yy1 = np.maximum(y1[i], y1[rest])
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xx2 = np.minimum(x2[i], x2[rest])
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yy2 = np.minimum(y2[i], y2[rest])
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inter = np.maximum(0.0, xx2 - xx1 + 1) * np.maximum(0.0, yy2 - yy1 + 1)
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iou = inter / (areas[i] + areas[rest] - inter)
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order = rest[iou <= thresh]
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return keep
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class FaceEngine:
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def __init__(self):
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self._det = None
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self._rec = None
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self._det_in = ""
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self._rec_in = ""
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self._lock = None
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def _load(self):
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if self._det is not None:
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return
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import threading
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self._lock = threading.Lock()
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with self._lock:
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if self._det is not None:
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return
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if not DET_FILE.exists() or not REC_FILE.exists():
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MODEL_DIR.mkdir(parents=True, exist_ok=True)
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import time as _time
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import urllib.request
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req = urllib.request.Request(ZIP_URL, headers={"User-Agent": "Mozilla/5.0"})
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for attempt in range(3):
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try:
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blob = urllib.request.urlopen(req, timeout=600).read()
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break
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except Exception:
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if attempt == 2:
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raise
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_time.sleep(2 * (attempt + 1))
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with zipfile.ZipFile(io.BytesIO(blob)) as zf:
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zf.extractall(MODEL_DIR)
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opts = ort.SessionOptions()
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opts.intra_op_num_threads = 2
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self._det = ort.InferenceSession(str(DET_FILE), opts, providers=["CPUExecutionProvider"])
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self._rec = ort.InferenceSession(str(REC_FILE), opts, providers=["CPUExecutionProvider"])
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self._det_in = self._det.get_inputs()[0].name
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self._rec_in = self._rec.get_inputs()[0].name
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def _detect(self, img: Image.Image):
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"""SCRFD decode (insightface). Returns (box (x1,y1,x2,y2), kps (5,2)) in original coords, or None."""
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h, w = img.height, img.width
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im_ratio = h / w
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if im_ratio > 1.0:
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new_h = DET_SIZE
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new_w = int(new_h / im_ratio)
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else:
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new_w = DET_SIZE
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new_h = int(new_w * im_ratio)
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det_scale = new_h / h
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det = np.zeros((DET_SIZE, DET_SIZE, 3), dtype=np.uint8)
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det[:new_h, :new_w] = np.asarray(img.convert("RGB").resize((new_w, new_h)))
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blob = ((det.astype(np.float32) - DET_INPUT_MEAN) / DET_INPUT_STD).transpose(2, 0, 1)[None]
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outs = self._det.run(None, {self._det_in: blob})
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scores_list, bboxes_list, kpss_list = [], [], []
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for idx, stride in enumerate(DET_STRIDES):
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scores = outs[idx][:, 0]
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keep = scores >= DET_SCORE_THRESH
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if not keep.any():
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continue
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H = DET_SIZE // stride
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W = DET_SIZE // stride
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centers = (np.stack(np.mgrid[:H, :W][::-1], axis=-1).astype(np.float32) * stride).reshape(-1, 2)
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centers = np.stack([centers] * NUM_ANCHORS, axis=1).reshape(-1, 2)
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c = centers[keep]
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b = outs[idx + 3][keep] * stride
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boxes = np.stack([c[:, 0] - b[:, 0], c[:, 1] - b[:, 1],
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c[:, 0] + b[:, 2], c[:, 1] + b[:, 3]], axis=-1)
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k = outs[idx + 6][keep] * stride
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kpss = np.stack([c[:, 0:1] + k[:, 0::2], c[:, 1:2] + k[:, 1::2]], axis=-1).reshape(-1, 5, 2)
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scores_list.append(scores[keep])
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bboxes_list.append(boxes / det_scale)
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kpss_list.append(kpss / det_scale)
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if not scores_list:
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return None
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scores = np.concatenate(scores_list)
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bboxes = np.concatenate(bboxes_list)
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kpss = np.concatenate(kpss_list)
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order = np.argsort(-scores, kind="stable")
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keep = _nms(bboxes[order], scores[order], DET_NMS_THRESH)
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if not keep:
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return None
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sel = [order[k] for k in keep]
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areas = (bboxes[sel, 2] - bboxes[sel, 0]) * (bboxes[sel, 3] - bboxes[sel, 1])
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cx = (bboxes[sel, 0] + bboxes[sel, 2]) / 2 - w / 2
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cy = (bboxes[sel, 1] + bboxes[sel, 3]) / 2 - h / 2
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values = areas - 2.0 * (cx ** 2 + cy ** 2)
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best = sel[int(np.argmax(values))]
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return bboxes[best], kpss[best]
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@staticmethod
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def _align(img: Image.Image, pts) -> Image.Image:
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src = pts.astype(np.float64)
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dst = REF5
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A = []
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bvec = []
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for (x, y), (X, Y) in zip(src, dst):
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A.append([x, -y, 1, 0])
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A.append([y, x, 0, 1])
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bvec.append(X)
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bvec.append(Y)
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sol, *_ = np.linalg.lstsq(np.array(A), np.array(bvec), rcond=None)
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a, b, c, d = sol
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s = a * a + b * b
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if s < 1e-6:
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return img
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ap = a / s
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bp = b / s
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cp = -(a * c + b * d) / s
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dp = -b / s
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ep = a / s
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fp = (b * c - a * d) / s
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return img.transform((REC_SIZE, REC_SIZE), Image.AFFINE, (ap, bp, cp, dp, ep, fp),
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Image.BICUBIC, fillcolor=(128, 128, 128))
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def embed(self, img_bytes: bytes) -> np.ndarray | None:
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"""512-d L2-normalized embedding of the most prominent face, or None."""
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self._load()
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try:
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img = Image.open(io.BytesIO(img_bytes))
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img.load()
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except Exception:
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return None
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det = self._detect(img)
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if not det:
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return None
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box, pts = det
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x1, y1, x2, y2 = [int(v) for v in box]
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x1 = max(0, x1)
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y1 = max(0, y1)
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x2 = min(img.width, x2)
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y2 = min(img.height, y2)
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if x2 - x1 < 20 or y2 - y1 < 20:
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return None
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if pts is not None:
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face = self._align(img, pts)
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else:
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face = img.crop((x1, y1, x2, y2)).resize((REC_SIZE, REC_SIZE))
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arr = np.asarray(face.convert("RGB"), dtype=np.float32)
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arr = (arr - REC_INPUT_MEAN) / REC_INPUT_STD
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arr = arr.transpose(2, 0, 1)[None]
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emb = self._rec.run(None, {self._rec_in: arr})[0][0]
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n = np.linalg.norm(emb)
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if n < 1e-6:
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return None
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return (emb / n).astype(np.float32)
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@staticmethod
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def similarity(a: np.ndarray, b: np.ndarray) -> float:
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return float(np.dot(a, b))
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engine = FaceEngine()
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+155
-2
@@ -1,10 +1,12 @@
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"""CIS Enhanced - FastAPI app."""
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import json
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import os
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import time
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from datetime import datetime, timedelta
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from pathlib import Path
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from dotenv import load_dotenv
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from fastapi import FastAPI, Query, Request
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from fastapi import FastAPI, File, Form, Query, Request, UploadFile
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from fastapi.responses import HTMLResponse, JSONResponse, Response
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from fastapi.templating import Jinja2Templates
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@@ -17,9 +19,12 @@ for _var in ("CIS_USER", "CIS_PASSWORD"):
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from fastapi.staticfiles import StaticFiles # noqa: E402
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import numpy as np # noqa: E402
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from .cis import CISClient, SLOTS # noqa: E402
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from .face import engine # noqa: E402
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from .util import (current_semester, decode_matrikel, events_to_json, fmt_range, # noqa: E402
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lv_color, week_label, week_start)
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lv_color, week_label, week_start)
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BASE = os.environ["CIS_BASE_URL"].rstrip("/")
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client = CISClient(os.environ["CIS_USER"], os.environ["CIS_PASSWORD"], BASE)
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@@ -158,6 +163,154 @@ def liste(request: Request,
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})
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# ---------- Fotos / Gesichter ----------
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DATA_DIR = Path(__file__).resolve().parent.parent / "data"
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EMB_CACHE = DATA_DIR / "embeddings.json"
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MAX_UPLOAD = 8 * 1024 * 1024
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def _emb_cache() -> dict:
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if EMB_CACHE.exists():
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try:
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return json.loads(EMB_CACHE.read_text())
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except Exception:
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pass
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return {}
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def _embed_person(pid: str, photo: bytes) -> np.ndarray | None:
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cache = _emb_cache()
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if pid in cache:
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return np.asarray(cache[pid], dtype=np.float32)
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v = engine.embed(photo)
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if v is None:
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return None
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cache[pid] = v.tolist()
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try:
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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EMB_CACHE.write_text(json.dumps(cache))
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except OSError:
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pass
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return v
|
||||
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def _photo_wall(kz: str, sem: str, ver: str) -> list[dict]:
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"""Students of a verband with person_id + whether a photo exists (cached check)."""
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students = client.namensliste(kz, sem, ver)
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wall = []
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for s in students:
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pid = client.person_id_for(s.matrikel)
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wall.append({
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"nachname": s.nachname, "vorname": s.vorname,
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"matrikel": s.matrikel, "person_id": pid,
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"has_photo": bool(pid),
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})
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return wall
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||||
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||||
@app.get("/fotos", response_class=HTMLResponse)
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||||
def fotos_page(request: Request,
|
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kz: str = Query("640"),
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||||
sem: str = Query("1"),
|
||||
ver: str = Query("V")):
|
||||
meta = _meta()
|
||||
stg = next((x for x in meta["stgs"] if x["kz"] == kz), None)
|
||||
verbands = client.verbands_for(kz, sem)
|
||||
wall = _photo_wall(kz, sem, ver)
|
||||
return templates.TemplateResponse(request, "fotos.html", {
|
||||
"request": request,
|
||||
"meta": meta,
|
||||
"kz": kz,
|
||||
"sem": sem,
|
||||
"ver": ver,
|
||||
"verbands": verbands,
|
||||
"stg": stg,
|
||||
"wall": wall,
|
||||
"wall_json": wall,
|
||||
"own_uid": OWN_UID,
|
||||
})
|
||||
|
||||
|
||||
@app.get("/fotos/img/{person_id}")
|
||||
def fotos_img(person_id: str):
|
||||
if not person_id.isdigit():
|
||||
return JSONResponse({"error": "Ungültige ID"}, status_code=400)
|
||||
res = client.photo_bytes(person_id)
|
||||
if not res:
|
||||
return JSONResponse({"error": "Kein Foto vorhanden"}, status_code=404)
|
||||
data, ct = res
|
||||
return Response(data, media_type=ct)
|
||||
|
||||
|
||||
@app.post("/fotos/suche", response_class=HTMLResponse)
|
||||
def fotos_suche(request: Request,
|
||||
kz: str = Form("640"),
|
||||
sem: str = Form("1"),
|
||||
ver: str = Form("V"),
|
||||
file: UploadFile = File(...)):
|
||||
data = file.file.read()
|
||||
if not data:
|
||||
return templates.TemplateResponse(request, "fotos_result.html", {
|
||||
"request": request, "error": "Leere Datei hochgeladen.",
|
||||
"kz": kz, "sem": sem, "ver": ver, "results": [], "meta": _meta(),
|
||||
"stg": next((x for x in _meta()["stgs"] if x["kz"] == kz), None),
|
||||
"verbands": client.verbands_for(kz, sem),
|
||||
"own_uid": OWN_UID,
|
||||
}, status_code=400)
|
||||
if len(data) > MAX_UPLOAD:
|
||||
return templates.TemplateResponse(request, "fotos_result.html", {
|
||||
"request": request, "error": "Datei zu groß (max. 8 MB).",
|
||||
"kz": kz, "sem": sem, "ver": ver, "results": [], "meta": _meta(),
|
||||
"stg": next((x for x in _meta()["stgs"] if x["kz"] == kz), None),
|
||||
"verbands": client.verbands_for(kz, sem),
|
||||
"own_uid": OWN_UID,
|
||||
}, status_code=400)
|
||||
emb = engine.embed(data)
|
||||
if emb is None:
|
||||
return templates.TemplateResponse(request, "fotos_result.html", {
|
||||
"request": request, "error": "Kein Gesicht im hochgeladenen Bild gefunden.",
|
||||
"kz": kz, "sem": sem, "ver": ver, "results": [], "meta": _meta(),
|
||||
"stg": next((x for x in _meta()["stgs"] if x["kz"] == kz), None),
|
||||
"verbands": client.verbands_for(kz, sem),
|
||||
"own_uid": OWN_UID,
|
||||
}, status_code=422)
|
||||
wall = _photo_wall(kz, sem, ver)
|
||||
results = []
|
||||
t0 = time.time()
|
||||
for p in wall:
|
||||
pid = p["person_id"]
|
||||
if not pid:
|
||||
continue
|
||||
res = client.photo_bytes(pid)
|
||||
if not res:
|
||||
continue
|
||||
e = _embed_person(pid, res[0])
|
||||
if e is None:
|
||||
continue
|
||||
sim = engine.similarity(emb, e)
|
||||
results.append({**p, "sim": round(sim, 4),
|
||||
"img": f"/fotos/img/{pid}"})
|
||||
results.sort(key=lambda r: r["sim"], reverse=True)
|
||||
meta = _meta()
|
||||
return templates.TemplateResponse(request, "fotos_result.html", {
|
||||
"request": request,
|
||||
"error": None,
|
||||
"took": round(time.time() - t0, 1),
|
||||
"query": {"kz": kz, "sem": sem, "ver": ver},
|
||||
"kz": kz, "sem": sem, "ver": ver,
|
||||
"results": results[:30],
|
||||
"meta": meta,
|
||||
"stg": next((x for x in meta["stgs"] if x["kz"] == kz), None),
|
||||
"verbands": client.verbands_for(kz, sem),
|
||||
"own_uid": OWN_UID,
|
||||
})
|
||||
|
||||
|
||||
@app.get("/api/person_id")
|
||||
def api_person_id(uid: str = Query(...)):
|
||||
return {"uid": uid, "person_id": client.person_id_for(uid)}
|
||||
|
||||
|
||||
@app.get("/api/meta")
|
||||
def api_meta():
|
||||
return _meta()
|
||||
|
||||
@@ -189,6 +189,44 @@ table.data .num { text-align: right; font-variant-numeric: tabular-nums; }
|
||||
.decoder-result { margin-top: 14px; display: none; }
|
||||
.decoder-result.show { display: block; }
|
||||
|
||||
/* ---------- fotos ---------- */
|
||||
.hint { color: var(--muted); font-size: 13.5px; margin: 0 0 14px; }
|
||||
.form-stack { flex-direction: column; align-items: stretch; gap: 12px; }
|
||||
.photo-wall { display: grid; grid-template-columns: repeat(auto-fill, minmax(150px, 1fr)); gap: 14px; }
|
||||
.photo-tile { text-align: center; }
|
||||
.photo-frame {
|
||||
aspect-ratio: 3 / 4; border-radius: var(--radius); overflow: hidden;
|
||||
background: var(--bg); border: 1px solid var(--line); display: flex;
|
||||
align-items: center; justify-content: center; margin-bottom: 8px;
|
||||
}
|
||||
.photo-frame img { width: 100%; height: 100%; object-fit: cover; display: block; }
|
||||
.avatar-initials {
|
||||
font-size: 30px; font-weight: 800; color: var(--blue-dark);
|
||||
background: var(--blue-light); width: 100%; height: 100%;
|
||||
display: flex; align-items: center; justify-content: center;
|
||||
}
|
||||
.photo-name { font-size: 13.5px; font-weight: 600; color: var(--ink); line-height: 1.3; }
|
||||
.photo-own { font-size: 11.5px; color: var(--green); font-weight: 700; margin-top: 2px; }
|
||||
.photo-tile.has-own { border: none; }
|
||||
|
||||
.match-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(180px, 1fr)); gap: 16px; }
|
||||
.match-tile {
|
||||
background: var(--card); border: 1px solid var(--line); border-radius: var(--radius);
|
||||
overflow: hidden; padding: 0;
|
||||
}
|
||||
.match-tile img { width: 100%; aspect-ratio: 1; object-fit: cover; display: block; background: var(--bg); }
|
||||
.match-name { padding: 8px 10px 4px; font-size: 13.5px; font-weight: 600; }
|
||||
.match-name a { color: var(--ink); }
|
||||
.match-sim { padding: 0 10px 12px; display: flex; align-items: center; gap: 8px; }
|
||||
.sim-bar { flex: 1; height: 8px; background: #eef2f7; border-radius: 6px; overflow: hidden; }
|
||||
.sim-bar > div { height: 100%; background: #94a3b8; border-radius: 6px; }
|
||||
.sim-num { font-size: 12.5px; font-weight: 700; color: var(--muted); min-width: 42px; text-align: right; }
|
||||
.match-tile.match-mid .sim-bar > div { background: #d97706; }
|
||||
.match-tile.match-mid .sim-num { color: #d97706; }
|
||||
.match-tile.match-strong { border-color: var(--green); box-shadow: 0 0 0 2px rgba(21,128,61,.15); }
|
||||
.match-tile.match-strong .sim-bar > div { background: var(--green); }
|
||||
.match-tile.match-strong .sim-num { color: var(--green); }
|
||||
|
||||
.footer { border-top: 1px solid var(--line); padding: 16px 0; color: var(--muted); font-size: 13px; background: #fff; }
|
||||
|
||||
@media (max-width: 720px) {
|
||||
|
||||
@@ -18,6 +18,7 @@
|
||||
<a href="/plan" class="{{ 'active' if path.startswith('/plan') }}">Studentenplan</a>
|
||||
<a href="/stg" class="{{ 'active' if path.startswith('/stg') }}">Studiengänge</a>
|
||||
<a href="/liste" class="{{ 'active' if path.startswith('/liste') }}">Namenslisten</a>
|
||||
<a href="/fotos" class="{{ 'active' if path.startswith('/fotos') }}">Fotos</a>
|
||||
<a href="/" class="{{ 'active' if path == '/' }}">Start</a>
|
||||
</nav>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,75 @@
|
||||
{% extends "base.html" %}
|
||||
{% block title %}Fotos{% endblock %}
|
||||
{% block content %}
|
||||
<h1 class="page-title">Fotos
|
||||
{% if stg %}<span class="badge">{{ stg.code }} - {{ stg.name }}</span>{% endif %}
|
||||
<span class="badge badge-muted">{{ ver == 'V' and 'Vollzeit' or ver }}</span>
|
||||
</h1>
|
||||
|
||||
<div class="card">
|
||||
<form class="form-row" method="get" action="/fotos">
|
||||
<div class="field">
|
||||
<label>Studiengang</label>
|
||||
<select name="kz" required>
|
||||
{% for s in meta.stgs %}
|
||||
<option value="{{ s.kz }}" {{ 'selected' if s.kz == kz }}>{{ s.code }} - {{ s.name }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<div class="field">
|
||||
<label>Studien-Semester</label>
|
||||
<select name="sem">
|
||||
{% for n in range(1, 9) %}
|
||||
<option value="{{ n }}" {{ 'selected' if (n | string) == sem }}>{{ n }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<div class="field">
|
||||
<label>Verband</label>
|
||||
<select name="ver">
|
||||
{% for v in verbands %}
|
||||
<option value="{{ v }}" {{ 'selected' if v == ver }}>{{ v }}{{ verband_label(v) }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<button class="btn btn-primary" type="submit">Laden</button>
|
||||
</form>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>Fotowand <span class="count-pill">{{ wall | selectattr('has_photo') | list | length }}/{{ wall | length }}</span></h2>
|
||||
<div class="photo-wall" id="photo-wall">
|
||||
{% for p in wall %}
|
||||
<div class="photo-tile" data-uid="{{ p.matrikel }}" data-initials="{{ (p.nachname[:1] ~ p.vorname[:1]) | upper }}">
|
||||
<div class="photo-frame">
|
||||
{% if p.person_id %}
|
||||
<img loading="lazy" src="/fotos/img/{{ p.person_id }}" alt="{{ p.nachname }}"
|
||||
onerror="this.closest('.photo-frame').innerHTML='<div class="avatar-initials">' + this.closest('.photo-tile').dataset.initials + '</div>'">
|
||||
{% else %}
|
||||
<div class="avatar-initials">{{ (p.nachname[:1] ~ p.vorname[:1]) | upper }}</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
<div class="photo-name">{{ p.nachname }}{% if p.vorname %}, {{ p.vorname }}{% endif %}</div>
|
||||
{% if p.matrikel == own_uid %}<div class="photo-own">Das bist du</div>{% endif %}
|
||||
</div>
|
||||
{% else %}
|
||||
<p class="empty">Keine Studierenden in diesem Verband/Semester.</p>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>Gesichtssuche</h2>
|
||||
<p class="hint">Foto von dir hochladen - die App findet die passenden Profile in diesem Verband.</p>
|
||||
<form class="form-row form-stack" method="post" action="/fotos/suche" enctype="multipart/form-data">
|
||||
<input type="hidden" name="kz" value="{{ kz }}">
|
||||
<input type="hidden" name="sem" value="{{ sem }}">
|
||||
<input type="hidden" name="ver" value="{{ ver }}">
|
||||
<div class="field">
|
||||
<label>Foto</label>
|
||||
<input type="file" name="file" accept="image/*" required>
|
||||
</div>
|
||||
<button class="btn btn-primary" type="submit">Suchen</button>
|
||||
</form>
|
||||
</div>
|
||||
{% endblock %}
|
||||
@@ -0,0 +1,74 @@
|
||||
{% extends "base.html" %}
|
||||
{% block title %}Gesichtssuche{% endblock %}
|
||||
{% block content %}
|
||||
<h1 class="page-title">Gesichtssuche
|
||||
{% if stg %}<span class="badge">{{ stg.code }} - {{ stg.name }}</span>{% endif %}
|
||||
</h1>
|
||||
|
||||
<div class="card">
|
||||
<form class="form-row" method="get" action="/fotos">
|
||||
<div class="field">
|
||||
<label>Studiengang</label>
|
||||
<select name="kz">
|
||||
{% for s in meta.stgs %}
|
||||
<option value="{{ s.kz }}" {{ 'selected' if s.kz == kz }}>{{ s.code }} - {{ s.name }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<div class="field">
|
||||
<label>Studien-Semester</label>
|
||||
<select name="sem">
|
||||
{% for n in range(1, 9) %}
|
||||
<option value="{{ n }}" {{ 'selected' if (n | string) == sem }}>{{ n }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<div class="field">
|
||||
<label>Verband</label>
|
||||
<select name="ver">
|
||||
{% for v in verbands %}
|
||||
<option value="{{ v }}" {{ 'selected' if v == ver }}>{{ v }}{{ verband_label(v) }}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<button class="btn btn-primary" type="submit">Zurück zur Fotowand</button>
|
||||
</form>
|
||||
</div>
|
||||
|
||||
{% if error %}
|
||||
<div class="card">
|
||||
<p class="empty" style="color:#b3261e">{{ error }}</p>
|
||||
</div>
|
||||
{% else %}
|
||||
<div class="card">
|
||||
<h2>
|
||||
Ergebnisse
|
||||
<span class="count-pill">{{ results | selectattr('sim', 'ge', 0.35) | list | length }} Treffer</span>
|
||||
{% if took is defined %}<span class="badge badge-muted">{{ took }} s</span>{% endif %}
|
||||
</h2>
|
||||
{% if results | selectattr('sim', 'ge', 0.35) | list | length %}
|
||||
<div class="match-grid">
|
||||
{% for r in results[:30] %}
|
||||
<div class="match-tile {{ 'match-strong' if r.sim >= 0.5 else ('match-mid' if r.sim >= 0.35 else '') }}">
|
||||
<img src="{{ r.img }}" alt="{{ r.nachname }}">
|
||||
<div class="match-name">
|
||||
<a href="/plan?uid={{ r.matrikel }}">{{ r.nachname }}{% if r.vorname %}, {{ r.vorname }}{% endif %}</a>
|
||||
</div>
|
||||
<div class="match-sim">
|
||||
<div class="sim-bar"><div style="width: {{ [r.sim * 100, 0] | max | round(0) }}%"></div></div>
|
||||
<span class="sim-num">{{ [r.sim * 100, 0] | max | round(0) }} %</span>
|
||||
</div>
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
<p class="hint" style="margin-top:12px">
|
||||
<strong>Hinweis:</strong> Hohe Werte (grün, ≥ 50 %) deuten auf dieselbe Person hin.
|
||||
Werte um 35–50 % (gelb) sind mögliche Treffer - bitte prüfen. Unter 35 % wird nicht als Treffer gezählt.
|
||||
</p>
|
||||
{% else %}
|
||||
<p class="empty">Kein Treffer mit ≥ 35 % Ähnlichkeit gefunden.
|
||||
Prüfe, ob das richtige Semester/Verband gewählt ist und das Foto ein klar sichtbares Gesicht zeigt.</p>
|
||||
{% endif %}
|
||||
</div>
|
||||
{% endif %}
|
||||
{% endblock %}
|
||||
@@ -5,4 +5,9 @@ services:
|
||||
env_file: .env
|
||||
ports:
|
||||
- "8000:8000"
|
||||
volumes:
|
||||
- cis-data:/app/data
|
||||
restart: unless-stopped
|
||||
|
||||
volumes:
|
||||
cis-data:
|
||||
|
||||
@@ -4,3 +4,7 @@ requests
|
||||
beautifulsoup4
|
||||
jinja2
|
||||
python-dotenv
|
||||
python-multipart
|
||||
numpy
|
||||
pillow
|
||||
onnxruntime
|
||||
|
||||
Reference in New Issue
Block a user