feat: add Docker setup and CI workflows for headless server

.dockerignore

@@ -0,0 +1,19 @@
+# Exclude development and unnecessary files from the Docker build context
+.git
+__pycache__
+*.pyc
+*.pyo
+.gitignore
+.dockerignore
+README.md
+MyApplication6.rar
+submissions
+data/
+notebooks/catboost_info/
+notebooks/*.ipynb
+notebooks/FastMTCNN.py
+notebooks/tester.py
+notebooks/saved_dictionary.pkl
+c4715817-515e-4815-aa0d-bfcc75d45388.jfif
+models/
+.env

.gitea/workflows/docker-build.yml

@@ -0,0 +1,29 @@
+name: Build and push Docker image (Gitea)
+
+on:
+  push:
+    branches: [ main ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: docker/setup-buildx-action@v3
+      - uses: docker/login-action@v3
+        with:
+          registry: git.byte-mate.ru
+          username: ${{ gitea.actor }}
+          password: ${{ secrets.GITEA_TOKEN }}
+      - uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: |
+            git.byte-mate.ru/Coder/UfaHack2024:latest
+            git.byte-mate.ru/Coder/UfaHack2024:${{ gitea.sha }}
+
+# Notes:
+#   - Create the GITEA_TOKEN secret in the Gitea repository settings.
+#   - A Gitea Actions runner must be registered on the server for this
+#     workflow to run.

.github/workflows/docker-build.yml

@@ -0,0 +1,31 @@
+name: Build and push Docker image (GitHub)
+
+on:
+  push:
+    branches: [ main ]
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: DrHo1y/UfaHack2024
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - uses: actions/checkout@v4
+      - uses: docker/setup-buildx-action@v3
+      - uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: |
+            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest
+            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}

.gitignore

@@ -1,3 +1,8 @@
 .idea
 data
-data — копия
+data — копия
+__pycache__/
+*.pyc
+*.pyo
+.env
+models/

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY server/ server/
+COPY notebooks/model/ models/
+
+EXPOSE 12345
+
+ENV HOST=0.0.0.0 PORT=12345 MODEL_DIR=/app/models DATA_DIR=/app/data
+
+CMD ["python", "-u", "server/server.py"]

README.md

@@ -91,5 +91,36 @@ pip install -r requirements.txt
 ## Приложение для Android
 В корне репозитория находится архив `MyApplication6.rar` — проект Android-приложения на Kotlin. Клиент подключается к серверу (`PredictServer.py`) по TCP-сокету, отправляет фотографию и получает результат распознавания.
 
+## Docker / локальная разработка
+
+В репозитории добавлена конфигурация для запуска headless-сервера распознавания лиц в Docker-контейнере (без GUI).
+
+### Быстрый старт
+
+```bash
+docker compose up --build
+```
+
+После сборки и запуска сервер будет доступен на порту `12345` (адрес `127.0.0.1:12345`).
+
+### Переменные окружения
+
+| Переменная  | По умолчанию   | Описание                         |
+|-------------|----------------|----------------------------------|
+| `HOST`      | `0.0.0.0`      | Адрес для привязки сокета        |
+| `PORT`      | `12345`        | Порт TCP-сервера                 |
+| `MODEL_DIR` | `/app/models`  | Каталог с моделями CatBoost      |
+| `DATA_DIR`  | `/data`        | Каталог с данными (фотографиями) |
+
+### Важно
+
+- Файлы `.cbm` (catboost_usa.cbm и др.) **не включены** в репозиторий. Поместите их в каталог `./models/` на хосте перед запуском контейнера. Без них контейнер запустится, но распознавание будет недоступно (в логах появится предупреждение).
+- Каталоги `./models/` и `./data/` создаются Docker автоматически, если их нет на хосте.
+
+### CI / CD
+
+- **GitHub Actions** — при пуше в ветку `main` автоматически собирает образ и публикует его в `ghcr.io/DrHo1y/UfaHack2024`.
+- **Gitea Actions** — аналогичный workflow для Gitea (требует зарегистрированного runner и секрета `GITEA_TOKEN`).
+
 ## Благодарности
 Для обучения модели использовали [CatBoost](https://catboost.ai/)

docker-compose.yml

@@ -0,0 +1,18 @@
+services:
+  server:
+    build: .
+    ports:
+      - "12345:12345"
+    environment:
+      HOST: "0.0.0.0"
+      PORT: "12345"
+      MODEL_DIR: "/app/models"
+      DATA_DIR: "/data"
+    volumes:
+      - ./server:/app/server
+      - ./models:/app/models
+      - ./data:/data
+# Notes:
+#   - Place .cbm model files in ./models/ on the host before starting.
+#   - Docker creates missing host directories (./models/, ./data/) automatically
+#     (they will be owned by root on Linux).

requirements.txt

@@ -2,3 +2,14 @@ deepface
 customtkinter
 torch
 torchvision
+catboost
+mtcnn
+opencv-python
+opencv-contrib-python
+pandas
+numpy
+Pillow
+facenet-pytorch
+imutils
+tqdm
+scikit-learn

server/server.py

@@ -0,0 +1,186 @@
+"""
+Headless TCP server for face recognition (UfaHack2024).
+Reuses the recognition logic from the desktop app but runs standalone in Docker.
+No GUI dependencies (customtkinter / Tkinter).
+"""
+
+import os
+import socket
+import tempfile
+import pickle
+import logging
+import sys
+
+import cv2
+import numpy as np
+import pandas as pd
+from deepface import DeepFace
+from mtcnn import MTCNN
+from catboost import CatBoostClassifier
+
+# ---------------------------------------------------------------------------
+# Configuration from environment
+# ---------------------------------------------------------------------------
+HOST = os.environ.get("HOST", "0.0.0.0")
+PORT = int(os.environ.get("PORT", "12345"))
+MODEL_DIR = os.environ.get("MODEL_DIR", "/app/models")
+DATA_DIR = os.environ.get("DATA_DIR", "/app/data")
+
+# ---------------------------------------------------------------------------
+# Logging to stdout so Docker logs capture everything
+# ---------------------------------------------------------------------------
+logging.basicConfig(
+    stream=sys.stdout,
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+log = logging.getLogger("server")
+
+# ---------------------------------------------------------------------------
+# Globals (loaded once at startup)
+# ---------------------------------------------------------------------------
+detector = None
+catboost_model = None
+name_dict = None
+
+
+def load_models():
+    """Load MTCNN detector, CatBoost model and name dictionary.
+    Logs warnings instead of crashing when files are absent so the container
+    starts and waits for connections even without .cbm files."""
+    global detector, catboost_model, name_dict
+
+    detector = MTCNN()
+    log.info("MTCNN detector initialised")
+
+    model_path = os.path.join(MODEL_DIR, "catboost_usa.cbm")
+    if os.path.isfile(model_path):
+        try:
+            catboost_model = CatBoostClassifier()
+            catboost_model.load_model(model_path)
+            log.info("CatBoost model loaded from %s", model_path)
+        except Exception as exc:
+            log.warning("Failed to load CatBoost model from %s: %s — recognition will be unavailable", model_path, exc)
+            catboost_model = None
+    else:
+        log.warning("CatBoost model not found at %s — recognition will be unavailable until it is provided", model_path)
+
+    dict_path = os.path.join(MODEL_DIR, "saved_dictionary.pkl")
+    if os.path.isfile(dict_path):
+        try:
+            with open(dict_path, "rb") as f:
+                name_dict = pickle.load(f)
+            log.info("Name dictionary loaded from %s", dict_path)
+        except Exception as exc:
+            log.warning("Failed to load name dictionary from %s: %s", dict_path, exc)
+            name_dict = None
+    else:
+        log.warning("Name dictionary not found at %s — recognition will be unavailable until it is provided", dict_path)
+
+
+def recognise_face(image_array):
+    """Run the full recognition pipeline on an RGB image array.
+    Returns (name, confidence_info) or an error message string."""
+    if catboost_model is None or name_dict is None:
+        return "recognition unavailable (models not loaded)"
+
+    detections = detector.detect_faces(image_array)
+    if not detections:
+        return "no faces detected"
+
+    # Use the first face with confidence > 0.9
+    for detection in detections:
+        confidence = detection["confidence"]
+        if confidence > 0.9:
+            x, y, w, h = detection["box"]
+            detected_face = image_array[int(y) : int(y + h), int(x) : int(x + w)]
+
+            embedding = DeepFace.represent(
+                detected_face, model_name="Facenet", enforce_detection=False
+            )
+            ebd = embedding[0]["embedding"]
+
+            # Build DataFrame exactly as Predict_photo.py does
+            dicter3 = {1: ebd}
+            data_usa = pd.DataFrame.from_dict(dicter3.items())
+            data_usa.rename(columns={0: "id", 1: "embd"}, inplace=True, errors="ignore")
+            new_cols = pd.DataFrame(data_usa["embd"].apply(pd.Series))
+            df_usa = pd.concat([data_usa, new_cols], axis=1)
+            df_usa.drop(["embd"], axis=1, inplace=True, errors="ignore")
+            X = df_usa.drop(["id"], axis=1)
+
+            result = catboost_model.predict(X)
+            idx = result[0][0]
+            name = name_dict.get(idx, f"unknown (index {idx})")
+            log.info("Recognised: %s (confidence %.3f)", name, confidence)
+            return name
+
+    return "no face with sufficient confidence (>0.9)"
+
+
+def handle_client(conn, addr):
+    """Receive a photo from one client, recognise the face and send the name back."""
+    log.info("Accepted connection from %s", addr)
+
+    # Read the full photo bytes until the connection closes
+    chunks = []
+    while True:
+        chunk = conn.recv(65536)
+        if not chunk:
+            break
+        chunks.append(chunk)
+    photo_bytes = b"".join(chunks)
+
+    if not photo_bytes:
+        log.info("Empty request from %s — closing", addr)
+        conn.close()
+        return
+
+    log.info("Received %d bytes from %s", len(photo_bytes), addr)
+
+    # Write photo to OS temp dir (not FDJ.jpg in CWD)
+    tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+    try:
+        tmp.write(photo_bytes)
+        tmp.close()
+
+        img = cv2.imread(tmp.name)
+        if img is None:
+            result = "failed to decode image"
+            log.warning("Could not decode image from %s", addr)
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (1080, 720))
+            result = recognise_face(img)
+
+        # Send plain UTF-8 bytes (NOT bit-string encoding — bug fixed)
+        conn.sendall(result.encode("utf-8"))
+    finally:
+        os.unlink(tmp.name)
+
+    conn.close()
+    log.info("Closed connection from %s", addr)
+
+
+def main():
+    log.info("Starting UfaHack2024 headless server")
+    log.info("HOST=%s, PORT=%s, MODEL_DIR=%s, DATA_DIR=%s", HOST, PORT, MODEL_DIR, DATA_DIR)
+
+    load_models()
+
+    server_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    server_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    server_sock.bind((HOST, PORT))
+    server_sock.listen(5)
+    log.info("Listening on %s:%s", HOST, PORT)
+
+    while True:
+        conn, addr = server_sock.accept()
+        try:
+            handle_client(conn, addr)
+        except Exception as exc:
+            log.exception("Error handling client %s: %s", addr, exc)
+
+
+if __name__ == "__main__":
+    main()