init

api_routes.py

@@ -8,6 +8,7 @@ import json
 import os
 import time
 import uuid
+import subprocess
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
 from typing import Any, Dict, List, Optional
@@ -38,7 +39,7 @@ from config import logger, OUTPUT_DIR, IMAGES_DIR, DEEPFACE_AVAILABLE, \
     UPSCALE_SIZE, CLIP_AVAILABLE, REALESRGAN_MODEL, REMBG_AVAILABLE, \
     ANIME_STYLE_AVAILABLE, SAVE_QUALITY, \
     AUTO_INIT_ANALYZER, AUTO_INIT_GFPGAN, AUTO_INIT_DDCOLOR, \
-    AUTO_INIT_REALESRGAN, \
+    AUTO_INIT_REALESRGAN, MODELS_PATH, \
     AUTO_INIT_REMBG, AUTO_INIT_ANIME_STYLE, RVM_AVAILABLE, AUTO_INIT_RVM, \
     FACE_SCORE_MAX_IMAGES, FEMALE_AGE_ADJUSTMENT, \
     FEMALE_AGE_ADJUSTMENT_THRESHOLD, CELEBRITY_SOURCE_DIR, \
@@ -3381,6 +3382,63 @@ async def manual_cleanup_images():
         raise HTTPException(status_code=500, detail=f"手动清理任务执行失败: {str(e)}")
 
 
+def _extract_tar_archive(archive_path: str, target_dir: str) -> Dict[str, str]:
+    """在独立线程中执行tar命令，避免阻塞事件循环。"""
+    cmd = ["tar", "-xzf", archive_path, "-C", target_dir]
+    cmd_display = " ".join(cmd)
+    logger.info(f"开始执行解压命令: {cmd_display}")
+    completed = subprocess.run(
+        cmd, capture_output=True, text=True, check=False
+    )
+    if completed.returncode != 0:
+        stderr = (completed.stderr or "").strip()
+        raise RuntimeError(f"tar命令执行失败: {stderr or '未知错误'}")
+    logger.info(f"解压命令执行成功: {cmd_display}")
+    return {
+        "command": cmd_display,
+        "stdout": (completed.stdout or "").strip(),
+        "stderr": (completed.stderr or "").strip(),
+    }
+
+
+@api_router.post("/datasets/chinese-celeb/extract", tags=["系统管理"])
+@log_api_params
+async def extract_chinese_celeb_dataset():
+    """
+    解压 MODELS_PATH 下的 chinese_celeb_dataset.tar.gz 到 /opt/data/chinese_celeb_dataset。
+    """
+    archive_path = os.path.join(MODELS_PATH, "chinese_celeb_dataset.tar.gz")
+    target_dir = "/opt/data"
+
+    if not os.path.isfile(archive_path):
+        raise HTTPException(status_code=404, detail=f"数据集文件不存在: {archive_path}")
+
+    try:
+        os.makedirs(target_dir, exist_ok=True)
+    except OSError as exc:
+        logger.error(f"创建目标目录失败: {target_dir}, {exc}")
+        raise HTTPException(status_code=500, detail=f"创建目标目录失败: {exc}")
+
+    loop = asyncio.get_event_loop()
+    try:
+        extract_result = await loop.run_in_executor(
+            executor, _extract_tar_archive, archive_path, target_dir
+        )
+    except Exception as exc:
+        logger.error(f"解压 chinese_celeb_dataset 失败: {exc}")
+        raise HTTPException(status_code=500, detail=f"解压失败: {exc}")
+
+    return {
+        "success": True,
+        "message": "chinese_celeb_dataset 解压完成",
+        "archive_path": archive_path,
+        "target_dir": target_dir,
+        "command": extract_result.get("command"),
+        "stdout": extract_result.get("stdout"),
+        "stderr": extract_result.get("stderr"),
+    }
+
+
 @api_router.post("/celebrity/load", tags=["Face Recognition"])
 @log_api_params
 async def load_celebrity_database():

config.py

@@ -266,7 +266,7 @@ os.environ.setdefault("MODELSCOPE_CACHE", MODELSCOPE_CACHE_DIR)
 os.environ.setdefault("MODELSCOPE_HOME", MODELSCOPE_CACHE_DIR)
 os.environ.setdefault("MODELSCOPE_CACHE_HOME", MODELSCOPE_CACHE_DIR)
 
-DEEPFACE_HOME = os.environ.get("DEEPFACE_HOME", "~/apps/ai")
+DEEPFACE_HOME = os.environ.get("DEEPFACE_HOME", "/opt/data/models")
 os.environ["DEEPFACE_HOME"] = DEEPFACE_HOME
 
 # 设置GFPGAN相关模型下载路径