feat: implement date-based subdirectory storage with 'picture/' prefix for all image outputs

api_routes.py

@@ -329,6 +329,7 @@ from utils import (
     compress_image_by_file_size,
     convert_image_format,
     upload_file_to_bos,
+    get_date_subfolder,
     ensure_bos_resources,
     download_bos_directory,
 )
@@ -740,10 +741,13 @@ async def _record_output_file(
                 score=score_value,
                 extra_metadata=extra,
             )
+            # 使用与数据库一致的路径转换
+            from database import _normalize_file_path
+            display_path = _normalize_file_path(file_path) or os.path.basename(file_path)
             duration = time.perf_counter() - start_time
             logger.info(
                 "MySQL记录完成 file=%s category=%s nickname=%s score=%.4f bos_uploaded=%s cost=%.3fs",
-                os.path.basename(file_path),
+                display_path,
                 category or "auto",
                 nickname or "",
                 score_value,
@@ -756,6 +760,17 @@ async def _record_output_file(
     asyncio.create_task(_write_record())
 
 
+def _get_save_path(filename: str, base_dir: str = IMAGES_DIR) -> Tuple[str, str]:
+    """
+    返回完整的保存路径和相对于 base_dir 的规范化路径（带日期前缀）。
+    :return: (abs_path, rel_path)
+    """
+    date_sub = get_date_subfolder(base_dir)
+    abs_path = os.path.join(base_dir, date_sub, filename)
+    rel_path = f"{date_sub}/{filename}"
+    return abs_path, rel_path
+
+
 async def _refresh_celebrity_cache(sample_image_path: str,
     db_path: str) -> None:
     """刷新DeepFace数据库缓存"""
@@ -1009,8 +1024,8 @@ async def upload_file(
                     logger.warning(
                         "GFPGAN 修复器不可用，跳过修复，按原样保存证件照")
                     # 按原样保存
-                    saved_filename = f"{unique_id}_save_id_photo{file_extension}"
-                    saved_path = os.path.join(IMAGES_DIR, saved_filename)
+                    filename_raw = f"{unique_id}_save_id_photo{file_extension}"
+                    saved_path, saved_filename = _get_save_path(filename_raw)
                     with open(saved_path, "wb") as f:
                         f.write(contents)
                     # bos_uploaded = upload_file_to_bos(saved_path)
@@ -1022,8 +1037,8 @@ async def upload_file(
                     restored_image = await process_cpu_intensive_task(
                         photo_restorer.restore_image, image)
                     # 以 webp 高质量保存，命名与证件照区分
-                    saved_filename = f"{unique_id}_save_id_photo_restore.webp"
-                    saved_path = os.path.join(IMAGES_DIR, saved_filename)
+                    filename_raw = f"{unique_id}_save_id_photo_restore.webp"
+                    saved_path, saved_filename = _get_save_path(filename_raw)
                     if not save_image_high_quality(restored_image, saved_path,
                                                    quality=SAVE_QUALITY):
                         raise HTTPException(status_code=500,
@@ -1058,8 +1073,8 @@ async def upload_file(
             except Exception as e:
                 logger.error(f"证件照上传修复流程失败，改为直接保存: {e}")
                 # 失败兜底：直接保存原文件
-                saved_filename = f"{unique_id}_save_id_photo{file_extension}"
-                saved_path = os.path.join(IMAGES_DIR, saved_filename)
+                filename_raw = f"{unique_id}_save_id_photo{file_extension}"
+                saved_path, saved_filename = _get_save_path(filename_raw)
                 try:
                     with open(saved_path, "wb") as f:
                         f.write(contents)
@@ -1084,8 +1099,8 @@ async def upload_file(
                 }
 
         # 默认：普通文件直接保存原始内容
-        saved_filename = f"{unique_id}_save_file{file_extension}"
-        saved_path = os.path.join(IMAGES_DIR, saved_filename)
+        filename_raw = f"{unique_id}_save_file{file_extension}"
+        saved_path, saved_filename = _get_save_path(filename_raw)
         try:
             with open(saved_path, "wb") as f:
                 f.write(contents)
@@ -1123,8 +1138,8 @@ async def analyze_face(
     np_arr = np.frombuffer(contents, np.uint8)
     image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
     original_md5_hash = f"ugc_{str(uuid.uuid4()).replace('-', '')}"
-    original_image_filename = f"{original_md5_hash}_original.webp"
-    original_image_path = os.path.join(IMAGES_DIR, original_image_filename)
+    original_image_filename_raw = f"{original_md5_hash}_original.webp"
+    original_image_path, original_image_filename = _get_save_path(original_image_filename_raw)
     save_image_high_quality(image, original_image_path, quality=SAVE_QUALITY, upload_to_bos=False)
     try:
         with open(original_image_path, "rb") as f:
@@ -1308,7 +1323,9 @@ async def analyze_face(
 
                 # 生成MD5哈希
                 original_md5_hash = f"ugc_{str(uuid.uuid4()).replace('-', '')}"
-                original_image_filename = f"{original_md5_hash}_original.webp"
+                original_image_filename_raw = f"{original_md5_hash}_original.webp"
+                # 获取带日期的保存路径和带日期的相对路径
+                original_image_path, original_image_filename = _get_save_path(original_image_filename_raw, OUTPUT_DIR)
 
                 logger.info(
                     f"Processing image {idx+1}/{len(image_data_list)}, md5={original_md5_hash}, size={image_size_kb:.2f} KB"
@@ -1332,7 +1349,7 @@ async def analyze_face(
                 result["annotated_image_filename"] = None
 
                 if result.get("success") and annotated_image_np is not None:
-                    original_image_path = os.path.join(OUTPUT_DIR, original_image_filename)
+                    # 注意：这里已经使用之前生成的 original_image_path
                     save_start = time.perf_counter()
                     save_success = save_image_high_quality(
                         annotated_image_np, original_image_path, quality=SAVE_QUALITY
@@ -2260,7 +2277,7 @@ async def restore_old_photo(
         processed_height, processed_width = final_image.shape[:2]
 
         # 保存最终处理后的图像到IMAGES_DIR（与人脸评分使用相同路径）
-        restored_path = os.path.join(IMAGES_DIR, restored_filename)
+        restored_path, restored_filename = _get_save_path(restored_filename)
         save_success = save_image_high_quality(
             final_image, restored_path, quality=SAVE_QUALITY
         )
@@ -2370,7 +2387,7 @@ async def colorize_photo(
         processed_height, processed_width = colorized_image.shape[:2]
 
         # 保存上色后的图像到IMAGES_DIR
-        colored_path = os.path.join(IMAGES_DIR, colored_filename)
+        colored_path, colored_filename = _get_save_path(colored_filename)
         save_success = save_image_high_quality(
             colorized_image, colored_path, quality=SAVE_QUALITY
         )
@@ -2556,8 +2573,8 @@ async def anime_stylize_photo(
             bos_uploaded_flag = upload_file_to_bos(output_path)
             return True, bos_uploaded_flag
 
-        original_filename = f"{original_md5_hash}_anime_style.webp"
-        original_path = os.path.join(IMAGES_DIR, original_filename)
+        original_filename_raw = f"{original_md5_hash}_anime_style.webp"
+        original_path, original_filename = _get_save_path(original_filename_raw)
         if not os.path.exists(original_path):
             original_saved, original_bos_uploaded = _save_webp_and_upload(
                 image, original_path, "动漫风格原图"
@@ -2570,7 +2587,8 @@ async def anime_stylize_photo(
             original_bos_uploaded = False
 
         styled_uuid = f"ugc_{uuid.uuid4().hex}"
-        styled_filename = f"{styled_uuid}_anime_style_{style_type}.webp"
+        styled_filename_raw = f"{styled_uuid}_anime_style_{style_type}.webp"
+        styled_path, styled_filename = _get_save_path(styled_filename_raw)
 
         # 获取风格描述
         style_descriptions = anime_stylizer.get_available_styles()
@@ -2604,7 +2622,7 @@ async def anime_stylize_photo(
             raise HTTPException(status_code=500, detail=f"动漫风格化处理失败: {str(e)}")
 
         # 保存风格化后的图像到IMAGES_DIR
-        styled_path = os.path.join(IMAGES_DIR, styled_filename)
+        # styled_path 已经在前面通过 _get_save_path 生成
         save_success, bos_uploaded = _save_webp_and_upload(
             stylized_image, styled_path, "动漫风格结果图"
         )
@@ -2699,7 +2717,7 @@ async def grayscale_photo(
             raise HTTPException(status_code=500, detail=f"黑白化处理失败: {str(e)}")
 
         # 保存黑白化后的图像到IMAGES_DIR
-        grayscale_path = os.path.join(IMAGES_DIR, grayscale_filename)
+        grayscale_path, grayscale_filename = _get_save_path(grayscale_filename)
         save_success = save_image_high_quality(
             grayscale_image, grayscale_path, quality=SAVE_QUALITY
         )
@@ -2816,7 +2834,7 @@ async def upscale_photo(
         upscaled_height, upscaled_width = upscaled_image.shape[:2]
 
         # 保存超清后的图像到IMAGES_DIR（与其他接口保持一致）
-        upscaled_path = os.path.join(IMAGES_DIR, upscaled_filename)
+        upscaled_path, upscaled_filename = _get_save_path(upscaled_filename)
         save_success = save_image_high_quality(
             upscaled_image, upscaled_path, quality=SAVE_QUALITY
         )
@@ -3001,7 +3019,7 @@ async def remove_background(
         processed_height, processed_width = processed_image.shape[:2]
 
         # 保存抠图后的图像到IMAGES_DIR（与facescore保持一致）
-        processed_path = os.path.join(IMAGES_DIR, processed_filename)
+        processed_path, processed_filename = _get_save_path(processed_filename)
         bos_uploaded = False
 
         # 根据是否有透明背景选择保存方式
@@ -3179,7 +3197,7 @@ async def rvm_remove_background(
         processed_height, processed_width = processed_image.shape[:2]
 
         # 保存抠图后的图像到IMAGES_DIR（与facescore保持一致）
-        processed_path = os.path.join(IMAGES_DIR, processed_filename)
+        processed_path, processed_filename = _get_save_path(processed_filename)
         bos_uploaded = False
 
         # 根据是否有透明背景选择保存方式
@@ -3437,8 +3455,8 @@ async def split_grid_image(
 
                 # 生成格子文件名
                 grid_index = row * cols + col + 1  # 从1开始编号
-                grid_filename = f"{original_md5_hash}_grid_{grid_name}_{grid_index:02d}.webp"
-                grid_path = os.path.join(IMAGES_DIR, grid_filename)
+                grid_filename_raw = f"{original_md5_hash}_grid_{grid_name}_{grid_index:02d}.webp"
+                grid_path, grid_filename = _get_save_path(grid_filename_raw)
 
                 # 保存格子图片
                 save_success = save_image_high_quality(grid_image, grid_path, quality=SAVE_QUALITY)
@@ -3460,8 +3478,8 @@ async def split_grid_image(
                     )
 
         # 同时保存原图到IMAGES_DIR供向量化使用
-        original_filename = f"{original_md5_hash}_original.webp"
-        original_path = os.path.join(IMAGES_DIR, original_filename)
+        original_filename_raw = f"{original_md5_hash}_original.webp"
+        original_path, original_filename = _get_save_path(original_filename_raw)
         if save_image_high_quality(image, original_path, quality=SAVE_QUALITY):
             await _record_output_file(
                 file_path=original_path,
@@ -3597,16 +3615,18 @@ async def compress_image(
             raise HTTPException(status_code=500, detail=f"压缩处理失败: {str(e)}")
 
         # 保存压缩后的图像到IMAGES_DIR
-        compressed_path = os.path.join(IMAGES_DIR, compressed_filename)
+        compressed_path, compressed_filename = _get_save_path(compressed_filename)
         try:
             with open(compressed_path, "wb") as f:
                 f.write(compressed_bytes)
+            save_success = True
             bos_uploaded = upload_file_to_bos(compressed_path)
             logger.info(f"Compressed image saved successfully: {compressed_path}")
 
             # 异步执行图片向量化并入库，不阻塞主流程
             if CLIP_AVAILABLE:
                 asyncio.create_task(handle_image_vector_async(compressed_path, compressed_filename))
+            
             await _record_output_file(
                 file_path=compressed_path,
                 nickname=nickname,
@@ -4429,10 +4449,15 @@ async def face_similarity_verification(
         md5_hash2 = f"ugc_{str(uuid.uuid4()).replace('-', '')}"
 
         # 生成文件名
-        original_filename1 = f"{md5_hash1}_original1.webp"
-        original_filename2 = f"{md5_hash2}_original2.webp"
-        face_filename1 = f"{md5_hash1}_face1.webp"
-        face_filename2 = f"{md5_hash2}_face2.webp"
+        original_filename1_raw = f"{md5_hash1}_original1.webp"
+        original_filename2_raw = f"{md5_hash2}_original2.webp"
+        face_filename1_raw = f"{md5_hash1}_face1.webp"
+        face_filename2_raw = f"{md5_hash2}_face2.webp"
+
+        original_path1, original_filename1 = _get_save_path(original_filename1_raw)
+        original_path2, original_filename2 = _get_save_path(original_filename2_raw)
+        face_path1, face_filename1 = _get_save_path(face_filename1_raw)
+        face_path2, face_filename2 = _get_save_path(face_filename2_raw)
 
         logger.info(f"Starting face similarity verification: {file1.filename} vs {file2.filename}")
         t1 = time.perf_counter()
@@ -4466,7 +4491,7 @@ async def face_similarity_verification(
                 raise HTTPException(status_code=400, detail="第二张图片中未检测到人脸，请上传包含清晰人脸的图片")
 
         # 保存原始图片到IMAGES_DIR（先不上传 BOS，供 DeepFace 使用）
-        original_path1 = os.path.join(IMAGES_DIR, original_filename1)
+        # original_path1 和 original_path2 已经在前面通过 _get_save_path 生成
         if not save_image_high_quality(
             image1,
             original_path1,
@@ -4475,7 +4500,6 @@ async def face_similarity_verification(
         ):
             raise HTTPException(status_code=500, detail="保存第一张原始图片失败")
 
-        original_path2 = os.path.join(IMAGES_DIR, original_filename2)
         if not save_image_high_quality(
             image2,
             original_path2,
@@ -4631,8 +4655,7 @@ async def face_similarity_verification(
                 face_img1 = image1[y1:y1_end, x1:x1_end]
                 face_img2 = image2[y2:y2_end, x2:x2_end]
 
-                face_path1 = os.path.join(IMAGES_DIR, face_filename1)
-                face_path2 = os.path.join(IMAGES_DIR, face_filename2)
+                # face_path1 和 face_path2 已经通过 _get_save_path 生成
                 
                 # 直接保存裁剪图，不进行特征点绘制
                 if save_image_high_quality(face_img1, face_path1, quality=SAVE_QUALITY):

face_analyzer.py

@@ -11,7 +11,7 @@ from config import logger, MODELS_PATH, OUTPUT_DIR, DEEPFACE_AVAILABLE, \
     YOLO_AVAILABLE
 from facial_analyzer import FacialFeatureAnalyzer
 from models import ModelType
-from utils import save_image_high_quality
+from utils import save_image_high_quality, get_date_subfolder
 
 if DEEPFACE_AVAILABLE:
     from deepface import DeepFace
@@ -981,7 +981,8 @@ class EnhancedFaceAnalyzer:
                 pass
 
             # 保存裁剪的人脸
-            cropped_face_filename = f"{original_image_hash}_face_{i + 1}.webp"
+            date_sub = get_date_subfolder(OUTPUT_DIR)
+            cropped_face_filename = f"{date_sub}/{original_image_hash}_face_{i + 1}.webp"
             cropped_face_path = os.path.join(OUTPUT_DIR, cropped_face_filename)
             try:
                 save_image_high_quality(face_cropped, cropped_face_path)

utils.py

@@ -6,6 +6,7 @@ import re
 import shutil
 import threading
 import time
+from datetime import datetime
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Optional
 from collections import OrderedDict
@@ -480,13 +481,20 @@ def upload_file_to_bos(file_path: str, object_name: str | None = None) -> bool:
     if object_name:
         object_key = object_name.strip("/ ")
     else:
-        base_name = os.path.basename(expanded_path)
+        # 如果文件在 _IMAGES_DIR_ABS 之下，保持其相对路径作为 object_key，包含可能的子目录（如日期目录）
+        try:
+            # 这里的 _IMAGES_DIR_ABS 已经提前在 utils.py 定义了
+            rel_path = os.path.relpath(expanded_path, _IMAGES_DIR_ABS)
+            # 路径分隔符统一使用正斜杠，防止 Windows 兼容性问题
+            object_key = rel_path.replace(os.sep, "/")
+        except (ValueError, Exception):
+            # 兜底：如果无法计算相对路径，则使用基础文件名
+            object_key = os.path.basename(expanded_path)
+
         if BOS_IMAGE_DIR:
             object_key = "/".join(
-                part.strip("/ ") for part in (BOS_IMAGE_DIR, base_name) if part
+                part.strip("/ ") for part in (BOS_IMAGE_DIR, object_key) if part
             )
-        else:
-            object_key = base_name
 
     mtime_ns = getattr(file_stat, "st_mtime_ns", int(file_stat.st_mtime * 1_000_000_000))
     cache_signature = (mtime_ns, file_stat.st_size)
@@ -602,6 +610,21 @@ def image_to_base64(image: np.ndarray) -> str:
     return f"data:image/webp;base64,{img_base64}"
 
 
+def get_date_subfolder(base_dir: str) -> str:
+    """
+    返回 picture/YYYYMMDD 格式的子目录名，并确保该目录在 base_dir 下存在。
+    :param base_dir: 基础目录路径
+    :return: 子目录名称 (如 "picture/20240305")
+    """
+    date_str = datetime.now().strftime("%Y%m%d")
+    # 增加一层 picture 目录
+    sub_folder = os.path.join("picture", date_str)
+    target_dir = os.path.join(base_dir, sub_folder)
+    os.makedirs(target_dir, exist_ok=True)
+    # 统一使用正斜杠返回，确保跨平台和 BOS 路径一致
+    return sub_folder.replace(os.sep, "/")
+
+
 def save_base64_to_unique_file(
     base64_string: str, output_dir: str = "output_images"
 ) -> str | None: