update at 2026-03-28 16:46:40

README.md

@@ -14,7 +14,9 @@ python remove_background.py
 # 处理单个文件
 python remove_background.py input.jpg output.png
 
-# 查看所有参数-m, --model)
+# 查看所有参数
+python remove_background.py -h
+```
 
 不同模型适用于不同场景：
 
@@ -39,7 +41,19 @@ python remove_background.py input.jpg output.png -m birefnet-portrait
 
 # 使用快速模型
 python remove_background.py input.jpg output.png -m u2netp
-``
+
+# 书画类文字偏浅的温和参数示例
+python remove_background.py images output \
+  --remove-subject --black_subject --gray_subject --save_mask \
+  --black-threshold 30\
+  --gray-saturation-threshold 30 --gray-value-threshold 30 \
+  --edge-grow 2 \
+  --feather --feather-radius 4 \
+  --aot-pretrain experiments/G0000000.pt \
+  --aot-max-size 1000
+```
+
+查看已下载模型：
 ```bash
 ls -lh ~/.u2net/
 ```
@@ -75,6 +89,40 @@ rm -rf ~/.u2net/
 rm ~/.u2net/u2net.onnx
 ```
 
+## AOT-GAN 修补后端
+
+`--remove-subject` 默认使用 AOT-GAN 修补。
+AOT-GAN 依赖 PyTorch（官方仓库测试 Python 3.8 / torch 1.8.1）。建议使用独立虚拟环境或确保兼容版本。
+
+```bash
+# 安装依赖（示例）
+pip install torch torchvision
+```
+
+下载预训练权重后，运行示例：
+```bash
+python remove_background.py "images/IMG_9259 2.JPG" \
+  --remove-subject --black-subject --gray-subject --save-mask \
+  --aot-pretrain experiments/places2.pth
+```
+
+CPU 无 GPU 时的加速建议（只裁剪主体区域并限制最大边）：
+```bash
+python remove_background.py "images/IMG_9259 2.JPG" \
+  --remove-subject --black-subject --gray-subject --save-mask \
+  --aot-pretrain experiments/places2.pth \
+  --aot-crop --aot-crop-pad 24 --aot-max-size 1400
+```
+
+减少“补脸”倾向：启用随机噪声预填充
+```bash
+python remove_background.py "images/IMG_9259 2.JPG" \
+  --remove-subject --black-subject --gray-subject --save-mask \
+  --aot-pretrain experiments/places2.pth \
+  --aot-crop --aot-crop-pad 64 --aot-max-size 900 \
+  --aot-noise-prefill --aot-noise-strength 1.0
+```
+
 ## 可调整参数说明
 
 ### 1. 模型选择 (model_name)
@@ -97,10 +145,11 @@ rm ~/.u2net/u2net.onnx
 
 Alpha Matting 是后处理步骤，可以显著改善边缘质量，特别是头发、毛发等细节。
 
-#### alpha_matting (True/False)
-- **作用**: 是否启用alpha matting
-- **默认**: False
-- **建议**: 如果边缘不自然，启用此选项
+#### alpha_matting（开关）
+- **作用**: 是否启用 alpha matting，提升边缘质量
+- **默认**: 关闭（不传 `-a/--alpha-matting`）
+- **启用方式**: 传入 `-a` 或 `--alpha-matting`
+- **效果**: 有利于细节边缘（毛发/细线），但速度稍慢
 
 #### alpha_matting_foreground_threshold (0-255)
 - **作用**: 前景阈值，控制哪些区域被认为是前景
@@ -128,9 +177,45 @@ Alpha Matting 是后处理步骤，可以显著改善边缘质量，特别是头
 
 ### 3. Mask后处理 (post_process_mask)
 
-- **作用**: 对mask进行额外的后处理
-- **默认**: False
-- **建议**: 可以尝试启用看效果是否改善
+- **作用**: 对 mask 进行额外后处理
+- **默认**: 关闭（不传 `-p/--post-process`）
+- **启用方式**: 传入 `-p` 或 `--post-process`
+- **效果**: 有助于减少毛边，但可能略损失细节
+
+### 4. 去主体补背景 (remove_subject)
+
+用于“去掉主体并补全背景”。当前仅使用 AOT-GAN 修补。
+
+- **remove_subject（开关）**: 启用去主体补背景（默认关闭，传 `--remove-subject` 开启）
+- **aot_root**: AOT-GAN 目录（默认: `AOT-GAN-for-Inpainting`）
+- **aot_pretrain**: AOT-GAN 权重文件路径（必填）
+- **aot_device**: AOT-GAN 设备（默认: `cpu`）
+- **aot_block_num**: AOTBlock 数量（默认: 8）
+- **aot_rates**: AOTBlock 膨胀率（默认: `1+2+4+8`）
+- **aot_crop（开关）**: 仅对 mask 覆盖区域裁剪修补（默认关闭，传 `--aot-crop` 开启）
+- **aot_crop_pad (像素)**: 裁剪边缘留白像素（默认: 0）
+- **aot_max_size (像素)**: AOT 输入最大边限制（默认: 0 表示不限制）
+- **aot_noise_prefill（开关）**: AOT使用随机噪声预填充（默认关闭）
+- **aot_noise_strength (系数)**: 噪声强度（默认: 1.0）
+- **mask_dilate (像素)**: mask 膨胀大小（默认: 3）。越大去除范围越大，风险更高
+- **mask_blur (像素)**: mask 模糊大小（默认: 3）。越大边缘越柔和但易过度
+- **mask_threshold (0-255)**: alpha 阈值（默认: 10）。越大保留越多主体
+- **edge_grow (像素)**: 主体边缘额外扩张（默认: 0）。用于清理残留边缘
+- **save_mask（开关）**: 保存 mask 方便检查（默认关闭，传 `--save-mask` 开启）
+- **black_subject（开关）**: 将黑色内容也视为主体（默认关闭，传 `--black-subject` 开启）
+- **black_threshold (0-255)**: 黑色阈值（默认: 50）。越大越容易把浅灰当黑
+- **gray_subject（开关）**: 将灰阶内容也视为主体（默认关闭，传 `--gray-subject` 开启）
+- **gray_saturation_threshold (0-255)**: 灰阶饱和度阈值（默认: 30）。越大越容易把彩色当灰
+- **gray_value_threshold (0-255)**: 灰阶亮度阈值（默认: 200）。越大越容易把浅灰当灰
+- **feather（开关）**: 启用边缘过渡（默认关闭，传 `--feather` 开启）
+- **feather_radius (像素)**: 过渡半径（默认: 5）。越大过渡越柔和但可能变糊
+- **说明**: 过渡仅在 mask 外侧进行，避免把主体边缘带回
+
+### 5. 参数调优建议（针对书画/字迹）
+- 先开启 `--remove-subject`，仅看主体遮罩是否覆盖到字迹
+- 文字残留：提高 `--black-threshold` 或 `--gray-*` 阈值
+- 过度修补：降低 `--black-threshold`、`--gray-value-threshold`，并减小 `--mask-dilate/--mask-blur`
+- 边缘不自然：尝试开启 `--feather` 并使用较小的 `--feather-radius`
 
 ## 常见问题解决
 

pyproject.toml

@@ -0,0 +1,16 @@
+[tool.mypy]
+python_version = "3.12"
+ignore_missing_imports = true
+exclude = '^(generative_inpainting|images|output)/'
+
+[tool.ruff]
+exclude = [
+  "AOT-GAN-for-Inpainting",
+  "generative_inpainting",
+  "images",
+  "output",
+  "__pycache__",
+  ".venv",
+  "venv",
+  ".git",
+]

remove_background.py

@@ -3,10 +3,288 @@
 使用rembg库自动去除图片背景
 """
 import os
+import sys
 import argparse
 from pathlib import Path
+import numpy as np
+import cv2
+from PIL import Image, ImageOps
+
+# 避免 numba 在某些环境下缓存失败
+os.environ.setdefault("NUMBA_CACHE_DIR", "/tmp/numba_cache")
+
 from rembg import remove, new_session
-from PIL import Image
+
+AOT_MODEL_CACHE: dict[tuple[str, str, int, tuple[int, ...]], tuple[object, object]] = {}
+
+def _resolve_aot_paths(aot_root, aot_pretrain):
+    """解析 AOT-GAN 的路径配置。"""
+    aot_root = Path(aot_root).resolve()
+    if aot_pretrain is None:
+        aot_pretrain = None
+    else:
+        aot_pretrain = Path(aot_pretrain)
+        if not aot_pretrain.is_absolute():
+            # 预训练权重相对路径以当前工作目录为基准
+            aot_pretrain = (Path.cwd() / aot_pretrain).resolve()
+    return aot_root, aot_pretrain
+
+def _parse_aot_rates(rates_str):
+    parts = [p for p in rates_str.split("+") if p]
+    return [int(p) for p in parts]
+
+def _get_aot_model(aot_root, aot_pretrain, device="cpu", block_num=8, rates=None):
+    """加载/缓存 AOT-GAN 模型。"""
+    aot_root, aot_pretrain = _resolve_aot_paths(aot_root, aot_pretrain)
+    if not aot_root.exists():
+        raise FileNotFoundError(f"AOT-GAN目录不存在: {aot_root}")
+    if aot_pretrain is None:
+        raise ValueError("AOT-GAN需要指定预训练权重路径（--aot-pretrain）")
+    if not aot_pretrain.exists():
+        raise FileNotFoundError(f"AOT-GAN权重不存在: {aot_pretrain}")
+    if rates is None:
+        rates = [1, 2, 4, 8]
+    rates_tuple = tuple(rates)
+    key = (str(aot_pretrain), device, int(block_num), rates_tuple)
+    if key in AOT_MODEL_CACHE:
+        return AOT_MODEL_CACHE[key]
+
+    src_root = aot_root / "src"
+    if str(src_root) not in sys.path:
+        sys.path.insert(0, str(src_root))
+    import importlib
+    try:
+        import torch
+    except ImportError as exc:
+        raise ImportError("未找到 PyTorch，请先按README安装依赖。") from exc
+
+    net = importlib.import_module("model.aotgan")
+    class _Args:
+        pass
+    args = _Args()
+    args.block_num = int(block_num)
+    args.rates = rates
+    model = net.InpaintGenerator(args)
+    state = torch.load(str(aot_pretrain), map_location=device)
+    if isinstance(state, dict):
+        if "state_dict" in state:
+            state = state["state_dict"]
+        elif "model" in state:
+            state = state["model"]
+        elif "generator" in state:
+            state = state["generator"]
+    if isinstance(state, dict) and any(k.startswith("module.") for k in state.keys()):
+        state = {k.replace("module.", "", 1): v for k, v in state.items()}
+    model.load_state_dict(state, strict=True)
+    model.to(device)
+    model.eval()
+    AOT_MODEL_CACHE[key] = (model, device)
+    return AOT_MODEL_CACHE[key]
+
+def _mask_bbox(mask):
+    ys, xs = np.where(mask > 0)
+    if len(xs) == 0:
+        return None
+    return int(xs.min()), int(ys.min()), int(xs.max()), int(ys.max())
+
+def _expand_bbox(bbox, pad, width, height):
+    if bbox is None:
+        return None
+    x0, y0, x1, y1 = bbox
+    pad = max(0, int(pad))
+    x0 = max(0, x0 - pad)
+    y0 = max(0, y0 - pad)
+    x1 = min(width - 1, x1 + pad)
+    y1 = min(height - 1, y1 + pad)
+    return x0, y0, x1, y1
+
+def _expand_bbox_min_size(bbox, pad, width, height, min_size):
+    """在给定 pad 基础上，确保裁剪区域至少为 min_size。"""
+    expanded = _expand_bbox(bbox, pad, width, height)
+    if expanded is None:
+        return None
+    x0, y0, x1, y1 = expanded
+    roi_w = x1 - x0 + 1
+    roi_h = y1 - y0 + 1
+    need_w = max(0, int(min_size) - roi_w)
+    need_h = max(0, int(min_size) - roi_h)
+    if need_w == 0 and need_h == 0:
+        return expanded
+    extra = max((need_w + 1) // 2, (need_h + 1) // 2)
+    return _expand_bbox(bbox, pad + extra, width, height)
+
+def _build_noise_prefill(img, mask_t, strength):
+    """为mask区域生成噪声预填充，img取值范围为[-1, 1]。"""
+    import torch
+    img01 = (img + 1.0) / 2.0
+    unmasked = 1.0 - mask_t
+    denom = unmasked.sum()
+    if denom.item() < 1.0:
+        mean = torch.full((1, 3, 1, 1), 0.5, device=img.device)
+        std = torch.full((1, 3, 1, 1), 0.2, device=img.device)
+    else:
+        mean = (img01 * unmasked).sum(dim=(0, 2, 3), keepdim=True) / denom
+        var = ((img01 - mean) ** 2 * unmasked).sum(dim=(0, 2, 3), keepdim=True) / denom
+        std = torch.sqrt(var + 1e-6)
+    noise = mean + std * float(strength) * torch.randn_like(img01)
+    noise = noise.clamp(0.0, 1.0)
+    return noise * 2.0 - 1.0
+
+def _inpaint_with_aot_core(
+    bgr,
+    mask,
+    aot_root,
+    aot_pretrain,
+    device="cpu",
+    block_num=8,
+    rates=None,
+    noise_prefill=False,
+    noise_strength=1.0,
+):
+    """使用 AOT-GAN 进行修补，返回 BGR 图像（与输入同尺寸）。"""
+    if mask.dtype != np.uint8:
+        mask = mask.astype(np.uint8)
+    if mask.max() <= 1:
+        mask = mask * 255
+    if mask.max() == 0:
+        return bgr
+
+    h, w = mask.shape
+    grid = 4
+    h2 = (h // grid) * grid
+    w2 = (w // grid) * grid
+    if h2 == 0 or w2 == 0:
+        return bgr
+
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    img_crop = rgb[:h2, :w2, :]
+    mask_crop = mask[:h2, :w2]
+
+    import torch
+    img = torch.from_numpy(img_crop).permute(2, 0, 1).float() / 255.0
+    img = img * 2.0 - 1.0
+    mask_t = torch.from_numpy((mask_crop > 0).astype(np.float32)).unsqueeze(0)
+    img = img.unsqueeze(0)
+    mask_t = mask_t.unsqueeze(0)
+    if device != "cpu":
+        img = img.to(device)
+        mask_t = mask_t.to(device)
+
+    model, _ = _get_aot_model(
+        aot_root=aot_root,
+        aot_pretrain=aot_pretrain,
+        device=device,
+        block_num=block_num,
+        rates=rates,
+    )
+    with torch.no_grad():
+        if noise_prefill:
+            noise = _build_noise_prefill(img, mask_t, noise_strength)
+            image_masked = img * (1 - mask_t) + noise * mask_t
+        else:
+            image_masked = img * (1 - mask_t) + mask_t
+        pred = model(image_masked, mask_t)
+        comp = pred * mask_t + img * (1 - mask_t)
+    comp = comp[0].clamp(-1.0, 1.0)
+    comp = (comp + 1.0) / 2.0 * 255.0
+    comp = comp.permute(1, 2, 0).byte().cpu().numpy()
+    result_bgr = cv2.cvtColor(comp, cv2.COLOR_RGB2BGR)
+
+    if h2 == h and w2 == w:
+        return result_bgr
+    output_full = bgr.copy()
+    output_full[:h2, :w2, :] = result_bgr
+    return output_full
+
+def _inpaint_with_aot(
+    bgr,
+    mask,
+    aot_root,
+    aot_pretrain,
+    device="cpu",
+    block_num=8,
+    rates=None,
+    crop=False,
+    crop_pad=0,
+    max_size=0,
+    noise_prefill=False,
+    noise_strength=1.0,
+):
+    """使用 AOT-GAN 进行修补，支持裁剪与限幅以加速。"""
+    min_side = 32
+    if mask.dtype != np.uint8:
+        mask = mask.astype(np.uint8)
+    if mask.max() <= 1:
+        mask = mask * 255
+    if mask.max() == 0:
+        return bgr
+
+    h, w = mask.shape
+    x0 = y0 = 0
+    x1 = w - 1
+    y1 = h - 1
+    if crop:
+        bbox = _mask_bbox(mask)
+        if bbox is None:
+            return bgr
+        expanded = _expand_bbox_min_size(bbox, crop_pad, w, h, min_side)
+        if expanded is None:
+            return bgr
+        x0, y0, x1, y1 = expanded
+        roi_w = x1 - x0 + 1
+        roi_h = y1 - y0 + 1
+        if roi_w < min_side or roi_h < min_side:
+            # 裁剪区域过小会导致 AOT 失败，回退到全图修补
+            crop = False
+            x0 = y0 = 0
+            x1 = w - 1
+            y1 = h - 1
+
+    bgr_roi = bgr[y0 : y1 + 1, x0 : x1 + 1]
+    mask_roi = mask[y0 : y1 + 1, x0 : x1 + 1]
+    roi_h, roi_w = bgr_roi.shape[:2]
+
+    scale = 1.0
+    max_size = int(max_size) if max_size else 0
+    if max_size > 0:
+        max_dim = max(roi_h, roi_w)
+        if max_dim > max_size:
+            scale = max_size / float(max_dim)
+            new_w = max(1, int(round(roi_w * scale)))
+            new_h = max(1, int(round(roi_h * scale)))
+            if min(new_w, new_h) < min_side:
+                scale = 1.0
+                new_w = roi_w
+                new_h = roi_h
+            interp = cv2.INTER_AREA if scale < 1.0 else cv2.INTER_LINEAR
+            if scale != 1.0:
+                bgr_roi = cv2.resize(bgr_roi, (new_w, new_h), interpolation=interp)
+                mask_roi = cv2.resize(mask_roi, (new_w, new_h), interpolation=cv2.INTER_NEAREST)
+
+    filled_roi = _inpaint_with_aot_core(
+        bgr_roi,
+        mask_roi,
+        aot_root=aot_root,
+        aot_pretrain=aot_pretrain,
+        device=device,
+        block_num=block_num,
+        rates=rates,
+        noise_prefill=noise_prefill,
+        noise_strength=noise_strength,
+    )
+
+    if scale != 1.0:
+        filled_roi = cv2.resize(
+            filled_roi,
+            (roi_w, roi_h),
+            interpolation=cv2.INTER_LINEAR,
+        )
+
+    if not crop:
+        return filled_roi
+    output_full = bgr.copy()
+    output_full[y0 : y1 + 1, x0 : x1 + 1] = filled_roi
+    return output_full
 
 # 支持HEIC格式
 try:
@@ -16,17 +294,6 @@ try:
 except ImportError:
     HEIC_SUPPORTED = False
 
-def str2bool(v):
-    """将字符串转换为布尔值"""
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('布尔值应为true或false')
-
 def remove_background(input_path, output_path, session=None, **kwargs):
     """
     去除图片背景
@@ -40,7 +307,7 @@ def remove_background(input_path, output_path, session=None, **kwargs):
     print(f"正在处理: {input_path}")
     
     # 读取输入图片
-    input_image = Image.open(input_path)
+    input_image = ImageOps.exif_transpose(Image.open(input_path))
     
     # 去除背景
     output_image = remove(input_image, session=session, **kwargs)
@@ -50,10 +317,195 @@ def remove_background(input_path, output_path, session=None, **kwargs):
     
     print(f"已保存: {output_path}")
 
-def process_images_folder(input_folder, output_folder, model_name="u2net", 
-                         alpha_matting=False, alpha_matting_foreground_threshold=240,
-                         alpha_matting_background_threshold=10, alpha_matting_erode_size=10,
-                         post_process_mask=False):
+def _pil_to_bgr(image):
+    """将PIL图片转换为OpenCV BGR格式"""
+    rgb = image.convert("RGB")
+    arr = np.array(rgb)
+    return cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
+
+def _alpha_to_mask(alpha_channel, threshold=10):
+    """将alpha通道转换为二值mask（0或255）"""
+    mask = (alpha_channel > threshold).astype(np.uint8) * 255
+    return mask
+
+def _ensure_rgba_size(image, target_size):
+    if image.mode != "RGBA":
+        image = image.convert("RGBA")
+    if image.size != target_size:
+        image = image.resize(target_size, resample=Image.NEAREST)
+    return image
+
+def _prepare_mask(mask, mask_dilate=3, mask_blur=3, edge_grow=0):
+    """生成硬边mask与处理后mask（用于填补/过渡）"""
+    if mask_dilate and mask_dilate > 0:
+        kernel = np.ones((mask_dilate, mask_dilate), np.uint8)
+        mask = cv2.dilate(mask, kernel, iterations=1)
+    if edge_grow and edge_grow > 0:
+        kernel = np.ones((3, 3), np.uint8)
+        mask = cv2.dilate(mask, kernel, iterations=int(edge_grow))
+
+    mask_hard = mask.copy()
+    mask_used = mask_hard
+    if mask_blur and mask_blur > 0:
+        k = mask_blur if mask_blur % 2 == 1 else mask_blur + 1
+        mask_used = cv2.GaussianBlur(mask_hard, (k, k), 0)
+        mask_used = (mask_used > 0).astype(np.uint8) * 255
+    return mask_hard, mask_used
+
+def remove_subject_and_inpaint(
+    input_path,
+    output_path,
+    session=None,
+    mask_dilate=3,
+    mask_blur=3,
+    mask_threshold=10,
+    edge_grow=0,
+    aot_root="AOT-GAN-for-Inpainting",
+    aot_pretrain=None,
+    aot_device="cpu",
+    aot_block_num=8,
+    aot_rates="1+2+4+8",
+    aot_crop=False,
+    aot_crop_pad=0,
+    aot_max_size=0,
+    aot_noise_prefill=False,
+    aot_noise_strength=1.0,
+    black_subject=False,
+    black_threshold=50,
+    gray_subject=False,
+    gray_saturation_threshold=30,
+    gray_value_threshold=200,
+    feather=False,
+    feather_radius=5,
+    save_mask=False,
+    mask_output_path=None,
+    **kwargs,
+):
+    """
+    去掉主体并补全背景
+
+    Args:
+        input_path: 输入图片路径
+        output_path: 输出图片路径
+        session: rembg会话对象（可选）
+        aot_root: AOT-GAN目录
+        aot_pretrain: AOT-GAN权重文件路径
+        aot_device: AOT-GAN设备
+        aot_block_num: AOT-GAN AOTBlock 数量
+        aot_rates: AOT-GAN AOTBlock 膨胀率
+        aot_crop: AOT仅裁剪mask区域进行修补
+        aot_crop_pad: AOT裁剪边缘留白像素
+        aot_max_size: AOT输入最大边限制（0为不限制）
+        aot_noise_prefill: AOT使用随机噪声预填充
+        aot_noise_strength: 噪声强度系数
+        mask_dilate: mask膨胀大小
+        mask_blur: mask模糊大小（奇数）
+        mask_threshold: alpha阈值
+        save_mask: 是否保存mask
+        mask_output_path: mask保存路径
+        **kwargs: rembg参数
+    """
+    print(f"正在处理(去主体补背景): {input_path}")
+
+    input_image = ImageOps.exif_transpose(Image.open(input_path))
+
+    # 使用rembg获取主体mask
+    output_image = remove(input_image, session=session, **kwargs)
+    output_image = _ensure_rgba_size(output_image, input_image.size)
+
+    alpha = np.array(output_image.getchannel("A"))
+    mask = _alpha_to_mask(alpha, threshold=mask_threshold)
+
+    bgr = _pil_to_bgr(input_image)
+    if black_subject:
+        gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+        black_mask = (gray <= black_threshold).astype(np.uint8) * 255
+        mask = cv2.bitwise_or(mask, black_mask)
+    if gray_subject:
+        hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
+        s = hsv[:, :, 1]
+        v = hsv[:, :, 2]
+        gray_mask = ((s <= gray_saturation_threshold) & (v <= gray_value_threshold)).astype(np.uint8) * 255
+        mask = cv2.bitwise_or(mask, gray_mask)
+
+    mask_hard, mask_used = _prepare_mask(
+        mask,
+        mask_dilate=mask_dilate,
+        mask_blur=mask_blur,
+        edge_grow=edge_grow,
+    )
+
+    rates = _parse_aot_rates(aot_rates)
+    filled = _inpaint_with_aot(
+        bgr,
+        mask_used,
+        aot_root=aot_root,
+        aot_pretrain=aot_pretrain,
+        device=aot_device,
+        block_num=aot_block_num,
+        rates=rates,
+        crop=aot_crop,
+        crop_pad=aot_crop_pad,
+        max_size=aot_max_size,
+        noise_prefill=aot_noise_prefill,
+        noise_strength=aot_noise_strength,
+    )
+
+    if feather and feather_radius > 0:
+        # 仅在mask外侧做过渡，避免把原主体带回
+        mask_bin = (mask_hard > 0).astype(np.uint8) * 255
+        dist_out = cv2.distanceTransform(255 - mask_bin, cv2.DIST_L2, 3)
+        alpha = np.ones_like(dist_out, dtype=np.float32)
+        outside = mask_bin == 0
+        alpha[outside] = np.clip(1.0 - (dist_out[outside] / float(feather_radius)), 0.0, 1.0)
+        alpha = alpha[:, :, None]
+        blended = (alpha * filled + (1.0 - alpha) * bgr).astype(np.uint8)
+        result = cv2.cvtColor(blended, cv2.COLOR_BGR2RGB)
+    else:
+        result = cv2.cvtColor(filled, cv2.COLOR_BGR2RGB)
+    Image.fromarray(result).save(output_path)
+
+    if save_mask:
+        if mask_output_path is None:
+            mask_output_path = str(Path(output_path).with_suffix("")) + "_mask.png"
+        Image.fromarray(mask_used).save(mask_output_path)
+        print(f"已保存mask: {mask_output_path}")
+
+    print(f"已保存: {output_path}")
+
+def process_images_folder(
+    input_folder,
+    output_folder,
+    model_name="u2net",
+    alpha_matting=False,
+    alpha_matting_foreground_threshold=240,
+    alpha_matting_background_threshold=10,
+    alpha_matting_erode_size=10,
+    post_process_mask=False,
+    remove_subject=False,
+    mask_dilate=3,
+    mask_blur=3,
+    mask_threshold=10,
+    edge_grow=0,
+    aot_root="AOT-GAN-for-Inpainting",
+    aot_pretrain=None,
+    aot_device="cpu",
+    aot_block_num=8,
+    aot_rates="1+2+4+8",
+    aot_crop=False,
+    aot_crop_pad=0,
+    aot_max_size=0,
+    aot_noise_prefill=False,
+    aot_noise_strength=1.0,
+    black_subject=False,
+    black_threshold=50,
+    gray_subject=False,
+    gray_saturation_threshold=30,
+    gray_value_threshold=200,
+    feather=False,
+    feather_radius=5,
+    save_mask=False,
+):
     """
     批量处理文件夹中的所有图片
     
@@ -105,26 +557,100 @@ def process_images_folder(input_folder, output_folder, model_name="u2net",
         print(f"  - 背景阈值: {alpha_matting_background_threshold}")
         print(f"  - 侵蚀大小: {alpha_matting_erode_size}")
     print(f"Mask后处理: {'启用' if post_process_mask else '禁用'}")
+    print(f"去主体补背景: {'启用' if remove_subject else '禁用'}")
+    if remove_subject:
+        print(f"  - AOT目录: {aot_root}")
+        print(f"  - AOT权重: {aot_pretrain}")
+        print(f"  - AOT设备: {aot_device}")
+        print(f"  - AOT块数: {aot_block_num}")
+        print(f"  - AOT膨胀率: {aot_rates}")
+        print(f"  - AOT裁剪: {'是' if aot_crop else '否'}")
+        if aot_crop:
+            print(f"  - AOT裁剪边界: {aot_crop_pad}")
+        print(f"  - AOT最大边: {aot_max_size}")
+        print(f"  - AOT噪声预填充: {'是' if aot_noise_prefill else '否'}")
+        if aot_noise_prefill:
+            print(f"  - AOT噪声强度: {aot_noise_strength}")
+        print(f"  - mask膨胀: {mask_dilate}")
+        print(f"  - mask模糊: {mask_blur}")
+        print(f"  - mask阈值: {mask_threshold}")
+        print(f"  - 边缘扩张: {edge_grow}")
+        print(f"  - 黑色内容作为主体: {'是' if black_subject else '否'}")
+        if black_subject:
+            print(f"  - 黑色阈值: {black_threshold}")
+        print(f"  - 灰阶内容作为主体: {'是' if gray_subject else '否'}")
+        if gray_subject:
+            print(f"  - 灰阶饱和度阈值: {gray_saturation_threshold}")
+            print(f"  - 灰阶亮度阈值: {gray_value_threshold}")
+        print(f"  - 边缘过渡: {'是' if feather else '否'}")
+        if feather:
+            print(f"  - 过渡半径: {feather_radius}")
+        print(f"  - 保存mask: {'是' if save_mask else '否'}")
     print("-" * 50)
     
     # 处理每张图片
     for i, image_file in enumerate(image_files, 1):
         try:
-            # 输出文件名（保持原始名称，改为PNG格式以支持透明背景）
-            output_filename = image_file.stem + '_nobg.png'
+            if remove_subject:
+                suffix = image_file.suffix.lower()
+                if suffix in {".jpg", ".jpeg", ".png", ".bmp", ".webp"}:
+                    output_filename = image_file.stem + "_bgfill" + suffix
+                else:
+                    output_filename = image_file.stem + "_bgfill.jpg"
+            else:
+                # 去背景默认使用PNG格式以支持透明背景
+                output_filename = image_file.stem + "_nobg.png"
             output_path = Path(output_folder) / output_filename
             
             print(f"[{i}/{len(image_files)}] ", end="")
-            remove_background(
-                str(image_file), 
-                str(output_path),
-                session=session,
-                alpha_matting=alpha_matting,
-                alpha_matting_foreground_threshold=alpha_matting_foreground_threshold,
-                alpha_matting_background_threshold=alpha_matting_background_threshold,
-                alpha_matting_erode_size=alpha_matting_erode_size,
-                post_process_mask=post_process_mask
-            )
+            if remove_subject:
+                mask_output_path = None
+                if save_mask:
+                    mask_output_path = str(Path(output_folder) / (image_file.stem + "_mask.png"))
+                remove_subject_and_inpaint(
+                    str(image_file),
+                    str(output_path),
+                    session=session,
+                    alpha_matting=alpha_matting,
+                    alpha_matting_foreground_threshold=alpha_matting_foreground_threshold,
+                    alpha_matting_background_threshold=alpha_matting_background_threshold,
+                    alpha_matting_erode_size=alpha_matting_erode_size,
+                    post_process_mask=post_process_mask,
+                    mask_dilate=mask_dilate,
+                    mask_blur=mask_blur,
+                    mask_threshold=mask_threshold,
+                    edge_grow=edge_grow,
+                    aot_root=aot_root,
+                    aot_pretrain=aot_pretrain,
+                    aot_device=aot_device,
+                    aot_block_num=aot_block_num,
+                    aot_rates=aot_rates,
+                    aot_crop=aot_crop,
+                    aot_crop_pad=aot_crop_pad,
+                    aot_max_size=aot_max_size,
+                    aot_noise_prefill=aot_noise_prefill,
+                    aot_noise_strength=aot_noise_strength,
+                    black_subject=black_subject,
+                    black_threshold=black_threshold,
+                    gray_subject=gray_subject,
+                    gray_saturation_threshold=gray_saturation_threshold,
+                    gray_value_threshold=gray_value_threshold,
+                    feather=feather,
+                    feather_radius=feather_radius,
+                    save_mask=save_mask,
+                    mask_output_path=mask_output_path,
+                )
+            else:
+                remove_background(
+                    str(image_file),
+                    str(output_path),
+                    session=session,
+                    alpha_matting=alpha_matting,
+                    alpha_matting_foreground_threshold=alpha_matting_foreground_threshold,
+                    alpha_matting_background_threshold=alpha_matting_background_threshold,
+                    alpha_matting_erode_size=alpha_matting_erode_size,
+                    post_process_mask=post_process_mask,
+                )
             
         except Exception as e:
             print(f"处理 {image_file.name} 时出错: {e}")
@@ -171,9 +697,8 @@ if __name__ == "__main__":
                        help='选择使用的模型 (默认: isnet-general-use)')
     
     # Alpha Matting参数
-    parser.add_argument('-a', '--alpha-matting', type=str2bool, nargs='?', const=True, default=True,
-                       metavar='true/false',
-                       help='启用alpha matting后处理（默认: true）。用法: -a 或 -a true 或 -a false')
+    parser.add_argument('-a', '--alpha-matting', '--alpha_matting', action='store_true',
+                       help='启用alpha matting后处理（默认: false）')
     parser.add_argument('-ft', '--foreground-threshold', type=int, default=245,
                        help='前景阈值 (0-255)，值越大保留越多细节 (默认: 245)')
     parser.add_argument('-bt', '--background-threshold', type=int, default=8,
@@ -182,9 +707,56 @@ if __name__ == "__main__":
                        help='侵蚀大小，用于平滑边缘，值越大越平滑但可能丢失细节 (默认: 2)')
     
     # 其他选项
-    parser.add_argument('-p', '--post-process', type=str2bool, nargs='?', const=True, default=True,
-                       metavar='true/false',
-                       help='启用mask后处理（默认: true）。用法: -p 或 -p true 或 -p false')
+    parser.add_argument('-p', '--post-process', '--post_process', action='store_true',
+                       help='启用mask后处理（默认: false）')
+
+    # 去主体补背景参数
+    parser.add_argument('--remove-subject', '--remove_subject', action='store_true',
+                        help='去掉主体并补全背景（默认: false）')
+    parser.add_argument('--aot-root', type=str, default='AOT-GAN-for-Inpainting',
+                        help='AOT-GAN目录（默认: AOT-GAN-for-Inpainting）')
+    parser.add_argument('--aot-pretrain', type=str, default=None,
+                        help='AOT-GAN预训练权重文件路径（必填，相对路径基于当前目录）')
+    parser.add_argument('--aot-device', type=str, default='cpu',
+                        help='AOT-GAN设备（默认: cpu）')
+    parser.add_argument('--aot-block-num', type=int, default=8,
+                        help='AOTBlock数量（默认: 8）')
+    parser.add_argument('--aot-rates', type=str, default='1+2+4+8',
+                        help='AOTBlock膨胀率（默认: 1+2+4+8）')
+    parser.add_argument('--aot-crop', action='store_true',
+                        help='AOT仅对mask区域裁剪修补（默认: false）')
+    parser.add_argument('--aot-crop-pad', type=int, default=0,
+                        help='AOT裁剪边缘留白像素（默认: 0）')
+    parser.add_argument('--aot-max-size', type=int, default=0,
+                        help='AOT输入最大边限制，0为不限制（默认: 0）')
+    parser.add_argument('--aot-noise-prefill', action='store_true',
+                        help='AOT使用随机噪声预填充（默认: false）')
+    parser.add_argument('--aot-noise-strength', type=float, default=1.0,
+                        help='AOT噪声强度系数（默认: 1.0）')
+    parser.add_argument('--mask-dilate', type=int, default=3,
+                        help='mask膨胀大小（默认: 3）')
+    parser.add_argument('--mask-blur', type=int, default=3,
+                        help='mask模糊大小（默认: 3，建议奇数）')
+    parser.add_argument('--mask-threshold', type=int, default=10,
+                        help='alpha阈值（默认: 10）')
+    parser.add_argument('--edge-grow', type=int, default=0,
+                        help='主体边缘扩张像素（默认: 0）')
+    parser.add_argument('--save-mask', '--save_mask', action='store_true',
+                        help='保存mask到output目录（默认: false）')
+    parser.add_argument('--black-subject', '--black_subject', action='store_true',
+                        help='将黑色内容也视为主体（默认: false）')
+    parser.add_argument('--black-threshold', type=int, default=50,
+                        help='黑色阈值(0-255，灰度越小越黑，默认: 50)')
+    parser.add_argument('--gray-subject', '--gray_subject', action='store_true',
+                        help='将灰阶内容也视为主体（默认: false）')
+    parser.add_argument('--gray-saturation-threshold', type=int, default=30,
+                        help='灰阶饱和度阈值(0-255，越小越接近灰阶，默认: 30)')
+    parser.add_argument('--gray-value-threshold', type=int, default=200,
+                        help='灰阶亮度阈值(0-255，越小越暗，默认: 200)')
+    parser.add_argument('--feather', action='store_true',
+                        help='启用边缘过渡融合（默认: false）')
+    parser.add_argument('--feather-radius', type=int, default=5,
+                        help='边缘过渡半径（默认: 5）')
     
     args = parser.parse_args()
     
@@ -201,13 +773,33 @@ if __name__ == "__main__":
     
     # 处理单个文件
     if input_path.is_file():
+        suffix = input_path.suffix.lower()
+        if args.remove_subject:
+            if suffix in {".jpg", ".jpeg", ".png", ".bmp", ".webp"}:
+                output_name = input_path.stem + "_bgfill" + suffix
+            else:
+                output_name = input_path.stem + "_bgfill.jpg"
+        else:
+            output_name = input_path.stem + "_nobg.png"
+
         # 确定输出路径
         if args.output is None:
-            output_path = input_path.parent / 'output' / (input_path.stem + '_nobg.png')
+            if args.remove_subject:
+                output_path = input_path.parent / "output" / output_name
+            else:
+                output_path = input_path.parent / 'output' / output_name
             output_path.parent.mkdir(parents=True, exist_ok=True)
         else:
-            output_path = Path(args.output)
-            output_path.parent.mkdir(parents=True, exist_ok=True)
+            output_candidate = Path(args.output)
+            if output_candidate.exists() and output_candidate.is_dir():
+                output_path = output_candidate / output_name
+                output_path.parent.mkdir(parents=True, exist_ok=True)
+            elif output_candidate.suffix == "":
+                output_candidate.mkdir(parents=True, exist_ok=True)
+                output_path = output_candidate / output_name
+            else:
+                output_path = output_candidate
+                output_path.parent.mkdir(parents=True, exist_ok=True)
         
         print(f"输入文件: {input_path}")
         print(f"输出文件: {output_path}")
@@ -218,22 +810,89 @@ if __name__ == "__main__":
             print(f"  - 背景阈值: {args.background_threshold}")
             print(f"  - 侵蚀大小: {args.erode_size}")
         print(f"Mask后处理: {'启用' if args.post_process else '禁用'}")
+        print(f"去主体补背景: {'启用' if args.remove_subject else '禁用'}")
+        if args.remove_subject:
+            print(f"  - AOT目录: {args.aot_root}")
+            print(f"  - AOT权重: {args.aot_pretrain}")
+            print(f"  - AOT设备: {args.aot_device}")
+            print(f"  - AOT块数: {args.aot_block_num}")
+            print(f"  - AOT膨胀率: {args.aot_rates}")
+            print(f"  - AOT裁剪: {'是' if args.aot_crop else '否'}")
+            if args.aot_crop:
+                print(f"  - AOT裁剪边界: {args.aot_crop_pad}")
+            print(f"  - AOT最大边: {args.aot_max_size}")
+            print(f"  - AOT噪声预填充: {'是' if args.aot_noise_prefill else '否'}")
+            if args.aot_noise_prefill:
+                print(f"  - AOT噪声强度: {args.aot_noise_strength}")
+            print(f"  - mask膨胀: {args.mask_dilate}")
+            print(f"  - mask模糊: {args.mask_blur}")
+            print(f"  - mask阈值: {args.mask_threshold}")
+            print(f"  - 边缘扩张: {args.edge_grow}")
+            print(f"  - 黑色内容作为主体: {'是' if args.black_subject else '否'}")
+            if args.black_subject:
+                print(f"  - 黑色阈值: {args.black_threshold}")
+            print(f"  - 灰阶内容作为主体: {'是' if args.gray_subject else '否'}")
+            if args.gray_subject:
+                print(f"  - 灰阶饱和度阈值: {args.gray_saturation_threshold}")
+                print(f"  - 灰阶亮度阈值: {args.gray_value_threshold}")
+            print(f"  - 边缘过渡: {'是' if args.feather else '否'}")
+            if args.feather:
+                print(f"  - 过渡半径: {args.feather_radius}")
+            print(f"  - 保存mask: {'是' if args.save_mask else '否'}")
         print("-" * 50)
         
         # 创建会话
         session = new_session(args.model)
         
         # 处理图片
-        remove_background(
-            str(input_path),
-            str(output_path),
-            session=session,
-            alpha_matting=args.alpha_matting,
-            alpha_matting_foreground_threshold=args.foreground_threshold,
-            alpha_matting_background_threshold=args.background_threshold,
-            alpha_matting_erode_size=args.erode_size,
-            post_process_mask=args.post_process
-        )
+        if args.remove_subject:
+            mask_output_path = None
+            if args.save_mask:
+                mask_output_path = str(output_path.with_suffix("")) + "_mask.png"
+            remove_subject_and_inpaint(
+                str(input_path),
+                str(output_path),
+                session=session,
+                alpha_matting=args.alpha_matting,
+                alpha_matting_foreground_threshold=args.foreground_threshold,
+                alpha_matting_background_threshold=args.background_threshold,
+                alpha_matting_erode_size=args.erode_size,
+                post_process_mask=args.post_process,
+                mask_dilate=args.mask_dilate,
+                mask_blur=args.mask_blur,
+                mask_threshold=args.mask_threshold,
+                edge_grow=args.edge_grow,
+                aot_root=args.aot_root,
+                aot_pretrain=args.aot_pretrain,
+                aot_device=args.aot_device,
+                aot_block_num=args.aot_block_num,
+                aot_rates=args.aot_rates,
+                aot_crop=args.aot_crop,
+                aot_crop_pad=args.aot_crop_pad,
+                aot_max_size=args.aot_max_size,
+                aot_noise_prefill=args.aot_noise_prefill,
+                aot_noise_strength=args.aot_noise_strength,
+                black_subject=args.black_subject,
+                black_threshold=args.black_threshold,
+                gray_subject=args.gray_subject,
+                gray_saturation_threshold=args.gray_saturation_threshold,
+                gray_value_threshold=args.gray_value_threshold,
+                feather=args.feather,
+                feather_radius=args.feather_radius,
+                save_mask=args.save_mask,
+                mask_output_path=mask_output_path,
+            )
+        else:
+            remove_background(
+                str(input_path),
+                str(output_path),
+                session=session,
+                alpha_matting=args.alpha_matting,
+                alpha_matting_foreground_threshold=args.foreground_threshold,
+                alpha_matting_background_threshold=args.background_threshold,
+                alpha_matting_erode_size=args.erode_size,
+                post_process_mask=args.post_process,
+            )
         
         print("-" * 50)
         print(f"处理完成！结果保存在: {output_path}")
@@ -250,7 +909,30 @@ if __name__ == "__main__":
             alpha_matting_foreground_threshold=args.foreground_threshold,
             alpha_matting_background_threshold=args.background_threshold,
             alpha_matting_erode_size=args.erode_size,
-            post_process_mask=args.post_process
+            post_process_mask=args.post_process,
+            remove_subject=args.remove_subject,
+            mask_dilate=args.mask_dilate,
+            mask_blur=args.mask_blur,
+            mask_threshold=args.mask_threshold,
+            edge_grow=args.edge_grow,
+            aot_root=args.aot_root,
+            aot_pretrain=args.aot_pretrain,
+            aot_device=args.aot_device,
+            aot_block_num=args.aot_block_num,
+            aot_rates=args.aot_rates,
+            aot_crop=args.aot_crop,
+            aot_crop_pad=args.aot_crop_pad,
+            aot_max_size=args.aot_max_size,
+            aot_noise_prefill=args.aot_noise_prefill,
+            aot_noise_strength=args.aot_noise_strength,
+            black_subject=args.black_subject,
+            black_threshold=args.black_threshold,
+            gray_subject=args.gray_subject,
+            gray_saturation_threshold=args.gray_saturation_threshold,
+            gray_value_threshold=args.gray_value_threshold,
+            feather=args.feather,
+            feather_radius=args.feather_radius,
+            save_mask=args.save_mask,
         )
     
     else:

requirements.txt

@@ -1,3 +1,4 @@
 rembg[gpu]
 pillow
 pillow-heif
+opencv-python

tests/test_remove_background.py

@@ -0,0 +1,78 @@
+import importlib
+import sys
+import types
+import unittest
+
+
+def _import_remove_background():
+    if "remove_background" in sys.modules:
+        del sys.modules["remove_background"]
+    rembg_stub = types.ModuleType("rembg")
+    rembg_stub.remove = lambda *args, **kwargs: None
+    rembg_stub.new_session = lambda *args, **kwargs: None
+    sys.modules["rembg"] = rembg_stub
+    return importlib.import_module("remove_background")
+
+
+class RemoveBackgroundTests(unittest.TestCase):
+    def setUp(self):
+        self.mod = _import_remove_background()
+
+    def test_alpha_to_mask_threshold(self):
+        alpha = self.mod.np.array([[0, 9, 10, 255]], dtype=self.mod.np.uint8)
+        mask = self.mod._alpha_to_mask(alpha, threshold=10)
+        self.assertEqual(mask.shape, alpha.shape)
+        self.assertEqual(mask[0, 0], 0)
+        self.assertEqual(mask[0, 1], 0)
+        self.assertEqual(mask[0, 2], 0)
+        self.assertEqual(mask[0, 3], 255)
+
+    def test_prepare_mask_outputs_binary(self):
+        mask = self.mod.np.zeros((5, 5), dtype=self.mod.np.uint8)
+        mask[2, 2] = 255
+        mask_hard, mask_used = self.mod._prepare_mask(mask, mask_dilate=0, mask_blur=3)
+        self.assertEqual(mask_hard.shape, mask.shape)
+        self.assertEqual(mask_used.shape, mask.shape)
+        self.assertTrue(self.mod.np.all(self.mod.np.isin(mask_used, [0, 255])))
+
+    def test_resolve_aot_paths_defaults(self):
+        aot_root, aot_pretrain = self.mod._resolve_aot_paths(
+            "AOT-GAN-for-Inpainting", None
+        )
+        self.assertTrue(str(aot_root).endswith("AOT-GAN-for-Inpainting"))
+        self.assertIsNone(aot_pretrain)
+
+    def test_resolve_aot_paths_relative_pretrain(self):
+        aot_root, aot_pretrain = self.mod._resolve_aot_paths(
+            "AOT-GAN-for-Inpainting", "experiments/foo.pt"
+        )
+        self.assertTrue(str(aot_root).endswith("AOT-GAN-for-Inpainting"))
+        self.assertTrue(str(aot_pretrain).endswith("experiments/foo.pt"))
+
+    def test_parse_aot_rates(self):
+        rates = self.mod._parse_aot_rates("1+2+4+8")
+        self.assertEqual(rates, [1, 2, 4, 8])
+
+    def test_mask_bbox_empty(self):
+        mask = self.mod.np.zeros((3, 3), dtype=self.mod.np.uint8)
+        self.assertIsNone(self.mod._mask_bbox(mask))
+
+    def test_mask_bbox_and_expand(self):
+        mask = self.mod.np.zeros((5, 5), dtype=self.mod.np.uint8)
+        mask[1:3, 2:4] = 255
+        bbox = self.mod._mask_bbox(mask)
+        self.assertEqual(bbox, (2, 1, 3, 2))
+        expanded = self.mod._expand_bbox(bbox, 2, 5, 5)
+        self.assertEqual(expanded, (0, 0, 4, 4))
+        expanded_min = self.mod._expand_bbox_min_size(bbox, 0, 5, 5, 4)
+        self.assertEqual(expanded_min, (1, 0, 4, 3))
+
+    def test_ensure_rgba_size_resizes(self):
+        img = self.mod.Image.new("RGB", (2, 3), color=(0, 0, 0))
+        out = self.mod._ensure_rgba_size(img, (4, 5))
+        self.assertEqual(out.mode, "RGBA")
+        self.assertEqual(out.size, (4, 5))
+
+
+if __name__ == "__main__":
+    unittest.main()