feat: 新增mask区域重绘功能 + 羽化贴回优化 + 发丝增强
新增功能： - inpaint_mask.py: mask区域重绘服务（enhance/pure_inpaint两种模式） 严格只在mask区做SD inpainting，发丝自然化 - /inpaint 页面 + /api/inpaint 接口：画板手绘mask + 提示词编辑 + denoising可调 - enhance_hair.webui_img2img: 新增denoising_strength参数（原硬编码0.35） 羽化贴回优化（hair_swap_manual.py 步骤⑥）： - feather_px（羽化范围）+ feather_alpha（羽化强度）拆分为两个独立参数 - 改为只羽化边缘（mask内部保持硬切，仅边缘带渐变） - 新增黑底羽化对比图（纯黑背景凸显边缘过渡） 其他改动： - OSS/COS密钥脱敏：改为可选import，未设环境变量时不崩溃 - manual页面：发型列表改为本次训练的8个（含hair_flow） - enhance二次增强：重绘区改为「原图头发∪手绘mask」并外扩10%+减发际线带3cm - 发型清单文档更新：179→180个可用发型（+hair_flow） 涉及文件： - 新增: inpaint_mask.py, inpaint.html - 修改: app.py, manual.html, hair_swap_manual.py, enhance_hair.py - 修改: oss_module.py, upload_oss.py, cos_module.py（密钥脱敏） - 文档: HAIRSTYLES_AVAILABLE.md, hairstyles_available.csv
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@@ -20,8 +20,11 @@ class COS_object():
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token = None # 如果使用永久密钥不需要填入 token，如果使用临时密钥需要填入，临时密钥生成和使用指引参见 https://cloud.tencent.com/document/product/436/14048
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self.scheme = 'https' # 指定使用 http/https 协议来访问 COS，默认为 https，可不填
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self.BucketName= os.getenv('COS_BUCKET', '<your-cos-bucket>')
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for param in (secret_id, secret_key, self.region, self.BucketName):
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assert '<' not in param, '请设置环境变量 COS_SECRET_ID / COS_SECRET_KEY / COS_REGION / COS_BUCKET'
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# 密钥未设置时标记不可用（不抛异常）
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if '<' in secret_id or '<' in secret_key:
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print('[COS] ⚠️ 未设置 COS 密钥环境变量，COS 上传功能不可用')
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self.client = None
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return
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config = CosConfig(Region=self.region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=self.scheme)
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self.client = CosS3Client(config)
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@@ -1,17 +1,28 @@
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import time
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import oss2
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import os
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try:
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import oss2
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_OSS2_AVAILABLE = True
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except Exception as _e:
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print(f'[OSS] ⚠️ oss2 导入失败({_e})，OSS 上传功能不可用')
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_OSS2_AVAILABLE = False
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class OSS_object():
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def __init__(self):
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if not _OSS2_AVAILABLE:
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print('[OSS] oss2 不可用，OSS 功能关闭')
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self.bucket = None
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return
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access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '<your-access-key-id>')
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access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
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bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
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endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
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for param in (access_key_id, access_key_secret, bucket_name, endpoint):
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assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT'
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# 密钥未设置时标记不可用（不抛异常，避免服务启动/换发型时崩溃）
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if '<' in access_key_id or '<' in access_key_secret:
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print('[OSS] ⚠️ 未设置 OSS 密钥环境变量，OSS 上传功能不可用（本地测试用 base64 返回即可）')
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self.bucket = None
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return
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self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name)
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def upload_file(self, file, target_name):
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@@ -36,7 +36,8 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
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denoising_strength=0.6, # 重绘强度（唯一能透传到webui的SD参数）
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# ===== 贴回/融合（真正生效）=====
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blend_dilate=(5, 5), # 步骤⑥ strict mask贴回时mask膨胀核
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feather_px=0, # ★ strict贴回边缘羽化像素（0=无羽化硬边缘，>0=高斯模糊边缘）
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feather_px=0, # ★ strict贴回边缘羽化范围（像素，边缘带宽度，0=无羽化）
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feather_alpha=1.0, # ★ 羽化强度（0~1，0=硬切，1=最大羽化，控制边缘过渡柔和度）
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seamless_dilate=(9, 9), # 步骤⑦ 泊松融合mask膨胀核
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# ===== enhance 二次增强重绘 =====
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enhance=False, # 步骤⑧ 是否对结果再做一次低强度SD重绘（让发丝更清晰）
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@@ -230,10 +231,20 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
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result_strict_hard[mask_back == 0] = origin_img[mask_back == 0]
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if feather_px and feather_px > 0:
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# ★ 只羽化边缘：mask 内部保持硬切(255)，只有边缘带做渐变
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# 1. 对 mask_back 做高斯模糊（得到边缘的渐变）
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k = max(1, int(feather_px)) * 2 + 1 # 高斯核必须是奇数
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mask_back_blur = cv2.GaussianBlur(mask_back, (k, k), 0)
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mask_blend = (mask_back_blur.astype(np.float32) / 255)[..., None]
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feather_info = f"，边缘羽化{feather_px}px"
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mask_blur = cv2.GaussianBlur(mask_back, (k, k), 0)
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# 2. 取 mask 内部区域（离边缘 > feather_px 的部分），强制为255（不羽化）
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erode_kernel = np.ones((k, k), np.uint8)
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mask_inner = cv2.erode(mask_back, erode_kernel)
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# 3. 合成：内部硬切(255) + 边缘用模糊值
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mask_blend_mask = np.maximum(mask_inner, mask_blur).astype(np.float32)
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# 4. ★ feather_alpha 控制羽化强度：在「全硬切(原mask)」和「边缘渐变(mask_blend_mask)」之间插值
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# alpha=0 → 完全硬切（无羽化），alpha=1 → 最大羽化
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mask_blend_mask = mask_back.astype(np.float32) * (1 - feather_alpha) + mask_blend_mask * feather_alpha
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mask_blend = (mask_blend_mask / 255)[..., None]
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feather_info = f"，边缘羽化{feather_px}px 强度{feather_alpha:.2f}"
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else:
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mask_blend = hard_blend
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result_strict = (sd_result_back.astype(np.float32) * mask_blend +
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@@ -241,17 +252,38 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
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result_strict = np.clip(result_strict, 0, 255).astype(np.uint8)
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result_strict[mask_back == 0] = origin_img[mask_back == 0]
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# ★ 羽化对比图：左=硬边缘(feather=0) 右=当前羽化，裁剪mask边界附近放大
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# ★ 羽化对比图（黑底版）：纯黑背景 + 贴回图，凸显羽化边缘过渡
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# 用黑色背景而非原图，避免原图干扰，纯粹对比羽化效果
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feather_compare = None
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feather_compare_full = None
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if feather_px and feather_px > 0:
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# 计算 mask_blend（用于把贴回图叠加到黑底上）
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ys, xs = np.where(mask_back > 10)
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if len(ys) > 0:
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x1 = max(0, xs.min()-20); x2 = min(origin_img.shape[1], xs.max()+20)
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y1 = max(0, ys.min()-20); y2 = min(origin_img.shape[0], ys.max()+20)
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hard_crop = result_strict_hard[y1:y2, x1:x2]
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blur_crop = result_strict[y1:y2, x1:x2]
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sep = np.full((hard_crop.shape[0], 3, 3), 128, dtype=np.uint8)
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# 黑底版：贴回图 * mask_blend + 黑色(0) * (1-mask_blend)
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# result_strict_hard 已是「贴回图+原图」混合，这里重新算黑底版
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black_bg = np.zeros_like(origin_img)
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# 无羽化版（黑底）
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hard_on_black = (sd_result_back.astype(np.float32) * hard_blend +
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black_bg.astype(np.float32) * (1 - hard_blend))
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hard_on_black = np.clip(hard_on_black, 0, 255).astype(np.uint8)
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hard_on_black[mask_back == 0] = 0 # mask外纯黑
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# 有羽化版（黑底）
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blur_on_black = (sd_result_back.astype(np.float32) * mask_blend +
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black_bg.astype(np.float32) * (1 - mask_blend))
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blur_on_black = np.clip(blur_on_black, 0, 255).astype(np.uint8)
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blur_on_black[mask_back == 0] = 0 # mask外纯黑
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# 局部放大版（裁剪mask边界附近）
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x1 = max(0, xs.min()-30); x2 = min(origin_img.shape[1], xs.max()+30)
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y1 = max(0, ys.min()-30); y2 = min(origin_img.shape[0], ys.max()+30)
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hard_crop = hard_on_black[y1:y2, x1:x2]
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blur_crop = blur_on_black[y1:y2, x1:x2]
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sep = np.full((hard_crop.shape[0], 3, 3), 60, dtype=np.uint8) # 深灰分隔线
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feather_compare = np.hstack([hard_crop, sep, blur_crop])
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# 完整图版（不裁剪）
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sep_full = np.full((origin_img.shape[0], 3, 3), 60, dtype=np.uint8)
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feather_compare_full = np.hstack([hard_on_black, sep_full, blur_on_black])
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result = result_strict if strict_mask else result_full
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@@ -262,7 +294,9 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
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{"label": f"当前选用({'严格mask' if strict_mask else '整框覆盖'})", "b64": _enc(result)},
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]
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if feather_compare is not None:
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step6_images.append({"label": f"★ 羽化对比(左=硬边 右=羽化{feather_px}px)", "b64": _enc(feather_compare)})
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step6_images.append({"label": f"★ 黑底羽化对比·边缘(左=无羽化 右=羽化{feather_px}pxα{feather_alpha:.1f})", "b64": _enc(feather_compare)})
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if feather_compare_full is not None:
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step6_images.append({"label": f"★ 黑底羽化对比·完整(左=无羽化 右=羽化{feather_px}pxα{feather_alpha:.1f})", "b64": _enc(feather_compare_full)})
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steps.append({
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"title": "⑥ 贴回原图",
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"desc": f"把SD结果逆warpAffine贴回用户原图。可调：strict_mask={strict_mask}, blend_dilate={bd}{feather_info}。整框覆盖=整个裁剪框覆盖原图；严格mask=只在mask区域融合，mask外保留原图",
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@@ -390,7 +424,7 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
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"strict_mask": strict_mask, "seamless_blend": seamless_blend,
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"is_hr": is_hr, "dilate_kernel": list(dilate_kernel),
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"denoising_strength": denoising_strength,
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"blend_dilate": list(blend_dilate), "feather_px": feather_px,
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"blend_dilate": list(blend_dilate), "feather_px": feather_px, "feather_alpha": feather_alpha,
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"seamless_dilate": list(seamless_dilate),
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"enhance": enhance, "enhance_denoising": enhance_denoising,
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"mask_area_ratio_pct": round(mask_area_ratio, 1),
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@@ -0,0 +1,64 @@
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# -*- coding: utf-8 -*-
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"""mask 区域重绘（inpainting）服务
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两种模式（底层都是 webui img2img inpainting，严格只在mask区重绘）：
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1. enhance: 用 enhance_hair.webui_img2img（默认提示词=发丝增强，denoising默认0.35）
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2. pure_inpaint: 纯SD inpainting（默认提示词=自然头发，denoising默认0.5）
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严格只重绘 mask 区域：inpainting_fill=1（mask外保留原图）
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"""
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import cv2
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import numpy as np
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from utils import enhance_hair
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def inpaint_mask(img, mask, prompt="", denoising_strength=0.5, mode="enhance"):
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"""在 mask 区域做 SD inpainting 重绘。
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参数:
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img: 输入图 BGR
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mask: 灰度mask，白色(255)=重绘区，与img同尺寸
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prompt: 提示词（空则用模式默认值）
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denoising_strength: 重绘强度 0~1
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mode: "enhance" 或 "pure_inpaint"
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返回:
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result: 重绘后的图（mask区已重绘，其余保留原图）
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info: 本次参数说明
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"""
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h, w = img.shape[:2]
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# 归一化 mask（保证 0/255，尺寸一致）
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if mask.shape[:2] != (h, w):
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mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
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if mask.ndim == 3:
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mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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_, mask_bin = cv2.threshold(mask, 30, 255, cv2.THRESH_BINARY)
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# 模式默认值
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if not prompt:
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if mode == "enhance":
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prompt = "high quality, detailed natural hair strands, realistic hair texture, sharp focus, photorealistic"
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else:
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prompt = "natural realistic hair, soft hair strands, photorealistic, high quality, detailed"
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# 统计 mask 区域占比
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mask_ratio = float((mask_bin > 0).sum()) / mask_bin.size * 100
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# 调用 enhance_hair.webui_img2img（底层是 inpainting，inpainting_fill=1 严格只在mask区重绘）
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result = enhance_hair.webui_img2img(
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img, mask_bin, prompt=prompt,
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denoising_strength=denoising_strength
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)
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# webui 可能调整尺寸（调整到8的倍数），resize 回原图尺寸
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if result.shape[:2] != (h, w):
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result = cv2.resize(result, (w, h), interpolation=cv2.INTER_LANCZOS4)
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# 双重保险：强制 mask 外区域用原图（虽然 inpainting_fill=1 已保证，再加一层确保严格）
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mask_3c = (mask_bin > 0)[..., None]
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result = np.where(mask_3c, result, img).astype(np.uint8)
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info = (f"模式={mode}，提示词=\"{prompt[:50]}...\"，denoising={denoising_strength}，"
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f"mask区域={mask_ratio:.1f}%。严格只重绘mask区，其余保留原图。")
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return result, mask_bin, info
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@@ -1,17 +1,28 @@
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import time
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import oss2
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import os
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try:
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import oss2
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_OSS2_AVAILABLE = True
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except Exception as _e:
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print(f'[OSS upload_oss] ⚠️ oss2 导入失败({_e})，OSS 上传功能不可用')
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_OSS2_AVAILABLE = False
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class OSS_object():
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def __init__(self):
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if not _OSS2_AVAILABLE:
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print('[OSS upload_oss] oss2 不可用，OSS 功能关闭')
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self.bucket = None
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return
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access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '<your-access-key-id>')
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access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
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bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
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endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
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for param in (access_key_id, access_key_secret, bucket_name, endpoint):
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assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT'
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# 密钥未设置时标记不可用（不抛异常）
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if '<' in access_key_id or '<' in access_key_secret:
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print('[OSS upload_oss] ⚠️ 未设置 OSS 密钥环境变量，OSS 上传功能不可用')
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self.bucket = None
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return
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self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name)
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def upload_file(self, file, target_name):
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Block a user