初始化：换发型/换发色/训练发型服务
包含： - hair_service_sd: 主服务（换发型/换发色/生发，端口8801） - photo_service: LoRA调度+训练（端口32678） - hair_grow_service: 调试测试页（端口8888，含4个测试页） - 批量训练脚本（batch_train_hairstyles.py） - 发际线mask自动识别（hairline_mask.py，4种方案） - 手绘mask换发型（hair_swap_manual.py） - 文档：README.md + LARGE_FILES.md + docs/ 大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md OSS/COS密钥已脱敏为环境变量，原文件备份在本地
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# -*- coding: utf-8 -*-
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"""换发型（调试版，全参数可调 + 每步可视化）
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基于 hair_swap_viz.py，但把所有内部硬编码参数都暴露为入参，便于前端调试。
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工作流：输入 → 粗推理 → mask合并 → warpAffine → SD推理 → 贴回 → 消除接缝
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"""
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import os
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import json
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import glob
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import time
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import shutil
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import base64
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import cv2
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import numpy as np
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from gen_super_image import webui_img2img
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from common.logger import config
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from utils.landmark_processor import high_quality_warpAffine
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from utils.landmark_processor import pts_1k_to_137
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def hair_swap_debug(origin_img, hair_id, hairstyle_process, landmark_processor,
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task_id,
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# ===== 流程开关（真正生效）=====
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cut_bang=True, # 步骤③ 是否减刘海圆
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strict_mask=False, # 步骤⑥ 严格按mask贴回
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seamless_blend=True, # 步骤⑦ 泊松融合消除接缝
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# ===== 尺寸/对齐（真正生效）=====
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is_hr=True, # 高清(1152x1536) vs 标清(576x768)
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dilate_kernel=(6, 18), # 步骤④ mask膨胀核 (x,y)
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# ===== SD 推理（仅 denoising 可调，其余webui端固定）=====
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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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seamless_dilate=(9, 9), # 步骤⑦ 泊松融合mask膨胀核
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):
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"""换发型 + 全参数可视化。
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返回:
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result_img: 最终结果图 BGR
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steps: [{title, desc, images:[{label, b64}]}]
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params_used: 实际使用的参数（回显）
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"""
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start_all = time.time()
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steps = []
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hairstyle_dir = config.get('default', 'hairstyleDir')
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user_dir = config.get('default', 'userDir')
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train_dir = config.get('default', 'train_dir')
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userInfo_dir = config.get('default', 'userInfo_dir')
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hair_material_dir = os.path.join(train_dir, hair_id)
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material_save_path = os.path.join(hairstyle_dir, hair_id)
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ref_img_path = os.path.join(material_save_path, "ref_rgb_8uc3_768.png")
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if not os.path.exists(ref_img_path):
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raise FileNotFoundError(f"发型材质不存在: {ref_img_path}")
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user_img_name = f"swapdbg_{task_id}.jpg"
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new_user_img_path = os.path.join(user_dir, user_img_name)
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if os.path.exists(new_user_img_path):
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os.remove(new_user_img_path)
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cv2.imwrite(new_user_img_path, origin_img)
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origin_img = cv2.imread(new_user_img_path)
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ref_img = cv2.imread(ref_img_path)
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# === 步骤1: 输入 ===
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steps.append({
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"title": "① 输入",
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"desc": "用户人像 + 目标发型参考图。参考图来自训练材质 ref_rgb_8uc3_768.png",
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"images": [
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{"label": "用户原图", "b64": _enc(origin_img)},
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{"label": f"发型参考图({hair_id})", "b64": _enc(cv2.resize(ref_img, (origin_img.shape[1], origin_img.shape[0])))},
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]
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})
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# === 步骤2: 粗推理 infer_hairstyle_diy_jy ===
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start = time.time()
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work_dir = os.path.join(userInfo_dir, task_id)
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os.makedirs(work_dir, exist_ok=True)
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import torch
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with torch.no_grad():
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img_res, status, _, landmarks_1k, isEyeOccluded = hairstyle_process.infer_hairstyle_diy_jy(
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origin_img, ref_img, os.path.join(work_dir, task_id), f"{task_id}.png")
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if status != 0:
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raise RuntimeError("发型粗推理失败")
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t_coarse = time.time() - start
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user_material_dir = os.path.join(work_dir, task_id)
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hair_matting_path = os.path.join(user_material_dir, "hair_mask_2.png")
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user_orig_mask_path = os.path.join(user_material_dir, "user_orig_mask.png")
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origin_matting = cv2.imread(user_orig_mask_path, cv2.IMREAD_GRAYSCALE)
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new_matting = cv2.imread(hair_matting_path, cv2.IMREAD_GRAYSCALE)
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steps.append({
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"title": "② 粗推理 (infer_hairstyle_diy_jy)",
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"desc": f"用 SPADE 风格迁移模型把目标发型粗略合成到用户脸上。这步只产生粗略效果，细节由后续 SD 推理完善。耗时 {t_coarse:.1f}s",
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"images": [
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{"label": "粗推理结果", "b64": _enc(img_res)},
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]
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})
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# === 步骤3: mask 合并 ===
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start = time.time()
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no_bang_result = origin_matting.copy()
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bang_info = "未减刘海"
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if cut_bang:
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try:
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landmarks_137 = pts_1k_to_137(np.asarray(landmarks_1k))
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eye_centers = []
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for i in [7, 14]:
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if i < len(landmarks_137):
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eye_centers.append(landmarks_137[i])
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if len(eye_centers) == 2:
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cx = int((eye_centers[0][0] + eye_centers[1][0]) / 2)
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cy = int((eye_centers[0][1] + eye_centers[1][1]) / 2)
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r = int(np.linalg.norm(np.array(eye_centers[0]) - np.array(eye_centers[1])))
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cv2.circle(no_bang_result, (cx, cy), r, 0, -1)
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bang_info = f"已减刘海圆(中心={cx},{cy} 半径={r})"
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except Exception as e:
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bang_info = f"刘海圆计算失败({e})，未减"
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matting_merge = np.max(np.stack([no_bang_result, new_matting], axis=2), axis=2).astype(np.uint8)
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steps.append({
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"title": "③ 生成重绘 mask",
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"desc": f"决定哪些区域要重新绘制。用户原头发mask({bang_info}) ∪ 新发型mask = 重绘区域。可调：cut_bang={cut_bang}",
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"images": [
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{"label": "用户原头发mask", "b64": _enc_mask(origin_matting)},
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{"label": "减刘海后", "b64": _enc_mask(no_bang_result)},
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{"label": "新发型mask", "b64": _enc_mask(new_matting)},
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{"label": "合并mask(重绘区)", "b64": _enc_mask(matting_merge)},
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]
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})
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# === 步骤4: warpAffine 变换 ===
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dst_size = (1152, 1536) if is_hr else (576, 768)
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box_info = hairstyle_process.get_body_info(img_res)
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box_w = box_info[2] - box_info[0]
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box_h = box_info[3] - box_info[1]
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scale = min(dst_size[1] / max(box_h, 1), dst_size[0] / max(box_w, 1))
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rotate_center = [(box_info[2] + box_info[0]) * 0.5, (box_info[3] + box_info[1]) * 0.5]
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M = cv2.getRotationMatrix2D(rotate_center, 0, scale)
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M[:, 2] += np.float32([dst_size[0] * 0.5, dst_size[1] * 0.5]) - np.float32(rotate_center)
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crop_result = high_quality_warpAffine(img_res, M, dst_size)
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crop_matting = cv2.warpAffine(matting_merge, M, dst_size)
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mask = (crop_matting > 10).astype(np.float32)
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dk = tuple(max(1, int(x)) for x in dilate_kernel)
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mask_dilate = cv2.dilate(mask, np.ones(dk, np.uint8))
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mask_dilate = np.clip(mask_dilate * 255, 0, 255).astype(np.uint8)
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final_img = crop_result
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steps.append({
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"title": "④ warpAffine 对齐裁剪",
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"desc": f"检测身体框，计算缩放/平移矩阵，把图和mask对齐裁剪到 {dst_size[0]}x{dst_size[1]} 送入 webui。可调：is_hr={is_hr}(决定尺寸), dilate_kernel={dk}",
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"images": [
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{"label": f"裁剪后图({dst_size[0]}x{dst_size[1]})", "b64": _enc(_shrink(crop_result))},
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{"label": f"mask膨胀后(dilate={dk})", "b64": _enc_mask(_shrink(mask_dilate, nearest=True))},
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]
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})
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# === 步骤5: 读取 config/prompt ===
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config_json_path = os.path.join(material_save_path, "config.json")
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with open(config_json_path, "r") as f:
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in_gender = json.load(f)["gender"]
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images_dir = os.path.join(hair_material_dir, "images")
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txt_dir = os.path.join(images_dir, os.listdir(images_dir)[0])
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txt_path = glob.glob(txt_dir + '/*.txt')[0]
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with open(txt_path, 'r') as f:
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p_tag = f.readline()
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if "titor hairstyle, faceless, no human, gray background, simple background" in p_tag:
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p_tag = p_tag[p_tag.find("simple background, ") + len("simple background, "):]
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else:
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p_tag = ""
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# === 步骤6: webui 推理（走 photo_service + LoRA）===
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start = time.time()
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sd_result = webui_img2img(
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img=final_img, mask_img=mask_dilate,
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in_gender=in_gender, task_id=task_id,
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hair_id=hair_id, lora_material_path=hair_material_dir,
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tag=p_tag, is_hr=is_hr,
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denoising_strength=denoising_strength, inference_port="57860")
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t_sd = time.time() - start
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steps.append({
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"title": "⑤ SD 推理 (webui img2img inpainting + LoRA)",
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"desc": f"加载发型LoRA，在mask区域用SD重绘新发型。这是生成发丝细节的关键步骤。耗时 {t_sd:.1f}s。可调：denoising={denoising_strength}（其余 cfg=7/steps=20/sampler=DPM++ 2M Karras/mask_blur=11/seed=123456789 在webui端固定）",
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"images": [
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{"label": "webui输出(SD重绘结果)", "b64": _enc(_shrink(sd_result))},
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]
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})
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# === 步骤7: warpAffine 贴回原图 ===
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M_inv = cv2.invertAffineTransform(M)
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result_full = origin_img.copy()
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cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
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dst=result_full, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.INTER_LANCZOS4)
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result_strict = origin_img.copy()
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sd_result_back = np.zeros_like(origin_img)
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cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
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dst=sd_result_back, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0), flags=cv2.INTER_LANCZOS4)
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mask_back = np.zeros(origin_img.shape[:2], dtype=np.uint8)
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cv2.warpAffine(mask_dilate, M_inv, (origin_img.shape[1], origin_img.shape[0]),
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dst=mask_back, borderMode=cv2.BORDER_CONSTANT, borderValue=0, flags=cv2.INTER_NEAREST)
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bd = tuple(max(1, int(x)) for x in blend_dilate)
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mask_back = cv2.dilate(mask_back, np.ones(bd, np.uint8))
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mask_blend = (mask_back.astype(np.float32) / 255)[..., None]
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result_strict = (sd_result_back.astype(np.float32) * mask_blend +
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origin_img.astype(np.float32) * (1 - mask_blend))
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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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result = result_strict if strict_mask else result_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}。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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"images": [
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{"label": "用户原图", "b64": _enc(origin_img)},
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{"label": "整框覆盖", "b64": _enc(result_full)},
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{"label": "严格mask贴回", "b64": _enc(result_strict)},
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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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})
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# === 步骤8: 消除接缝（泊松无缝融合）===
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if strict_mask and seamless_blend:
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start_enhance = time.time()
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try:
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sd_back = np.zeros_like(origin_img)
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cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
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dst=sd_back, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0), flags=cv2.INTER_LANCZOS4)
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mask_back2 = np.zeros(origin_img.shape[:2], dtype=np.uint8)
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cv2.warpAffine(mask_dilate, M_inv, (origin_img.shape[1], origin_img.shape[0]),
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dst=mask_back2, borderMode=cv2.BORDER_CONSTANT, borderValue=0, flags=cv2.INTER_NEAREST)
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sd2 = tuple(max(1, int(x)) for x in seamless_dilate)
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mask_back2 = cv2.dilate(mask_back2, np.ones(sd2, np.uint8))
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ys2, xs2 = np.where(mask_back2 > 10)
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if len(ys2) > 10:
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cx2 = int((xs2.min() + xs2.max()) / 2)
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cy2 = int((ys2.min() + ys2.max()) / 2)
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result = cv2.seamlessClone(sd_back, origin_img, mask_back2, (cx2, cy2), cv2.NORMAL_CLONE)
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t_seamless = time.time() - start_enhance
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steps.append({
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"title": "⑦ 泊松融合消除接缝 (seamlessClone)",
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"desc": f"严格mask模式下边缘有接缝/色差。泊松融合保持SD结果内部梯度，把边缘梯度过渡到原图，消除突变。耗时 {t_seamless:.1f}s。可调：seamless_dilate={sd2}",
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"images": [
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{"label": "融合前(有接缝)", "b64": _enc(result_strict)},
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{"label": "融合后(最终)", "b64": _enc(result)},
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]
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})
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except Exception as e:
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steps.append({
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"title": "⑦ 泊松融合（失败）",
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"desc": f"泊松融合失败({e})，回退到严格mask结果",
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"images": [{"label": "最终结果", "b64": _enc(result)}]
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})
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# 清理临时文件
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try:
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shutil.rmtree(user_material_dir)
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os.remove(new_user_img_path)
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except Exception:
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pass
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params_used = {
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"cut_bang": cut_bang, "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), "seamless_dilate": list(seamless_dilate),
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"gender": in_gender, "tag": p_tag, "dst_size": list(dst_size),
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"total_time": round(time.time() - start_all, 1),
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}
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print(f"[swap_debug] 总耗时: {params_used['total_time']}s")
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return result, steps, params_used
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def _shrink(img, max_side=1024, nearest=False):
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"""缩小图片便于前端展示"""
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h, w = img.shape[:2]
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if max(h, w) <= max_side:
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return img
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sc = max_side / max(h, w)
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interp = cv2.INTER_NEAREST if nearest else cv2.INTER_AREA
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return cv2.resize(img, (int(w * sc), int(h * sc)), interpolation=interp)
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def _enc(img):
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_, buf = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 80])
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return base64.b64encode(buf).decode()
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def _enc_mask(mask):
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if mask.ndim == 2:
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colored = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
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else:
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colored = mask
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_, buf = cv2.imencode(".jpg", colored, [cv2.IMWRITE_JPEG_QUALITY, 80])
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return base64.b64encode(buf).decode()
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