change_hair/project/hair_service_sd/hair_swap_hairline.py

# -*- coding: utf-8 -*-
"""换发型（发际线带重绘实验版）

与 hair_swap_debug.py 工作流完全一致，唯一区别在步骤③：
  - debug版：重绘区域 = 原头发mask ∪ 新发型mask（整个头发区域）
  - 本版：  重绘区域 = 原mask边界带 ∪ 新mask边界带（只重绘发际线交界带，主体保留原图）

工作流：输入 → 粗推理 → 发际线带mask → warpAffine → SD推理 → 贴回 → 消除接缝
"""
import os
import json
import glob
import time
import shutil
import base64

import cv2
import numpy as np

from gen_super_image import webui_img2img
from common.logger import config
from utils.landmark_processor import high_quality_warpAffine
from utils.landmark_processor import pts_1k_to_137


def hair_swap_hairline(origin_img, hair_id, hairstyle_process, landmark_processor,
                       task_id,
                       # ===== 流程开关（真正生效）=====
                       method="mediapipe",     # 发际线mask方案: boundary_band/mediapipe/landmark_1k/deeplab
                       strict_mask=False,       # 步骤⑥ 严格按mask贴回
                       seamless_blend=True,     # 步骤⑦ 泊松融合消除接缝
                       # ===== 尺寸/对齐（真正生效）=====
                       is_hr=True,              # 高清(1152x1536) vs 标清(576x768)
                       dilate_kernel=(6, 18),   # 步骤④ mask膨胀核 (x,y)
                       # ===== SD 推理（仅 denoising 可调，其余webui端固定）=====
                       denoising_strength=0.6,  # 重绘强度（唯一能透传到webui的SD参数）
                       # ===== 贴回/融合（真正生效）=====
                       blend_dilate=(5, 5),     # 步骤⑥ strict mask贴回时mask膨胀核
                       seamless_dilate=(9, 9),  # 步骤⑦ 泊松融合mask膨胀核
                       # ===== 发际线带专属参数 =====
                       band_width=15,           # 边界带宽(像素)，形态学梯度核大小
                       preview_only=False,      # ★ 只验证mask（跳过SD推理，几秒出结果，默认False）
                       # ===== landmark_1k 方案形状参数 =====
                       height_ratio=0.432,      # 高度缩放比（越小越窄）
                       width_ratio=0.144,       # 左右各扩展比（相对眉宽，越大越宽）
                       corner_ratio=0.25,       # 圆角半径比（相对短边）
                       vertical_offset=0.0,     # 上下平移比（相对额头高度，正值下移）
                       # ===== webui SD 参数 =====
                       refiner_switch_at=0.5,   # refiner切换点（0~1，仅高清模式生效，0=不用refiner）
                       ):
    """换发型 + 全参数可视化。

    返回:
        result_img: 最终结果图 BGR
        steps: [{title, desc, images:[{label, b64}]}]
        params_used: 实际使用的参数（回显）
    """
    start_all = time.time()
    steps = []

    hairstyle_dir = config.get('default', 'hairstyleDir')
    user_dir = config.get('default', 'userDir')
    train_dir = config.get('default', 'train_dir')
    userInfo_dir = config.get('default', 'userInfo_dir')

    hair_material_dir = os.path.join(train_dir, hair_id)
    material_save_path = os.path.join(hairstyle_dir, hair_id)
    ref_img_path = os.path.join(material_save_path, "ref_rgb_8uc3_768.png")
    if not os.path.exists(ref_img_path):
        raise FileNotFoundError(f"发型材质不存在: {ref_img_path}")

    user_img_name = f"swapdbg_{task_id}.jpg"
    new_user_img_path = os.path.join(user_dir, user_img_name)
    if os.path.exists(new_user_img_path):
        os.remove(new_user_img_path)
    cv2.imwrite(new_user_img_path, origin_img)
    origin_img = cv2.imread(new_user_img_path)

    ref_img = cv2.imread(ref_img_path)

    # === 步骤1: 输入 ===
    steps.append({
        "title": "① 输入",
        "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",
        "images": [
            {"label": "用户原图", "b64": _enc(origin_img)},
            {"label": f"发型参考图({hair_id})", "b64": _enc(cv2.resize(ref_img, (origin_img.shape[1], origin_img.shape[0])))},
        ]
    })

    # === 步骤2: 粗推理 infer_hairstyle_diy_jy ===
    start = time.time()
    work_dir = os.path.join(userInfo_dir, task_id)
    os.makedirs(work_dir, exist_ok=True)
    import torch
    with torch.no_grad():
        img_res, status, _, landmarks_1k, isEyeOccluded = hairstyle_process.infer_hairstyle_diy_jy(
            origin_img, ref_img, os.path.join(work_dir, task_id), f"{task_id}.png")
    if status != 0:
        raise RuntimeError("发型粗推理失败")
    t_coarse = time.time() - start

    user_material_dir = os.path.join(work_dir, task_id)
    hair_matting_path = os.path.join(user_material_dir, "hair_mask_2.png")
    user_orig_mask_path = os.path.join(user_material_dir, "user_orig_mask.png")
    origin_matting = cv2.imread(user_orig_mask_path, cv2.IMREAD_GRAYSCALE)
    new_matting = cv2.imread(hair_matting_path, cv2.IMREAD_GRAYSCALE)

    steps.append({
        "title": "② 粗推理 (infer_hairstyle_diy_jy)",
        "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",
        "images": [
            {"label": "粗推理结果", "b64": _enc(img_res)},
        ]
    })

    # === 步骤3: 发际线带 mask（核心：用所选方案自动识别发际线区域）===
    start = time.time()

    # 调用 hairline_mask 统一入口
    import hairline_mask as hm
    matting_merge, method_info, debug_imgs = hm.detect_hairline(
        origin_matting, landmarks_1k, img_res, band_width=band_width, method=method,
        height_ratio=height_ratio, width_ratio=width_ratio, corner_ratio=corner_ratio,
        vertical_offset=vertical_offset)

    if matting_merge is None:
        # 方案失败：回退到边界带法
        matting_merge, method_info, debug_imgs = hm.detect_hairline(
            origin_matting, landmarks_1k, img_res, band_width=band_width, method="boundary_band")
        method_info = f"[回退到边界带] " + method_info

    # 收集步骤③的展示图
    step3_images = []
    step3_images.append({"label": "原头发mask(参考)", "b64": _enc_mask(origin_matting)})
    for label, arr in debug_imgs.items():
        if arr.ndim == 2:
            step3_images.append({"label": label, "b64": _enc_mask(arr)})
        else:
            step3_images.append({"label": label, "b64": _enc(arr)})
    step3_images.append({"label": "发际线重绘区", "b64": _enc_mask(matting_merge)})
    # ★ mask半透明叠加到粗推理图（目标发型图）
    overlay_res = hm.make_overlay(img_res, matting_merge, alpha=0.45)
    step3_images.append({"label": "★ 叠加目标发型图", "b64": _enc(overlay_res)})
    # ★ mask半透明叠加到用户原图（直观看重绘区域在用户原图上的位置）
    overlay_orig = hm.make_overlay(origin_img, matting_merge, alpha=0.45)
    step3_images.append({"label": "★ 叠加用户原图", "b64": _enc(overlay_orig)})

    steps.append({
        "title": f"③ 发际线mask [方案: {method}]",
        "desc": f"{method_info}。其余头发主体保留原图。可调：band_width={band_width}px。看「★ 叠加用户原图」直观判断重绘区位置。",
        "images": step3_images
    })

    # ★ preview_only 模式：只验证 mask，跳过后续 SD 推理（几秒出结果）
    if preview_only:
        params_used = {
            "method": method, "band_width": band_width, "preview_only": True,
            "height_ratio": height_ratio, "width_ratio": width_ratio,
            "corner_ratio": corner_ratio, "vertical_offset": vertical_offset,
            "is_hr": is_hr, "total_time": round(time.time() - start_all, 1),
        }
        print(f"[swap_hairline] preview_only 完成，耗时 {params_used['total_time']}s")
        return overlay_orig, steps, params_used

    # === 步骤4: warpAffine 变换 ===
    dst_size = (1152, 1536) if is_hr else (576, 768)
    box_info = hairstyle_process.get_body_info(img_res)
    box_w = box_info[2] - box_info[0]
    box_h = box_info[3] - box_info[1]
    scale = min(dst_size[1] / max(box_h, 1), dst_size[0] / max(box_w, 1))
    rotate_center = [(box_info[2] + box_info[0]) * 0.5, (box_info[3] + box_info[1]) * 0.5]
    M = cv2.getRotationMatrix2D(rotate_center, 0, scale)
    M[:, 2] += np.float32([dst_size[0] * 0.5, dst_size[1] * 0.5]) - np.float32(rotate_center)

    crop_result = high_quality_warpAffine(img_res, M, dst_size)
    crop_matting = cv2.warpAffine(matting_merge, M, dst_size)
    mask = (crop_matting > 10).astype(np.float32)
    dk = tuple(max(1, int(x)) for x in dilate_kernel)
    mask_dilate = cv2.dilate(mask, np.ones(dk, np.uint8))
    mask_dilate = np.clip(mask_dilate * 255, 0, 255).astype(np.uint8)
    final_img = crop_result

    steps.append({
        "title": "④ warpAffine 对齐裁剪",
        "desc": f"检测身体框，计算缩放/平移矩阵，把图和【发际线带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}",
        "images": [
            {"label": f"裁剪后图({dst_size[0]}x{dst_size[1]})", "b64": _enc(_shrink(crop_result))},
            {"label": f"发际线带mask膨胀后(dilate={dk})", "b64": _enc_mask(_shrink(mask_dilate, nearest=True))},
        ]
    })

    # === 步骤5: 读取 config/prompt ===
    config_json_path = os.path.join(material_save_path, "config.json")
    with open(config_json_path, "r") as f:
        in_gender = json.load(f)["gender"]
    images_dir = os.path.join(hair_material_dir, "images")
    txt_dir = os.path.join(images_dir, os.listdir(images_dir)[0])
    txt_path = glob.glob(txt_dir + '/*.txt')[0]
    with open(txt_path, 'r') as f:
        p_tag = f.readline()
        if "titor hairstyle, faceless, no human, gray background, simple background" in p_tag:
            p_tag = p_tag[p_tag.find("simple background, ") + len("simple background, "):]
        else:
            p_tag = ""

    # === 步骤6: webui 推理（走 photo_service + LoRA）===
    start = time.time()
    sd_result = webui_img2img(
        img=final_img, mask_img=mask_dilate,
        in_gender=in_gender, task_id=task_id,
        hair_id=hair_id, lora_material_path=hair_material_dir,
        tag=p_tag, is_hr=is_hr,
        denoising_strength=denoising_strength, inference_port="57860",
        refiner_switch_at=refiner_switch_at)
    t_sd = time.time() - start

    steps.append({
        "title": "⑤ SD 推理 (webui img2img inpainting + LoRA)",
        "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）",
        "images": [
            {"label": "webui输出(SD重绘结果)", "b64": _enc(_shrink(sd_result))},
        ]
    })

    # === 步骤7: warpAffine 贴回原图 ===
    M_inv = cv2.invertAffineTransform(M)
    result_full = origin_img.copy()
    cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
                   dst=result_full, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.INTER_LANCZOS4)

    result_strict = origin_img.copy()
    sd_result_back = np.zeros_like(origin_img)
    cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
                   dst=sd_result_back, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0), flags=cv2.INTER_LANCZOS4)
    mask_back = np.zeros(origin_img.shape[:2], dtype=np.uint8)
    cv2.warpAffine(mask_dilate, M_inv, (origin_img.shape[1], origin_img.shape[0]),
                   dst=mask_back, borderMode=cv2.BORDER_CONSTANT, borderValue=0, flags=cv2.INTER_NEAREST)
    bd = tuple(max(1, int(x)) for x in blend_dilate)
    mask_back = cv2.dilate(mask_back, np.ones(bd, np.uint8))
    mask_blend = (mask_back.astype(np.float32) / 255)[..., None]
    result_strict = (sd_result_back.astype(np.float32) * mask_blend +
                     origin_img.astype(np.float32) * (1 - mask_blend))
    result_strict = np.clip(result_strict, 0, 255).astype(np.uint8)
    result_strict[mask_back == 0] = origin_img[mask_back == 0]

    result = result_strict if strict_mask else result_full

    steps.append({
        "title": "⑥ 贴回原图",
        "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",
        "images": [
            {"label": "用户原图", "b64": _enc(origin_img)},
            {"label": "整框覆盖", "b64": _enc(result_full)},
            {"label": "严格mask贴回", "b64": _enc(result_strict)},
            {"label": f"当前选用({'严格mask' if strict_mask else '整框覆盖'})", "b64": _enc(result)},
        ]
    })

    # === 步骤8: 消除接缝（泊松无缝融合）===
    if strict_mask and seamless_blend:
        start_enhance = time.time()
        try:
            sd_back = np.zeros_like(origin_img)
            cv2.warpAffine(sd_result, M_inv, (origin_img.shape[1], origin_img.shape[0]),
                           dst=sd_back, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0), flags=cv2.INTER_LANCZOS4)
            mask_back2 = np.zeros(origin_img.shape[:2], dtype=np.uint8)
            cv2.warpAffine(mask_dilate, M_inv, (origin_img.shape[1], origin_img.shape[0]),
                           dst=mask_back2, borderMode=cv2.BORDER_CONSTANT, borderValue=0, flags=cv2.INTER_NEAREST)
            sd2 = tuple(max(1, int(x)) for x in seamless_dilate)
            mask_back2 = cv2.dilate(mask_back2, np.ones(sd2, np.uint8))
            ys2, xs2 = np.where(mask_back2 > 10)
            if len(ys2) > 10:
                cx2 = int((xs2.min() + xs2.max()) / 2)
                cy2 = int((ys2.min() + ys2.max()) / 2)
                result = cv2.seamlessClone(sd_back, origin_img, mask_back2, (cx2, cy2), cv2.NORMAL_CLONE)
                t_seamless = time.time() - start_enhance
                steps.append({
                    "title": "⑦ 泊松融合消除接缝 (seamlessClone)",
                    "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}",
                    "images": [
                        {"label": "融合前(有接缝)", "b64": _enc(result_strict)},
                        {"label": "融合后(最终)", "b64": _enc(result)},
                    ]
                })
        except Exception as e:
            steps.append({
                "title": "⑦ 泊松融合（失败）",
                "desc": f"泊松融合失败({e})，回退到严格mask结果",
                "images": [{"label": "最终结果", "b64": _enc(result)}]
            })

    # 清理临时文件
    try:
        shutil.rmtree(user_material_dir)
        os.remove(new_user_img_path)
    except Exception:
        pass

    params_used = {
        "method": method, "strict_mask": strict_mask, "seamless_blend": seamless_blend,
        "is_hr": is_hr, "dilate_kernel": list(dilate_kernel),
        "denoising_strength": denoising_strength,
        "blend_dilate": list(blend_dilate), "seamless_dilate": list(seamless_dilate),
        "band_width": band_width,
        "height_ratio": height_ratio, "width_ratio": width_ratio,
        "corner_ratio": corner_ratio, "vertical_offset": vertical_offset,
        "refiner_switch_at": refiner_switch_at,
        "gender": in_gender, "tag": p_tag, "dst_size": list(dst_size),
        "total_time": round(time.time() - start_all, 1),
    }
    print(f"[swap_hairline] 总耗时: {params_used['total_time']}s")
    return result, steps, params_used


def _boundary_band(mask, kernel):
    """提取 mask 的边界带（形态学梯度：dilate - erode）。

    输入软mask(0-255)，输出边界带(0或255)，带宽≈2*kernel_size。
    """
    binary = (mask > 10).astype(np.uint8) * 255
    dilated = cv2.dilate(binary, kernel)
    eroded = cv2.erode(binary, kernel)
    band = cv2.subtract(dilated, eroded)
    return band


def _shrink(img, max_side=1024, nearest=False):
    """缩小图片便于前端展示"""
    h, w = img.shape[:2]
    if max(h, w) <= max_side:
        return img
    sc = max_side / max(h, w)
    interp = cv2.INTER_NEAREST if nearest else cv2.INTER_AREA
    return cv2.resize(img, (int(w * sc), int(h * sc)), interpolation=interp)


def _enc(img):
    _, buf = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 80])
    return base64.b64encode(buf).decode()


def _enc_mask(mask):
    if mask.ndim == 2:
        colored = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
    else:
        colored = mask
    _, buf = cv2.imencode(".jpg", colored, [cv2.IMWRITE_JPEG_QUALITY, 80])
    return base64.b64encode(buf).decode()