xsl
9b7324cd26
将所有绝对路径从 /home/ubuntu 改回 /home/xsl，覆盖启动脚本、 configure.ini 及训练/测试脚本。纯路径替换，无功能性改动。 本分支(xsl5090)用于本机(RTX 5090)运行；master 维持 /home/ubuntu 供远程 ubuntu 机器使用。
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""为新发型生成 hair_template_material（first##_matting.png + .pkl）
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换发型功能4 需要：
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hair_template_material/<hair_id>/first##<name>.png 原图
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hair_template_material/<hair_id>/first##<name>_matting.png 头发抠图mask
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hair_template_material/<hair_id>/first##<name>.pkl 1k关键点
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用法:
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cd /home/xsl/change_hair/project/hair_service_sd
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python gen_template_material.py --hair-id new_test_001 --img /path/to/template.jpg
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"""
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import os
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import sys
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import ssl
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import uuid
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import pickle
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import argparse
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ssl._create_default_https_context = ssl._create_unverified_context
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HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
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os.chdir(HAIR_SERVICE_DIR)
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sys.path.insert(0, HAIR_SERVICE_DIR)
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os.environ.setdefault("HF_HUB_OFFLINE", "1")
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import cv2
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import numpy as np
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import torch
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_orig_torch_load = torch.load
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def _patched_torch_load(*args, **kwargs):
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if 'map_location' in kwargs and callable(kwargs['map_location']) and not isinstance(kwargs['map_location'], str):
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kwargs['map_location'] = 'cpu'
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return _orig_torch_load(*args, **kwargs)
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torch.load = _patched_torch_load
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from models.detector import RetinaFaceDetector
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from models.MomocvFaceAlignment1K import MomocvFaceAlignment1K
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from utils.landmark_processor import get_max_rect
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from core.process_modules import Generator_Matte
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from common.logger import config
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--hair-id", required=True)
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parser.add_argument("--img", required=True, help="模板图(发型图)")
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args = parser.parse_args()
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template_dir = config.get('default', 'hair_template_material_dir')
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out_dir = os.path.join(template_dir, args.hair_id)
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os.makedirs(out_dir, exist_ok=True)
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img = cv2.imread(args.img)
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if img is None:
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print(f"✗ 读取失败: {args.img}"); sys.exit(1)
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print(f"模板图: {img.shape}")
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# 1. 人脸检测 + 1k关键点
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print("加载模型...")
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detector = RetinaFaceDetector(gpu_id=0)
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aligner = MomocvFaceAlignment1K(gpu_id=0)
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matte_gen = Generator_Matte(gpu=True, device_id=0)
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print("模型加载完成")
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dets, landms = detector.forward(img)
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if len(dets) == 0:
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print("✗ 未检测到人脸"); sys.exit(1)
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det = dets[get_max_rect(dets)]
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landmarks_1k = aligner.stable_forward(img.copy(), [det])
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pt1k = landmarks_1k[0]
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# 2. 头发抠图
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with torch.no_grad():
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_, alpha, _ = matte_gen.matte_inference(img, pt1k)
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if alpha.shape != img.shape[:2]:
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alpha = cv2.resize(alpha, (img.shape[1], img.shape[0]))
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# 3. 保存三个文件（first##前缀）
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file_uuid = f"first##{uuid.uuid4()}"
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png_path = os.path.join(out_dir, f"{file_uuid}.png")
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matting_path = os.path.join(out_dir, f"{file_uuid}_matting.png")
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pkl_path = os.path.join(out_dir, f"{file_uuid}.pkl")
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cv2.imwrite(png_path, img)
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cv2.imwrite(matting_path, alpha)
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with open(pkl_path, 'wb') as f:
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# 存 numpy array（不能用 tolist()，代码用 landmarks[index, :] 索引需要 ndarray）
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pickle.dump({'human_pt1k': np.asarray(pt1k, dtype=np.float32)}, f)
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print(f"✓ 生成完成:")
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print(f" {png_path}")
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print(f" {matting_path}")
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print(f" {pkl_path}")
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if __name__ == "__main__":
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main()
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