xsl
443cfa298f
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
25 lines
922 B
Python
25 lines
922 B
Python
from seg.hairseg_single_model import Evaluator
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import os
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import cv2
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import numpy as np
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if __name__ == "__main__":
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data_path = "/home/liyang/project/matting/合格/origin"
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dst_path = "/home/liyang/project/matting/合格/origin_seg_res1102"
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if not os.path.exists(dst_path):
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os.mkdir(dst_path)
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seg_model = Evaluator(gpu_id=0, output_img_size=512, nclass=3)
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for imgs in os.listdir(data_path):
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if imgs.endswith(".txt"):
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continue
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img_path = os.path.join(data_path, imgs)
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img = cv2.imread(img_path)
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if img_path.endswith('.png'):
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kpts_1k = np.loadtxt(img_path.replace('.png', '_landmark1k.txt'))
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elif img_path.endswith('.jpg'):
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kpts_1k = np.loadtxt(img_path.replace('.jpg', '_landmark1k.txt'))
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output_img_size = 512
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mask = seg_model.eval(img, kpts_1k)
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cv2.imwrite(os.path.join(dst_path, imgs), mask)
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