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
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
import os
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import time
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import torch
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import torch.nn.parallel
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import numpy as np
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from utils import landmark_processor
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import cv2
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# modelRoot = "/home/yangchaojie/Desktop/hairstyle/hairstyle_infer/weights"
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modelRoot = "./weights"
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label_map = [
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[0, 0, 0],
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[255, 0, 0],
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[0, 0, 255],
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[0, 255, 0],
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[0, 255, 255],
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# [0, 255, 0]
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]
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class Generator_BaldSeg_5c(object):
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def __init__(self, gpu_flag, gpu_id):
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if not gpu_flag:
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self.device = torch.device("cpu")
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else:
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self.device = torch.device('cuda:{0}'.format(gpu_id))
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# load seg model
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self.model_dir = modelRoot
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self.pre_trained_model = os.path.join(self.model_dir, "ori_hair_checkpoint_7660_0611.pt")
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self.net = torch.jit.load(self.pre_trained_model, map_location=self.device).to(self.device)
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self.net.eval()
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self.output_img_size = 512
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self.img_ratio = 0.4
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def label_to_mask(self, label_np):
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label_np = label_np.astype(np.int32)[:, :, np.newaxis]
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mask = np.zeros((label_np.shape[0], label_np.shape[1], 3), dtype=np.uint8)
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for id, color in enumerate(label_map):
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index = (label_np == id).all(axis=2)
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mask[index] = color
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return mask
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def forward(self, image, alpha, landmarks1k):
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image_to_face_mat = landmark_processor.get_transform_mat_full_face_ratio_deeplab(landmarks1k, self.output_img_size, self.img_ratio)
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img_ = (image * (1 - alpha[:, :, np.newaxis].astype(np.float32) / 255)).astype(np.uint8)
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img_cuted = cv2.warpAffine(img_, image_to_face_mat, (self.output_img_size, self.output_img_size), flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_CONSTANT, borderValue=[255, 255, 255])
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inv_image_to_face_mat = cv2.invertAffineTransform(image_to_face_mat)
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img = (img_cuted.astype(np.float32) / 255).transpose((2, 0, 1))
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img = np.expand_dims(img, axis=0)
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img = torch.from_numpy(img).to(self.device) #cuda(self.gpu_id)
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with torch.no_grad():
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output = self.net(img)
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pred = output.detach().cpu().numpy().squeeze().astype(np.float32) #torch.max(output[:1], 1)[1].detach().cpu().numpy().squeeze().astype(np.float32)
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mask = self.label_to_mask(pred)
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# cv2.imshow("img_cuted: ", img_cuted)
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# cv2.imshow("mask: ", mask)
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# cv2.waitKey()
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black_img = np.zeros(image.shape).astype(np.uint8)
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cv2.warpAffine(mask, inv_image_to_face_mat, (image.shape[1], image.shape[0]),
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dst=black_img, flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_TRANSPARENT)
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return black_img |