初始化换发型项目：3个微服务代码 + 部署脚本

包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除，由网盘单独上传。
2026-07-11 18:11:49 +08:00
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+import os
+import torch
+
+from seg.networks.deeplabv3_plus import get_deeplabv3_plus
+import numpy as np
+import cv2
+import time
+from utils import landmark_processor
+
+def label_to_mask(label_np):
+    label_np = label_np.astype(np.int32)[:, :, np.newaxis]
+    mask = np.zeros((label_np.shape[0], label_np.shape[1], 3), dtype=np.uint8)
+    for id, color in enumerate(label_map):
+        index = (label_np == id).all(axis=2)
+        mask[index] = color
+    return mask
+
+label_map = [
+    [0, 0, 0],  #
+    [128, 128, 128],
+    [255, 255, 255],
+]
+
+class Evaluator(object):
+    def __init__(self, gpu_id, output_img_size, nclass, seg_model_path=None):
+        self.device = torch.device('cuda:{}'.format(gpu_id) if gpu_id is not None else 'cpu')
+        # print("gpu_id: ", gpu_id)
+
+        # create network
+        self.model = get_deeplabv3_plus(backbone='xception', nclass=nclass)
+        model_path = os.path.join(seg_model_path)
+        self.model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))
+        # print("seg device: ", self.model.device)
+        self.model.to(self.device)
+        self.model.eval()
+
+        # images = torch.randn((1, 3, 512, 512)).to(self.device)
+        # torch.onnx.export(self.model, images,
+        #                   "deeplabv3_hair512_360_0520_wl.onnx",
+        #                   verbose=True,
+        #                   opset_version=11,
+        #                   input_names=['data'],
+        #                   do_constant_folding=True,
+        #                   output_names=['output'])
+
+        # exit()
+
+        self.output_img_size = output_img_size
+        self.nclass = nclass
+
+
+    def process_data(self, img):
+        img = (img.astype(np.float32) / 255).transpose((2, 0, 1))
+        img = torch.from_numpy(img).unsqueeze(0)
+
+        return img
+
+    def eval(self, img, pts1k):
+        orig_h, orig_w, _ = img.shape
+
+        M1 = landmark_processor.get_transform_mat_hair(pts1k, self.output_img_size, ratio=0.28, h_ratio=0.3)
+        crop_img = cv2.warpAffine(img, M1, (self.output_img_size, self.output_img_size), flags=cv2.INTER_LANCZOS4)
+        crop_img = self.process_data(crop_img)
+        crop_img = crop_img.to(self.device)
+
+        with torch.no_grad():
+            # torch.cuda.synchronize()
+            outputs = self.model(crop_img)
+            pred = torch.argmax(outputs[0], 1)
+
+            pred = pred[0].detach().cpu().numpy()
+            predict = pred.astype(np.float32)
+
+            pred_mask = label_to_mask(predict)
+
+            M1_invert = cv2.invertAffineTransform(M1)
+            img_pred = cv2.warpAffine(pred_mask, M1_invert, (orig_w, orig_h), flags=cv2.INTER_CUBIC)  #flags=cv2.INTER_NEAREST
+            orig_mask = img_pred.copy()
+
+        # show_concat = np.concatenate((img, orig_mask), axis=1)
+        # cv2.imshow("show_concat", show_concat)
+        # cv2.waitKey()
+        return orig_mask
+
+
+