初始化：换发型/换发色/训练发型服务

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

project/photo_service/webui_im2im.py

@@ -0,0 +1,106 @@
+import io
+import cv2
+import base64
+import requests
+from PIL import Image
+import numpy as np
+
+"""
+    To use this example make sure you've done the following steps before executing:
+    1. Ensure automatic1111 is running in api mode with the controlnet extension. 
+       Use the following command in your terminal to activate:
+            ./webui.sh --no-half --api
+    2. Validate python environment meet package dependencies.
+       If running in a local repo you'll likely need to pip install cv2, requests and PIL 
+"""
+
+
+class ControlnetRequestImg2Img:
+    def __init__(self, prompt, net_prompt):
+        self.url = "http://127.0.0.1:7860/sdapi/v1/img2img"
+        self.prompt = prompt
+        self.neg_prompt = net_prompt
+        self.body = None
+
+    def build_body(self, dst_width, dst_height, cfg_scale, base_img):
+
+        self.body = {
+            "prompt": self.prompt,
+            "negative_prompt": self.neg_prompt,
+            "sampler_name": "Restart",
+            "batch_size": 1,
+            "steps": 30,
+            "width": dst_width,
+            "height": dst_height,
+            "cfg_scale": cfg_scale,
+            "seed": -1,
+            "init_images": [
+                self.encode_image_to_base64(base_img)
+            ],
+            "denoising_strength": 0.4,
+            "alwayson_scripts": {
+                "controlnet": {
+                    "args": [
+                        {
+                            "enabled": True,
+                            "module": "openpose_full",
+                            "model": "openpose",
+                            "weight": 1.0,
+                            # "image": self.read_image(),
+                            "resize_mode": "Crop and Resize",
+                            "low_vram": False,
+                            "processor_res": 512,
+                            "guidance_start": 0.0,
+                            "guidance_end": 1.0,
+                            "control_mode": "Balanced",
+                            "pixel_perfect": True
+                        }
+                    ]
+                }
+            }
+        }
+
+    def send_request(self):
+        response = requests.post(url=self.url, json=self.body)
+        return response.json()
+
+    def encode_image_to_base64(self, img):
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+    def read_image(self):
+        img = cv2.imread(self.img_path)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+    
+    def read_mask(self):
+        img = cv2.imread(self.mask)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+def encode_image_to_base64(img):
+    retval, bytes = cv2.imencode('.jpg', img)
+    encoded_image = base64.b64encode(bytes).decode('utf-8')
+    return encoded_image
+
+
+
+if __name__ == '__main__':
+    path = '/home/chinatszrn/Downloads/photo_service/service_data/template_data/template01.png'
+    img = cv2.imread(path)
+    prompt = '<lora:5b05d5eeee0188f436d7131c4f0ff52b:0.8>,easyphoto_face, easyphoto, 1person,face,suit'
+    neg_prompt = '(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark'
+
+
+    control_net = ControlnetRequestImg2Img(prompt, neg_prompt)
+    control_net.build_body(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=3.5, base_img=img)
+    output = control_net.send_request()
+    result = output['images'][0]
+
+    image_array = np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8)
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    cv2.imshow('image', image)
+    cv2.waitKey()