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。
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colomi
Executable
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import cv2
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import os
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import numpy as np
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import tqdm
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from utils import landmark_processor
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import base64
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import requests
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from PIL import Image
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import io
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def encode_numpy_to_base64(img):
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retval, bytes = cv2.imencode('.png', img)
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encoded_image = base64.b64encode(bytes).decode('utf-8')
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return encoded_image
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def webui_img2img(img, mask, prompt=''):
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url = "http://127.0.0.1:57860/sdapi/v1/img2img"
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request_dict = {
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"prompt": prompt,
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"negative_prompt": '(nsfw:1.5), ng_deepnegative_v1_75t, (badhandv4:1.2), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, '
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'bad hands, ((monochrome)), ((grayscale)) watermark, large breast, big breast, bad_pictures,easynegative, faceless, no human, white background, simple background, ',
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"sampler_name": "DPM++ 2M Karras",
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"batch_size": 1,
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"steps": 20,
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"width": img.shape[1],
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"height": img.shape[0],
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"cfg_scale": 7.0,
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"seed": 123456789,
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"mask_blur": 11,
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"init_images": [
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encode_numpy_to_base64(img)
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],
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"inpaint_full_res": False,
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"inpainting_fill": 1,
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"inpainting_mask_invert": 0,
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"mask": encode_numpy_to_base64(mask),
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# "refiner_checkpoint":"majicmixRealistic_v7.safetensors",
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# "refiner_switch_at": 0.4,
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"denoising_strength": 0.7,
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"alwayson_scripts": {
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# "controlnet": {
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# "args": [
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# {
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# "enabled": True,
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# "module": "openpose_full",
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# "model": "openpose",
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# "weight": 1.0,
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# # "image": self.read_image(),
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# "resize_mode": "Crop and Resize",
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# "low_vram": False,
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# "processor_res": 512,
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# "guidance_start": 0.0,
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# "guidance_end": 1.0,
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# "control_mode": "Balanced",
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# "pixel_perfect": False
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# }
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# ]
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# }
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}
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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result = ret_json['images'][0]
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img = cv2.imdecode(np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8), cv2.IMREAD_COLOR)
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return img
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def webui_super_res_img(img, ratio):
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url = "http://127.0.0.1:57860/sdapi/v1/extra-single-image"
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request_dict = {
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"resize_mode": 0,
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"show_extras_results": False,
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"gfpgan_visibility": 0,
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"codeformer_visibility": 1,
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"codeformer_weight": 1,
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"upscaling_resize": ratio,
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"upscaler_1": "8x_NMKD-Superscale_150000_G",
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"upscale_first": False,
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"image": encode_numpy_to_base64(img)
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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result = ret_json['image']
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img = cv2.imdecode(np.frombuffer(base64.b64decode(result), np.uint8), cv2.IMREAD_COLOR)
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return img
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def webui_tag_by_clip(img):
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url = "http://127.0.0.1:57860/sdapi/v1/interrogate"
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request_dict = {
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"image": encode_numpy_to_base64(img),
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"model": "clip"
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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return ret_json['caption']
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if __name__ == '__main__':
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hair_dir = '/mnt/database2/jiangqian/0808/exp2-data-zrn-0808/1816523294655647746'
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data2process_list = []
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# 遍历查找hair_dir下的所有npy文件
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for root, dirs, files in os.walk(hair_dir):
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for file in files:
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if file.endswith('.npy'):
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# 关键点路径
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pt1k_path = os.path.join(root, file)
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origin_img_path = pt1k_path[:-4] + '.png'
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origin_matting_path = pt1k_path[:-4] + '_origin_matting.png'
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new_matting_path = pt1k_path[:-4] + '_new_matting.png'
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result_img_path = pt1k_path[:-4] + '_res.png'
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# 判断上面的文件是否存在
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if (not os.path.exists(origin_img_path) or not os.path.exists(origin_matting_path)
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or not os.path.exists(new_matting_path) or not os.path.exists(result_img_path)):
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continue
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ref_hair_path = pt1k_path[:-4] + '_orig_hair.png'
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# if not os.path.exists(ref_hair_path):
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# continue
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lora_model_path = pt1k_path[:-4] + '_hairstyle_lora.safetensors'
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# if not os.path.exists(lora_model_path):
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# continue
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data2process_list.append([pt1k_path, origin_img_path, origin_matting_path,
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new_matting_path, result_img_path, ref_hair_path, lora_model_path])
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crop_size = 768
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webui_lora_dir = '/home/student/Documents/workspace_cxt_tianjing_hair/miaoya/webui_home/stable-diffusion-webui/models/Lora'
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for pt1k_path, origin_img_path, origin_matting_path, new_matting_path, result_img_path, ref_hair_path, lora_model_path in tqdm.tqdm(data2process_list):
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# if '508417f3-2c71-45cf-a75b-969b27ec7d8f' not in pt1k_path: continue
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# 读取关键点
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pt1k = np.load(pt1k_path)
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# 读取原图
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origin_img = cv2.imread(origin_img_path)
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tmp_scale = 1920 / max(origin_img.shape[0], origin_img.shape[1])
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if tmp_scale < 1.0:
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origin_img = cv2.resize(origin_img, (0, 0), fx=tmp_scale, fy=tmp_scale, interpolation=cv2.INTER_LANCZOS4)
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print("origin_img shape:", origin_img.shape)
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# cv2.imshow("origin_img", origin_img)
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# 读取原图抠图
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origin_matting = cv2.imread(origin_matting_path, cv2.IMREAD_GRAYSCALE)
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# 读取新图抠图
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new_matting = cv2.imread(new_matting_path, cv2.IMREAD_GRAYSCALE)
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# 读取结果图
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result_img = cv2.imread(result_img_path)
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print("result_img shape:", result_img.shape)
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# cv2.imshow("result_img", result_img)
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# # 读取参考头发
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# ref_hair = cv2.imread(ref_hair_path)
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# if max(ref_hair.shape[:2]) < 300: continue
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# 如何图像不清晰，进行超分辨率处理
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# if max(origin_img.shape[:2]) < 1500:
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# scale_ratio = 2000 / max(origin_img.shape[:2])
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# result_img = webui_super_res_img(result_img, scale_ratio)
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# origin_img = cv2.resize(origin_img, (result_img.shape[1], result_img.shape[0]),
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# interpolation=cv2.INTER_LANCZOS4)
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# origin_matting = cv2.resize(origin_matting, (result_img.shape[1], result_img.shape[0]))
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# new_matting = cv2.resize(new_matting, (result_img.shape[1], result_img.shape[0]))
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# pt1k = pt1k * scale_ratio
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# 获取头发处理的局部区域图像
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# M = landmark_processor.get_transform_mat_hair_ratio_v1(pt1k, crop_size, ratio=0.30, h_offset=0.32)
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scale = 768 / max(origin_img.shape[0], origin_img.shape[1])
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M = cv2.getRotationMatrix2D((0, 0), 0, scale)
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dst_size = (int(origin_img.shape[1] * scale), int(origin_img.shape[0] * scale))
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# 高质量的从原图中截取头发区域
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crop_origin = landmark_processor.high_quality_warpAffine(origin_img, M, dst_size)
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cv2.imwrite("./crop_origin.png", crop_origin)
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crop_result = landmark_processor.high_quality_warpAffine(result_img, M, dst_size)
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cv2.imwrite("./crop_result.png", crop_result)
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# tmp = cv2.warpAffine(origin_img, M, dst_size, flags=cv2.INTER_AREA)
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# 构造重绘的mask
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matting_merge = np.concatenate([origin_matting[:,:, np.newaxis], new_matting[:,:, np.newaxis]], axis=2)
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matting_merge = np.max(matting_merge, axis=2)
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# matting_merge = new_matting
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crop_matting = cv2.warpAffine(matting_merge, M, dst_size)
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mask = (crop_matting > 10).astype(np.float32)
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mask_dilate = cv2.dilate(mask, np.ones((3, 11), np.uint8))
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final_img = crop_result
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mask_dilate = np.clip(mask_dilate * 255, 0, 255).astype(np.uint8)
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# file_name = os.path.basename(pt1k_path)[:-4]
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# save_dir = '/home/chinatszrn/Downloads/abc/ref_hair/dst_res/style3_tmp'
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# cv2.imwrite(os.path.join(save_dir, file_name + '.png'), final_img)
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# cv2.imwrite(os.path.join(save_dir, file_name + '_mask.png'), mask_dilate)
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# # cv2.imwrite(os.path.join(save_dir, file_name + '_ref_hair.png'), ref_hair)
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# continue
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# # 拷贝lora模型
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# os.system('cp {} {}'.format(lora_model_path, webui_lora_dir))
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# # 构建prompt提示词
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# lora_model_name = os.path.basename(pt1k_path)[:-4]
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# 对final_img进行打标
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# tag_result = webui_tag_by_clip(final_img)
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tag_result = ''
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# 开始重绘
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prompt = f'<lora:1816523294655647746_hairstyle_lora:1> titor hairstyle, easyphoto, ' + tag_result
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# 对发型进行重绘
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# cv2.imshow("final_img_0", final_img)
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# cv2.imshow("mask_dilate", mask_dilate)
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# cv2.waitKey(100)
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sd_result = webui_img2img(final_img, mask_dilate, prompt)
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final_img = origin_img.copy()
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# 将重绘结果恢复到原图
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M_inv = cv2.invertAffineTransform(M)
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cv2.warpAffine(sd_result, M_inv, (final_img.shape[1], final_img.shape[0]), dst=final_img,
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borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.INTER_LANCZOS4)
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# cv2.imshow("final_img", final_img)
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# cv2.waitKey(0)
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cv2.imwrite(result_img_path[:-4] + '_sd.png', final_img)
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# ref_hair_scale = final_img.shape[0] / ref_hair.shape[0]
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# ref_hair = cv2.resize(ref_hair, (0, 0), fx=ref_hair_scale, fy=ref_hair_scale, interpolation=cv2.INTER_LANCZOS4)
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# img2show = np.concatenate([origin_img, ref_hair, final_img], axis=1)
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#
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# # 显示结果
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# save_dir = '/home/chinatszrn/Downloads/exp'
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# cv2.imwrite(os.path.join(save_dir, lora_model_name + '.png'), img2show)
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# # cv2.imshow("origin_img", cv2.resize(img2show, (0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA))
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# cv2.imshow("sd_result", cv2.resize(sd_result, (0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA))
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# cv2.waitKey(1000)
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