https://github.com/fenglinglwb/mat

文章目录

• 基础镜像
• fastapi
• 总结
• 图片批量访问去除水印的请求代码
• 使用感受

基础镜像

docker run -it -p 7898:7860 --gpus device=3 kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-xformers bash 

git clone https://github.com/fenglinglwb/MAT.git   cd MAT/  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple 

apt-get update && apt-get install ffmpeg libsm6 libxext6  -y 

一系列操作后得到一个环境镜像，FFHQ_512.pkl在其中：

docker push kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat 

可以用这个镜像尝试inpaint效果：

docker run -it -p 7898:7860 --gpus device=3 kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat bash 

执行inpaint：

cd /workspace/MAT  python generate_image.py --network pretrained/FFHQ_512.pkl --dpath images --mpath masks --outdir samples 

原图

mask图：

结果去除图：

fastapi

安装了一些fastapi的环境：

kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat-apibase 

进而写dockerfile：

FROM kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat-apibase EXPOSE 7860 ENTRYPOINT cd /workspace/MAT/ && python /workspace/MAT/mianfastapi.py 

build：

docker build  -f Dockerfile1 . -t kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat-api 

只需要执行这个镜像就可以启动服务：

docker run -d -p 7898:7860 --gpus device=3 kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat-api  

总结

启动服务：

docker run -d -p 7898:7860 --gpus device=3 kevinchina/deeplearning:pytorch2.3.0-cuda12.1-cudnn8-devel-mat-api  

访问服务：

import requests  url = "http://10.136.19.26:7898/inpaint" image_path = "image.jpg" mask_path = "mask.jpg"  # 读取图像和掩码文件 with open(image_path, "rb") as img_file, open(mask_path, "rb") as mask_file:     files = {         "image": img_file,         "mask": mask_file     }      # 发送POST请求     response = requests.post(url, files=files)      # 检查响应状态码     if response.status_code == 200:         # 保存生成的图像         with open("output.png", "wb") as out_file:             out_file.write(response.content)         print("生成的图像已保存为 output.png")     else:         print(f"请求失败，状态码: {response.status_code}")         print(response.text)  

图片批量访问去除水印的请求代码

import base64 import io import os import traceback  import requests import cv2 import numpy as np import json from PIL import Image from PIL import ImageDraw import numpy as np import cv2 from tqdm import tqdm import json   def listPathAllfiles(dirname):     result = []     for maindir, subdir, file_name_list in os.walk(dirname):         for filename in file_name_list:             apath = os.path.join(maindir, filename)             result.append(apath)     return result   url = "http://10.136.19.26:7898/inpaint" # image_path = "image.jpg" # mask_path = "mask.jpg" # # # 读取图像和掩码文件 # with open(image_path, "rb") as img_file, open(mask_path, "rb") as mask_file: #     files = { #         "image": img_file, #         "mask": mask_file #     } # #     # 发送POST请求 #     response = requests.post(url, files=files) # #     # 检查响应状态码 #     if response.status_code == 200: #         # 保存生成的图像 #         with open("output.png", "wb") as out_file: #             out_file.write(response.content) #         print("生成的图像已保存为 output.png") #     else: #         print(f"请求失败，状态码: {response.status_code}") #         print(response.text)   src = r"/ssd/xiedong/xiezhenceshi/xiezhen_datasets" save_img_dst_output_inpaint_alpha = r"/ssd/xiedong/xiezhenceshi/inpaint_alpha" os.makedirs(save_img_dst_output_inpaint_alpha, exist_ok=True) files = listPathAllfiles(src) files.sort() files = [file for file in files if file.endswith(".jpg")]  for src_image_file in tqdm(files):     try:         ocr_ret_file = src_image_file.replace(".jpg", ".json")         output_image_file_alpha = src_image_file.replace(src, save_img_dst_output_inpaint_alpha)         if not os.path.exists(ocr_ret_file):             print(f"ocr_ret_file not exists: {ocr_ret_file}")             continue         if os.path.exists(output_image_file_alpha):             print(f"output_image_file_alpha exists: {output_image_file_alpha}")             continue         output_image_file_alpha_father = os.path.dirname(output_image_file_alpha)         os.makedirs(output_image_file_alpha_father, exist_ok=True)          # 造一个mask图片在本地         ocr_json_data = json.load(open(ocr_ret_file, "r", encoding="utf-8"))         image = cv2.imread(src_image_file)         # 只要中心512*512的图         image_zitu = image[image.shape[0] // 2 - 256:image.shape[0] // 2 + 256,                      image.shape[1] // 2 - 256:image.shape[1] // 2 + 256]         mask = np.zeros(image.shape, dtype=np.uint8)         for item in ocr_json_data:             box = item[0]             cv2.fillPoly(mask, np.array([box], dtype=np.int32), (255, 255, 255))         # 只要中心512*512的图         mask_zitu = mask[mask.shape[0] // 2 - 256:mask.shape[0] // 2 + 256,                     mask.shape[1] // 2 - 256:mask.shape[1] // 2 + 256]         # 取反mask_zitu的选择         mask_zitu = cv2.bitwise_not(mask_zitu)         src_image_file_rb = cv2.imencode('.jpg', image_zitu)[1].tobytes()         mask_file_rb = cv2.imencode('.jpg', mask_zitu)[1].tobytes()          files = {             "image": src_image_file_rb,             "mask": mask_file_rb         }          # 发送POST请求         response = requests.post(url, files=files)          # 检查响应状态码         if response.status_code == 200:             # 保存生成的图像             # with open("output.png", "wb") as out_file:             #     out_file.write(response.content)             # print("生成的图像已保存为 output.png")             image_inpaint = Image.open(io.BytesIO(response.content)).convert('RGB')             image_inpaint_cv2 = np.array(image_inpaint)             image_inpaint_cv2 = cv2.cvtColor(image_inpaint_cv2, cv2.COLOR_RGB2BGR)              # 贴回到原图             image[image.shape[0] // 2 - 256:image.shape[0] // 2 + 256, \             image.shape[1] // 2 - 256:image.shape[1] // 2 + 256] = image_inpaint_cv2              cv2.imwrite(output_image_file_alpha, image)         else:             print(f"请求失败，状态码: {response.status_code}")             print(response.text)     except:         traceback.print_exc()  

使用感受

不行，基本传统的inpaint就是很垃圾，效果不行无法投入使用，生成还得看StableDiffusion，但StableDiffusion就是很慢，如果有希望把LCM和小的SD模型用起来，就很nice了。