微信商城网站建设多少钱,北京网站改版报价,如何做网站后台管理,网营科技是外包公司吗无论是自己、家人或是朋友、客户的照片#xff0c;免不了有些是黑白的、被污损的、模糊的#xff0c;总想着修复一下。作为一个程序员 或者 程序员的家属#xff0c;当然都有责任满足他们的需求、实现他们的想法。除了这个#xff0c;学习了本文的成果#xff0c;或许你还…无论是自己、家人或是朋友、客户的照片免不了有些是黑白的、被污损的、模糊的总想着修复一下。作为一个程序员 或者 程序员的家属当然都有责任满足他们的需求、实现他们的想法。除了这个学习了本文的成果或许你还可以用来赚点小钱。
比如这样 或是这样 是不是很酷那么。。。
需要什么编程技能什么知识
答案你不需要会编程序你只需要认识26个字母 和 大约4GB左右的硬盘空间. 1 CODEFORMER概要可略过允许你看不懂
GitHub - sczhou/CodeFormer: [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Releases · sczhou/CodeFormer · GitHub
下面是项目网址可访问无需下载哦。
https://github.com/sczhou/CodeFormer/ https://github.com/sczhou/CodeFormer/releases 1.1 论文Paper可略过允许你看不懂
Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)
Shangchen Zhou, Kelvin C.K. Chan, Chongyi Li, Chen Change Loy
PAPER: https://arxiv.org/abs/2206.11253
Zhou: Shangchen Zhou
Chan: Kelvin C.K. Chan
Li: Chongyi Li
Loy: Home | Chen Change Loy
of Lab, Nanyang Technological University
新加坡南阳科技大学NTU
基本原理可略过 ⭐ If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! 1.2 进化史Update可略过
2023.07.20: Integrated to OpenXLab. Try out online demo! OpenXLabDemo: OpenXLabOpenXLab浦源 - 应用中心2023.04.19: Training codes and config files are public available now.2023.04.09: Add features of inpainting and colorization for cropped and aligned face images.2023.02.10: Include dlib as a new face detector option, it produces more accurate face identity.2022.10.05: Support video input --input_path [YOUR_VIDEO.mp4]. Try it to enhance your videos! ?2022.09.14: Integrated to Hugging Face. Try out online demo! Hugging Face2022.09.09: Integrated to Replicate. Try out online demo! Replicate 1.3 功能 TODO虽然不可略过但瞄一眼即可
Add training code and config filesAdd checkpoint and script for face inpaintingAdd checkpoint and script for face colorizationAdd background image enhancement 1.3.1 老照片修复Try Enhancing Old Photos / Fixing AI-arts 1.3.2 人脸重建画质修复画质提升Face Restoration 1.3.3 黑白上色Face Color Enhancement and Restoration 1.3.4 照片划痕修复Face Inpainting 1.4 依赖项与安装Dependencies and Installation
可或不可略过快速浏览即可
1.4.1 依赖项 Dependencies Pytorch 1.7.1[Option] CUDA 10.1 可选没有CUDA也可以运行。Other required packages in requirements.txt 1.4.2 安装 Install看看就行了看后面的教程再干活 # git clone this repository
git clone https://github.com/sczhou/CodeFormer
cd CodeFormer # create new anaconda env
conda create -n codeformer python3.8 -y
conda activate codeformer # install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib (only for face detection or cropping with dlib) 1.5 快速指南Quick Inference
可略过后面有更快的指南 下载预训练模型Download Pre-trained Models:
下面的网址 与 方法 都很慢用后面分享的网盘会快一些
Download the facelib and dlib pretrained models from [Releases | Google Drive | OneDrive] to the weights/facelib folder. You can manually download the pretrained models OR download by running the following command:
Release CodeFormer V0.1.0 Release · sczhou/CodeFormer · GitHub
https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?uspsharing
OneDrive python scripts/download_pretrained_models.py facelib
python scripts/download_pretrained_models.py dlib (only for dlib face detector) Download the CodeFormer pretrained models from [Releases | Google Drive | OneDrive] to the weights/CodeFormer folder. You can manually download the pretrained models OR download by running the following command:
Release CodeFormer V0.1.0 Release · sczhou/CodeFormer · GitHub
https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?uspsharing
OneDrive python scripts/download_pretrained_models.py CodeFormer 准备测试数据Prepare Testing Data:
作者已经帮你下载好了
You can put the testing images in the inputs/TestWhole folder. If you would like to test on cropped and aligned faces, you can put them in the inputs/cropped_faces folder. You can get the cropped and aligned faces by running the following command: # you may need to install dlib via: conda install -c conda-forge dlib
python scripts/crop_align_face.py -i [input folder] -o [output folder] 1.6 测试Testing:
可略过后面有更快的指南下同
[Note] If you want to compare CodeFormer in your paper, please run the following command indicating --has_aligned (for cropped and aligned face), as the command for the whole image will involve a process of face-background fusion that may damage hair texture on the boundary, which leads to unfair comparison. Fidelity weight w lays in [0, 1]. Generally, smaller w tends to produce a higher-quality result, while larger w yields a higher-fidelity result. The results will be saved in the results folder. 1.6.1 人脸重建Face Restoration (cropped and aligned face)
脸部重建局部512x512
# For cropped and aligned faces (512x512)
python inference_codeformer.py -w 0.5 --has_aligned --input_path [image folder]|[image path] 全图
Whole Image Enhancement # For whole image
# Add --bg_upsampler realesrgan to enhance the background regions with Real-ESRGAN
# Add --face_upsample to further upsample restorated face with Real-ESRGAN
python inference_codeformer.py -w 0.7 --input_path [image folder]|[image path] Video Enhancement # For Windows/Mac users, please install ffmpeg first
conda install -c conda-forge ffmpeg
# For video clips
# Video path should end with .mp4|.mov|.avi
python inference_codeformer.py --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path [video path] 1.6.2 黑白上色Face Colorization (cropped and aligned face) # For cropped and aligned faces (512x512)
# Colorize black and white or faded photo
python inference_colorization.py --input_path [image folder]|[image path] 1.6.3 人脸修复Face Inpainting (cropped and aligned face) # For cropped and aligned faces (512x512)
# Inputs could be masked by white brush using an image editing app (e.g., Photoshop)
# (check out the examples in inputs/masked_faces)
python inference_inpainting.py --input_path [image folder]|[image path] 1.7 自训练Training:
费力不讨好的事别干了。再说你也没有资源
The training commands can be found in the documents: English | 简体中文.
https://github.com/sczhou/CodeFormer/blob/master/docs/train.md
https://github.com/sczhou/CodeFormer/blob/master/docs/train_CN.md 1.8 发行版本Release V0.1.0
可略过后面有更快的指南
下面这些是你需要下载的模型。。。。。。
但是作者已经帮你下载好了。
CodeFormer V0.1.0 Release Latest
This release is mainly for storing pre-trained models, etc. Assets 22
codeformer.pth 359 MB Aug 10, 2022
codeformer_colorization.pth 355 MB Apr 3
codeformer_inpainting.pth 354 MB Apr 9
codeformer_stage2.pth 365 MB Jul 23
detection_mobilenet0.25_Final.pth 1.71 MB Aug 10, 2022
detection_Resnet50_Final.pth 104 MB Aug 10, 2022
latent_gt_code1024.pth 302 MB Apr 17
latent_gt_code512.pth 302 MB Apr 17
mmod_human_face_detector-4cb19393.dat 713 KB Feb 10
motion-blur-kernels-32.pth 65.4 KB Apr 17
parsing_bisenet.pth 50.8 MB Aug 10, 2022
parsing_parsenet.pth 81.4 MB Aug 10, 2022
RealESRGAN_x2plus.pth 64 MB Sep 13, 2022
shape_predictor_5_face_landmarks-c4b1e980.dat 8.73 MB Feb 10
shape_predictor_68_face_landmarks-fbdc2cb8.dat 95.1 MB Apr 7
vqgan_code1024.pth 243 MB Feb 25
vqgan_code512.pth 243 MB Apr 17
vqgan_discriminator.pth 26.6 MB Apr 18
yolov5l-face.pth 178 MB Aug 10, 2022
yolov5n-face.pth 6.81 MB Aug 10, 2022
Source code(zip) Aug 9, 2022
Source code(tar.gz) Aug 9, 2022 1.9 权利License
不可略过请爱惜他人成果
This project is licensed under NTU S-Lab License 1.0/a. Redistribution and use should follow this license.
https://github.com/sczhou/CodeFormer/blob/master/LICENSE 1.10 相关知识Acknowledgement
不可略过来龙去脉要搞清爽的
This project is based on [BasicSR]. Some codes are brought from [Unleashing Transformers], [YOLOv5-face], and [FaceXLib]. We also adopt [Real-ESRGAN] to support background image enhancement. Thanks for their awesome works.
BasicSR GitHub - XPixelGroup/BasicSR: Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
Transformers GitHub - samb-t/unleashing-transformers: Code for the ECCV 2022 paper Unleashing Transformers
YOLOv5-face GitHub - deepcam-cn/yolov5-face: YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)
FaceXLib GitHub - xinntao/facexlib: FaceXlib aims at providing ready-to-use face-related functions based on current STOA open-source methods.
Real-ESRGAN GitHub - xinntao/Real-ESRGAN: Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. 1.11 联系 Contact
try it!
If you have any questions, please feel free to reach me out at shangchenzhougmail.com. ---------------------------------------------------------------------- 2 以下字字珠玑不bu可ke略lue过guo...! 2.1 安装 python
90年代末期在一知半解的情况下在某985大学给大家做了一次 python 讲座最后告诉大家这个语言没有什么前途听者大多稀里糊涂。没想到借助 AI这个垃圾语言又死灰复燃了。
python 用于一些算法的验证等等特别适合在大学、研究机构使用工业场景就很吃力了。
CodeFormer 是python写的。
当然要先下载、安装 python。
提示不同的python程序需要不同版本的python。这里随便先安装一个不用太在意。
访问
http://www.python.org文件不太大一会儿就能下载完成 下载 windows 64 bit版本 下载稳定版本 stable releases 然后在你的计算机 下载 目录就会有一个 python-.......exe 文件。双击安装。 务必记住安装过程中请将所有的可选项 【✔️】都点开再点击【下一步】什么的
再请记住安装差不多的时候有一个 Disable path length limit! 的项目一定记得点击 2.2 安装Anaconda
官网www.anaconda.com 访问但不要下载哦太慢啦用镜像。下面有 看一下网页左下角的文件名。 Ana.......64.exe 什么的。 访问清华的镜像
https://mirrors.tuna.tsinghua.edu.cn/anaconda/achive/
技巧CTRLF然后输入 2023.09 可以快速定位。如果是其他版本按上面你看到的 版本信息取其中几个具有区别性的字母或数字来搜搜。 Anaconda 比较大 1 GB 多了慢慢下吧。
下载后双击运行 exe 文件即可。后面照样选择全部的【可选项】下一步。。。。。。 2.3 无需下载安装 PyCharm收费
很多python教程指导大家下载、安装 PyCharm其实这个软件没有大用处。
那咱们用什么编辑器编写软件呢。
推荐用
1微软 Visual Studio 2022 或者 Visual Studio Code
2或个人最喜欢用 EditPlus 绿色版本或其他任何可以写文本文件的软件
3或用 Windows 写字板 也是可以的。 2.4 开始工作
做完了 2.1 与 2.2 再安装代码、模型及设置最基本的运行环境。后面就快了。 2.4.1 下载 CodeFormer 及其模型库 复习一下差点忘记了本文的主角
作者为大家下载了全部的代码 和 需要的 模型文件大约是 2GB 左右主要是模型数据比较大。
下面的网盘连接可以免费下载不需要积分与铜钱。 链接https://pan.baidu.com/s/1QLnLKvVQGnmedP7WdxGZ8w?pwdmiff 提取码miff --来自百度网盘超级会员V5的分享https://pan.baidu.com/s/1QLnLKvVQGnmedP7WdxGZ8w?pwdmiff 下载后先创建文件夹这是我的文件夹结构你如果能跑不限啊 C:/Yohoo/ C:/Yohoo/Face Restoration
将文件复制到该文件夹下再解压成: C:/Yohoo/Face Restoration/CodeFormer-Master
即可。 2.4.2 设置 CodeFormer 运行环境
Windows 10 或 Windows 11应用程序。 这样就进入了 Conda Prompt 界面如下 进入 CodeFormer-master 目录 dir 可以看到一些文件 下面开始逐句执行一些代码借此设置运行环境。 第一句
conda create -n codeformer python3.8 -y
你可以复制本语句(CTRLC)到黑色窗口后粘贴CTRLV
如此 然后回车下同 第二句
conda activate codeformer
第三句
pip3 install -r requirements.txt
如果出现错误一般是你没到达 codeformer-master 目录。 第四句
python basicsr/setup.py develop
第五句
conda install -c conda-forge dlib 上面这些过程一般将持续 20 分钟。 2.5 见证奇迹的时候 画质修复
第一步在 mydata 目录下创建 src 文件夹。将需要处理的图片复制到该目录下。 第二步输入代码
python inference_codeformer.py -w 0.7 --input_path mydata/src
一会结果就能出来保存到 results 文件夹下。 上面的这个图有彩蛋哦 环境设置只需要一次
后面如果多次使用 CodeFormer 则需要每次都
conda activate codeformer 这里只做一个试验其他的功能看上面的 1.6 节 代码即可。 总结几条
1python 一点儿都不简单说简单的都是为了买书、买视频
2python 除了AI领域其他的领域没有多大用处建议学其他的语言
3即使把 python 玩出花来也成不了大神。 效果不好不是我的错也不是 python 的错。
所有的都是数据的错。
