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Monday, 19 June 2023

魔术橡皮擦「Magic Eraser」离线版，AI帮你轻松删除图片中不需要的元素

推荐了最近爆火的网站：魔术橡皮擦「Magic Eraser」，这个网站可以在几秒钟就帮你把图片中不需要的元素清除干净，比用PS来慢慢修图要方便太多了。


最近吾爱论坛的大佬@zhaoyun007 根据这个项目背后的开源技术，自己开发了一款Windows离线版的 「魔术橡皮擦」，不需要将图片上传到别人的网站，用自己的电脑就能处理.

使用方法也非常简单，导入需要处理的图片，然后用鼠标将图片中不需要的元素涂抹覆盖，点击右上角的转换按钮等待几秒钟即可~

由于是本地跑模型，所以你的CPU性能越好，处理速度也就越快，一般十来秒的就能搞定。
注意：如果第一次处理后有瑕疵，可以反复涂抹几次瑕疵部位。

转换后的图片，会和软件保存在同一个目录下，并会在原件名后增加magic后缀显示。

这个网站和离线版工具都是根据三星AI研究所的开源项目 LaMa 开发，感兴趣的小伙伴也可以自己去研究研究这个项目。

下载地址：https://www.123pan.com/s/uYh9-AMWQ3


魔术橡皮擦「Magic Eraser」：https://www.magiceraser.io


项目地址：https://github.com/saic-mdal/lama 

(在线抠图网站：魔术橡皮檫Magic Eraser-瞬间删除图像中不需要的部分.
要保留照片原本完美的样子，又要移除图片中某个不需要的人物，物体时，透过 Magic Eraser 在线移除工具就可以了，只要将照片上传到网站上，简单操作涂抹要移除的部分， 马上就可以瞬间完成完美的照片，不需要注册也不需要钱，完全免费使用！
Magic Eraser 在线移除人像
首先进入网站，点击「Upload lmage」上传要修改的照片。
上传照片后，就可以直接把照片中想要移除的物体通过刷子涂抹掉（刷子的大小可以根据要移除图片的大小来自由调节），然后点击“搽除”等待系统处理后，就会看到处理成像的照片非常完美，最后只要点选右下角的「Download」即可下载保存，非常实用。
是不是非常惊艳呢？有需要的小伙伴赶快试试！
Magic Eraser官网主页：魔术橡皮擦 – 在几秒钟内从图像中删除不需要的东西 (magiceraser.io) )

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LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

advimman.github.io/lama-project/ 

LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions

by Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky.

🔥🔥🔥
LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.

[Project page] [arXiv] [Supplementary] [BibTeX] [Casual GAN Papers Summary]


Try out in Google Colab

LaMa development

(Feel free to share your paper by creating an issue)

Non-official 3rd party apps:

(Feel free to share your app/implementation/demo by creating an issue)

Environment setup

Clone the repo: git clone https://github.com/advimman/lama.git

There are three options of an environment:

  1. Python virtualenv:

    virtualenv inpenv --python=/usr/bin/python3
source inpenv/bin/activate
pip install torch==1.8.0 torchvision==0.9.0

cd lama
pip install -r requirements.txt

  2. Conda

    % Install conda for Linux, for other OS download miniconda at https://docs.conda.io/en/latest/miniconda.html
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda
$HOME/miniconda/bin/conda init bash

cd lama
conda env create -f conda_env.yml
conda activate lama
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -y
pip install pytorch-lightning==1.2.9

  3. Docker: No actions are needed 🎉.

Inference

Run

cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

1. Download pre-trained models

Install tool for yandex disk link extraction:

pip3 install wldhx.yadisk-direct

The best model (Places2, Places Challenge):

curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip
unzip big-lama.zip

All models (Places & CelebA-HQ):

curl -L $(yadisk-direct https://disk.yandex.ru/d/EgqaSnLohjuzAg) -o lama-models.zip
unzip lama-models.zip

2. Prepare images and masks

Download test images:

curl -L $(yadisk-direct https://disk.yandex.ru/d/xKQJZeVRk5vLlQ) -o LaMa_test_images.zip
unzip LaMa_test_images.zip
OR prepare your data: 1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder.

3. Predict

On the host machine:

python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output

OR in the docker

The following command will pull the docker image from Docker Hub and execute the prediction script

bash docker/2_predict.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output device=cpu

Docker cuda: TODO

4. Predict with Refinement

On the host machine:

python3 bin/predict.py refine=True model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output

Train and Eval

Make sure you run:

cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

Then download models for perceptual loss:

mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/
wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth

Places

⚠️ NB: FID/SSIM/LPIPS metric values for Places that we see in LaMa paper are computed on 30000 images that we produce in evaluation section below. For more details on evaluation data check [Section 3. Dataset splits in Supplementary] ⚠️

On the host machine:

# Download data from http://places2.csail.mit.edu/download.html
# Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images section
wget http://data.csail.mit.edu/places/places365/train_large_places365standard.tar
wget http://data.csail.mit.edu/places/places365/val_large.tar
wget http://data.csail.mit.edu/places/places365/test_large.tar

# Unpack train/test/val data and create .yaml config for it
bash fetch_data/places_standard_train_prepare.sh
bash fetch_data/places_standard_test_val_prepare.sh

# Sample images for test and viz at the end of epoch
bash fetch_data/places_standard_test_val_sample.sh
bash fetch_data/places_standard_test_val_gen_masks.sh

# Run training
python3 bin/train.py -cn lama-fourier location=places_standard

# To evaluate trained model and report metrics as in our paper
# we need to sample previously unseen 30k images and generate masks for them
bash fetch_data/places_standard_evaluation_prepare_data.sh

# Infer model on thick/thin/medium masks in 256 and 512 and run evaluation 
# like this:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \
indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \
outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckpt

python3 bin/evaluate_predicts.py \
$(pwd)/configs/eval2_gpu.yaml \
$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \
$(pwd)/inference/random_thick_512 \
$(pwd)/inference/random_thick_512_metrics.csv

Docker: TODO

CelebA

On the host machine:

# Make shure you are in lama folder
cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

# Download CelebA-HQ dataset
# Download data256x256.zip from https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P

# unzip & split into train/test/visualization & create config for it
bash fetch_data/celebahq_dataset_prepare.sh

# generate masks for test and visual_test at the end of epoch
bash fetch_data/celebahq_gen_masks.sh

# Run training
python3 bin/train.py -cn lama-fourier-celeba data.batch_size=10

# Infer model on thick/thin/medium masks in 256 and run evaluation 
# like this:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier-celeba_/ \
indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \
outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt

Docker: TODO

Places Challenge

On the host machine:

# This script downloads multiple .tar files in parallel and unpacks them
# Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) 
bash places_challenge_train_download.sh

TODO: prepare
TODO: train 
TODO: eval

Docker: TODO

Create your data

Please check bash scripts for data preparation and mask generation from CelebaHQ section, if you stuck at one of the following steps.

On the host machine:

# Make shure you are in lama folder
cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

# You need to prepare following image folders:
$ ls my_dataset
train
val_source # 2000 or more images
visual_test_source # 100 or more images
eval_source # 2000 or more images

# LaMa generates random masks for the train data on the flight,
# but needs fixed masks for test and visual_test for consistency of evaluation.

# Suppose, we want to evaluate and pick best models 
# on 512x512 val dataset  with thick/thin/medium masks 
# And your images have .jpg extention:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \ # thick, thin, medium
my_dataset/val_source/ \
my_dataset/val/random_<size>_512.yaml \# thick, thin, medium
--ext jpg

# So the mask generator will: 
# 1. resize and crop val images and save them as .png
# 2. generate masks

ls my_dataset/val/random_medium_512/
image1_crop000_mask000.png
image1_crop000.png
image2_crop000_mask000.png
image2_crop000.png
...

# Generate thick, thin, medium masks for visual_test folder:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
my_dataset/visual_test_source/ \
my_dataset/visual_test/random_<size>_512/ \ #thick, thin, medium
--ext jpg


ls my_dataset/visual_test/random_thick_512/
image1_crop000_mask000.png
image1_crop000.png
image2_crop000_mask000.png
image2_crop000.png
...

# Same process for eval_source image folder:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
my_dataset/eval_source/ \
my_dataset/eval/random_<size>_512/ \ #thick, thin, medium
--ext jpg



# Generate location config file which locate these folders:

touch my_dataset.yaml
echo "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yaml
echo "out_root_dir: $(pwd)/experiments/" >> my_dataset.yaml
echo "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yaml
mv my_dataset.yaml ${PWD}/configs/training/location/


# Check data config for consistency with my_dataset folder structure:
$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist
...
train:
  indir: ${location.data_root_dir}/train
  ...
val:
  indir: ${location.data_root_dir}/val
  img_suffix: .png
visual_test:
  indir: ${location.data_root_dir}/visual_test
  img_suffix: .png


# Run training
python3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10

# Evaluation: LaMa training procedure picks best few models according to 
# scores on my_dataset/val/ 

# To evaluate one of your best models (i.e. at epoch=32) 
# on previously unseen my_dataset/eval do the following 
# for thin, thick and medium:

# infer:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \
indir=$(pwd)/my_dataset/eval/random_<size>_512/ \
outdir=$(pwd)/inference/my_dataset/random_<size>_512 \
model.checkpoint=epoch32.ckpt

# metrics calculation:
python3 bin/evaluate_predicts.py \
$(pwd)/configs/eval2_gpu.yaml \
$(pwd)/my_dataset/eval/random_<size>_512/ \
$(pwd)/inference/my_dataset/random_<size>_512 \
$(pwd)/inference/my_dataset/random_<size>_512_metrics.csv

OR in the docker:

TODO: train
TODO: eval

Hints

Generate different kinds of masks

The following command will execute a script that generates random masks.

bash docker/1_generate_masks_from_raw_images.sh \
    configs/data_gen/random_medium_512.yaml \
    /directory_with_input_images \
    /directory_where_to_store_images_and_masks \
    --ext png

The test data generation command stores images in the format, which is suitable for prediction.

The table below describes which configs we used to generate different test sets from the paper. Note that we do not fix a random seed, so the results will be slightly different each time.


 Places 512x512 CelebA 256x256
Narrow random_thin_512.yaml random_thin_256.yaml
Medium random_medium_512.yaml random_medium_256.yaml
Wide random_thick_512.yaml random_thick_256.yaml

Feel free to change the config path (argument #1) to any other config in configs/data_gen or adjust config files themselves.

Override parameters in configs

Also you can override parameters in config like this:

python3 bin/train.py -cn <config> data.batch_size=10 run_title=my-title

Where .yaml file extension is omitted

Models options

Config names for models from paper (substitude into the training command):

* big-lama
* big-lama-regular
* lama-fourier
* lama-regular
* lama_small_train_masks

Which are seated in configs/training/folder

Links

Training time & resources

TODO

Acknowledgments

from https://github.com/advimman/lama 

-----------------------------------------------------

开源图像修复工具 污点擦除 – Lama Cleaner

Lama Cleaner，一款GitHub上免费开源的图像修复工具，由 SOTA AI 模型提供支持的图像修复工具，基于cv2的经典图像修复算法，支持中央处理器和图形处理器，多种策略修复图像，可以从照片中删除任何不需要的物体、缺陷、人物或擦除和替换（由稳定扩散驱动）你照片上的任何东西，A 姐体验了一下，非常不错。

Lama Cleaner：https://huggingface.co/spaces/Sanster/Lama-Cleaner-lama

GitHub地址：https://github.com/Sanster/lama-cleaner

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Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

www.iopaint.com/

IOPaint

A free and open-source inpainting & outpainting tool powered by SOTA AI model.

total download version python version HuggingFace Spaces Open in Colab

Erase Replace Object

IOPaint-erase-markdown.mp4

iopaint-inpaint-markdown.mp4
Draw Text Out-painting

AnyText-markdown.mp4

outpainting.mp4

Quick Start

Start webui

IOPaint provides a convenient webui for using the latest AI models to edit your images. You can install and start IOPaint easily by running following command:

# In order to use GPU, install cuda version of pytorch first.
# pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118
# AMD GPU users, please utilize the following command, only works on linux, as pytorch is not yet supported on Windows with ROCm.
# pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/rocm5.6

pip3 install iopaint
iopaint start --model=lama --device=cpu --port=8080

That's it, you can start using IOPaint by visiting http://localhost:8080 in your web browser.

Batch processing

You can also use IOPaint in the command line to batch process images:

iopaint run --model=lama --device=cpu \
--input=/path/to/image_folder \
--mask=/path/to/mask_folder \
--output=output_dir

--input is the folder containing input images, --mask is the folder containing corresponding mask images. When --mask is a path to a mask file, all images will be processed using this mask.

You can see more information about the available models and plugins supported by IOPaint below.

Features

from https://github.com/Sanster/IOPaint

 

 


 

 

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