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EISeg

Python 3.6 License Downloads

简体中文| English

Latest Developments

  • Our paper on interactive segmentation named EdgeFlow is accepted by ICCV 2021 Workshop.
  • We release EISeg 0.3.0 with more functions and support for Polygon editing.

Introduction

EISeg (Efficient Interactive Segmentation) is an efficient and intelligent interactive segmentation annotation software developed based on PaddlePaddle. It relies on the interactivate image segmentation methods RITM and EdgeFlow .It covers a large number of high-quality segmentation models in different directions such as high-performance and lightweight, providing convenience to the rapid annotation of semantic and instance labels with reduced cost. In addition, by applying the annotations obtained by EISeg to other segmentation models provided by PaddleSeg for training, high-performance models with customized scenarios can be created, integrating the whole process of segmentation tasks from data annotation to model training and prediction.

eiseg_demo

Model Preparation

Please download the model parameters before using EIseg. EISeg provides four annotation models trained on COCO+LVIS and large-scale portrait data to meet the needs of both generic and portrait scenarios. The model architecture corresponds to the network selection module in EISeg interactive tools, and users need to select different network structures and loading parameters in accordance with their own needs.

Model Type Applicable Scenarios Model Architecture Download Link
High Performance Model Image annotation in generic scenarios HRNet18_OCR64 hrnet18_ocr64_cocolvis
Lightweight Model Image annotation in generic scenarios HRNet18s_OCR48 hrnet18s_ocr48_cocolvis
High Performance Model Annotation in portrait scenarios HRNet18_OCR64 hrnet18_ocr64_human
Lightweight Model Annotation in portrait scenarios HRNet18s_OCR48 hrnet18s_ocr48_human

Installation

EISeg provides multiple ways of installation, among which pip and [run code](#run code) are compatible with Windows, Mac OS and Linux. It is recommended to install in a virtual environment created by conda for fear of environmental conflicts.

System Requirements:

  • PaddlePaddle >= 2.2.0

For more details of the installation of PaddlePaddle, please refer to our official website。

Clone

Clone PaddleSeg to your local system through git:

git clone https://github.com/PaddlePaddle/PaddleSeg.git

Enable EISeg by running eiseg after installing the needed environment:

cd PaddleSeg\contrib\EISeg
python -m eiseg

Or you can run exe.py in eiseg:

cd PaddleSeg\contrib\EISeg\eiseg
python exe.py

PIP

Install pip as follows：

pip install eiseg

pip will install dependencies automatically. After that, enter the following at the command line:

eiseg

Now, you can run pip.

Windows exe

EISeg uses QPT to package. You can download the latest EISeg from here, unzip it, and run the program by double-clicking its .exe. The program will initialize the packages needed for installation in its first run, please be patient.

Using

After opening the software, make the following settings before annotating:

  1. Load Model Parameter

    Select the appropriate network and load the corresponding model parameters. Currently, networks in EISeg are HRNet18s_OCR48 and HRNet18_OCR64, which provide model parameters for portrait and generic scenarios respectively. Successful loading is shown at the status bar in the lower right corner, while a mismatch between the network parameters and model parameters will trigger a warning of failure load, requiring to be reloaded. The correctly loaded model parameters will be recorded in Recent Model Parameters, which can be easily switched, and the exiting model parameter will be loaded automatically the next time you open the software.

  2. Load Image

    Open the image or image folder. Things go well when you see that the main screen image is loaded correctly and the image path is rightly shown in Data List.

  3. Add/Load Label

    Add/load labels. New labels can be created by Add Label, which are divided into 4 columns corresponding to pixel value, description, color and deletion. The newly created labels can be saved as txt files by Save Label List, and other collaborators can import labels by Load Label List. Labels imported by loading will be loaded automatically after restarting the software.

  4. Autosave

    You can choose the right folder and have the autosave set up, so that the annotated image will be saved automatically when switching images.

Start the annotation when the above are all set up. Here are the commonly used keys/shortcut keys by default, press E to modify them as you need.

Keys/Shortcut Keys Function
Left Mouse Button Add Positive Sample Points
Right Mouse Button Add Negative Sample Points
Middle Mouse Button Image Panning
Ctrl+Middle Mouse Button（wheel） Image Zooming
S Previous Image
F Next Image
Space Finish Annotation/Switch State
Ctrl+Z Undo
Ctrl+Shift+Z Clear
Ctrl+Y Redo
Ctrl+A Open Image
Shift+A Open Folder
E Open Shortcut Key List
Backspace Delete Polygon
Double Click（point） Delete Point
Double Click（edge） Add Point

Instruction of New Functions

  • Polygon
  1. Click Space key to complete interactive annotation, then appears the polygon boundary; when you need to continue the interactive process inside the polygon, click Space to switch to interactive mode so the polygon cannot be selected and changed.
  2. The polygon can be dragged and deleted. Use the left mouse to drag the anchor point, double-click the anchor point to delete it, and double-click a side to add an anchor point.
  3. With Keep Maximum Connected Blocks on, only the largest area will remain in the image, the rest of the small areas will not be displayed and saved.
  • Save Format
  1. Polygons will be recorded and automatically loaded after setting JSON Save or COCO Save.
  2. With no specified save path, the image is save to the label folder under the current image folder by default.
  3. If there are images with the same name but different suffixes, you can open labels and images with the same extensions.
  4. You can also save as grayscale, pseudo-color or matting image, see tools 7-9 in the toolbar
  • Generate mask
  1. Labels can be dragged by holding down the second column, and the final generated mask will be overwritten from top to bottom according to the label list.
  • Interface Module
  1. You can select the interface module to be presented in Display, and the normal exit status and location of the interface module will be recorded, and loaded automatically when you open it next time.

Version Updates

  • 2021.09.16 0.3.0：【1】Complete the function of polygon editing with support for editing the results of interactive annotation；【2】Support CH/EN interface；【3】Support saving as grayscale/pseudo-color labels and COCO format；【4】More flexible interface dragging；【5】Achieve the dragging of label bar, and the generated mask is overwritten from top to bottom.
  • 2021.07.07 0.2.0: Newly added contrib：EISeg，which enables rapid interactive annotation of portrait and generic images.

Developer

Yuying Hao, Lin Han, Yizhou Chen, Yiakwy, GT, Zhiliang Yu

Academic Citation

If you find our project useful in your research, please consider citing ：

@article{hao2021edgeflow,
  title={EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow},
  author={Hao, Yuying and Liu, Yi and Wu, Zewu and Han, Lin and Chen, Yizhou and Chen, Guowei and Chu, Lutao and Tang, Shiyu and Yu, Zhiliang and Chen, Zeyu and others},
  journal={arXiv preprint arXiv:2109.09406},
  year={2021}
}