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2D Image Anotation

The following content is about how to use EISeg to annotate 2D images. Model preparation and how to use can be seen as follow:

Model Preparation

Please download the model parameters before using EIseg. EISeg 0.5.0 provides seven direction models trained on COCO+LVIS, large-scale portrait data, mapping_challenge, MRSpineSeg, Chest Xray, LiTS and Self-built aluminum plate quality inspection data set to meet the labeling needs of generic and portrait scenarios as well as architecture, medical and industrial images. 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 static_hrnet18_ocr64_cocolvis
Lightweight Model Image annotation in generic scenarios HRNet18s_OCR48 static_hrnet18s_ocr48_cocolvis
High Performance Model Annotation in portrait scenarios HRNet18_OCR64 static_hrnet18_ocr64_human
High Performance Model Image annotation in generic scenarios EdgeFlow static_edgeflow_cocolvis
Lightweight Model Annotation in portrait scenarios HRNet18s_OCR48 static_hrnet18s_ocr48_human
Lightweight Model Annotation of remote sensing building HRNet18s_OCR48 static_hrnet18_ocr48_rsbuilding_instance
High Performance Model Annotation of chest Xray in medical scenarios Resnet50_DeeplabV3+ static_resnet50_deeplab_chest_xray *
Lightweight Model Annotation of liver in medical scenarios HRNet18s_OCR48 static_hrnet18s_ocr48_lits
Lightweight Model Annotation of Spinal Structures in medical scenarios HRNet18s_OCR48 static_hrnet18s_ocr48_MRSpineSeg
Lightweight Model Annotation of Aluminum plate defects in industrial scenarios HRNet18s_OCR48 static_hrnet18s_ocr48_aluminium

NOTE： The downloaded model structure *.pdmodel and the corresponding model parameters *.pdiparams should be put into the same directory. When loading the model, you only need to decide the location of the model parameter at the end of *.pdiparams, and *.pdmodel will be loaded automatically. When using EdgeFlow model, please turn off Use Mask, and check Use Mask when adopting other models. For High Performance Model, we recommend to utilize the computer with gpu for a smoother annotation experience.

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. After downloading and decompressing the right model and parameters, the model structure *.pdmodel and the corresponding model parameters *.pdiparams should be put into the same directory, and only the location of the model parameter at the end of *.pdiparamsneed to be selected when loading the model. The initialization of the static model takes some time, please wait patiently until the model is loaded. 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. Annotation

    During interactive annotation, users add positive and negative points with left and right mouse clicks, respectively. After finishing interactive segmentation, you can push Space button and the tool generates a polygon frame around the target border. Users can adjust the polygon vertexes to further improve segmentation accuracy.

  5. 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

    • 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.
    • The polygon can be 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.
    • 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

    • Polygons will be recorded and automatically loaded after setting JSON Save or COCO Save.
    • With no specified save path, the image is save to the label folder under the current image folder by default.
    • If there are images with the same name but different suffixes, you can open labels and images with the same extensions.
    • You can also save as grayscale, pseudo-color or matting image, see tools 7-9 in the toolbar.

  • Generate mask

    • 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

    • 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.

  • Vertical Segmentation

    EISeg now supports remote sensing images and medical images segmentation, and additional dependencies need to be installed for their functioning.

  • Scripting Tool

    EISeg currently provides scripting tools including annotation to PaddleX dataset, delineation of COCO format and semantic labels to instance labels, etc. See Scripting Tools Usage for more details.