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Annotating Tutorial

Whether it is semantic segmentation, panoramic segmentation, or instance segmentation, we all need sufficient training data. If you want to use an unlabeled original dataset for segmentation tasks, you must first annotate the original image. If you are using a dataset with segmentation annotations such as Cityscapes, you can skip this step.

• This document will provide tutorials for using 2 kinds of labeling tools: EISeg, LabelMe.

1、LabelMe

• 1.Installation

After the user has collected the images for training, evaluation, and prediction, he needs to use the data labeling tool [LabelMe] (https://github.com/wkentaro/labelme) to complete the data labeling. LabelMe supports the use of Windows/macOS/Linux three systems, and the label format under the three systems is the same. For the specific installation process, please refer to the Official Installation Guide.

• 2.Using

Open the terminal and enter labelme, and an interactive interface of LabelMe will appear. You can preview the marked image given by LabelMe before starting to label the custom dataset.

• Preview annotated Images

Get LabelMe source code：

git clone https://github.com/wkentaro/labelme

Enter labelme in the terminal and the LableMe interactive interface will appear. Click OpenDir to open <path/to/labelme>/examples/semantic_segmentation/data_annotated, where <path/to/labelme> is the cloned labelme After opening, it shows the truth label of semantic segmentation.

• Start Annotating

Please follow the steps below to label the dataset:

​ (1) Click OpenDir to open the directory where the picture to be labeled is located, click Create Polygons, draw polygons along the edges of the target, and enter the target category after completion. During the marking process, if a point is drawn wrong, you can press the undo shortcut key to undo the point. The undo shortcut key under Mac is command+Z.

​ (2) Right-click and select Edit Polygons to move the position of the polygon as a whole or to move a certain point; right-click and select Edit Label to modify the category of each target. Please perform this step according to your own needs, if you don't need to modify it, you can skip it.

​ (3) After marking all targets in the picture, click Save to save the json file, please put the json file and the picture in the same folder, and click Next Image to mark the next picture.

The truth value files produced by LableMe can refer to the [folder] (https://github.com/PaddlePaddle/PaddleSeg/blob/release/v0.8.0/docs/annotation/labelme_demo) given by us.

Note：

Marking method for targets with holes in the middle: After marking the target outline, draw a polygon along the edge of the hole area and assign it to another category, and if it is a background, assign it to _background_. as follows:

• 3.Data format conversion

Finally, use the data conversion script we provide to convert the data format produced by the above-mentioned annotation tool into the data format required for model training.

• The dataset directory structure after data format conversion is as follows:

my_dataset                 # Root directory
|-- annotations            # Ground-truth
|   |-- xxx.png            # Pixel-level truth information
|   |...
|-- class_names.txt        # The category name of the dataset
|-- xxx.jpg(png or other)  # Original image of dataset
|-- ...
|-- xxx.json               # Json file,used to save annotation information
|-- ...

• 4.Run the following code to convert the annotated data into a dataset that meets the above format:

  python tools/labelme2seg.py <PATH/TO/LABEL_JSON_FILE>

Among them, <PATH/TO/LABEL_JSON_FILE> is the directory of the folder where the picture and the json file produced by LabelMe are located, and it is also the directory of the folder where the converted label set is located.

We have built a example, you can run the following code to experience:

python tools/labelme2seg.py legacy/docs/annotation/labelme_demo/

The converted dataset can refer to the [folder] (https://github.com/PaddlePaddle/PaddleSeg/blob/release/v0.8.0/docs/annotation/labelme_demo) given by us. Among them, the file class_names.txt is the name of all the annotation categories in the dataset, including the background class; the folder annotations saves the pixel-level truth information of each picture, the background class _background_ corresponds to 0, and other targets The category starts from 1 and increases up to 255.

2、EISeg

For the use of EISeg, please refer to Document