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Whether it is semantic segmentation, panoramic segmentation, or instance segmentation, we all need sufficient training data.
In this tutorial, we introduce the usage of LabelMe and convert the labeled image to the required format by PaddleSeg.
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.
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.
Figure 1 : Schematic diagram of LableMe interactive interface
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.
Figure 2 : Schematic diagram of annotated images
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.
Figure 3 : Schematic diagram of marking a single target
(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.
Figure 4 : Schematic diagram of modified annotation
(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.
Figure 5 : Schematic diagram of the truth file produced by LableMe
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:
Figure 6 : Schematic diagram of labeling with holes in the target
Finally, use the data conversion script to convert the data to the format required by PaddleSeg.
Run the following code to convert the annotated data. <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.
python tools/data/labelme2seg.py <PATH/TO/LABEL_JSON_FILE>
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
|-- ...
Figure 7 : Schematic diagram of the structure of the dataset catalog after format conversion