# Semantic Segmentation Example

## Annotation

```bash
labelme data_annotated --labels labels.txt --nodata
```

![](.readme/annotation.jpg)


## Convert to VOC-like Dataset

```bash
# It generates:
#   - data_dataset_voc/JPEGImages
#   - data_dataset_voc/SegmentationClass
#   - data_dataset_voc/SegmentationClassVisualization
./labelme2voc.py labels.txt data_annotated data_dataset_voc
```

<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClass/2011_000003.png" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" />

Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)  


Note that the reason why the label file is mostly black is it contains only very low label values (ex. `0, 4, 14`), and
`255` indicates the `__ignore__` label value (`-1` in the npy file).  
You can see the label PNG file by following.

```bash
labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png
```

<img src=".readme/draw_label_png.jpg" width="33%" />