1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
|
# Instance Segmentation Example
## Annotation
```bash
labelme data_annotated --labels labels.txt --nodata --validatelabel exact --config '{shift_auto_shape_color: -2}'
labelme data_annotated --labels labels.txt --nodata --labelflags '{.*: [occluded, truncated], person: [male]}'
```
## Convert to VOC-format Dataset
```bash
# It generates:
# - data_dataset_voc/JPEGImages
# - data_dataset_voc/SegmentationClass
# - data_dataset_voc/SegmentationClassNpy
# - data_dataset_voc/SegmentationClassVisualization
# - data_dataset_voc/SegmentationObject
# - data_dataset_voc/SegmentationObjectNpy
# - data_dataset_voc/SegmentationObjectVisualization
./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
```
<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationObjectVisualization/2011_000003.jpg" width="33%" />
Fig 1. JPEG image (left), JPEG class label visualization (center), JPEG instance label visualization (right)
Note that the label file 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/SegmentationClass/2011_000003.png # left
labelme_draw_label_png data_dataset_voc/SegmentationObject/2011_000003.png # right
```
<img src=".readme/draw_label_png_class.jpg" width="33%" /> <img src=".readme/draw_label_png_object.jpg" width="33%" />
## Convert to COCO-format Dataset
```bash
# It generates:
# - data_dataset_coco/JPEGImages
# - data_dataset_coco/annotations.json
./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt
```
|
