# Prepare Datasets for FrozenSeg

A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
This document explains how to setup the builtin datasets so they can be used by the above APIs.
[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
and how to add new datasets to them.

FrozenSeg has builtin support for a few datasets.
The datasets are assumed to exist in a directory specified by the environment variable
`DETECTRON2_DATASETS`.
Under this directory, detectron2 will look for datasets in the structure described below, if needed.
```
$DETECTRON2_DATASETS/
  # panoptic datasets
  ADEChallengeData2016/
  coco/
  cityscapes/
  mapillary_vistas/
  bdd100k/
  # semantic datasets
  VOCdevkit/
  ADE20K_2021_17_01/
  pascal_ctx_d2/
  pascal_voc_d2/
```

You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
If left unset, the default is `./datasets` relative to your current working directory.


## Expected dataset structure for [COCO](https://cocodataset.org/#download):

```
coco/
  annotations/
    instances_{train,val}2017.json
    panoptic_{train,val}2017.json
  {train,val}2017/
    # image files that are mentioned in the corresponding json
  panoptic_{train,val}2017/  # png annotations
  panoptic_semseg_{train,val}2017/  # generated by the script mentioned below
```

Install panopticapi by:
```
pip install git+https://github.com/cocodataset/panopticapi.git
```
Then, run `python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py`, to extract semantic annotations from panoptic annotations (only used for evaluation).


## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
```
cityscapes/
  gtFine/
    train/
      aachen/
        color.png, instanceIds.png, labelIds.png, polygons.json,
        labelTrainIds.png
      ...
    val/
    test/
    # below are generated Cityscapes panoptic annotation
    cityscapes_panoptic_train.json
    cityscapes_panoptic_train/
    cityscapes_panoptic_val.json
    cityscapes_panoptic_val/
    cityscapes_panoptic_test.json
    cityscapes_panoptic_test/
  leftImg8bit/
    train/
    val/
    test/
```
Install cityscapes scripts by:
```
pip install git+https://github.com/mcordts/cityscapesScripts.git
```

Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
```
CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
```
These files are not needed for instance segmentation.

Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
```
CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
```
These files are not needed for semantic and instance segmentation.


## Expected dataset structure for [ADE20k (A150)](http://sceneparsing.csail.mit.edu/):
```
ADEChallengeData2016/
  images/
  annotations/
  objectInfo150.txt
  # download instance annotation
  annotations_instance/
  # generated by prepare_ade20k_sem_seg.py
  annotations_detectron2/
  # below are generated by prepare_ade20k_pan_seg.py
  ade20k_panoptic_{train,val}.json
  ade20k_panoptic_{train,val}/
  # below are generated by prepare_ade20k_ins_seg.py
  ade20k_instance_{train,val}.json
```

The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.

Install panopticapi by:
```bash
pip install git+https://github.com/cocodataset/panopticapi.git
```

Download the instance annotation from http://sceneparsing.csail.mit.edu/:
```bash
wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar
```

Then, run `python datasets/prepare_ade20k_pan_seg.py`, to combine semantic and instance annotations for panoptic annotations.

And run `python datasets/prepare_ade20k_ins_seg.py`, to extract instance annotations in COCO format.


## Expected dataset structure for [Mapillary Vistas](https://www.mapillary.com/dataset/vistas):
```
mapillary_vistas/
  training/
    images/
    instances/
    labels/
    panoptic/
  validation/
    images/
    instances/
    labels/
    panoptic/
```

No preprocessing is needed for Mapillary Vistas on semantic and panoptic segmentation.

## Expected dataset structure for [BDD100K](https://doc.bdd100k.com/download.html#id1)
```
bdd100k/
  images/
    10k/
      train/
      val/
      test/
  json
  labels/
    pan_seg/
    sem_seg/
```

`coco-format` annotations is obtained by running:


```
cd $DETECTRON2_DATASETS
wget https://github.com/chenxi52/FrozenSeg/releases/download/latest/bdd100k_json.zip
unzip bdd100k_json.zip
```


## Expected dataset structure for [ADE20k-Full (A-847)](https://groups.csail.mit.edu/vision/datasets/ADE20K/):
```
ADE20K_2021_17_01/
  images/
  index_ade20k.pkl
  objects.txt
  # generated by prepare_ade20k_full_sem_seg.py
  images_detectron2/
  annotations_detectron2/
```

Register and download the dataset from https://groups.csail.mit.edu/vision/datasets/ADE20K/:
```bash
cd $DETECTRON2_DATASETS
wget your/personal/download/link/{username}_{hash}.zip
unzip {username}_{hash}.zip
```

Generate the directories `ADE20K_2021_17_01/images_detectron2` and `ADE20K_2021_17_01/annotations_detectron2` by running: 
```bash
python datasets/prepare_ade20k_full_sem_seg.py
```

## Expected dataset structure for [PASCAL Context Full (PC-459)](https://www.cs.stanford.edu/~roozbeh/pascal-context/) and [PASCAL VOC (PAS-21)](http://host.robots.ox.ac.uk/pascal/VOC/):

```bash
VOCdevkit/
  VOC2012/
    Annotations/
    JPEGImages/
    ImageSets/
      Segmentation/
  VOC2010/
    JPEGImages/
    trainval/
    trainval_merged.json
# generated by prepare_pascal_voc_sem_seg.py
pascal_voc_d2/
  images/
  annotations_pascal21/
  # pascal 20 excludes the background class
  annotations_pascal20/
# generated by prepare_pascal_ctx_sem_seg.py
pascal_ctx_d2/
  images/
  annotations_ctx59/
  # generated by prepare_pascal_ctx_full_sem_seg.py
  annotations_ctx459/

```
### PASCAL VOC (PAS-21)

Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/:
```bash
cd $DETECTRON2_DATASETS
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
# generate folder VOCdevkit/VOC2012
tar -xvf VOCtrainval_11-May-2012.tar
```

Generate directory `pascal_voc_d2` running: 
```bash
python datasets/prepare_pascal_voc_sem_seg.py
```

### PASCAL Context Full (PC-459)

Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/ and annotation from https://www.cs.stanford.edu/~roozbeh/pascal-context/:
```bash
cd $DETECTRON2_DATASETS
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
# generate folder VOCdevkit/VOC2010
tar -xvf VOCtrainval_03-May-2010.tar 
wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
# generate folder VOCdevkit/VOC2010/trainval
tar -xvzf trainval.tar.gz -C VOCdevkit/VOC2010 
wget https://codalabuser.blob.core.windows.net/public/trainval_merged.json -P VOCdevkit/VOC2010/
```

Install [Detail API](https://github.com/zhanghang1989/detail-api) by:
```bash
git clone https://github.com/zhanghang1989/detail-api.git
rm detail-api/PythonAPI/detail/_mask.c
pip install -e detail-api/PythonAPI/
```

Generate directory `pascal_ctx_d2/images` running:
```bash
python datasets/prepare_pascal_ctx_sem_seg.py
```

Generate directory `pascal_ctx_d2/annotations_ctx459` running:
```bash
python datasets/prepare_pascal_ctx_full_sem_seg.py
```