# Global Wheat 2020 dataset http://www.global-wheat.com/
# Train command: python train.py --data GlobalWheat2020.yaml
# Default dataset location is next to YOLOv5:
#   /parent_folder
#     /datasets/GlobalWheat2020
#     /yolov5


# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: # 3422 images
  - ../../data/exitPPS/train/images/

val: # 748 images (WARNING: train set contains ethz_1)
  - ../../data/exitPPS/val/images/

test: # 1276 images
  - ../../data/exitPPS/val/images/


# number of classes
nc: 9

# class names
names: [ 'ex','dEx','qV','wF','oth','bdg','rsh','fam','st']


# download command/URL (optional) --------------------------------------------------------------------------------------
# download: |
#   from utils.general import download, Path

#   # Download
#   dir = Path('../datasets/GlobalWheat2020')  # dataset directory
#   urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
#           'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
#   download(urls, dir=dir)

#   # Make Directories
#   for p in 'annotations', 'images', 'labels':
#       (dir / p).mkdir(parents=True, exist_ok=True)

#   # Move
#   for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
#            'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
#       (dir / p).rename(dir / 'images' / p)  # move to /images
#       f = (dir / p).with_suffix('.json')  # json file
#       if f.exists():
#           f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations