# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail

# Example usage: python train.py --data SKU-110K.yaml

# parent

# ├── yolov5

# └── datasets

#     └── SKU-110K  ← downloads here (13.6 GB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: ../datasets/SKU-110K  # dataset root dir

train: train.txt  # train images (relative to 'path')  8219 images

val: val.txt  # val images (relative to 'path')  588 images

test: test.txt  # test images (optional)  2936 images

# Classes

names:

  0: object

# Download script/URL (optional) ---------------------------------------------------------------------------------------

download: |

  import shutil

  from tqdm import tqdm

  from utils.general import np, pd, Path, download, xyxy2xywh

  # Download

  dir = Path(yaml['path'])  # dataset root dir

  parent = Path(dir.parent)  # download dir

  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']

  download(urls, dir=parent, delete=False)

  # Rename directories

  if dir.exists():

      shutil.rmtree(dir)

  (parent / 'SKU110K_fixed').rename(dir)  # rename dir

  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir

  # Convert labels

  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names

  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':

      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations

      images, unique_images = x[:, 0], np.unique(x[:, 0])

      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:

          f.writelines(f'./images/{s}\n' for s in unique_images)

      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):

          cls = 0  # single-class dataset

          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:

              for r in x[images == im]:

                  w, h = r[6], r[7]  # image width, height

                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance

                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label