person_monitor/dependence/yolov5/data/xView.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
# Example usage: python train.py --data xView.yaml
# parent
# ├── yolov5
# └── datasets
#     └── xView  ← downloads here (20.7 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/xView  # dataset root dir
train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images

# Classes
nc: 60  # number of classes
names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
        'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
        'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
        'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
        'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
        'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
        'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
        'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
        'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower']  # class names


# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
  import json
  import os
  from pathlib import Path

  import numpy as np
  from PIL import Image
  from tqdm import tqdm

  from utils.datasets import autosplit
  from utils.general import download, xyxy2xywhn


  def convert_labels(fname=Path('xView/xView_train.geojson')):
      # Convert xView geoJSON labels to YOLO format
      path = fname.parent
      with open(fname) as f:
          print(f'Loading {fname}...')
          data = json.load(f)

      # Make dirs
      labels = Path(path / 'labels' / 'train')
      os.system(f'rm -rf {labels}')
      labels.mkdir(parents=True, exist_ok=True)

      # xView classes 11-94 to 0-59
      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]

      shapes = {}
      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
          p = feature['properties']
          if p['bounds_imcoords']:
              id = p['image_id']
              file = path / 'train_images' / id
              if file.exists():  # 1395.tif missing
                  try:
                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
                      cls = p['type_id']
                      cls = xview_class2index[int(cls)]  # xView class to 0-60
                      assert 59 >= cls >= 0, f'incorrect class index {cls}'

                      # Write YOLO label
                      if id not in shapes:
                          shapes[id] = Image.open(file).size
                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
                      with open((labels / id).with_suffix('.txt'), 'a') as f:
                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
                  except Exception as e:
                      print(f'WARNING: skipping one label for {file}: {e}')


  # Download manually from https://challenge.xviewdataset.org
  dir = Path(yaml['path'])  # dataset root dir
  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
  # download(urls, dir=dir, delete=False)

  # Convert labels
  convert_labels(dir / 'xView_train.geojson')

  # Move images
  images = Path(dir / 'images')
  images.mkdir(parents=True, exist_ok=True)
  Path(dir / 'train_images').rename(dir / 'images' / 'train')
  Path(dir / 'val_images').rename(dir / 'images' / 'val')

  # Split
  autosplit(dir / 'images' / 'train')