Global Wheat Detection 2020 Dataset Auto-Download (ultralytics#2968)

* Create GlobalWheat2020.yaml

* Update and rename visdrone.yaml to VisDrone.yaml

* Update GlobalWheat2020.yaml

(cherry picked from commit 33712d6)

data/GlobalWheat2020.yaml

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+# 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
+  - ../datasets/GlobalWheat2020/images/arvalis_1
+  - ../datasets/GlobalWheat2020/images/arvalis_2
+  - ../datasets/GlobalWheat2020/images/arvalis_3
+  - ../datasets/GlobalWheat2020/images/ethz_1
+  - ../datasets/GlobalWheat2020/images/rres_1
+  - ../datasets/GlobalWheat2020/images/inrae_1
+  - ../datasets/GlobalWheat2020/images/usask_1
+
+val: # 748 images (WARNING: train set contains ethz_1)
+  - ../datasets/GlobalWheat2020/images/ethz_1
+
+test: # 1276
+  - ../datasets/GlobalWheat2020/images/utokyo_1
+  - ../datasets/GlobalWheat2020/images/utokyo_2
+  - ../datasets/GlobalWheat2020/images/nau_1
+  - ../datasets/GlobalWheat2020/images/uq_1
+
+# number of classes
+nc: 1
+
+# class names
+names: [ 'wheat_head' ]
+
+
+# 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

data/VisDrone.yaml

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+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# Train command: python train.py --data VisDrone.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /VisDrone
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../VisDrone/VisDrone2019-DET-train/images  # 6471 images
+val: ../VisDrone/VisDrone2019-DET-val/images  # 548 images
+test: ../VisDrone/VisDrone2019-DET-test-dev/images  # 1610 images
+
+# number of classes
+nc: 10
+
+# class names
+names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, os, Path
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path('../VisDrone')  # dataset directory
+  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+  download(urls, dir=dir)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels