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app/yolov5/data/xView.yaml
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app/yolov5/data/xView.yaml
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
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# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
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# Example usage: python train.py --data xView.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── xView ← downloads here (20.7 GB)
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# 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, ..]
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path: ../datasets/xView # dataset root dir
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train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
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val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
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# Classes
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names:
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0: Fixed-wing Aircraft
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1: Small Aircraft
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2: Cargo Plane
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3: Helicopter
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4: Passenger Vehicle
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5: Small Car
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6: Bus
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7: Pickup Truck
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8: Utility Truck
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9: Truck
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10: Cargo Truck
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11: Truck w/Box
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12: Truck Tractor
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13: Trailer
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14: Truck w/Flatbed
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15: Truck w/Liquid
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16: Crane Truck
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17: Railway Vehicle
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18: Passenger Car
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19: Cargo Car
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20: Flat Car
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21: Tank car
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22: Locomotive
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23: Maritime Vessel
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24: Motorboat
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25: Sailboat
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26: Tugboat
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27: Barge
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28: Fishing Vessel
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29: Ferry
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30: Yacht
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31: Container Ship
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32: Oil Tanker
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33: Engineering Vehicle
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34: Tower crane
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35: Container Crane
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36: Reach Stacker
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37: Straddle Carrier
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38: Mobile Crane
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39: Dump Truck
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40: Haul Truck
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41: Scraper/Tractor
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42: Front loader/Bulldozer
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43: Excavator
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44: Cement Mixer
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45: Ground Grader
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46: Hut/Tent
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47: Shed
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48: Building
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49: Aircraft Hangar
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50: Damaged Building
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51: Facility
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52: Construction Site
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53: Vehicle Lot
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54: Helipad
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55: Storage Tank
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56: Shipping container lot
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57: Shipping Container
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58: Pylon
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59: Tower
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import json
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import os
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from tqdm import tqdm
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from utils.datasets import autosplit
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from utils.general import download, xyxy2xywhn
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def convert_labels(fname=Path('xView/xView_train.geojson')):
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# Convert xView geoJSON labels to YOLO format
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path = fname.parent
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with open(fname) as f:
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print(f'Loading {fname}...')
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data = json.load(f)
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# Make dirs
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labels = Path(path / 'labels' / 'train')
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os.system(f'rm -rf {labels}')
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labels.mkdir(parents=True, exist_ok=True)
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# xView classes 11-94 to 0-59
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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,
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12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
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29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
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47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
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shapes = {}
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for feature in tqdm(data['features'], desc=f'Converting {fname}'):
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p = feature['properties']
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if p['bounds_imcoords']:
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id = p['image_id']
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file = path / 'train_images' / id
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if file.exists(): # 1395.tif missing
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try:
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box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
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assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
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cls = p['type_id']
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cls = xview_class2index[int(cls)] # xView class to 0-60
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assert 59 >= cls >= 0, f'incorrect class index {cls}'
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# Write YOLO label
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if id not in shapes:
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shapes[id] = Image.open(file).size
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box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
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with open((labels / id).with_suffix('.txt'), 'a') as f:
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f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
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except Exception as e:
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print(f'WARNING: skipping one label for {file}: {e}')
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# Download manually from https://challenge.xviewdataset.org
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dir = Path(yaml['path']) # dataset root dir
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# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
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# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
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# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
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# download(urls, dir=dir, delete=False)
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# Convert labels
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convert_labels(dir / 'xView_train.geojson')
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# Move images
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images = Path(dir / 'images')
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images.mkdir(parents=True, exist_ok=True)
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Path(dir / 'train_images').rename(dir / 'images' / 'train')
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Path(dir / 'val_images').rename(dir / 'images' / 'val')
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# Split
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autosplit(dir / 'images' / 'train')
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