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153 lines
5.0 KiB
153 lines
5.0 KiB
# Ultralytics YOLO 🚀, AGPL-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: yolo train data=xView.yaml |
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# parent |
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# ├── ultralytics |
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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 ultralytics.data.utils import autosplit |
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from ultralytics.utils.ops import 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) |
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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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