Add YOLOv5 dataset yamls (#207)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>single_channel
Glenn Jocher
2 years ago
committed by
GitHub
parent
e371e81aa0
commit
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
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# Example usage: python train.py --data Argoverse.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── Argoverse ← downloads here (31.3 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/Argoverse # dataset root dir
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
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test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: bus
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5: truck
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6: traffic_light
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7: stop_sign
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import json
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from tqdm import tqdm
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from utils.general import download, Path
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def argoverse2yolo(set):
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labels = {}
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a = json.load(open(set, "rb"))
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for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
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img_id = annot['image_id']
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img_name = a['images'][img_id]['name']
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img_label_name = f'{img_name[:-3]}txt'
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cls = annot['category_id'] # instance class id
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x_center, y_center, width, height = annot['bbox']
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x_center = (x_center + width / 2) / 1920.0 # offset and scale
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y_center = (y_center + height / 2) / 1200.0 # offset and scale
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width /= 1920.0 # scale
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height /= 1200.0 # scale
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img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
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if not img_dir.exists():
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img_dir.mkdir(parents=True, exist_ok=True)
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k = str(img_dir / img_label_name)
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if k not in labels:
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labels[k] = []
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for k in labels:
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with open(k, "w") as f:
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f.writelines(labels[k])
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
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download(urls, dir=dir, delete=False)
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# Convert
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annotations_dir = 'Argoverse-HD/annotations/'
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(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
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for d in "train.json", "val.json":
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argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
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# Example usage: python train.py --data GlobalWheat2020.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── GlobalWheat2020 ← downloads here (7.0 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/GlobalWheat2020 # dataset root dir
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train: # train images (relative to 'path') 3422 images
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- images/arvalis_1
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- images/arvalis_2
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- images/arvalis_3
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- images/ethz_1
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- images/rres_1
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- images/inrae_1
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- images/usask_1
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val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
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- images/ethz_1
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test: # test images (optional) 1276 images
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- images/utokyo_1
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- images/utokyo_2
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- images/nau_1
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- images/uq_1
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# Classes
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names:
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0: wheat_head
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from utils.general import download, Path
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / p).rename(dir / 'images' / p) # move to /images
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f = (dir / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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File diff suppressed because it is too large
Load Diff
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Objects365 dataset https://www.objects365.org/ by Megvii
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# Example usage: python train.py --data Objects365.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
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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/Objects365 # dataset root dir
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train: images/train # train images (relative to 'path') 1742289 images
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val: images/val # val images (relative to 'path') 80000 images
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test: # test images (optional)
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# Classes
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names:
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0: Person
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1: Sneakers
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2: Chair
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3: Other Shoes
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4: Hat
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5: Car
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6: Lamp
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7: Glasses
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8: Bottle
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9: Desk
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10: Cup
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11: Street Lights
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12: Cabinet/shelf
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13: Handbag/Satchel
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14: Bracelet
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15: Plate
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16: Picture/Frame
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17: Helmet
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18: Book
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19: Gloves
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20: Storage box
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21: Boat
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22: Leather Shoes
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23: Flower
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24: Bench
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25: Potted Plant
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26: Bowl/Basin
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27: Flag
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28: Pillow
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29: Boots
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30: Vase
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31: Microphone
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32: Necklace
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33: Ring
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34: SUV
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35: Wine Glass
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36: Belt
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37: Monitor/TV
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38: Backpack
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39: Umbrella
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40: Traffic Light
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41: Speaker
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42: Watch
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43: Tie
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44: Trash bin Can
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45: Slippers
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46: Bicycle
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47: Stool
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48: Barrel/bucket
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49: Van
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50: Couch
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51: Sandals
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52: Basket
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53: Drum
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54: Pen/Pencil
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55: Bus
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56: Wild Bird
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57: High Heels
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58: Motorcycle
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59: Guitar
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60: Carpet
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61: Cell Phone
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62: Bread
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63: Camera
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64: Canned
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65: Truck
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66: Traffic cone
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67: Cymbal
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68: Lifesaver
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69: Towel
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70: Stuffed Toy
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71: Candle
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72: Sailboat
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73: Laptop
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74: Awning
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75: Bed
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76: Faucet
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77: Tent
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78: Horse
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79: Mirror
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80: Power outlet
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81: Sink
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82: Apple
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83: Air Conditioner
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84: Knife
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85: Hockey Stick
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86: Paddle
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87: Pickup Truck
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88: Fork
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89: Traffic Sign
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90: Balloon
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91: Tripod
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92: Dog
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93: Spoon
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94: Clock
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95: Pot
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96: Cow
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97: Cake
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98: Dinning Table
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99: Sheep
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100: Hanger
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101: Blackboard/Whiteboard
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102: Napkin
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103: Other Fish
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104: Orange/Tangerine
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105: Toiletry
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106: Keyboard
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107: Tomato
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108: Lantern
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109: Machinery Vehicle
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110: Fan
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111: Green Vegetables
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112: Banana
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113: Baseball Glove
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114: Airplane
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115: Mouse
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116: Train
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117: Pumpkin
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118: Soccer
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119: Skiboard
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120: Luggage
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121: Nightstand
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122: Tea pot
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123: Telephone
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124: Trolley
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125: Head Phone
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126: Sports Car
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127: Stop Sign
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128: Dessert
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129: Scooter
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130: Stroller
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131: Crane
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132: Remote
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133: Refrigerator
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134: Oven
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135: Lemon
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136: Duck
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137: Baseball Bat
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138: Surveillance Camera
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139: Cat
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140: Jug
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141: Broccoli
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142: Piano
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143: Pizza
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144: Elephant
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145: Skateboard
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146: Surfboard
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147: Gun
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148: Skating and Skiing shoes
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149: Gas stove
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150: Donut
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151: Bow Tie
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152: Carrot
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153: Toilet
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154: Kite
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155: Strawberry
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156: Other Balls
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157: Shovel
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158: Pepper
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159: Computer Box
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160: Toilet Paper
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161: Cleaning Products
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162: Chopsticks
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163: Microwave
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164: Pigeon
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165: Baseball
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166: Cutting/chopping Board
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167: Coffee Table
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168: Side Table
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169: Scissors
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170: Marker
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171: Pie
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172: Ladder
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173: Snowboard
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174: Cookies
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175: Radiator
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176: Fire Hydrant
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177: Basketball
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178: Zebra
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179: Grape
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180: Giraffe
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181: Potato
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182: Sausage
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183: Tricycle
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184: Violin
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185: Egg
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186: Fire Extinguisher
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187: Candy
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188: Fire Truck
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189: Billiards
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190: Converter
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191: Bathtub
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192: Wheelchair
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193: Golf Club
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194: Briefcase
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195: Cucumber
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196: Cigar/Cigarette
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197: Paint Brush
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198: Pear
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199: Heavy Truck
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200: Hamburger
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201: Extractor
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202: Extension Cord
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203: Tong
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204: Tennis Racket
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205: Folder
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206: American Football
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207: earphone
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208: Mask
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209: Kettle
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210: Tennis
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211: Ship
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212: Swing
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213: Coffee Machine
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214: Slide
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215: Carriage
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216: Onion
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217: Green beans
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218: Projector
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219: Frisbee
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220: Washing Machine/Drying Machine
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221: Chicken
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222: Printer
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223: Watermelon
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224: Saxophone
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225: Tissue
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226: Toothbrush
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227: Ice cream
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228: Hot-air balloon
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229: Cello
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230: French Fries
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231: Scale
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232: Trophy
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233: Cabbage
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234: Hot dog
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235: Blender
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236: Peach
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237: Rice
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238: Wallet/Purse
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239: Volleyball
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240: Deer
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241: Goose
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242: Tape
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243: Tablet
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244: Cosmetics
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245: Trumpet
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246: Pineapple
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247: Golf Ball
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248: Ambulance
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249: Parking meter
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250: Mango
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251: Key
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252: Hurdle
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253: Fishing Rod
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254: Medal
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255: Flute
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256: Brush
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257: Penguin
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258: Megaphone
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259: Corn
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260: Lettuce
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261: Garlic
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262: Swan
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263: Helicopter
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264: Green Onion
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265: Sandwich
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||||||
|
266: Nuts
|
||||||
|
267: Speed Limit Sign
|
||||||
|
268: Induction Cooker
|
||||||
|
269: Broom
|
||||||
|
270: Trombone
|
||||||
|
271: Plum
|
||||||
|
272: Rickshaw
|
||||||
|
273: Goldfish
|
||||||
|
274: Kiwi fruit
|
||||||
|
275: Router/modem
|
||||||
|
276: Poker Card
|
||||||
|
277: Toaster
|
||||||
|
278: Shrimp
|
||||||
|
279: Sushi
|
||||||
|
280: Cheese
|
||||||
|
281: Notepaper
|
||||||
|
282: Cherry
|
||||||
|
283: Pliers
|
||||||
|
284: CD
|
||||||
|
285: Pasta
|
||||||
|
286: Hammer
|
||||||
|
287: Cue
|
||||||
|
288: Avocado
|
||||||
|
289: Hamimelon
|
||||||
|
290: Flask
|
||||||
|
291: Mushroom
|
||||||
|
292: Screwdriver
|
||||||
|
293: Soap
|
||||||
|
294: Recorder
|
||||||
|
295: Bear
|
||||||
|
296: Eggplant
|
||||||
|
297: Board Eraser
|
||||||
|
298: Coconut
|
||||||
|
299: Tape Measure/Ruler
|
||||||
|
300: Pig
|
||||||
|
301: Showerhead
|
||||||
|
302: Globe
|
||||||
|
303: Chips
|
||||||
|
304: Steak
|
||||||
|
305: Crosswalk Sign
|
||||||
|
306: Stapler
|
||||||
|
307: Camel
|
||||||
|
308: Formula 1
|
||||||
|
309: Pomegranate
|
||||||
|
310: Dishwasher
|
||||||
|
311: Crab
|
||||||
|
312: Hoverboard
|
||||||
|
313: Meat ball
|
||||||
|
314: Rice Cooker
|
||||||
|
315: Tuba
|
||||||
|
316: Calculator
|
||||||
|
317: Papaya
|
||||||
|
318: Antelope
|
||||||
|
319: Parrot
|
||||||
|
320: Seal
|
||||||
|
321: Butterfly
|
||||||
|
322: Dumbbell
|
||||||
|
323: Donkey
|
||||||
|
324: Lion
|
||||||
|
325: Urinal
|
||||||
|
326: Dolphin
|
||||||
|
327: Electric Drill
|
||||||
|
328: Hair Dryer
|
||||||
|
329: Egg tart
|
||||||
|
330: Jellyfish
|
||||||
|
331: Treadmill
|
||||||
|
332: Lighter
|
||||||
|
333: Grapefruit
|
||||||
|
334: Game board
|
||||||
|
335: Mop
|
||||||
|
336: Radish
|
||||||
|
337: Baozi
|
||||||
|
338: Target
|
||||||
|
339: French
|
||||||
|
340: Spring Rolls
|
||||||
|
341: Monkey
|
||||||
|
342: Rabbit
|
||||||
|
343: Pencil Case
|
||||||
|
344: Yak
|
||||||
|
345: Red Cabbage
|
||||||
|
346: Binoculars
|
||||||
|
347: Asparagus
|
||||||
|
348: Barbell
|
||||||
|
349: Scallop
|
||||||
|
350: Noddles
|
||||||
|
351: Comb
|
||||||
|
352: Dumpling
|
||||||
|
353: Oyster
|
||||||
|
354: Table Tennis paddle
|
||||||
|
355: Cosmetics Brush/Eyeliner Pencil
|
||||||
|
356: Chainsaw
|
||||||
|
357: Eraser
|
||||||
|
358: Lobster
|
||||||
|
359: Durian
|
||||||
|
360: Okra
|
||||||
|
361: Lipstick
|
||||||
|
362: Cosmetics Mirror
|
||||||
|
363: Curling
|
||||||
|
364: Table Tennis
|
||||||
|
|
||||||
|
|
||||||
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
|
download: |
|
||||||
|
from tqdm import tqdm
|
||||||
|
|
||||||
|
from utils.general import Path, check_requirements, download, np, xyxy2xywhn
|
||||||
|
|
||||||
|
check_requirements(('pycocotools>=2.0',))
|
||||||
|
from pycocotools.coco import COCO
|
||||||
|
|
||||||
|
# Make Directories
|
||||||
|
dir = Path(yaml['path']) # dataset root dir
|
||||||
|
for p in 'images', 'labels':
|
||||||
|
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||||
|
for q in 'train', 'val':
|
||||||
|
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
# Train, Val Splits
|
||||||
|
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
||||||
|
print(f"Processing {split} in {patches} patches ...")
|
||||||
|
images, labels = dir / 'images' / split, dir / 'labels' / split
|
||||||
|
|
||||||
|
# Download
|
||||||
|
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
||||||
|
if split == 'train':
|
||||||
|
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
|
||||||
|
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
|
||||||
|
elif split == 'val':
|
||||||
|
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
|
||||||
|
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
|
||||||
|
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
|
||||||
|
|
||||||
|
# Move
|
||||||
|
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
||||||
|
f.rename(images / f.name) # move to /images/{split}
|
||||||
|
|
||||||
|
# Labels
|
||||||
|
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
||||||
|
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||||
|
for cid, cat in enumerate(names):
|
||||||
|
catIds = coco.getCatIds(catNms=[cat])
|
||||||
|
imgIds = coco.getImgIds(catIds=catIds)
|
||||||
|
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||||
|
width, height = im["width"], im["height"]
|
||||||
|
path = Path(im["file_name"]) # image filename
|
||||||
|
try:
|
||||||
|
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
||||||
|
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
|
||||||
|
for a in coco.loadAnns(annIds):
|
||||||
|
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||||
|
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
||||||
|
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
||||||
|
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
||||||
|
except Exception as e:
|
||||||
|
print(e)
|
||||||
@ -0,0 +1,53 @@
|
|||||||
|
# Ultralytics YOLO 🚀, GPL-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
|
||||||
@ -0,0 +1,100 @@
|
|||||||
|
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||||
|
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||||
|
# Example usage: python train.py --data VOC.yaml
|
||||||
|
# parent
|
||||||
|
# ├── yolov5
|
||||||
|
# └── datasets
|
||||||
|
# └── VOC ← downloads here (2.8 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/VOC
|
||||||
|
train: # train images (relative to 'path') 16551 images
|
||||||
|
- images/train2012
|
||||||
|
- images/train2007
|
||||||
|
- images/val2012
|
||||||
|
- images/val2007
|
||||||
|
val: # val images (relative to 'path') 4952 images
|
||||||
|
- images/test2007
|
||||||
|
test: # test images (optional)
|
||||||
|
- images/test2007
|
||||||
|
|
||||||
|
# Classes
|
||||||
|
names:
|
||||||
|
0: aeroplane
|
||||||
|
1: bicycle
|
||||||
|
2: bird
|
||||||
|
3: boat
|
||||||
|
4: bottle
|
||||||
|
5: bus
|
||||||
|
6: car
|
||||||
|
7: cat
|
||||||
|
8: chair
|
||||||
|
9: cow
|
||||||
|
10: diningtable
|
||||||
|
11: dog
|
||||||
|
12: horse
|
||||||
|
13: motorbike
|
||||||
|
14: person
|
||||||
|
15: pottedplant
|
||||||
|
16: sheep
|
||||||
|
17: sofa
|
||||||
|
18: train
|
||||||
|
19: tvmonitor
|
||||||
|
|
||||||
|
|
||||||
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
|
download: |
|
||||||
|
import xml.etree.ElementTree as ET
|
||||||
|
|
||||||
|
from tqdm import tqdm
|
||||||
|
from utils.general import download, Path
|
||||||
|
|
||||||
|
|
||||||
|
def convert_label(path, lb_path, year, image_id):
|
||||||
|
def convert_box(size, box):
|
||||||
|
dw, dh = 1. / size[0], 1. / size[1]
|
||||||
|
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||||
|
return x * dw, y * dh, w * dw, h * dh
|
||||||
|
|
||||||
|
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
|
||||||
|
out_file = open(lb_path, 'w')
|
||||||
|
tree = ET.parse(in_file)
|
||||||
|
root = tree.getroot()
|
||||||
|
size = root.find('size')
|
||||||
|
w = int(size.find('width').text)
|
||||||
|
h = int(size.find('height').text)
|
||||||
|
|
||||||
|
names = list(yaml['names'].values()) # names list
|
||||||
|
for obj in root.iter('object'):
|
||||||
|
cls = obj.find('name').text
|
||||||
|
if cls in names and int(obj.find('difficult').text) != 1:
|
||||||
|
xmlbox = obj.find('bndbox')
|
||||||
|
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||||
|
cls_id = names.index(cls) # class id
|
||||||
|
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
|
||||||
|
|
||||||
|
|
||||||
|
# Download
|
||||||
|
dir = Path(yaml['path']) # dataset root dir
|
||||||
|
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||||
|
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||||
|
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||||
|
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||||
|
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
|
||||||
|
|
||||||
|
# Convert
|
||||||
|
path = dir / 'images/VOCdevkit'
|
||||||
|
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||||
|
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||||
|
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||||
|
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||||
|
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||||
|
|
||||||
|
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
|
||||||
|
image_ids = f.read().strip().split()
|
||||||
|
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||||
|
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||||
|
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||||
|
f.rename(imgs_path / f.name) # move image
|
||||||
|
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
||||||
@ -0,0 +1,70 @@
|
|||||||
|
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||||
|
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||||
|
# Example usage: python train.py --data VisDrone.yaml
|
||||||
|
# parent
|
||||||
|
# ├── yolov5
|
||||||
|
# └── datasets
|
||||||
|
# └── VisDrone ← downloads here (2.3 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/VisDrone # dataset root dir
|
||||||
|
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||||
|
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||||
|
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||||
|
|
||||||
|
# Classes
|
||||||
|
names:
|
||||||
|
0: pedestrian
|
||||||
|
1: people
|
||||||
|
2: bicycle
|
||||||
|
3: car
|
||||||
|
4: van
|
||||||
|
5: truck
|
||||||
|
6: tricycle
|
||||||
|
7: awning-tricycle
|
||||||
|
8: bus
|
||||||
|
9: motor
|
||||||
|
|
||||||
|
|
||||||
|
# Download script/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(yaml['path']) # dataset root dir
|
||||||
|
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, curl=True, threads=4)
|
||||||
|
|
||||||
|
# Convert
|
||||||
|
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||||
|
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
|
||||||
@ -0,0 +1,153 @@
|
|||||||
|
# Ultralytics YOLO 🚀, 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
|
||||||
|
names:
|
||||||
|
0: Fixed-wing Aircraft
|
||||||
|
1: Small Aircraft
|
||||||
|
2: Cargo Plane
|
||||||
|
3: Helicopter
|
||||||
|
4: Passenger Vehicle
|
||||||
|
5: Small Car
|
||||||
|
6: Bus
|
||||||
|
7: Pickup Truck
|
||||||
|
8: Utility Truck
|
||||||
|
9: Truck
|
||||||
|
10: Cargo Truck
|
||||||
|
11: Truck w/Box
|
||||||
|
12: Truck Tractor
|
||||||
|
13: Trailer
|
||||||
|
14: Truck w/Flatbed
|
||||||
|
15: Truck w/Liquid
|
||||||
|
16: Crane Truck
|
||||||
|
17: Railway Vehicle
|
||||||
|
18: Passenger Car
|
||||||
|
19: Cargo Car
|
||||||
|
20: Flat Car
|
||||||
|
21: Tank car
|
||||||
|
22: Locomotive
|
||||||
|
23: Maritime Vessel
|
||||||
|
24: Motorboat
|
||||||
|
25: Sailboat
|
||||||
|
26: Tugboat
|
||||||
|
27: Barge
|
||||||
|
28: Fishing Vessel
|
||||||
|
29: Ferry
|
||||||
|
30: Yacht
|
||||||
|
31: Container Ship
|
||||||
|
32: Oil Tanker
|
||||||
|
33: Engineering Vehicle
|
||||||
|
34: Tower crane
|
||||||
|
35: Container Crane
|
||||||
|
36: Reach Stacker
|
||||||
|
37: Straddle Carrier
|
||||||
|
38: Mobile Crane
|
||||||
|
39: Dump Truck
|
||||||
|
40: Haul Truck
|
||||||
|
41: Scraper/Tractor
|
||||||
|
42: Front loader/Bulldozer
|
||||||
|
43: Excavator
|
||||||
|
44: Cement Mixer
|
||||||
|
45: Ground Grader
|
||||||
|
46: Hut/Tent
|
||||||
|
47: Shed
|
||||||
|
48: Building
|
||||||
|
49: Aircraft Hangar
|
||||||
|
50: Damaged Building
|
||||||
|
51: Facility
|
||||||
|
52: Construction Site
|
||||||
|
53: Vehicle Lot
|
||||||
|
54: Helipad
|
||||||
|
55: Storage Tank
|
||||||
|
56: Shipping container lot
|
||||||
|
57: Shipping Container
|
||||||
|
58: Pylon
|
||||||
|
59: Tower
|
||||||
|
|
||||||
|
|
||||||
|
# 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.dataloaders 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')
|
||||||
Loading…
Reference in new issue