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Add optional dataset.yaml
path attribute (ultralytics#3753)
* Add optional dataset.yaml `path` attribute @kalenmike * pass locals to python scripts * handle lists * update coco128.yaml * Capitalize first letter * add test key * finalize GlobalWheat2020.yaml * finalize objects365.yaml * finalize SKU-110K.yaml * finalize SKU-110K.yaml * finalize VisDrone.yaml * NoneType fix * update download comment * voc to VOC * update * update VOC.yaml * update VOC.yaml * remove dashes * delete get_voc.sh * force coco and coco128 to ../datasets * Capitalize Argoverse_HD.yaml * Capitalize Objects365.yaml * update Argoverse_HD.yaml * coco segments fix * VOC single-thread * update Argoverse_HD.yaml * update data_dict in test handling * create root
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| # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ | ||
| # Train command: python train.py --data Argoverse_HD.yaml | ||
| # Default dataset location is next to YOLOv5: | ||
| # /parent | ||
| # /datasets/Argoverse | ||
| # /yolov5 | ||
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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, ..] | ||
| path: ../datasets/Argoverse # dataset root dir | ||
| train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images | ||
| val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images | ||
| test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview | ||
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| # Classes | ||
| nc: 8 # number of classes | ||
| names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] # class names | ||
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| # Download script/URL (optional) --------------------------------------------------------------------------------------- | ||
| download: | | ||
| import json | ||
| from tqdm import tqdm | ||
| from utils.general import download, Path | ||
| def argoverse2yolo(set): | ||
| labels = {} | ||
| a = json.load(open(set, "rb")) | ||
| for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): | ||
| img_id = annot['image_id'] | ||
| img_name = a['images'][img_id]['name'] | ||
| img_label_name = img_name[:-3] + "txt" | ||
| cls = annot['category_id'] # instance class id | ||
| x_center, y_center, width, height = annot['bbox'] | ||
| x_center = (x_center + width / 2) / 1920.0 # offset and scale | ||
| y_center = (y_center + height / 2) / 1200.0 # offset and scale | ||
| width /= 1920.0 # scale | ||
| height /= 1200.0 # scale | ||
| img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] | ||
| if not img_dir.exists(): | ||
| img_dir.mkdir(parents=True, exist_ok=True) | ||
| k = str(img_dir / img_label_name) | ||
| if k not in labels: | ||
| labels[k] = [] | ||
| labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") | ||
| for k in labels: | ||
| with open(k, "w") as f: | ||
| f.writelines(labels[k]) | ||
| # Download | ||
| dir = Path('../datasets/Argoverse') # dataset root dir | ||
| urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] | ||
| download(urls, dir=dir, delete=False) | ||
| # Convert | ||
| annotations_dir = 'Argoverse-HD/annotations/' | ||
| (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' | ||
| for d in "train.json", "val.json": | ||
| argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels |
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| # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ | ||
| # Train command: python train.py --data VOC.yaml | ||
| # Default dataset location is next to YOLOv5: | ||
| # /parent | ||
| # /datasets/VOC | ||
| # /yolov5 | ||
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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, ..] | ||
| 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 | ||
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| # Classes | ||
| nc: 20 # number of classes | ||
| names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', | ||
| 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] # class names | ||
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| # 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) | ||
| for obj in root.iter('object'): | ||
| cls = obj.find('name').text | ||
| if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images | ||
| url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images | ||
| url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images | ||
| download(urls, dir=dir / 'images', delete=False) | ||
| # Convert | ||
| path = dir / f'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) | ||
| image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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 |
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