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Reference for ultralytics/data/converter.py

Note

Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!


ultralytics.data.converter.coco91_to_coco80_class()

Converts 91-index COCO class IDs to 80-index COCO class IDs.

Returns:

Type Description
list

A list of 91 class IDs where the index represents the 80-index class ID and the value is the corresponding 91-index class ID.

Source code in ultralytics/data/converter.py
def coco91_to_coco80_class():
    """Converts 91-index COCO class IDs to 80-index COCO class IDs.

    Returns:
        (list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
            corresponding 91-index class ID.
    """
    return [
        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
        None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
        51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
        None, 73, 74, 75, 76, 77, 78, 79, None]




ultralytics.data.converter.coco80_to_coco91_class()

Converts 80-index (val2014) to 91-index (paper).
For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.

Example:
    ```python
    import numpy as np

    a = np.loadtxt('data/coco.names', dtype='str', delimiter='

') b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter=' ') x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet ```

Source code in ultralytics/data/converter.py
def coco80_to_coco91_class():  #
    """
    Converts 80-index (val2014) to 91-index (paper).
    For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.

    Example:
        ```python
        import numpy as np

        a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
        b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
        x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
        x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
        ```
    """
    return [
        1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
        64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]




ultralytics.data.converter.convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keypoints=False, cls91to80=True)

Converts COCO dataset annotations to a format suitable for training YOLOv5 models.

Parameters:

Name Type Description Default
labels_dir str

Path to directory containing COCO dataset annotation files.

'../coco/annotations/'
use_segments bool

Whether to include segmentation masks in the output.

False
use_keypoints bool

Whether to include keypoint annotations in the output.

False
cls91to80 bool

Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.

True
Example
from ultralytics.data.converter import convert_coco

convert_coco('../datasets/coco/annotations/', use_segments=True, use_keypoints=False, cls91to80=True)
Output

Generates output files in the specified output directory.

Source code in ultralytics/data/converter.py
def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keypoints=False, cls91to80=True):
    """Converts COCO dataset annotations to a format suitable for training YOLOv5 models.

    Args:
        labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
        use_segments (bool, optional): Whether to include segmentation masks in the output.
        use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
        cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.

    Example:
        ```python
        from ultralytics.data.converter import convert_coco

        convert_coco('../datasets/coco/annotations/', use_segments=True, use_keypoints=False, cls91to80=True)
        ```

    Output:
        Generates output files in the specified output directory.
    """

    # Create dataset directory
    save_dir = Path('yolo_labels')
    if save_dir.exists():
        shutil.rmtree(save_dir)  # delete dir
    for p in save_dir / 'labels', save_dir / 'images':
        p.mkdir(parents=True, exist_ok=True)  # make dir

    # Convert classes
    coco80 = coco91_to_coco80_class()

    # Import json
    for json_file in sorted(Path(labels_dir).resolve().glob('*.json')):
        fn = Path(save_dir) / 'labels' / json_file.stem.replace('instances_', '')  # folder name
        fn.mkdir(parents=True, exist_ok=True)
        with open(json_file) as f:
            data = json.load(f)

        # Create image dict
        images = {f'{x["id"]:d}': x for x in data['images']}
        # Create image-annotations dict
        imgToAnns = defaultdict(list)
        for ann in data['annotations']:
            imgToAnns[ann['image_id']].append(ann)

        # Write labels file
        for img_id, anns in TQDM(imgToAnns.items(), desc=f'Annotations {json_file}'):
            img = images[f'{img_id:d}']
            h, w, f = img['height'], img['width'], img['file_name']

            bboxes = []
            segments = []
            keypoints = []
            for ann in anns:
                if ann['iscrowd']:
                    continue
                # The COCO box format is [top left x, top left y, width, height]
                box = np.array(ann['bbox'], dtype=np.float64)
                box[:2] += box[2:] / 2  # xy top-left corner to center
                box[[0, 2]] /= w  # normalize x
                box[[1, 3]] /= h  # normalize y
                if box[2] <= 0 or box[3] <= 0:  # if w <= 0 and h <= 0
                    continue

                cls = coco80[ann['category_id'] - 1] if cls91to80 else ann['category_id'] - 1  # class
                box = [cls] + box.tolist()
                if box not in bboxes:
                    bboxes.append(box)
                if use_segments and ann.get('segmentation') is not None:
                    if len(ann['segmentation']) == 0:
                        segments.append([])
                        continue
                    elif len(ann['segmentation']) > 1:
                        s = merge_multi_segment(ann['segmentation'])
                        s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
                    else:
                        s = [j for i in ann['segmentation'] for j in i]  # all segments concatenated
                        s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
                    s = [cls] + s
                    if s not in segments:
                        segments.append(s)
                if use_keypoints and ann.get('keypoints') is not None:
                    keypoints.append(box + (np.array(ann['keypoints']).reshape(-1, 3) /
                                            np.array([w, h, 1])).reshape(-1).tolist())

            # Write
            with open((fn / f).with_suffix('.txt'), 'a') as file:
                for i in range(len(bboxes)):
                    if use_keypoints:
                        line = *(keypoints[i]),  # cls, box, keypoints
                    else:
                        line = *(segments[i]
                                 if use_segments and len(segments[i]) > 0 else bboxes[i]),  # cls, box or segments
                    file.write(('%g ' * len(line)).rstrip() % line + '\n')




ultralytics.data.converter.convert_dota_to_yolo_obb(dota_root_path)

Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.

The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory.

Parameters:

Name Type Description Default
dota_root_path str

The root directory path of the DOTA dataset.

required
Example
from ultralytics.data.converter import convert_dota_to_yolo_obb

convert_dota_to_yolo_obb('path/to/DOTA')
Notes

The directory structure assumed for the DOTA dataset: - DOTA - images - train - val - labels - train_original - val_original

After the function execution, the new labels will be saved in: - DOTA - labels - train - val

Source code in ultralytics/data/converter.py
def convert_dota_to_yolo_obb(dota_root_path: str):
    """
    Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.

    The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the
    associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory.

    Args:
        dota_root_path (str): The root directory path of the DOTA dataset.

    Example:
        ```python
        from ultralytics.data.converter import convert_dota_to_yolo_obb

        convert_dota_to_yolo_obb('path/to/DOTA')
        ```

    Notes:
        The directory structure assumed for the DOTA dataset:
            - DOTA
                - images
                    - train
                    - val
                - labels
                    - train_original
                    - val_original

        After the function execution, the new labels will be saved in:
            - DOTA
                - labels
                    - train
                    - val
    """
    dota_root_path = Path(dota_root_path)

    # Class names to indices mapping
    class_mapping = {
        'plane': 0,
        'ship': 1,
        'storage-tank': 2,
        'baseball-diamond': 3,
        'tennis-court': 4,
        'basketball-court': 5,
        'ground-track-field': 6,
        'harbor': 7,
        'bridge': 8,
        'large-vehicle': 9,
        'small-vehicle': 10,
        'helicopter': 11,
        'roundabout': 12,
        'soccer ball-field': 13,
        'swimming-pool': 14,
        'container-crane': 15,
        'airport': 16,
        'helipad': 17}

    def convert_label(image_name, image_width, image_height, orig_label_dir, save_dir):
        orig_label_path = orig_label_dir / f'{image_name}.txt'
        save_path = save_dir / f'{image_name}.txt'

        with orig_label_path.open('r') as f, save_path.open('w') as g:
            lines = f.readlines()
            for line in lines:
                parts = line.strip().split()
                if len(parts) < 9:
                    continue
                class_name = parts[8]
                class_idx = class_mapping[class_name]
                coords = [float(p) for p in parts[:8]]
                normalized_coords = [
                    coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)]
                formatted_coords = ['{:.6g}'.format(coord) for coord in normalized_coords]
                g.write(f"{class_idx} {' '.join(formatted_coords)}\n")

    for phase in ['train', 'val']:
        image_dir = dota_root_path / 'images' / phase
        orig_label_dir = dota_root_path / 'labels' / f'{phase}_original'
        save_dir = dota_root_path / 'labels' / phase

        save_dir.mkdir(parents=True, exist_ok=True)

        image_paths = list(image_dir.iterdir())
        for image_path in TQDM(image_paths, desc=f'Processing {phase} images'):
            if image_path.suffix != '.png':
                continue
            image_name_without_ext = image_path.stem
            img = cv2.imread(str(image_path))
            h, w = img.shape[:2]
            convert_label(image_name_without_ext, w, h, orig_label_dir, save_dir)




ultralytics.data.converter.min_index(arr1, arr2)

Find a pair of indexes with the shortest distance between two arrays of 2D points.

Parameters:

Name Type Description Default
arr1 array

A NumPy array of shape (N, 2) representing N 2D points.

required
arr2 array

A NumPy array of shape (M, 2) representing M 2D points.

required

Returns:

Type Description
tuple

A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively.

Source code in ultralytics/data/converter.py
def min_index(arr1, arr2):
    """
    Find a pair of indexes with the shortest distance between two arrays of 2D points.

    Args:
        arr1 (np.array): A NumPy array of shape (N, 2) representing N 2D points.
        arr2 (np.array): A NumPy array of shape (M, 2) representing M 2D points.

    Returns:
        (tuple): A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively.
    """
    dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
    return np.unravel_index(np.argmin(dis, axis=None), dis.shape)




ultralytics.data.converter.merge_multi_segment(segments)

Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment. This function connects these coordinates with a thin line to merge all segments into one.

Parameters:

Name Type Description Default
segments List[List]

Original segmentations in COCO's JSON file. Each element is a list of coordinates, like [segmentation1, segmentation2,...].

required

Returns:

Name Type Description
s List[ndarray]

A list of connected segments represented as NumPy arrays.

Source code in ultralytics/data/converter.py
def merge_multi_segment(segments):
    """
    Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment.
    This function connects these coordinates with a thin line to merge all segments into one.

    Args:
        segments (List[List]): Original segmentations in COCO's JSON file.
                               Each element is a list of coordinates, like [segmentation1, segmentation2,...].

    Returns:
        s (List[np.ndarray]): A list of connected segments represented as NumPy arrays.
    """
    s = []
    segments = [np.array(i).reshape(-1, 2) for i in segments]
    idx_list = [[] for _ in range(len(segments))]

    # record the indexes with min distance between each segment
    for i in range(1, len(segments)):
        idx1, idx2 = min_index(segments[i - 1], segments[i])
        idx_list[i - 1].append(idx1)
        idx_list[i].append(idx2)

    # use two round to connect all the segments
    for k in range(2):
        # forward connection
        if k == 0:
            for i, idx in enumerate(idx_list):
                # middle segments have two indexes
                # reverse the index of middle segments
                if len(idx) == 2 and idx[0] > idx[1]:
                    idx = idx[::-1]
                    segments[i] = segments[i][::-1, :]

                segments[i] = np.roll(segments[i], -idx[0], axis=0)
                segments[i] = np.concatenate([segments[i], segments[i][:1]])
                # deal with the first segment and the last one
                if i in [0, len(idx_list) - 1]:
                    s.append(segments[i])
                else:
                    idx = [0, idx[1] - idx[0]]
                    s.append(segments[i][idx[0]:idx[1] + 1])

        else:
            for i in range(len(idx_list) - 1, -1, -1):
                if i not in [0, len(idx_list) - 1]:
                    idx = idx_list[i]
                    nidx = abs(idx[1] - idx[0])
                    s.append(segments[i][nidx:])
    return s




Created 2023-07-16, Updated 2023-08-24
Authors: glenn-jocher (8), Laughing-q (1)