YOLOv8-16bit/ultralytics/data/converter.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

import json
from collections import defaultdict
from pathlib import Path

import cv2
import numpy as np
from tqdm import tqdm

from ultralytics.utils.checks import check_requirements
from ultralytics.utils.files import make_dirs


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]


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.
    """

    save_dir = make_dirs('yolo_labels')  # output directory
    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
                    if isinstance(ann['segmentation'], dict):
                        ann['segmentation'] = rle2polygon(ann['segmentation'])
                    if 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:
                    k = (np.array(ann['keypoints']).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
                    k = box + k
                    keypoints.append(k)

            # 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')


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)


def rle2polygon(segmentation):
    """
    Convert Run-Length Encoding (RLE) mask to polygon coordinates.

    Args:
        segmentation (dict, list): RLE mask representation of the object segmentation.

    Returns:
        (list): A list of lists representing the polygon coordinates for each contour.

    Note:
        Requires the 'pycocotools' package to be installed.
    """
    check_requirements('pycocotools')
    from pycocotools import mask

    m = mask.decode(segmentation)
    m[m > 0] = 255
    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
    polygons = []
    for contour in contours:
        epsilon = 0.001 * cv2.arcLength(contour, True)
        contour_approx = cv2.approxPolyDP(contour, epsilon, True)
        polygon = contour_approx.flatten().tolist()
        polygons.append(polygon)
    return polygons


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)


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