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Reference for ultralytics/data/split_dota.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/split_dota.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.data.split_dota.bbox_iof

bbox_iof(polygon1, bbox2, eps=1e-06)

Calculate Intersection over Foreground (IoF) between polygons and bounding boxes.

Parameters:

Name Type Description Default
polygon1 ndarray

Polygon coordinates, shape (n, 8).

required
bbox2 ndarray

Bounding boxes, shape (n, 4).

required
eps float

Small value to prevent division by zero. Defaults to 1e-6.

1e-06

Returns:

Type Description
ndarray

IoF scores, shape (n, 1) or (n, m) if bbox2 is (m, 4).

Note

Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4]. Bounding box format: [x_min, y_min, x_max, y_max].

Source code in ultralytics/data/split_dota.py
def bbox_iof(polygon1, bbox2, eps=1e-6):
    """
    Calculate Intersection over Foreground (IoF) between polygons and bounding boxes.

    Args:
        polygon1 (np.ndarray): Polygon coordinates, shape (n, 8).
        bbox2 (np.ndarray): Bounding boxes, shape (n, 4).
        eps (float, optional): Small value to prevent division by zero. Defaults to 1e-6.

    Returns:
        (np.ndarray): IoF scores, shape (n, 1) or (n, m) if bbox2 is (m, 4).

    Note:
        Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4].
        Bounding box format: [x_min, y_min, x_max, y_max].
    """
    check_requirements("shapely")
    from shapely.geometry import Polygon

    polygon1 = polygon1.reshape(-1, 4, 2)
    lt_point = np.min(polygon1, axis=-2)  # left-top
    rb_point = np.max(polygon1, axis=-2)  # right-bottom
    bbox1 = np.concatenate([lt_point, rb_point], axis=-1)

    lt = np.maximum(bbox1[:, None, :2], bbox2[..., :2])
    rb = np.minimum(bbox1[:, None, 2:], bbox2[..., 2:])
    wh = np.clip(rb - lt, 0, np.inf)
    h_overlaps = wh[..., 0] * wh[..., 1]

    left, top, right, bottom = (bbox2[..., i] for i in range(4))
    polygon2 = np.stack([left, top, right, top, right, bottom, left, bottom], axis=-1).reshape(-1, 4, 2)

    sg_polys1 = [Polygon(p) for p in polygon1]
    sg_polys2 = [Polygon(p) for p in polygon2]
    overlaps = np.zeros(h_overlaps.shape)
    for p in zip(*np.nonzero(h_overlaps)):
        overlaps[p] = sg_polys1[p[0]].intersection(sg_polys2[p[-1]]).area
    unions = np.array([p.area for p in sg_polys1], dtype=np.float32)
    unions = unions[..., None]

    unions = np.clip(unions, eps, np.inf)
    outputs = overlaps / unions
    if outputs.ndim == 1:
        outputs = outputs[..., None]
    return outputs





ultralytics.data.split_dota.load_yolo_dota

load_yolo_dota(data_root, split='train')

Load DOTA dataset.

Parameters:

Name Type Description Default
data_root str

Data root.

required
split str

The split data set, could be train or val.

'train'
Notes

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

Source code in ultralytics/data/split_dota.py
def load_yolo_dota(data_root, split="train"):
    """
    Load DOTA dataset.

    Args:
        data_root (str): Data root.
        split (str): The split data set, could be train or val.

    Notes:
        The directory structure assumed for the DOTA dataset:
            - data_root
                - images
                    - train
                    - val
                - labels
                    - train
                    - val
    """
    assert split in {"train", "val"}, f"Split must be 'train' or 'val', not {split}."
    im_dir = Path(data_root) / "images" / split
    assert im_dir.exists(), f"Can't find {im_dir}, please check your data root."
    im_files = glob(str(Path(data_root) / "images" / split / "*"))
    lb_files = img2label_paths(im_files)
    annos = []
    for im_file, lb_file in zip(im_files, lb_files):
        w, h = exif_size(Image.open(im_file))
        with open(lb_file) as f:
            lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
            lb = np.array(lb, dtype=np.float32)
        annos.append(dict(ori_size=(h, w), label=lb, filepath=im_file))
    return annos





ultralytics.data.split_dota.get_windows

get_windows(im_size, crop_sizes=(1024), gaps=(200), im_rate_thr=0.6, eps=0.01)

Get the coordinates of windows.

Parameters:

Name Type Description Default
im_size tuple

Original image size, (h, w).

required
crop_sizes List(int

Crop size of windows.

(1024)
gaps List(int

Gap between crops.

(200)
im_rate_thr float

Threshold of windows areas divided by image ares.

0.6
eps float

Epsilon value for math operations.

0.01
Source code in ultralytics/data/split_dota.py
def get_windows(im_size, crop_sizes=(1024,), gaps=(200,), im_rate_thr=0.6, eps=0.01):
    """
    Get the coordinates of windows.

    Args:
        im_size (tuple): Original image size, (h, w).
        crop_sizes (List(int)): Crop size of windows.
        gaps (List(int)): Gap between crops.
        im_rate_thr (float): Threshold of windows areas divided by image ares.
        eps (float): Epsilon value for math operations.
    """
    h, w = im_size
    windows = []
    for crop_size, gap in zip(crop_sizes, gaps):
        assert crop_size > gap, f"invalid crop_size gap pair [{crop_size} {gap}]"
        step = crop_size - gap

        xn = 1 if w <= crop_size else ceil((w - crop_size) / step + 1)
        xs = [step * i for i in range(xn)]
        if len(xs) > 1 and xs[-1] + crop_size > w:
            xs[-1] = w - crop_size

        yn = 1 if h <= crop_size else ceil((h - crop_size) / step + 1)
        ys = [step * i for i in range(yn)]
        if len(ys) > 1 and ys[-1] + crop_size > h:
            ys[-1] = h - crop_size

        start = np.array(list(itertools.product(xs, ys)), dtype=np.int64)
        stop = start + crop_size
        windows.append(np.concatenate([start, stop], axis=1))
    windows = np.concatenate(windows, axis=0)

    im_in_wins = windows.copy()
    im_in_wins[:, 0::2] = np.clip(im_in_wins[:, 0::2], 0, w)
    im_in_wins[:, 1::2] = np.clip(im_in_wins[:, 1::2], 0, h)
    im_areas = (im_in_wins[:, 2] - im_in_wins[:, 0]) * (im_in_wins[:, 3] - im_in_wins[:, 1])
    win_areas = (windows[:, 2] - windows[:, 0]) * (windows[:, 3] - windows[:, 1])
    im_rates = im_areas / win_areas
    if not (im_rates > im_rate_thr).any():
        max_rate = im_rates.max()
        im_rates[abs(im_rates - max_rate) < eps] = 1
    return windows[im_rates > im_rate_thr]





ultralytics.data.split_dota.get_window_obj

get_window_obj(anno, windows, iof_thr=0.7)

Get objects for each window.

Source code in ultralytics/data/split_dota.py
def get_window_obj(anno, windows, iof_thr=0.7):
    """Get objects for each window."""
    h, w = anno["ori_size"]
    label = anno["label"]
    if len(label):
        label[:, 1::2] *= w
        label[:, 2::2] *= h
        iofs = bbox_iof(label[:, 1:], windows)
        # Unnormalized and misaligned coordinates
        return [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]  # window_anns
    else:
        return [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]  # window_anns





ultralytics.data.split_dota.crop_and_save

crop_and_save(
    anno, windows, window_objs, im_dir, lb_dir, allow_background_images=True
)

Crop images and save new labels.

Parameters:

Name Type Description Default
anno dict

Annotation dict, including filepath, label, ori_size as its keys.

required
windows list

A list of windows coordinates.

required
window_objs list

A list of labels inside each window.

required
im_dir str

The output directory path of images.

required
lb_dir str

The output directory path of labels.

required
allow_background_images bool

Whether to include background images without labels.

True
Notes

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

Source code in ultralytics/data/split_dota.py
def crop_and_save(anno, windows, window_objs, im_dir, lb_dir, allow_background_images=True):
    """
    Crop images and save new labels.

    Args:
        anno (dict): Annotation dict, including `filepath`, `label`, `ori_size` as its keys.
        windows (list): A list of windows coordinates.
        window_objs (list): A list of labels inside each window.
        im_dir (str): The output directory path of images.
        lb_dir (str): The output directory path of labels.
        allow_background_images (bool): Whether to include background images without labels.

    Notes:
        The directory structure assumed for the DOTA dataset:
            - data_root
                - images
                    - train
                    - val
                - labels
                    - train
                    - val
    """
    im = cv2.imread(anno["filepath"])
    name = Path(anno["filepath"]).stem
    for i, window in enumerate(windows):
        x_start, y_start, x_stop, y_stop = window.tolist()
        new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
        patch_im = im[y_start:y_stop, x_start:x_stop]
        ph, pw = patch_im.shape[:2]

        label = window_objs[i]
        if len(label) or allow_background_images:
            cv2.imwrite(str(Path(im_dir) / f"{new_name}.jpg"), patch_im)
        if len(label):
            label[:, 1::2] -= x_start
            label[:, 2::2] -= y_start
            label[:, 1::2] /= pw
            label[:, 2::2] /= ph

            with open(Path(lb_dir) / f"{new_name}.txt", "w") as f:
                for lb in label:
                    formatted_coords = [f"{coord:.6g}" for coord in lb[1:]]
                    f.write(f"{int(lb[0])} {' '.join(formatted_coords)}\n")





ultralytics.data.split_dota.split_images_and_labels

split_images_and_labels(
    data_root, save_dir, split="train", crop_sizes=(1024), gaps=(200)
)

Split both images and labels.

Notes

The directory structure assumed for the DOTA dataset: - data_root - images - split - labels - split and the output directory structure is: - save_dir - images - split - labels - split

Source code in ultralytics/data/split_dota.py
def split_images_and_labels(data_root, save_dir, split="train", crop_sizes=(1024,), gaps=(200,)):
    """
    Split both images and labels.

    Notes:
        The directory structure assumed for the DOTA dataset:
            - data_root
                - images
                    - split
                - labels
                    - split
        and the output directory structure is:
            - save_dir
                - images
                    - split
                - labels
                    - split
    """
    im_dir = Path(save_dir) / "images" / split
    im_dir.mkdir(parents=True, exist_ok=True)
    lb_dir = Path(save_dir) / "labels" / split
    lb_dir.mkdir(parents=True, exist_ok=True)

    annos = load_yolo_dota(data_root, split=split)
    for anno in tqdm(annos, total=len(annos), desc=split):
        windows = get_windows(anno["ori_size"], crop_sizes, gaps)
        window_objs = get_window_obj(anno, windows)
        crop_and_save(anno, windows, window_objs, str(im_dir), str(lb_dir))





ultralytics.data.split_dota.split_trainval

split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0))

Split train and val set of DOTA.

Notes

The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val and the output directory structure is: - save_dir - images - train - val - labels - train - val

Source code in ultralytics/data/split_dota.py
def split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0,)):
    """
    Split train and val set of DOTA.

    Notes:
        The directory structure assumed for the DOTA dataset:
            - data_root
                - images
                    - train
                    - val
                - labels
                    - train
                    - val
        and the output directory structure is:
            - save_dir
                - images
                    - train
                    - val
                - labels
                    - train
                    - val
    """
    crop_sizes, gaps = [], []
    for r in rates:
        crop_sizes.append(int(crop_size / r))
        gaps.append(int(gap / r))
    for split in ["train", "val"]:
        split_images_and_labels(data_root, save_dir, split, crop_sizes, gaps)





ultralytics.data.split_dota.split_test

split_test(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0))

Split test set of DOTA, labels are not included within this set.

Notes

The directory structure assumed for the DOTA dataset: - data_root - images - test and the output directory structure is: - save_dir - images - test

Source code in ultralytics/data/split_dota.py
def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0,)):
    """
    Split test set of DOTA, labels are not included within this set.

    Notes:
        The directory structure assumed for the DOTA dataset:
            - data_root
                - images
                    - test
        and the output directory structure is:
            - save_dir
                - images
                    - test
    """
    crop_sizes, gaps = [], []
    for r in rates:
        crop_sizes.append(int(crop_size / r))
        gaps.append(int(gap / r))
    save_dir = Path(save_dir) / "images" / "test"
    save_dir.mkdir(parents=True, exist_ok=True)

    im_dir = Path(data_root) / "images" / "test"
    assert im_dir.exists(), f"Can't find {im_dir}, please check your data root."
    im_files = glob(str(im_dir / "*"))
    for im_file in tqdm(im_files, total=len(im_files), desc="test"):
        w, h = exif_size(Image.open(im_file))
        windows = get_windows((h, w), crop_sizes=crop_sizes, gaps=gaps)
        im = cv2.imread(im_file)
        name = Path(im_file).stem
        for window in windows:
            x_start, y_start, x_stop, y_stop = window.tolist()
            new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
            patch_im = im[y_start:y_stop, x_start:x_stop]
            cv2.imwrite(str(save_dir / f"{new_name}.jpg"), patch_im)



📅 Created 11 months ago ✏️ Updated 2 months ago