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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: ndarray, bbox2: ndarray, eps: float = 1e-06) -> np.ndarray

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

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`polygon1` | `ndarray` | Polygon coordinates with shape (N, 8). | required
`bbox2` | `ndarray` | Bounding boxes with shape (N, 4). | required
`eps` | `float` | Small value to prevent division by zero. | `1e-06`

Returns:

Type | Description
--- | ---
`ndarray` | IoF scores with shape (N, 1) or (N, M) if bbox2 is (M, 4).

Notes

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`

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    def bbox_iof(polygon1: np.ndarray, bbox2: np.ndarray, eps: float = 1e-6) -> np.ndarray:
        """
        Calculate Intersection over Foreground (IoF) between polygons and bounding boxes.

        Args:
            polygon1 (np.ndarray): Polygon coordinates with shape (N, 8).
            bbox2 (np.ndarray): Bounding boxes with shape (N, 4).
            eps (float, optional): Small value to prevent division by zero.

        Returns:
            (np.ndarray): IoF scores with shape (N, 1) or (N, M) if bbox2 is (M, 4).

        Notes:
            Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4].
            Bounding box format: [x_min, y_min, x_max, y_max].
        """
        check_requirements("shapely>=2.0.0")
        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: str, split: str = 'train') -> list[dict[str, Any]]

Load DOTA dataset annotations and image information.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_root` | `str` | Data root directory. | required
`split` | `str` | The split data set, could be 'train' or 'val'. | `'train'`

Returns:

Type | Description
--- | ---
`list[dict[str, Any]]` | List of annotation dictionaries containing image information.

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`

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    def load_yolo_dota(data_root: str, split: str = "train") -> list[dict[str, Any]]:
        """
        Load DOTA dataset annotations and image information.

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

        Returns:
            (list[dict[str, Any]]): List of annotation dictionaries containing image information.

        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, encoding="utf-8") 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: tuple[int, int],
        crop_sizes: tuple[int, ...] = (1024,),
        gaps: tuple[int, ...] = (200,),
        im_rate_thr: float = 0.6,
        eps: float = 0.01,
    ) -> np.ndarray

Get the coordinates of sliding windows for image cropping.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`im_size` | `tuple[int, int]` | Original image size, (H, W). | required
`crop_sizes` | `tuple[int, ...]` | Crop size of windows. | `(1024,)`
`gaps` | `tuple[int, ...]` | Gap between crops. | `(200,)`
`im_rate_thr` | `float` | Threshold of windows areas divided by image areas. | `0.6`
`eps` | `float` | Epsilon value for math operations. | `0.01`

Returns:

Type | Description
--- | ---
`ndarray` | Array of window coordinates with shape (N, 4) where each row is [x_start, y_start, x_stop, y_stop].

Source code in `ultralytics/data/split_dota.py`

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    def get_windows(
        im_size: tuple[int, int],
        crop_sizes: tuple[int, ...] = (1024,),
        gaps: tuple[int, ...] = (200,),
        im_rate_thr: float = 0.6,
        eps: float = 0.01,
    ) -> np.ndarray:
        """
        Get the coordinates of sliding windows for image cropping.

        Args:
            im_size (tuple[int, int]): Original image size, (H, W).
            crop_sizes (tuple[int, ...], optional): Crop size of windows.
            gaps (tuple[int, ...], optional): Gap between crops.
            im_rate_thr (float, optional): Threshold of windows areas divided by image areas.
            eps (float, optional): Epsilon value for math operations.

        Returns:
            (np.ndarray): Array of window coordinates with shape (N, 4) where each row is [x_start, y_start, x_stop, y_stop].
        """
        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: dict[str, Any], windows: ndarray, iof_thr: float = 0.7
    ) -> list[np.ndarray]

Get objects for each window based on IoF threshold.

Source code in `ultralytics/data/split_dota.py`

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    def get_window_obj(anno: dict[str, Any], windows: np.ndarray, iof_thr: float = 0.7) -> list[np.ndarray]:
        """Get objects for each window based on IoF threshold."""
        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: dict[str, Any],
        windows: ndarray,
        window_objs: list[ndarray],
        im_dir: str,
        lb_dir: str,
        allow_background_images: bool = True,
    ) -> None

Crop images and save new labels for each window.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`anno` | `dict[str, Any]` | Annotation dict, including 'filepath', 'label', 'ori_size' as its keys. | required
`windows` | `ndarray` | Array of windows coordinates with shape (N, 4). | required
`window_objs` | `list[ndarray]` | 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`

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    def crop_and_save(
        anno: dict[str, Any],
        windows: np.ndarray,
        window_objs: list[np.ndarray],
        im_dir: str,
        lb_dir: str,
        allow_background_images: bool = True,
    ) -> None:
        """
        Crop images and save new labels for each window.

        Args:
            anno (dict[str, Any]): Annotation dict, including 'filepath', 'label', 'ori_size' as its keys.
            windows (np.ndarray): Array of windows coordinates with shape (N, 4).
            window_objs (list[np.ndarray]): 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, optional): 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", encoding="utf-8") 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: str,
        save_dir: str,
        split: str = "train",
        crop_sizes: tuple[int, ...] = (1024,),
        gaps: tuple[int, ...] = (200,),
    ) -> None

Split both images and labels for a given dataset split.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_root` | `str` | Root directory of the dataset. | required
`save_dir` | `str` | Directory to save the split dataset. | required
`split` | `str` | The split data set, could be 'train' or 'val'. | `'train'`
`crop_sizes` | `tuple[int, ...]` | Tuple of crop sizes. | `(1024,)`
`gaps` | `tuple[int, ...]` | Tuple of gaps between crops. | `(200,)`

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`

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    def split_images_and_labels(
        data_root: str,
        save_dir: str,
        split: str = "train",
        crop_sizes: tuple[int, ...] = (1024,),
        gaps: tuple[int, ...] = (200,),
    ) -> None:
        """
        Split both images and labels for a given dataset split.

        Args:
            data_root (str): Root directory of the dataset.
            save_dir (str): Directory to save the split dataset.
            split (str, optional): The split data set, could be 'train' or 'val'.
            crop_sizes (tuple[int, ...], optional): Tuple of crop sizes.
            gaps (tuple[int, ...], optional): Tuple of gaps between crops.

        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: str,
        save_dir: str,
        crop_size: int = 1024,
        gap: int = 200,
        rates: tuple[float, ...] = (1.0,),
    ) -> None

Split train and val sets of DOTA dataset with multiple scaling rates.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_root` | `str` | Root directory of the dataset. | required
`save_dir` | `str` | Directory to save the split dataset. | required
`crop_size` | `int` | Base crop size. | `1024`
`gap` | `int` | Base gap between crops. | `200`
`rates` | `tuple[float, ...]` | Scaling rates for crop_size and gap. | `(1.0,)`

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`

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    def split_trainval(
        data_root: str, save_dir: str, crop_size: int = 1024, gap: int = 200, rates: tuple[float, ...] = (1.0,)
    ) -> None:
        """
        Split train and val sets of DOTA dataset with multiple scaling rates.

        Args:
            data_root (str): Root directory of the dataset.
            save_dir (str): Directory to save the split dataset.
            crop_size (int, optional): Base crop size.
            gap (int, optional): Base gap between crops.
            rates (tuple[float, ...], optional): Scaling rates for crop_size and gap.

        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: str,
        save_dir: str,
        crop_size: int = 1024,
        gap: int = 200,
        rates: tuple[float, ...] = (1.0,),
    ) -> None

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

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_root` | `str` | Root directory of the dataset. | required
`save_dir` | `str` | Directory to save the split dataset. | required
`crop_size` | `int` | Base crop size. | `1024`
`gap` | `int` | Base gap between crops. | `200`
`rates` | `tuple[float, ...]` | Scaling rates for crop_size and gap. | `(1.0,)`

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`

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    def split_test(
        data_root: str, save_dir: str, crop_size: int = 1024, gap: int = 200, rates: tuple[float, ...] = (1.0,)
    ) -> None:
        """
        Split test set of DOTA dataset, labels are not included within this set.

        Args:
            data_root (str): Root directory of the dataset.
            save_dir (str): Directory to save the split dataset.
            crop_size (int, optional): Base crop size.
            gap (int, optional): Base gap between crops.
            rates (tuple[float, ...], optional): Scaling rates for crop_size and gap.

        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 1 year ago ✏️ Updated 1 year ago