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| from collections import abc
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| from itertools import repeat
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| from numbers import Number
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| from typing import List
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|
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| import numpy as np
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|
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| from .ops import ltwh2xywh, ltwh2xyxy, resample_segments, xywh2ltwh, xywh2xyxy, xyxy2ltwh, xyxy2xywh
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| def _ntuple(n):
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| """From PyTorch internals."""
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|
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| def parse(x):
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| """Parse input to return n-tuple by repeating singleton values n times."""
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| return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))
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|
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| return parse
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| to_2tuple = _ntuple(2)
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| to_4tuple = _ntuple(4)
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| _formats = ["xyxy", "xywh", "ltwh"]
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| __all__ = ("Bboxes", "Instances")
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|
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| class Bboxes:
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| """
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| A class for handling bounding boxes.
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| The class supports various bounding box formats like 'xyxy', 'xywh', and 'ltwh'.
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| Bounding box data should be provided in numpy arrays.
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| Attributes:
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| bboxes (np.ndarray): The bounding boxes stored in a 2D numpy array with shape (N, 4).
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| format (str): The format of the bounding boxes ('xyxy', 'xywh', or 'ltwh').
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|
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| Note:
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| This class does not handle normalization or denormalization of bounding boxes.
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| """
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|
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| def __init__(self, bboxes, format="xyxy") -> None:
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| """
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| Initialize the Bboxes class with bounding box data in a specified format.
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|
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| Args:
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| bboxes (np.ndarray): Array of bounding boxes with shape (N, 4) or (4,).
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| format (str): Format of the bounding boxes, one of 'xyxy', 'xywh', or 'ltwh'.
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| """
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| assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
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| bboxes = bboxes[None, :] if bboxes.ndim == 1 else bboxes
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| assert bboxes.ndim == 2
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| assert bboxes.shape[1] == 4
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| self.bboxes = bboxes
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| self.format = format
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|
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| def convert(self, format):
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| """
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| Convert bounding box format from one type to another.
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|
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| Args:
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| format (str): Target format for conversion, one of 'xyxy', 'xywh', or 'ltwh'.
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| """
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| assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
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| if self.format == format:
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| return
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| elif self.format == "xyxy":
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| func = xyxy2xywh if format == "xywh" else xyxy2ltwh
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| elif self.format == "xywh":
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| func = xywh2xyxy if format == "xyxy" else xywh2ltwh
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| else:
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| func = ltwh2xyxy if format == "xyxy" else ltwh2xywh
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| self.bboxes = func(self.bboxes)
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| self.format = format
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|
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| def areas(self):
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| """Return box areas."""
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| return (
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| (self.bboxes[:, 2] - self.bboxes[:, 0]) * (self.bboxes[:, 3] - self.bboxes[:, 1])
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| if self.format == "xyxy"
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| else self.bboxes[:, 3] * self.bboxes[:, 2]
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| )
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| def mul(self, scale):
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| """
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| Multiply bounding box coordinates by scale factor(s).
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|
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| Args:
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| scale (int | tuple | list): Scale factor(s) for four coordinates.
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| If int, the same scale is applied to all coordinates.
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| """
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| if isinstance(scale, Number):
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| scale = to_4tuple(scale)
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| assert isinstance(scale, (tuple, list))
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| assert len(scale) == 4
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| self.bboxes[:, 0] *= scale[0]
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| self.bboxes[:, 1] *= scale[1]
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| self.bboxes[:, 2] *= scale[2]
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| self.bboxes[:, 3] *= scale[3]
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|
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| def add(self, offset):
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| """
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| Add offset to bounding box coordinates.
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|
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| Args:
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| offset (int | tuple | list): Offset(s) for four coordinates.
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| If int, the same offset is applied to all coordinates.
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| """
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| if isinstance(offset, Number):
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| offset = to_4tuple(offset)
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| assert isinstance(offset, (tuple, list))
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| assert len(offset) == 4
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| self.bboxes[:, 0] += offset[0]
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| self.bboxes[:, 1] += offset[1]
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| self.bboxes[:, 2] += offset[2]
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| self.bboxes[:, 3] += offset[3]
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|
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| def __len__(self):
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| """Return the number of boxes."""
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| return len(self.bboxes)
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|
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| @classmethod
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| def concatenate(cls, boxes_list: List["Bboxes"], axis=0) -> "Bboxes":
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| """
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| Concatenate a list of Bboxes objects into a single Bboxes object.
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| Args:
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| boxes_list (List[Bboxes]): A list of Bboxes objects to concatenate.
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| axis (int, optional): The axis along which to concatenate the bounding boxes.
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|
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| Returns:
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| (Bboxes): A new Bboxes object containing the concatenated bounding boxes.
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|
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| Note:
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| The input should be a list or tuple of Bboxes objects.
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| """
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| assert isinstance(boxes_list, (list, tuple))
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| if not boxes_list:
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| return cls(np.empty(0))
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| assert all(isinstance(box, Bboxes) for box in boxes_list)
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|
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| if len(boxes_list) == 1:
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| return boxes_list[0]
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| return cls(np.concatenate([b.bboxes for b in boxes_list], axis=axis))
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|
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| def __getitem__(self, index) -> "Bboxes":
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| """
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| Retrieve a specific bounding box or a set of bounding boxes using indexing.
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|
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| Args:
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| index (int | slice | np.ndarray): The index, slice, or boolean array to select
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| the desired bounding boxes.
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| Returns:
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| (Bboxes): A new Bboxes object containing the selected bounding boxes.
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|
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| Raises:
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| AssertionError: If the indexed bounding boxes do not form a 2-dimensional matrix.
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|
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| Note:
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| When using boolean indexing, make sure to provide a boolean array with the same
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| length as the number of bounding boxes.
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| """
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| if isinstance(index, int):
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| return Bboxes(self.bboxes[index].reshape(1, -1))
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| b = self.bboxes[index]
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| assert b.ndim == 2, f"Indexing on Bboxes with {index} failed to return a matrix!"
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| return Bboxes(b)
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|
|
|
|
| class Instances:
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| """
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| Container for bounding boxes, segments, and keypoints of detected objects in an image.
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|
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| Attributes:
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| _bboxes (Bboxes): Internal object for handling bounding box operations.
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| keypoints (np.ndarray): Keypoints with shape (N, 17, 3) in format (x, y, visible).
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| normalized (bool): Flag indicating whether the bounding box coordinates are normalized.
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| segments (np.ndarray): Segments array with shape (N, M, 2) after resampling.
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| Methods:
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| convert_bbox: Convert bounding box format.
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| scale: Scale coordinates by given factors.
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| denormalize: Convert normalized coordinates to absolute coordinates.
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| normalize: Convert absolute coordinates to normalized coordinates.
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| add_padding: Add padding to coordinates.
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| flipud: Flip coordinates vertically.
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| fliplr: Flip coordinates horizontally.
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| clip: Clip coordinates to stay within image boundaries.
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| remove_zero_area_boxes: Remove boxes with zero area.
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| update: Update instance variables.
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| concatenate: Concatenate multiple Instances objects.
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| Examples:
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| >>> instances = Instances(
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| ... bboxes=np.array([[10, 10, 30, 30], [20, 20, 40, 40]]),
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| ... segments=[np.array([[5, 5], [10, 10]]), np.array([[15, 15], [20, 20]])],
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| ... keypoints=np.array([[[5, 5, 1], [10, 10, 1]], [[15, 15, 1], [20, 20, 1]]]),
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| ... )
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| """
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|
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| def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True) -> None:
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| """
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| Initialize the object with bounding boxes, segments, and keypoints.
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|
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| Args:
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| bboxes (np.ndarray): Bounding boxes, shape (N, 4).
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| segments (List | np.ndarray, optional): Segmentation masks.
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| keypoints (np.ndarray, optional): Keypoints, shape (N, 17, 3) in format (x, y, visible).
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| bbox_format (str, optional): Format of bboxes.
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| normalized (bool, optional): Whether the coordinates are normalized.
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| """
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| self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
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| self.keypoints = keypoints
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| self.normalized = normalized
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| self.segments = segments
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|
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| def convert_bbox(self, format):
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| """
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| Convert bounding box format.
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|
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| Args:
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| format (str): Target format for conversion, one of 'xyxy', 'xywh', or 'ltwh'.
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| """
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| self._bboxes.convert(format=format)
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|
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| @property
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| def bbox_areas(self):
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| """Calculate the area of bounding boxes."""
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| return self._bboxes.areas()
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|
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| def scale(self, scale_w, scale_h, bbox_only=False):
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| """
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| Scale coordinates by given factors.
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|
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| Args:
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| scale_w (float): Scale factor for width.
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| scale_h (float): Scale factor for height.
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| bbox_only (bool, optional): Whether to scale only bounding boxes.
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| """
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| self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
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| if bbox_only:
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| return
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| self.segments[..., 0] *= scale_w
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| self.segments[..., 1] *= scale_h
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| if self.keypoints is not None:
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| self.keypoints[..., 0] *= scale_w
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| self.keypoints[..., 1] *= scale_h
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|
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| def denormalize(self, w, h):
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| """
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| Convert normalized coordinates to absolute coordinates.
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|
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| Args:
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| w (int): Image width.
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| h (int): Image height.
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| """
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| if not self.normalized:
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| return
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| self._bboxes.mul(scale=(w, h, w, h))
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| self.segments[..., 0] *= w
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| self.segments[..., 1] *= h
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| if self.keypoints is not None:
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| self.keypoints[..., 0] *= w
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| self.keypoints[..., 1] *= h
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| self.normalized = False
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|
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| def normalize(self, w, h):
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| """
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| Convert absolute coordinates to normalized coordinates.
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|
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| Args:
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| w (int): Image width.
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| h (int): Image height.
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| """
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| if self.normalized:
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| return
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| self._bboxes.mul(scale=(1 / w, 1 / h, 1 / w, 1 / h))
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| self.segments[..., 0] /= w
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| self.segments[..., 1] /= h
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| if self.keypoints is not None:
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| self.keypoints[..., 0] /= w
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| self.keypoints[..., 1] /= h
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| self.normalized = True
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|
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| def add_padding(self, padw, padh):
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| """
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| Add padding to coordinates.
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|
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| Args:
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| padw (int): Padding width.
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| padh (int): Padding height.
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| """
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| assert not self.normalized, "you should add padding with absolute coordinates."
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| self._bboxes.add(offset=(padw, padh, padw, padh))
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| self.segments[..., 0] += padw
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| self.segments[..., 1] += padh
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| if self.keypoints is not None:
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| self.keypoints[..., 0] += padw
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| self.keypoints[..., 1] += padh
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|
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| def __getitem__(self, index) -> "Instances":
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| """
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| Retrieve a specific instance or a set of instances using indexing.
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|
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| Args:
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| index (int | slice | np.ndarray): The index, slice, or boolean array to select the desired instances.
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|
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| Returns:
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| (Instances): A new Instances object containing the selected boxes, segments, and keypoints if present.
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|
|
| Note:
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| When using boolean indexing, make sure to provide a boolean array with the same
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| length as the number of instances.
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| """
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| segments = self.segments[index] if len(self.segments) else self.segments
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| keypoints = self.keypoints[index] if self.keypoints is not None else None
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| bboxes = self.bboxes[index]
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| bbox_format = self._bboxes.format
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| return Instances(
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| bboxes=bboxes,
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| segments=segments,
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| keypoints=keypoints,
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| bbox_format=bbox_format,
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| normalized=self.normalized,
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| )
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|
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| def flipud(self, h):
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| """
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| Flip coordinates vertically.
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|
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| Args:
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| h (int): Image height.
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| """
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| if self._bboxes.format == "xyxy":
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| y1 = self.bboxes[:, 1].copy()
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| y2 = self.bboxes[:, 3].copy()
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| self.bboxes[:, 1] = h - y2
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| self.bboxes[:, 3] = h - y1
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| else:
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| self.bboxes[:, 1] = h - self.bboxes[:, 1]
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| self.segments[..., 1] = h - self.segments[..., 1]
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| if self.keypoints is not None:
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| self.keypoints[..., 1] = h - self.keypoints[..., 1]
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|
|
| def fliplr(self, w):
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| """
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| Flip coordinates horizontally.
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|
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| Args:
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| w (int): Image width.
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| """
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| if self._bboxes.format == "xyxy":
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| x1 = self.bboxes[:, 0].copy()
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| x2 = self.bboxes[:, 2].copy()
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| self.bboxes[:, 0] = w - x2
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| self.bboxes[:, 2] = w - x1
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| else:
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| self.bboxes[:, 0] = w - self.bboxes[:, 0]
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| self.segments[..., 0] = w - self.segments[..., 0]
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| if self.keypoints is not None:
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| self.keypoints[..., 0] = w - self.keypoints[..., 0]
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|
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| def clip(self, w, h):
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| """
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| Clip coordinates to stay within image boundaries.
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|
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| Args:
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| w (int): Image width.
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| h (int): Image height.
|
| """
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| ori_format = self._bboxes.format
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| self.convert_bbox(format="xyxy")
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| self.bboxes[:, [0, 2]] = self.bboxes[:, [0, 2]].clip(0, w)
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| self.bboxes[:, [1, 3]] = self.bboxes[:, [1, 3]].clip(0, h)
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| if ori_format != "xyxy":
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| self.convert_bbox(format=ori_format)
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| self.segments[..., 0] = self.segments[..., 0].clip(0, w)
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| self.segments[..., 1] = self.segments[..., 1].clip(0, h)
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| if self.keypoints is not None:
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|
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| self.keypoints[..., 2][
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| (self.keypoints[..., 0] < 0)
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| | (self.keypoints[..., 0] > w)
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| | (self.keypoints[..., 1] < 0)
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| | (self.keypoints[..., 1] > h)
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| ] = 0.0
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|
|
| def remove_zero_area_boxes(self):
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| """
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| Remove zero-area boxes, i.e. after clipping some boxes may have zero width or height.
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|
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| Returns:
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| (np.ndarray): Boolean array indicating which boxes were kept.
|
| """
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| good = self.bbox_areas > 0
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| if not all(good):
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| self._bboxes = self._bboxes[good]
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| if len(self.segments):
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| self.segments = self.segments[good]
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| if self.keypoints is not None:
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| self.keypoints = self.keypoints[good]
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| return good
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|
|
| def update(self, bboxes, segments=None, keypoints=None):
|
| """
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| Update instance variables.
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|
|
| Args:
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| bboxes (np.ndarray): New bounding boxes.
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| segments (np.ndarray, optional): New segments.
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| keypoints (np.ndarray, optional): New keypoints.
|
| """
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| self._bboxes = Bboxes(bboxes, format=self._bboxes.format)
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| if segments is not None:
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| self.segments = segments
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| if keypoints is not None:
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| self.keypoints = keypoints
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|
|
| def __len__(self):
|
| """Return the length of the instance list."""
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| return len(self.bboxes)
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|
|
| @classmethod
|
| def concatenate(cls, instances_list: List["Instances"], axis=0) -> "Instances":
|
| """
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| Concatenate a list of Instances objects into a single Instances object.
|
|
|
| Args:
|
| instances_list (List[Instances]): A list of Instances objects to concatenate.
|
| axis (int, optional): The axis along which the arrays will be concatenated.
|
|
|
| Returns:
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| (Instances): A new Instances object containing the concatenated bounding boxes,
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| segments, and keypoints if present.
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|
|
| Note:
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| The `Instances` objects in the list should have the same properties, such as
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| the format of the bounding boxes, whether keypoints are present, and if the
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| coordinates are normalized.
|
| """
|
| assert isinstance(instances_list, (list, tuple))
|
| if not instances_list:
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| return cls(np.empty(0))
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| assert all(isinstance(instance, Instances) for instance in instances_list)
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|
|
| if len(instances_list) == 1:
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| return instances_list[0]
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|
|
| use_keypoint = instances_list[0].keypoints is not None
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| bbox_format = instances_list[0]._bboxes.format
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| normalized = instances_list[0].normalized
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|
|
| cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
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| seg_len = [b.segments.shape[1] for b in instances_list]
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| if len(frozenset(seg_len)) > 1:
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| max_len = max(seg_len)
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| cat_segments = np.concatenate(
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| [
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| resample_segments(list(b.segments), max_len)
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| if len(b.segments)
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| else np.zeros((0, max_len, 2), dtype=np.float32)
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| for b in instances_list
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| ],
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| axis=axis,
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| )
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| else:
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| cat_segments = np.concatenate([b.segments for b in instances_list], axis=axis)
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| cat_keypoints = np.concatenate([b.keypoints for b in instances_list], axis=axis) if use_keypoint else None
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| return cls(cat_boxes, cat_segments, cat_keypoints, bbox_format, normalized)
|
|
|
| @property
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| def bboxes(self):
|
| """Return bounding boxes."""
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| return self._bboxes.bboxes
|
|
|
