# Copyright (C) 2021-2024 Intel Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import math
from enum import IntEnum
from functools import partial
from itertools import zip_longest
from typing import (
Any,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
)
import attr
import numpy as np
import shapely.geometry as sg
from attr import asdict, attrs, field
from typing_extensions import Literal
from datumaro.components.media import Image
from datumaro.util.attrs_util import default_if_none, not_empty
[docs]
class AnnotationType(IntEnum):
unknown = 0
label = 1
mask = 2
points = 3
polygon = 4
polyline = 5
bbox = 6
caption = 7
cuboid_3d = 8
super_resolution_annotation = 9
depth_annotation = 10
ellipse = 11
hash_key = 12
feature_vector = 13
tabular = 14
rotated_bbox = 15
COORDINATE_ROUNDING_DIGITS = 2
CHECK_POLYGON_EQ_EPSILONE = 1e-7
NO_GROUP = 0
NO_OBJECT_ID = -1
[docs]
@attrs(slots=True, kw_only=True, order=False)
class Annotation:
"""
A base annotation class.
Derived classes must define the '_type' class variable with a value
from the AnnotationType enum.
"""
# Describes an identifier of the annotation
# Is not required to be unique within DatasetItem annotations or dataset
id: int = field(default=0, validator=default_if_none(int))
# Arbitrary annotation-specific attributes. Typically, includes
# metainfo and properties that are not covered by other fields.
# If possible, try to limit value types of values by the simple
# builtin types (int, float, bool, str) to increase compatibility with
# different formats.
# There are some established names for common attributes like:
# - "occluded" (bool)
# - "visible" (bool)
# Possible dataset attributes can be described in Categories.attributes.
attributes: Dict[str, Any] = field(factory=dict, validator=default_if_none(dict))
# Annotations can be grouped, which means they describe parts of a
# single object. The value of 0 means there is no group.
group: int = field(default=NO_GROUP, validator=default_if_none(int))
# obeject identifier over the multiple items
# e.g.) in a video, person 'A' could be annotated on the multiple frame images
# the user could assign >=0 value as id of person 'A'.
object_id: int = field(default=NO_OBJECT_ID, validator=default_if_none(int))
_type = AnnotationType.unknown
@property
def type(self) -> AnnotationType:
return self._type # must be set in subclasses
[docs]
def as_dict(self) -> Dict[str, Any]:
"Returns a dictionary { field_name: value }"
return asdict(self)
[docs]
def wrap(self, **kwargs):
"Returns a modified copy of the object"
return attr.evolve(self, **kwargs)
[docs]
@attrs(slots=True, kw_only=True, order=False)
class Categories:
"""
A base class for annotation metainfo. It is supposed to include
dataset-wide metainfo like available labels, label colors,
label attributes etc.
"""
# Describes the list of possible annotation-type specific attributes
# in a dataset.
attributes: Set[str] = field(factory=set, validator=default_if_none(set), eq=False)
[docs]
class GroupType(IntEnum):
EXCLUSIVE = 0
INCLUSIVE = 1
RESTRICTED = 2
[docs]
def to_str(self) -> str:
return self.name.lower()
[docs]
@classmethod
def from_str(cls, text: str) -> GroupType:
try:
return cls[text.upper()]
except KeyError:
raise ValueError(f"Invalid GroupType: {text}")
[docs]
@attrs(slots=True, order=False)
class LabelCategories(Categories):
[docs]
@attrs(slots=True, order=False)
class Category:
name: str = field(converter=str, validator=not_empty)
parent: str = field(default="", validator=default_if_none(str))
attributes: Set[str] = field(factory=set, validator=default_if_none(set))
[docs]
@attrs(slots=True, order=False)
class LabelGroup:
name: str = field(converter=str, validator=not_empty)
labels: List[str] = field(default=[], validator=default_if_none(list))
group_type: GroupType = field(
default=GroupType.EXCLUSIVE, validator=default_if_none(GroupType)
)
items: List[str] = field(factory=list, validator=default_if_none(list))
label_groups: List[LabelGroup] = field(factory=list, validator=default_if_none(list))
_indices: Dict[str, int] = field(factory=dict, init=False, eq=False)
[docs]
@classmethod
def from_iterable(
cls,
iterable: Iterable[
Union[
str,
Tuple[str],
Tuple[str, str],
Tuple[str, str, List[str]],
]
],
) -> LabelCategories:
"""
Creates a LabelCategories from iterable.
Args:
iterable: This iterable object can be:
- a list of str - will be interpreted as list of Category names
- a list of positional arguments - will generate Categories
with these arguments
Returns: a LabelCategories object
"""
temp_categories = cls()
for category in iterable:
if isinstance(category, str):
category = [category]
temp_categories.add(*category)
return temp_categories
def __attrs_post_init__(self):
self._reindex()
def _reindex(self):
indices = {}
for index, item in enumerate(self.items):
assert item.name not in self._indices
indices[item.name] = index
self._indices = indices
[docs]
def add(
self,
name: str,
parent: Optional[str] = None,
attributes: Optional[Set[str]] = None,
) -> int:
assert name
assert name not in self._indices, name
index = len(self.items)
self.items.append(self.Category(name, parent, attributes))
self._indices[name] = index
return index
[docs]
def add_label_group(
self,
name: str,
labels: List[str],
group_type: GroupType,
) -> int:
assert name
index = len(self.label_groups)
self.label_groups.append(self.LabelGroup(name, labels, group_type))
return index
[docs]
def find(self, name: str) -> Tuple[Optional[int], Optional[Category]]:
index = self._indices.get(name)
if index is not None:
return index, self.items[index]
return index, None
def __getitem__(self, idx: int) -> Category:
return self.items[idx]
def __contains__(self, value: Union[int, str]) -> bool:
if isinstance(value, str):
return self.find(value)[1] is not None
else:
return 0 <= value and value < len(self.items)
def __len__(self) -> int:
return len(self.items)
def __iter__(self) -> Iterator[Category]:
return iter(self.items)
[docs]
@attrs(slots=True, order=False)
class Label(Annotation):
_type = AnnotationType.label
label: int = field(converter=int)
[docs]
@attrs(slots=True, eq=False, order=False)
class HashKey(Annotation):
_type = AnnotationType.hash_key
hash_key: np.ndarray = field(validator=attr.validators.instance_of(np.ndarray))
@hash_key.validator
def _validate(self, attribute, value: np.ndarray):
"""Check whether value is a 1D Numpy array having 64 np.uint8 values"""
if value.ndim != 1 or value.shape[0] != 64 or value.dtype != np.uint8:
raise ValueError(value)
def __eq__(self, other):
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
return np.array_equal(self.hash_key, other.hash_key)
[docs]
@attrs(eq=False, order=False)
class FeatureVector(Annotation):
_type = AnnotationType.feature_vector
vector: np.ndarray = field(validator=attr.validators.instance_of(np.ndarray))
def __eq__(self, other):
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
return np.array_equal(self.hash_key, other.hash_key)
RgbColor = Tuple[int, int, int]
Colormap = Dict[int, RgbColor]
"""Represents { index -> color } mapping for segmentation masks"""
[docs]
@attrs(slots=True, eq=False, order=False)
class MaskCategories(Categories):
"""
Describes a color map for segmentation masks.
"""
[docs]
@classmethod
def generate(cls, size: int = 255, include_background: bool = True) -> MaskCategories:
"""
Generates MaskCategories with the specified size.
If include_background is True, the result will include the item
"0: (0, 0, 0)", which is typically used as a background color.
"""
from datumaro.util.mask_tools import generate_colormap
return cls(generate_colormap(size, include_background=include_background))
colormap: Colormap = field(factory=dict, validator=default_if_none(dict))
_inverse_colormap: Optional[Dict[RgbColor, int]] = field(
default=None, validator=attr.validators.optional(dict)
)
@property
def inverse_colormap(self) -> Dict[RgbColor, int]:
from datumaro.util.mask_tools import invert_colormap
if self._inverse_colormap is None:
if self.colormap is not None:
self._inverse_colormap = invert_colormap(self.colormap)
return self._inverse_colormap
def __contains__(self, idx: int) -> bool:
return idx in self.colormap
def __getitem__(self, idx: int) -> RgbColor:
return self.colormap[idx]
def __len__(self) -> int:
return len(self.colormap)
def __eq__(self, other):
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
for label_id, my_color in self.colormap.items():
other_color = other.colormap.get(label_id)
if not np.array_equal(my_color, other_color):
return False
return True
BinaryMaskImage = np.ndarray # 2d array of type bool
BinaryMaskImageCallable = Callable[[], BinaryMaskImage]
IndexMaskImage = np.ndarray # 2d array of type int
IndexMaskImageCallable = Callable[[], IndexMaskImage]
[docs]
@attrs(slots=True, eq=False, order=False)
class Mask(Annotation):
"""
Represents a 2d single-instance binary segmentation mask.
"""
_type = AnnotationType.mask
_image: Union[BinaryMaskImage, BinaryMaskImageCallable] = field()
label: Optional[int] = field(
converter=attr.converters.optional(int), default=None, kw_only=True
)
z_order: int = field(default=0, validator=default_if_none(int), kw_only=True)
def __attrs_post_init__(self):
if isinstance(self._image, np.ndarray):
self._image = self._image.astype(bool)
@property
def image(self) -> BinaryMaskImage:
image = self._image
if callable(image):
image = image()
return image
[docs]
def as_class_mask(
self,
label_id: Optional[int] = None,
ignore_index: int = 0,
dtype: Optional[np.dtype] = None,
) -> IndexMaskImage:
"""Produces a class index mask based on the binary mask.
Args:
label_id: Scalar value to represent the class index of the mask.
If not specified, `self.label` will be used. Defaults to None.
ignore_index: Scalar value to fill in the zeros in the binary mask.
Defaults to 0.
dtype: Data type for the resulting mask. If not specified,
it will be inferred from the provided `label_id` to hold its value.
For example, if `label_id=255`, the inferred dtype will be `np.uint8`.
Defaults to None.
Returns:
IndexMaskImage: Class index mask generated from the binary mask.
"""
if label_id is None:
label_id = self.label
from datumaro.util.mask_tools import make_index_mask
return make_index_mask(self.image, index=label_id, ignore_index=ignore_index, dtype=dtype)
[docs]
def as_instance_mask(
self,
instance_id: int,
ignore_index: int = 0,
dtype: Optional[np.dtype] = None,
) -> IndexMaskImage:
"""Produces an instance index mask based on the binary mask.
Args:
instance_id: Scalar value to represent the instance id.
ignore_index: Scalar value to fill in the zeros in the binary mask.
Defaults to 0.
dtype: Data type for the resulting mask. If not specified,
it will be inferred from the provided `label_id` to hold its value.
For example, if `label_id=255`, the inferred dtype will be `np.uint8`.
Defaults to None.
Returns:
IndexMaskImage: Instance index mask generated from the binary mask.
"""
from datumaro.util.mask_tools import make_index_mask
return make_index_mask(
self.image, index=instance_id, ignore_index=ignore_index, dtype=dtype
)
[docs]
def get_area(self) -> int:
return np.count_nonzero(self.image)
[docs]
def get_bbox(self) -> Tuple[int, int, int, int]:
"""
Computes the bounding box of the mask.
Returns: [x, y, w, h]
"""
from datumaro.util.mask_tools import find_mask_bbox
return find_mask_bbox(self.image)
[docs]
def paint(self, colormap: Colormap) -> np.ndarray:
"""
Applies a colormap to the mask and produces the resulting image.
"""
from datumaro.util.mask_tools import paint_mask
return paint_mask(self.as_class_mask(), colormap)
def __eq__(self, other):
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
return (
(self.label == other.label)
and (self.z_order == other.z_order)
and (np.array_equal(self.image, other.image))
)
[docs]
@attrs(slots=True, eq=False, order=False)
class RleMask(Mask):
"""
An RLE-encoded instance segmentation mask.
"""
_rle = field() # uses pycocotools RLE representation
_image = field(init=False, default=None)
@property
def image(self) -> BinaryMaskImage:
return self._decode(self.rle)
@property
def rle(self):
rle = self._rle
if callable(rle):
rle = rle()
return rle
@staticmethod
def _decode(rle):
from pycocotools import mask as mask_utils
return mask_utils.decode(rle)
[docs]
def get_area(self) -> int:
from pycocotools import mask as mask_utils
return mask_utils.area(self.rle)
[docs]
def get_bbox(self) -> Tuple[int, int, int, int]:
from pycocotools import mask as mask_utils
return mask_utils.toBbox(self.rle)
def __eq__(self, other):
if not isinstance(other, __class__):
return super().__eq__(other)
return self.rle == other.rle
CompiledMaskImage = np.ndarray # 2d of integers (of different precision)
[docs]
class CompiledMask:
"""
Represents class- and instance- segmentation masks with
all the instances (opposed to single-instance masks).
"""
[docs]
@staticmethod
def from_instance_masks(
instance_masks: Iterable[Mask],
instance_ids: Optional[Iterable[int]] = None,
instance_labels: Optional[Iterable[int]] = None,
) -> CompiledMask:
"""
Joins instance masks into a single mask. Masks are sorted by
z_order (ascending) prior to merging.
Parameters:
instance_ids: Instance id values for the produced instance mask.
By default, mask positions are used.
instance_labels: Instance label id values for the produced class
mask. By default, mask labels are used.
"""
from datumaro.util.mask_tools import make_index_mask
instance_ids = instance_ids or []
instance_labels = instance_labels or []
masks = sorted(
zip_longest(instance_masks, instance_ids, instance_labels), key=lambda m: m[0].z_order
)
max_index = len(masks) + 1
index_dtype = np.min_scalar_type(max_index)
masks = (
(m, 1 + i, id if id is not None else 1 + i, label if label is not None else m.label)
for i, (m, id, label) in enumerate(masks)
)
# This optimized version is supposed for:
# 1. Avoiding memory explosion on materialization of all masks
# 2. Optimizing mask materialization calls (RLE decoding)
# 3. Optimizing intermediate mask memory use
#
# Basically, a mask can be quite large (e.g. 10k x 10k @ int32 etc.),
# so we can only afford having just few copies in
# memory simultaneously.
it = iter(masks)
instance_map = [0]
class_map = [0]
m, idx, instance_id, class_id = next(it)
if not class_id:
idx = 0
index_mask = make_index_mask(m.image, idx, dtype=index_dtype)
instance_map.append(instance_id)
class_map.append(class_id)
for m, idx, instance_id, class_id in it:
if not class_id:
idx = 0
index_mask = np.where(m.image, idx, index_mask)
instance_map.append(instance_id)
class_map.append(class_id)
# Generate compiled masks
if np.array_equal(instance_map, range(max_index)):
merged_instance_mask = index_mask
else:
merged_instance_mask = np.array(instance_map, dtype=np.min_scalar_type(instance_map))[
index_mask
]
merged_class_mask = np.array(class_map, dtype=np.min_scalar_type(class_map))[index_mask]
return __class__(class_mask=merged_class_mask, instance_mask=merged_instance_mask)
def __init__(
self,
class_mask: Union[None, CompiledMaskImage, Callable[[], CompiledMaskImage]] = None,
instance_mask: Union[None, CompiledMaskImage, Callable[[], CompiledMaskImage]] = None,
):
self._class_mask = class_mask
self._instance_mask = instance_mask
@staticmethod
def _get_image(image):
if callable(image):
return image()
return image
@property
def class_mask(self) -> Optional[CompiledMaskImage]:
return self._get_image(self._class_mask)
@property
def instance_mask(self) -> Optional[CompiledMaskImage]:
return self._get_image(self._instance_mask)
@property
def instance_count(self) -> int:
return int(self.instance_mask.max())
[docs]
def get_instance_labels(self) -> Dict[int, int]:
"""
Matches the class and instance masks.
Returns: { instance id: class id }
"""
class_shift = 16
m = (self.class_mask.astype(np.uint32) << class_shift) + self.instance_mask.astype(
np.uint32
)
keys = np.unique(m)
instance_labels = {
int(k & ((1 << class_shift) - 1)): int(k >> class_shift)
for k in keys
if k & ((1 << class_shift) - 1) != 0
}
return instance_labels
@attrs(slots=True, order=False)
class _Shape(Annotation):
# Flattened list of point coordinates
points: List[float] = field(
converter=lambda x: np.around(
np.array(x, dtype=np.float32), COORDINATE_ROUNDING_DIGITS
).tolist()
)
label: Optional[int] = field(
converter=attr.converters.optional(int), default=None, kw_only=True
)
z_order: int = field(default=0, validator=default_if_none(int), kw_only=True)
def get_area(self):
raise NotImplementedError()
def as_polygon(self) -> List[float]:
raise NotImplementedError()
def get_bbox(self) -> Tuple[float, float, float, float]:
"Returns [x, y, w, h]"
points = self.points
if not points:
return None
xs = [p for p in points[0::2]]
ys = [p for p in points[1::2]]
x0 = min(xs)
x1 = max(xs)
y0 = min(ys)
y1 = max(ys)
return [x0, y0, x1 - x0, y1 - y0]
def get_points(self) -> Optional[List[Tuple[float, float]]]:
"""
Return points as a list of tuples, e.g. [(x0, y0), (x1, y1), ...]
"""
points = self.points
if not points:
return None
assert len(points) % 2 == 0, "points should have (2 x points) number of float values."
xs = [p for p in points[0::2]]
ys = [p for p in points[1::2]]
return [(x, y) for x, y in zip(xs, ys)]
[docs]
@attrs(slots=True, order=False)
class PolyLine(_Shape):
_type = AnnotationType.polyline
[docs]
def as_polygon(self):
return self.points[:]
[docs]
def get_area(self):
return 0
[docs]
@attrs(slots=True, init=False, order=False)
class Cuboid3d(Annotation):
_type = AnnotationType.cuboid_3d
_points: List[float] = field(default=None)
label: Optional[int] = field(
converter=attr.converters.optional(int), default=None, kw_only=True
)
@_points.validator
def _points_validator(self, attribute, points):
if points is None:
points = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]
else:
assert len(points) == 3 + 3 + 3, points
points = np.around(points, COORDINATE_ROUNDING_DIGITS).tolist()
self._points = points
def __init__(self, position, rotation=None, scale=None, **kwargs):
assert len(position) == 3, position
if not rotation:
rotation = [0] * 3
if not scale:
scale = [1] * 3
kwargs.pop("points", None)
self.__attrs_init__(points=[*position, *rotation, *scale], **kwargs)
@property
def position(self):
"""[x, y, z]"""
return self._points[0:3]
@position.setter
def _set_poistion(self, value):
# TODO: fix the issue with separate coordinate rounding:
# self.position[0] = 12.345676
# - the number assigned won't be rounded.
self.position[:] = np.around(value, COORDINATE_ROUNDING_DIGITS).tolist()
@property
def rotation(self):
"""[rx, ry, rz]"""
return self._points[3:6]
@rotation.setter
def _set_rotation(self, value):
self.rotation[:] = np.around(value, COORDINATE_ROUNDING_DIGITS).tolist()
@property
def scale(self):
"""[sx, sy, sz]"""
return self._points[6:9]
@scale.setter
def _set_scale(self, value):
self.scale[:] = np.around(value, COORDINATE_ROUNDING_DIGITS).tolist()
[docs]
@attrs(slots=True, order=False, eq=False)
class Polygon(_Shape):
_type = AnnotationType.polygon
def __attrs_post_init__(self):
# keep the message on a single line to produce informative output
assert len(self.points) % 2 == 0 and 3 <= len(self.points) // 2, (
"Wrong polygon points: %s" % self.points
)
[docs]
def get_area(self):
import pycocotools.mask as mask_utils
x, y, w, h = self.get_bbox()
rle = mask_utils.frPyObjects([self.points], y + h, x + w)
area = mask_utils.area(rle)[0]
return area
[docs]
def as_polygon(self) -> List[float]:
return self.points
def __eq__(self, other):
if not isinstance(other, __class__):
return False
if (
not Annotation.__eq__(self, other)
or self.label != other.label
or self.z_order != other.z_order
):
return False
self_points = self.get_points()
other_points = other.get_points()
self_polygon = sg.Polygon(self_points)
other_polygon = sg.Polygon(other_points)
# if polygon is not valid, compare points
if not (self_polygon.is_valid and other_polygon.is_valid):
return self_points == other_points
inter_area = self_polygon.intersection(other_polygon).area
return abs(self_polygon.area - inter_area) < CHECK_POLYGON_EQ_EPSILONE
[docs]
@attrs(slots=True, init=False, order=False)
class Bbox(_Shape):
_type = AnnotationType.bbox
def __init__(self, x, y, w, h, *args, **kwargs):
kwargs.pop("points", None) # comes from wrap()
self.__attrs_init__([x, y, x + w, y + h], *args, **kwargs)
@property
def x(self):
return self.points[0]
@property
def y(self):
return self.points[1]
@property
def w(self):
return self.points[2] - self.points[0]
@property
def h(self):
return self.points[3] - self.points[1]
[docs]
def get_area(self):
return self.w * self.h
[docs]
def get_bbox(self):
return [self.x, self.y, self.w, self.h]
[docs]
def as_polygon(self) -> List[float]:
x, y, w, h = self.get_bbox()
return [x, y, x + w, y, x + w, y + h, x, y + h]
[docs]
def iou(self, other: _Shape) -> Union[float, Literal[-1]]:
from datumaro.util.annotation_util import bbox_iou
return bbox_iou(self.get_bbox(), other.get_bbox())
[docs]
def wrap(item, **kwargs):
d = {"x": item.x, "y": item.y, "w": item.w, "h": item.h}
d.update(kwargs)
return attr.evolve(item, **d)
[docs]
@attrs(slots=True, init=False, order=False)
class RotatedBbox(_Shape):
_type = AnnotationType.rotated_bbox
def __init__(self, cx, cy, w, h, r, *args, **kwargs):
kwargs.pop("points", None) # comes from wrap()
self.__attrs_init__([cx, cy, w, h, r], *args, **kwargs)
[docs]
@classmethod
def from_rectangle(cls, points: List[Tuple[float, float]], *args, **kwargs):
assert len(points) == 4, "polygon for a rotated bbox should have only 4 coordinates."
# Calculate rotation angle
rot = math.atan2(points[1][1] - points[0][1], points[1][0] - points[0][0])
# Calculate the center of the bounding box
cx = (points[0][0] + points[2][0]) / 2
cy = (points[0][1] + points[2][1]) / 2
# Calculate the width and height
width = math.sqrt((points[1][0] - points[0][0]) ** 2 + (points[1][1] - points[0][1]) ** 2)
height = math.sqrt((points[2][0] - points[1][0]) ** 2 + (points[2][1] - points[1][1]) ** 2)
return cls(cx=cx, cy=cy, w=width, h=height, r=math.degrees(rot), *args, **kwargs)
@property
def cx(self):
return self.points[0]
@property
def cy(self):
return self.points[1]
@property
def w(self):
return self.points[2]
@property
def h(self):
return self.points[3]
@property
def r(self):
return self.points[4]
[docs]
def get_area(self):
return self.w * self.h
[docs]
def get_bbox(self):
polygon = self.as_polygon()
xs = [pt[0] for pt in polygon]
ys = [pt[1] for pt in polygon]
return [min(xs), min(ys), max(xs) - min(xs), max(ys) - min(ys)]
[docs]
def get_rotated_bbox(self):
return [self.cx, self.cy, self.w, self.h, self.r]
[docs]
def as_polygon(self) -> List[Tuple[float, float]]:
"""Convert [center_x, center_y, width, height, rotation] to 4 coordinates for a rotated bounding box."""
def _rotate_point(x, y, angle):
"""Rotate a point around another point."""
angle_rad = math.radians(angle)
cos_theta = math.cos(angle_rad)
sin_theta = math.sin(angle_rad)
nx = cos_theta * x - sin_theta * y
ny = sin_theta * x + cos_theta * y
return nx, ny
# Calculate corner points of the rectangle
corners = [
(-self.w / 2, -self.h / 2),
(self.w / 2, -self.h / 2),
(self.w / 2, self.h / 2),
(-self.w / 2, self.h / 2),
]
# Rotate each corner point
rotated_corners = [_rotate_point(p[0], p[1], self.r) for p in corners]
# Translate the rotated points to the original position
return [(p[0] + self.cx, p[1] + self.cy) for p in rotated_corners]
[docs]
def iou(self, other: _Shape) -> Union[float, Literal[-1]]:
from datumaro.util.annotation_util import bbox_iou
return bbox_iou(self.get_bbox(), other.get_bbox())
[docs]
def wrap(item, **kwargs):
d = {"x": item.x, "y": item.y, "w": item.w, "h": item.h, "r": item.r}
d.update(kwargs)
return attr.evolve(item, **d)
[docs]
@attrs(slots=True, order=False)
class PointsCategories(Categories):
"""
Describes (key-)point metainfo such as point names and joints.
"""
[docs]
@attrs(slots=True, order=False)
class Category:
# Names for specific points, e.g. eye, hose, mouth etc.
# These labels are not required to be in LabelCategories
labels: List[str] = field(factory=list, validator=default_if_none(list))
# Pairs of connected point indices
joints: Set[Tuple[int, int]] = field(factory=set, validator=default_if_none(set))
items: Dict[int, Category] = field(factory=dict, validator=default_if_none(dict))
[docs]
@classmethod
def from_iterable(
cls,
iterable: Union[
Tuple[int, List[str]],
Tuple[int, List[str], Set[Tuple[int, int]]],
],
) -> PointsCategories:
"""
Create PointsCategories from an iterable.
Args:
iterable: An Iterable with the following elements:
- a label id
- a list of positional arguments for Categories
Returns:
PointsCategories: PointsCategories object
"""
temp_categories = cls()
for args in iterable:
temp_categories.add(*args)
return temp_categories
[docs]
def add(
self,
label_id: int,
labels: Optional[Iterable[str]] = None,
joints: Iterable[Tuple[int, int]] = None,
):
if joints is None:
joints = []
joints = set(map(tuple, joints))
self.items[label_id] = self.Category(labels, joints)
def __contains__(self, idx: int) -> bool:
return idx in self.items
def __getitem__(self, idx: int) -> Category:
return self.items[idx]
def __len__(self) -> int:
return len(self.items)
[docs]
@attrs(slots=True, order=False)
class Points(_Shape):
"""
Represents an ordered set of points.
"""
[docs]
class Visibility(IntEnum):
absent = 0
hidden = 1
visible = 2
_type = AnnotationType.points
visibility: List[IntEnum] = field(default=None)
@visibility.validator
def _visibility_validator(self, attribute, visibility):
if visibility is None:
visibility = [self.Visibility.visible] * (len(self.points) // 2)
else:
for i, v in enumerate(visibility):
if not isinstance(v, self.Visibility):
visibility[i] = self.Visibility(v)
assert len(visibility) == len(self.points) // 2
self.visibility = visibility
def __attrs_post_init__(self):
assert len(self.points) % 2 == 0, self.points
[docs]
def get_area(self):
return 0
[docs]
def get_bbox(self):
xs = [
p
for p, v in zip(self.points[0::2], self.visibility)
if v != __class__.Visibility.absent
]
ys = [
p
for p, v in zip(self.points[1::2], self.visibility)
if v != __class__.Visibility.absent
]
x0 = min(xs, default=0)
x1 = max(xs, default=0)
y0 = min(ys, default=0)
y1 = max(ys, default=0)
return [x0, y0, x1 - x0, y1 - y0]
[docs]
@attrs(slots=True, order=False)
class Caption(Annotation):
"""
Represents arbitrary text annotations.
"""
_type = AnnotationType.caption
caption: str = field(converter=str)
@attrs(slots=True, order=False, eq=False)
class _ImageAnnotation(Annotation):
image: Image = field()
def __eq__(self, other):
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
return np.array_equal(self.image, other.image)
[docs]
@attrs(slots=True, order=False, eq=False)
class SuperResolutionAnnotation(_ImageAnnotation):
"""
Represents high resolution images.
"""
_type = AnnotationType.super_resolution_annotation
[docs]
@attrs(slots=True, order=False, eq=False)
class DepthAnnotation(_ImageAnnotation):
"""
Represents depth images.
"""
_type = AnnotationType.depth_annotation
[docs]
@attrs(slots=True, init=False, order=False)
class Ellipse(_Shape):
"""
Ellipse represents an ellipse that is encapsulated by a rectangle.
- x1 and y1 represent the top-left coordinate of the encapsulating rectangle
- x2 and y2 representing the bottom-right coordinate of the encapsulating rectangle
Parameters
----------
x1: float
left x coordinate of encapsulating rectangle
y1: float
top y coordinate of encapsulating rectangle
x2: float
right x coordinate of encapsulating rectangle
y2: float
bottom y coordinate of encapsulating rectangle
"""
_type = AnnotationType.ellipse
def __init__(self, x1: float, y1: float, x2: float, y2: float, *args, **kwargs):
kwargs.pop("points", None) # comes from wrap()
self.__attrs_init__([x1, y1, x2, y2], *args, **kwargs)
@property
def x1(self):
return self.points[0]
@property
def y1(self):
return self.points[1]
@property
def x2(self):
return self.points[2]
@property
def y2(self):
return self.points[3]
@property
def w(self):
return self.points[2] - self.points[0]
@property
def h(self):
return self.points[3] - self.points[1]
@property
def c_x(self):
return 0.5 * (self.points[0] + self.points[2])
@property
def c_y(self):
return 0.5 * (self.points[1] + self.points[3])
[docs]
def get_area(self):
return 0.25 * np.pi * self.w * self.h
[docs]
def get_bbox(self):
return [self.x1, self.y1, self.w, self.h]
[docs]
def get_points(self, num_points: int = 720) -> List[Tuple[float, float]]:
"""
Return points as a list of tuples, e.g. [(x0, y0), (x1, y1), ...].
Parameters
----------
num_points: int
The number of boundary points of the ellipse.
By default, one point is created for every 1 degree of interior angle (num_points=360).
"""
points = self.as_polygon(num_points)
return [(x, y) for x, y in zip(points[0::2], points[1::2])]
[docs]
def as_polygon(self, num_points: int = 720) -> List[float]:
"""
Return a polygon as a list of tuples, e.g. [x0, y0, x1, y1, ...].
Parameters
----------
num_points: int
The number of boundary points of the ellipse.
By default, one point is created for every 1 degree of interior angle (num_points=360).
"""
theta = np.linspace(0, 2 * np.pi, num=num_points)
l1 = 0.5 * self.w
l2 = 0.5 * self.h
x_points = self.c_x + l1 * np.cos(theta)
y_points = self.c_y + l2 * np.sin(theta)
points = []
for x, y in zip(x_points, y_points):
points += [x, y]
return points
[docs]
def iou(self, other: _Shape) -> Union[float, Literal[-1]]:
from datumaro.util.annotation_util import bbox_iou
return bbox_iou(self.get_bbox(), other.get_bbox())
[docs]
def wrap(item: Ellipse, **kwargs) -> Ellipse:
d = {"x1": item.x1, "y1": item.y1, "x2": item.x2, "y2": item.y2}
d.update(kwargs)
return attr.evolve(item, **d)
TableDtype = TypeVar("TableDtype", str, int, float)
[docs]
@attrs(slots=True, order=False, eq=False)
class TabularCategories(Categories):
"""
Describes tabular data metainfo such as column names and types.
"""
[docs]
@attrs(slots=True, order=False, eq=False)
class Category:
name: str = field(converter=str, validator=not_empty)
dtype: Type[TableDtype] = field()
labels: Set[Union[str, int]] = field(factory=set, validator=default_if_none(set))
def __eq__(self, other):
same_name = self.name == other.name
same_dtype = self.dtype.__name__ == other.dtype.__name__
same_labels = self.labels == other.labels
return same_name and same_dtype and same_labels
def __repr__(self):
return f"name: {self.name}, dtype: {self.dtype.__name__}, labels: {self.labels}"
items: List[Category] = field(factory=list, validator=default_if_none(list))
_indices_by_name: Dict[str, int] = field(factory=dict, init=False, eq=False)
[docs]
@classmethod
def from_iterable(
cls,
iterable: Iterable[
Union[Tuple[str, Type[TableDtype]], Tuple[str, Type[TableDtype], Set[str]]]
],
) -> TabularCategories:
"""
Creates a TabularCategories from iterable.
Args:
iterable: a list of (Category name, type) or (Category name, type, set of labels)
Returns: a TabularCategories object
"""
temp_categories = cls()
for category in iterable:
temp_categories.add(*category)
return temp_categories
[docs]
def add(
self,
name: str,
dtype: Type[TableDtype],
labels: Optional[Set[str]] = None,
) -> int:
"""
Add a Tabular Category.
Args:
name (str): Column name
dtype (type): Type of the corresponding column. (str, int, or float)
labels (optional, set(str)): Label values where the column can have.
Returns:
int: A index of added category.
"""
assert name
assert name not in self._indices_by_name
assert dtype
index = len(self.items)
self.items.append(self.Category(name, dtype, labels))
self._indices_by_name[name] = index
return index
[docs]
def find(self, name: str) -> Tuple[Optional[int], Optional[Category]]:
"""
Find Category information for the given column name.
Args:
name (str): Column name
Returns:
tuple(int, Category): A index and Category information.
"""
index = self._indices_by_name.get(name)
return index, self.items[index] if index is not None else None
def __getitem__(self, index: int) -> Category:
return self.items[index]
def __contains__(self, name: str) -> bool:
return self.find(name)[1] is not None
def __len__(self) -> int:
return len(self.items)
def __iter__(self) -> Iterator[Category]:
return iter(self.items)
def __eq__(self, other) -> bool:
if not super().__eq__(other):
return False
if not isinstance(other, __class__):
return False
return self.items == other.items
[docs]
@attrs(slots=True, order=False)
class Tabular(Annotation):
"""
Represents values of target columns in a tabular dataset.
"""
_type = AnnotationType.tabular
values: Dict[str, TableDtype] = field(converter=dict)
