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Enhance Keypoints Serialization and Refactor Codebase for Consistency and Readability #28

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Merged
kunkunlin1221 merged 1 commit into from
Aug 29, 2025
Merged

Enhance Keypoints Serialization and Refactor Codebase for Consistency and Readability #28

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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -164,4 +164,5 @@ temp_image.jpg

#
.DS_Store
.python-version
.python-version
tmp.jpg
2 changes: 1 addition & 1 deletion capybara/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,4 +5,4 @@
from .utils import *
from .vision import *

__version__ = '0.11.0'
__version__ = "0.11.0"
36 changes: 12 additions & 24 deletions capybara/mixins.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,11 +8,13 @@
import numpy as np
from dacite import from_dict

from .structures import Box, Boxes, Polygon, Polygons
from .structures import Box, Boxes, Keypoints, KeypointsList, Polygon, Polygons

__all__ = [
'EnumCheckMixin', 'DataclassCopyMixin', 'DataclassToJsonMixin',
'dict_to_jsonable',
"EnumCheckMixin",
"DataclassCopyMixin",
"DataclassToJsonMixin",
"dict_to_jsonable",
]


Expand All @@ -27,18 +29,14 @@ def dict_to_jsonable(
out[k] = jsonable_func[k](v)
else:
if isinstance(v, (Box, Boxes)):
out[k] = v.convert('XYXY').numpy().astype(
float).round().tolist()
elif isinstance(v, (Polygon, Polygons)):
out[k] = v.convert("XYXY").numpy().astype(float).round().tolist()
elif isinstance(v, (Keypoints, KeypointsList, Polygon, Polygons)):
out[k] = v.numpy().astype(float).round().tolist()
elif isinstance(v, (np.ndarray, np.generic)):
# include array and scalar, if you want jsonable image please use jsonable_func
out[k] = v.tolist()
elif isinstance(v, (list, tuple)):
out[k] = [
dict_to_jsonable(x, jsonable_func) if isinstance(
x, dict) else x
for x in v
]
out[k] = [dict_to_jsonable(x, jsonable_func) if isinstance(x, dict) else x for x in v]
elif isinstance(v, Enum):
out[k] = v.name
elif isinstance(v, Mapping):
Expand All @@ -55,7 +53,6 @@ def dict_to_jsonable(


class EnumCheckMixin:

@classmethod
def obj_to_enum(cls: Enum, obj: Any):
if isinstance(obj, str):
Expand All @@ -75,26 +72,17 @@ def obj_to_enum(cls: Enum, obj: Any):


class DataclassCopyMixin:

def __copy__(self):
return self.__class__(**{
field: getattr(self, field)
for field in self.__dataclass_fields__
})
return self.__class__(**{field: getattr(self, field) for field in self.__dataclass_fields__})

def __deepcopy__(self, memo):
out = asdict(self, dict_factory=OrderedDict)
return from_dict(data_class=self.__class__, data=out)


class DataclassToJsonMixin:

def __init__(self):
self.jsonable_func = None
jsonable_func = None

def be_jsonable(self, dict_factory=OrderedDict):
d = asdict(self, dict_factory=dict_factory)
return dict_to_jsonable(d, getattr(self, 'jsonable_func', None), dict_factory)

def regist_jsonable_func(self, jsonable_func: Optional[Dict[str, Callable]] = None):
self.jsonable_func = jsonable_func
return dict_to_jsonable(d, jsonable_func=self.jsonable_func, dict_factory=dict_factory)
81 changes: 40 additions & 41 deletions capybara/structures/keypoints.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,8 @@
import numpy as np

from ..typing import _Number
from .boxes import Box, Boxes

__all__ = ['Keypoints', 'KeypointsList']
__all__ = ["Keypoints", "KeypointsList"]


_Keypoints = Union[
Expand All @@ -25,17 +24,17 @@


class Keypoints:
'''
"""
This structure has shape (K, 3) or (K, 2) where K is the number of keypoints.
The visibility flag follows the COCO format and must be one of three integers:
* v=0: not labeled (in which case x=y=0)
* v=1: labeled but not visible
* v=2: labeled and visible
'''
"""

def __init__(self, array: _Keypoints, cmap='rainbow', is_normalized: bool = False):
def __init__(self, array: _Keypoints, cmap="rainbow", is_normalized: bool = False):
self._array = self._check_valid_array(array)
steps = np.linspace(0., 1., self._array.shape[-2])
steps = np.linspace(0.0, 1.0, self._array.shape[-2])
color_map = matplotlib.colormaps[cmap]
self._point_colors = np.array(color_map(steps, bytes=True))[..., :3].tolist()
self._is_normalized = is_normalized
Expand All @@ -62,16 +61,16 @@ def _check_valid_array(self, array: Any) -> np.ndarray:
if cond3:
array = array.numpy()
else:
array = np.array(array, dtype='float32')
array = np.array(array, dtype="float32")

if not array.ndim == 2:
raise ValueError(f"Input array ndim = {array.ndim} is not 2, which is invalid.")

if not array.shape[-1] in [2, 3]:
if array.shape[-1] not in [2, 3]:
raise ValueError(f"Input array's shape[-1] = {array.shape[-1]} is not in [2, 3], which is invalid.")

if array.shape[-1] == 3 and not ((array[..., 2] <= 2).all() and (array[..., 2] >= 0).all()):
raise ValueError('Given array is invalid because of its labels. (array[..., 2])')
raise ValueError("Given array is invalid because of its labels. (array[..., 2])")
return array.copy()

def numpy(self) -> np.ndarray:
Expand All @@ -92,7 +91,7 @@ def scale(self, fx: float, fy: float) -> "Keypoints":

def normalize(self, w: float, h: float) -> "Keypoints":
if self.is_normalized:
warn(f'Normalized keypoints are forced to do normalization.')
warn("Normalized keypoints are forced to do normalization.")
arr = self._array.copy()
arr[..., :2] = arr[..., :2] / (w, h)
kpts = self.__class__(arr)
Expand All @@ -101,37 +100,33 @@ def normalize(self, w: float, h: float) -> "Keypoints":

def denormalize(self, w: float, h: float) -> "Keypoints":
if not self.is_normalized:
warn(f'Non-normalized keypoints is forced to do denormalization.')
warn("Non-normalized keypoints is forced to do denormalization.")
arr = self._array.copy()
arr[..., :2] = arr[..., :2] * (w, h)
kpts = self.__class__(arr)
kpts._is_normalized = False
return kpts

@ property
@property
def is_normalized(self) -> bool:
return self._is_normalized

@ property
@property
def point_colors(self) -> List[Tuple[int, int, int]]:
return [
tuple([int(x) for x in cs])
for cs in self._point_colors
]
return [tuple([int(x) for x in cs]) for cs in self._point_colors]

@ point_colors.setter
@point_colors.setter
def set_point_colors(self, cmap: str):
steps = np.linspace(0., 1., self._array.shape[-2])
steps = np.linspace(0.0, 1.0, self._array.shape[-2])
self._point_colors = matplotlib.colormaps[cmap](steps, bytes=True)


class KeypointsList:

def __init__(self, array: _KeypointsList, cmap='rainbow', is_normalized: bool = False) -> None:
def __init__(self, array: _KeypointsList, cmap="rainbow", is_normalized: bool = False) -> None:
self._array = self._check_valid_array(array).copy()
self._is_normalized = is_normalized
if len(self._array):
steps = np.linspace(0., 1., self._array.shape[-2])
steps = np.linspace(0.0, 1.0, self._array.shape[-2])
self._point_colors = matplotlib.colormaps[cmap](steps, bytes=True)
else:
self._point_colors = None
Expand All @@ -146,7 +141,7 @@ def __getitem__(self, item) -> Any:

def __setitem__(self, item, value):
if not isinstance(value, (Keypoints, KeypointsList)):
raise TypeError(f'Input value is not a keypoint or keypoints')
raise TypeError("Input value is not a keypoint or keypoints")

if isinstance(item, (int, np.ndarray, list, slice)):
self._array[item] = value._array
Expand All @@ -166,9 +161,13 @@ def __eq__(self, value: object) -> bool:
def _check_valid_array(self, array: Any) -> np.ndarray:
cond1 = isinstance(array, np.ndarray)
cond2 = isinstance(array, list) and len(array) == 0
cond3 = isinstance(array, list) and \
all(isinstance(x, (np.ndarray, Keypoints)) for x in array) or \
all(isinstance(y, tuple) for x in array for y in x)
cond3 = (
isinstance(array, list)
and (
all(isinstance(x, (np.ndarray, Keypoints)) for x in array)
or all(isinstance(y, tuple) for x in array for y in x)
)
)
cond4 = isinstance(array, self.__class__)

if not (cond1 or cond2 or cond3 or cond4):
Expand All @@ -177,9 +176,9 @@ def _check_valid_array(self, array: Any) -> np.ndarray:
if cond4:
array = array.numpy()
elif len(array) and isinstance(array[0], Keypoints):
array = np.array([x.numpy() for x in array], dtype='float32')
array = np.array([x.numpy() for x in array], dtype="float32")
else:
array = np.array(array, dtype='float32')
array = np.array(array, dtype="float32")

if len(array) == 0:
return array
Expand All @@ -191,7 +190,7 @@ def _check_valid_array(self, array: Any) -> np.ndarray:
raise ValueError(f"Input array's shape[-1] = {array.shape[-1]} is not 2 or 3, which is invalid.")

if array.shape[-1] == 3 and not ((array[..., 2] <= 2).all() and (array[..., 2] >= 0).all()):
raise ValueError('Given array is invalid because of its labels. (array[..., 2])')
raise ValueError("Given array is invalid because of its labels. (array[..., 2])")

return array

Expand All @@ -213,7 +212,7 @@ def scale(self, fx: float, fy: float) -> Any:

def normalize(self, w: float, h: float) -> "KeypointsList":
if self.is_normalized:
warn(f'Normalized keypoints_list is forced to do normalization.')
warn("Normalized keypoints_list is forced to do normalization.")
arr = self._array.copy()
arr[..., :2] = arr[..., :2] / (w, h)
kpts_list = self.__class__(arr)
Expand All @@ -222,29 +221,29 @@ def normalize(self, w: float, h: float) -> "KeypointsList":

def denormalize(self, w: float, h: float) -> "KeypointsList":
if not self.is_normalized:
warn(f'Non-normalized box is forced to do denormalization.')
warn("Non-normalized box is forced to do denormalization.")
arr = self._array.copy()
arr[..., :2] = arr[..., :2] * (w, h)
kpts_list = self.__class__(arr)
kpts_list._is_normalized = False
return kpts_list

@ property
@property
def is_normalized(self) -> bool:
return self._is_normalized

@ property
@property
def point_colors(self):
return [tuple(c) for c in self._point_colors[..., :3].tolist()]

@ point_colors.setter
@point_colors.setter
def set_point_colors(self, cmap: str):
steps = np.linspace(0., 1., self._array.shape[-2])
steps = np.linspace(0.0, 1.0, self._array.shape[-2])
self._point_colors = matplotlib.colormaps[cmap](steps, bytes=True)

@ classmethod
@classmethod
def cat(cls, keypoints_lists: List["KeypointsList"]) -> "KeypointsList":
'''
"""
Concatenates a list of KeypointsList into a single KeypointsList

Raises:
Expand All @@ -254,14 +253,14 @@ def cat(cls, keypoints_lists: List["KeypointsList"]) -> "KeypointsList":

Returns:
Keypoints: the concatenated Keypoints
'''
"""
if not isinstance(keypoints_lists, list):
raise TypeError('Given keypoints_list should be a list.')
raise TypeError("Given keypoints_list should be a list.")

if len(keypoints_lists) == 0:
raise ValueError('Given keypoints_list is empty.')
raise ValueError("Given keypoints_list is empty.")

if not all(isinstance(keypoints_list, KeypointsList) for keypoints_list in keypoints_lists):
raise TypeError('All type of elements in keypoints_lists must be KeypointsList.')
raise TypeError("All type of elements in keypoints_lists must be KeypointsList.")

return cls(np.concatenate([keypoints_list.numpy() for keypoints_list in keypoints_lists], axis=0))
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