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Added Equi2Ico and Ico2Equi classes #4

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6 changes: 6 additions & 0 deletions equilib/__init__.py
Original file line number Diff line number Diff line change
@@ -4,15 +4,21 @@
from equilib.equi2cube.base import Equi2Cube, equi2cube
from equilib.equi2equi.base import Equi2Equi, equi2equi
from equilib.equi2pers.base import Equi2Pers, equi2pers
from equilib.equi2ico.base import Equi2Ico, equi2ico
from equilib.ico2equi.base import Ico2Equi, ico2equi
from equilib.info import __version__ # noqa

__all__ = [
"Cube2Equi",
"Equi2Cube",
"Equi2Equi",
"Equi2Pers",
"Equi2Ico",
"Ico2Equi",
"cube2equi",
"equi2cube",
"equi2equi",
"equi2pers",
"equi2ico",
"ico2equi"
]
1 change: 1 addition & 0 deletions equilib/equi2ico/__init__.py
Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
#!/usr/bin/env python3
150 changes: 150 additions & 0 deletions equilib/equi2ico/base.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,150 @@
#!/usr/bin/env python3

from typing import Dict, List, Union

import numpy as np

import torch

from .numpy import (
run as run_numpy,
)
from .torch import (
run as run_torch,
)

__all__ = ["Equi2Ico", "equi2ico"]

ArrayLike = Union[np.ndarray, torch.Tensor]
SubLvl = Union[int, List[int]]
IcoMaps = Union[
# single
np.ndarray,
torch.Tensor,
# single 'list'
List[np.ndarray],
List[torch.Tensor],
# batch 'list'
List[List[np.ndarray]],
List[List[torch.Tensor]],
# single 'dict'
Dict[int, np.ndarray],
Dict[int, np.ndarray],
# batch 'dict'
List[Dict[int, np.ndarray]],
List[Dict[int, np.ndarray]],
]

class Equi2Ico(object):
"""
params:
- w_face (int): icosahedron face width
- fov_x (float): fov of horizontal axis in degrees
- sub_level(int, list[int]): icosahedron subdivision level
- ico_format (str): ("dict", "list")
- mode (str)

inputs:
- equi (np.ndarray, torch.Tensor)

returns:
- ico_faces (np.ndarray, torch.Tensor, list, dict)
"""

def __init__(
self,
w_face: int,
fov_x: float,
sub_level: SubLvl,
ico_format: str,
mode: str = "bilinear",
) -> None:
self.w_face = w_face
self.fov_x = fov_x
self.sub_level = sub_level
self.ico_format = ico_format
self.mode = mode

def __call__(
self,
equi: ArrayLike,
) -> IcoMaps:
return equi2ico(
equi=equi,
w_face=self.w_face,
fov_x=self.fov_x,
sub_level=self.sub_level,
ico_format=self.ico_format,
mode=self.mode,
)

def equi2ico(
equi: ArrayLike,
w_face: int,
fov_x: float,
sub_level: SubLvl,
ico_format: str,
mode: str = "bilinear",
**kwargs,
) -> IcoMaps:
"""
params:
- equi (np.ndarray, torch.Tensor)
- w_face (int): icosahedron face width
- fov_x (float): fov of horizontal axis in degrees
- sub_level(int, list[int]): icosahedron subdivision level
- ico_format (str): ("dict", "list")
- mode (str)

returns:
- ico_faces (np.ndarray, torch.Tensor, dict, list)

"""

_type = None
if isinstance(equi, np.ndarray):
_type = "numpy"
elif torch.is_tensor(equi):
_type = "torch"
else:
raise ValueError

is_single = False
if len(equi.shape) == 3 and isinstance(sub_level, int):
# probably the input was a single image
equi = equi[None, ...]
sub_level = [sub_level]
is_single = True
elif len(equi.shape) == 3:
# probably a grayscale image
equi = equi[:, None, ...]

assert isinstance(sub_level, list), "ERR: rots is not a list"
if _type == "numpy":
out = run_numpy(
equi=equi,
sub_level=sub_level,
w_face=w_face,
fov_x=fov_x,
ico_format=ico_format,
mode=mode,
**kwargs,
)
elif _type == "torch":
out = run_torch(
equi=equi,
sub_level=sub_level,
w_face=w_face,
fov_x=fov_x,
ico_format=ico_format,
mode=mode,
**kwargs,
)
else:
raise ValueError

# make sure that the output batch dim is removed if it's only a single icomap
if is_single:
out = out[0]

return out
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