convert files to pass pylance, update readme

Author: haruishi43

.flake8

@@ -5,6 +5,8 @@ ignore =
   E203,
   E206,
   E501,
+  # E731 do not assign a lambda expression, use a defflake8()
+  E731,
   # F812: list comprehension redefines ...
   F812,
   # H101: Use TODO(NAME)
@@ -31,4 +33,10 @@ ignore =
   H501,
   # I100:
   I100,
+  # N812, non lowercase
+  N812,
+  # N817: camelcase
+  N817,
   W503,
+  # EXE001: Shebang is present but the file is not executable.
+  EXE001

README.md

@@ -10,6 +10,7 @@
   <a href="https://pypi.org/project/pyequilib/0.1.0/"><img src="https://badge.fury.io/py/pyequilib.svg"></a>
   <a href="https://pypi.org/project/equilib"><img src="https://img.shields.io/pypi/pyversions/pyequilib"></a>
   <a href="https://github.com/haruishi43/equilib/actions"><img src="https://github.com/haruishi43/equilib/workflows/ci/badge.svg"></a>
+  <a href="https://github.com/haruishi43/equilib/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/haruishi43/equilib"></a>
 </div>
 
 <img src=".img/equilib.png" alt="equilib" width="720"/>

tests/cube2equi/test_numpy.py

@@ -13,7 +13,7 @@
 OUT_H = 240
 
 
-def run(cube, cube_format):
+def run(cube, cube_format) -> np.ndarray:
     print("Input is {}:".format(cube_format))
     if isinstance(cube, list):
         c = cube[0]  # get the first of the batch

tests/cube2equi/test_torch.py

@@ -17,7 +17,7 @@
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def run(cube, cube_format):
+def run(cube, cube_format) -> torch.Tensor:
     print("Input is {}".format(cube_format))
     if isinstance(cube, list):
         c = cube[0]  # get the first of the batch

tests/equi2cube/test_numpy.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import time
+from typing import Union
 import os.path as osp
 
 import numpy as np
@@ -12,7 +13,7 @@
 WIDTH = 256
 
 
-def run(equi, rot, cube_format):
+def run(equi, rot, cube_format) -> Union[list, dict, np.ndarray]:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)

tests/equi2cube/test_torch.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import time
+from typing import Union
 import os.path as osp
 
 import numpy as np
@@ -17,7 +18,7 @@
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def run(equi, rot, cube_format):
+def run(equi, rot, cube_format) -> Union[dict, list, torch.Tensor]:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)

tests/equi2equi/test_numpy.py

@@ -13,7 +13,7 @@
 HEIGHT = 320
 
 
-def run(equi, rot):
+def run(equi, rot) -> np.ndarray:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)

tests/equi2equi/test_torch.py

@@ -19,7 +19,7 @@
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def run(equi, rot):
+def run(equi, rot) -> torch.Tensor:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)

tests/equi2pers/test_numpy.py

@@ -17,7 +17,7 @@
 FOV = 90
 
 
-def run(equi, rot):
+def run(equi, rot) -> np.ndarray:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)

tests/equi2pers/test_torch.py

@@ -24,7 +24,7 @@
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def run(equi, rot):
+def run(equi, rot) -> torch.Tensor:
     h_equi, w_equi = equi.shape[-2:]
     print("equirectangular image size:")
     print(h_equi, w_equi)