refactoring, and fixed global coordinate settings

README.md

@@ -155,6 +155,8 @@ __Right-handed rule XYZ global coordinate system__. `x-axis` faces forward and `
 - `pitch`: counter-clockwise rotation about the `y-axis`
 - `yaw`: counter-clockwise rotation about the `z-axis`
 
+You can chnage the right-handed coordinate system so that the `z-axis` faces down by adding `z_down=True` as a parameter.
+
 See demo scripts under `scripts`.
 
 
@@ -187,7 +189,7 @@ Check [CONTRIBUTING.md](./CONTRIBUTING.md) for more information
 
 - [ ] Documentations for each transform
 - [x] Add table and statistics for speed improvements
-- [ ] Batch processing for `numpy`
+- [ ] Batch processing for `numpy` (uses too much memory; alternative will be `multiprocessing`)
 - [ ] Mixed precision for `torch`
 - [ ] `c++` version of grid sampling
 

demo/equi2equi_numpy.py

@@ -23,7 +23,7 @@ def preprocess(
     img: Union[np.ndarray, Image.Image],
     is_cv2: bool = False,
 ) -> np.ndarray:
-    r"""Preprocesses image"""
+    """Preprocesses image"""
     if isinstance(img, np.ndarray) and is_cv2:
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     if isinstance(img, Image.Image):
@@ -37,7 +37,7 @@ def preprocess(
 
 
 def test_video(path: str) -> None:
-    r"""Test video"""
+    """Test video"""
     # Rotation:
     pi = np.pi
     inc = pi / 180
@@ -105,7 +105,7 @@ def test_video(path: str) -> None:
 
 
 def test_image(path: str) -> None:
-    r"""Test single image"""
+    """Test single image"""
     # Rotation:
     rot = {
         "roll": 0,  #

demo/equi2equi_torch.py

@@ -27,7 +27,7 @@ def preprocess(
     img: Union[np.ndarray, Image.Image],
     is_cv2: bool = False,
 ) -> torch.Tensor:
-    r"""Preprocesses image"""
+    """Preprocesses image"""
     if isinstance(img, np.ndarray) and is_cv2:
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     if isinstance(img, Image.Image):
@@ -66,7 +66,7 @@ def postprocess(
 
 
 def test_video(path: str) -> None:
-    r"""Test video"""
+    """Test video"""
     # Rotation:
     pi = np.pi
     inc = pi / 180
@@ -134,7 +134,7 @@ def test_video(path: str) -> None:
 
 
 def test_image(path: str) -> None:
-    r"""Test single image"""
+    """Test single image"""
     # Rotation:
     rot = {
         "roll": 0,  #

demo/equi2pers_numpy.py

@@ -23,7 +23,7 @@ def preprocess(
     img: Union[np.ndarray, Image.Image],
     is_cv2: bool = False,
 ) -> np.ndarray:
-    r"""Preprocesses image"""
+    """Preprocesses image"""
     if isinstance(img, np.ndarray) and is_cv2:
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     if isinstance(img, Image.Image):
@@ -42,7 +42,7 @@ def test_video(
     w_pers: int = 640,
     fov_x: float = 90.0,
 ) -> None:
-    r"""Test video"""
+    """Test video"""
     # Rotation:
     pi = np.pi
     inc = pi / 180
@@ -116,7 +116,7 @@ def test_image(
     w_pers: int = 640,
     fov_x: float = 90.0,
 ) -> None:
-    r"""Test single image"""
+    """Test single image"""
     # Rotation:
     rot = {
         "roll": 0,  #

demo/equi2pers_torch.py

@@ -27,7 +27,7 @@ def preprocess(
     img: Union[np.ndarray, Image.Image],
     is_cv2: bool = False,
 ) -> torch.Tensor:
-    r"""Preprocesses image"""
+    """Preprocesses image"""
     if isinstance(img, np.ndarray) and is_cv2:
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     if isinstance(img, Image.Image):
@@ -71,7 +71,7 @@ def test_video(
     w_pers: int = 640,
     fov_x: float = 90.0,
 ) -> None:
-    r"""Test video"""
+    """Test video"""
     # Rotation:
     pi = np.pi
     inc = pi / 180
@@ -145,7 +145,7 @@ def test_image(
     w_pers: int = 640,
     fov_x: float = 90.0,
 ) -> None:
-    r"""Test single image"""
+    """Test single image"""
     # Rotation:
     rot = {
         "roll": 0,  #

equilib/__init__.py

@@ -4,6 +4,7 @@
 from equilib.equi2cube.base import Equi2Cube, equi2cube
 from equilib.equi2equi.base import Equi2Equi, equi2equi
 from equilib.equi2pers.base import Equi2Pers, equi2pers
+from equilib.info import __version__
 
 __all__ = [
     "Cube2Equi",
@@ -15,9 +16,3 @@
     "equi2equi",
     "equi2pers",
 ]
-
-__version__ = "0.2.1"
-__author__ = "Haruya Ishikawa"
-__homepage__ = "www.hoge.com"
-__description__ = "equirectangular processing"
-__url__ = "www.hoge.com/equilib"

equilib/cube2equi/base.py

@@ -13,7 +13,7 @@
 
 
 class Cube2Equi(object):
-    r"""
+    """
     params:
     - w_out, h_out (int): equirectangular image size
     - cube_format (str): input cube format("dice", "horizon", "dict", "list")
@@ -74,18 +74,19 @@ def cube2equi(
     sampling_method: str = "default",
     mode: str = "bilinear",
 ) -> Union[np.ndarray, torch.Tensor]:
-    r"""
+    """
     params:
-    - cubemap: Union[
+    - cubemap (Union[
         np.ndarray,
         torch.Tensor,
         Dict[str, Union[np.ndarray, torch.Tensor]],
-        List[Union[np.ndarray, torch.Tensor]]]
+        List[Union[np.ndarray, torch.Tensor]]])
     - cube_format (str): ("dice", "horizon", "dict", "list")
     - w_out (int):
     - h_out (int):
     - sampling_method (str): "default"
     - mode (str): "bilinear"
+    - z_down (bool): False
 
     return:
     - equi (np.ndarray, torch.Tensor)

equilib/cube2equi/cube2equi_numpy.py

@@ -22,7 +22,7 @@ def cube_dict2h(
 
 
 def cube_dice2h(cube_dice: np.ndarray) -> np.ndarray:
-    r"""dice to horizion
+    """dice to horizion
 
     params:
     - cube_dice: (C, H, W)
@@ -57,7 +57,7 @@ def _to_horizon(cubemap: np.ndarray, cube_format: str) -> np.ndarray:
 
 
 def _equirect_facetype(h: int, w: int) -> np.ndarray:
-    r"""0F 1R 2B 3L 4U 5D"""
+    """0F 1R 2B 3L 4U 5D"""
 
     tp = np.roll(
         np.arange(4).repeat(w // 4)[None, :].repeat(h, 0), 3 * w // 8, 1
@@ -92,7 +92,7 @@ def _run_single(
     sampling_method: str,
     mode: str,
 ) -> np.ndarray:
-    r"""Run a single batch of transformation"""
+    """Run a single batch of transformation"""
     w_face = cubemap.shape[-2]
 
     theta, phi = create_equi_grid(h_out, w_out)
@@ -145,7 +145,7 @@ def run(
     sampling_method: str,
     mode: str,
 ) -> np.ndarray:
-    r"""Run cube to equirectangular image transformation
+    """Run cube to equirectangular image transformation
 
     params:
     - cubemap: np.ndarray