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Convertion between cubemap and equirectangular and also to perspective planar.

Project description

# py360convert

Features of this project:
- Convertion between cubemap and equirectangular
- Equirectangular to planar
- Pure python implementation and depend only on [numpy]( and [scipy](
- Vectorization implementation (in most of the place)
- `c2e` takes 300ms and `e2c` takes 160ms on 1.6 GHz Intel Core i5 CPU

## Requirements
- numpy
- scipy
- pillow (for example code to load/save image)

## Run with command line
You can run command line to use the functionality. Please See `python -h` for detailed. The python script is also an example code to see how to use this as a package in your code.

python --convert e2c --i assert/example_input.png --o assert/example_e2c.png --w 200
| Input Equirectangular | Output Cubemap |
| :---: | :----: |
| ![](assert/example_input.png) | ![](assert/example_e2c.png) |


python --convert c2e --i assert/example_e2c.png --o assert/example_c2e.png --w 800 --h 400
| Input Cubemap | Output Equirectangular |
| :---: | :----: |
| ![](assert/example_e2c.png) | ![](assert/example_c2e.png) |

You can see the blurring artifacts in the polar region because the equirectangular in above figure are resampled twice (`e2c` then `c2e`).


python --convert e2p --i assert/example_input.png --o assert/example_e2p.png --w 300 --h 300 --u_deg 120 --v_deg 23
| Input Equirectangular | Output Perspective |
| :---: | :----: |
| ![](assert/example_input.png) | ![](assert/example_e2p.png) |

## Doc

#### `e2c(e_img, face_w=256, mode='bilinear', cube_format='dice')`
Convert the given equirectangular to cubemap.
- `e_img`: Numpy array with shape [H, W, C].
- `face_w`: The width of each cube face.
- `mode`: `bilinear` or `nearest`.
- `cube_format`: See `c2e` explaination.

#### `e2p(e_img, fov_deg, u_deg, v_deg, out_hw, in_rot_deg=0, mode='bilinear')`
Take perspective image from given equirectangular.
- `e_img`: Numpy array with shape [H, W, C].
- `fov_deg`: Field of view given in int or tuple `(h_fov_deg, v_fov_deg)`.
- `u_deg`: Horizontal viewing angle in range [-pi, pi]. (- Left / + Right).
- `v_deg`: Vertical viewing angle in range [-pi/2, pi/2]. (- Down/ + Up).
- `out_hw`: Output image `(height, width)` in tuple.
- `in_rot_deg`: Inplane rotation.
- `mode`: `bilinear` or `nearest`.

#### `c2e(cubemap, h, w, cube_format='dice')`
Convert the given cubemap to equirectangular.
- `cubemap`: Numpy array or list/dict of numpy array (depend on `cube_format`).
- `h`: Output equirectangular height.
- `w`: Output equirectangular width.
- `cube_format`: 'dice' (default) or 'horizon' or 'dict' or 'list'. Telling the format of the given `cubemap`.

import numpy as np
from PIL import Image
import py360convert

cube_dice = np.array('assert/demo_cube.png'))

# You can make convertion between supported cubemap format
cube_h = py360convert.cube_dice2h(cube_dice) # the inverse is cube_h2dice
cube_dict = py360convert.cube_h2dict(cube_h) # the inverse is cube_dict2h
cube_list = py360convert.cube_h2list(cube_h) # the inverse is cube_list2h
print('cube_dice.shape:', cube_dice.shape)
print('cube_h.shape:', cube_h.shape)
print('cube_dict.keys():', cube_dict.keys())
print('cube_dict["F"].shape:', cube_dict["F"].shape)
print('len(cube_list):', len(cube_list))
print('cube_list[0].shape:', cube_list[0].shape)
cube_dice.shape: (768, 1024, 3)
cube_h.shape: (256, 1536, 3)
cube_dict.keys(): dict_keys(['F', 'R', 'B', 'L', 'U', 'D'])
cube_dict["F"].shape: (256, 256, 3)
len(cube_list): 6
cube_list[0].shape: (256, 256, 3)

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