Metadata-Version: 2.1
Name: py360convert-modified
Version: 1.0.1
Summary: Convertion between cubemap and equirectangular and also to perspective planar.
Author-email: Andrea Lombardo <andrealombardo2m@gmail.com>
License: MIT License
        
        Copyright (c) 2019 sunset
        
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Project-URL: Homepage, https://github.com/imandrealombardo/py360convert
Project-URL: Issues, https://github.com/imandrealombardo/py360convert/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Artistic Software
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy

# py360convert

## Credits

This project is a fork of [py360convert](https://github.com/sunset1995/py360convert) by Cheng Sun.

-----

**A new library including more fuctionality for 360 is under contruction by my colleague. This repo will be depreciated then.**

Features of this project:
- Convertion between cubemap and equirectangular  
    ![](assert/teaser_convertion.png)
- Equirectangular to planar  
    ![](assert/teaser_2planar.png)
- Pure python implementation and depend only on [numpy](http://www.numpy.org/) and [scipy](https://www.scipy.org/)
- 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)

## Install
```
pip install py360convert
```
Now at everywhere, you can `import py360convert` or use the command line tool `convert360`.

### Command line examples
You can run command line tool to use the functionality. Please See `convert360 -h` for detailed. The python script is also an example code to see how to use this as a package in your code.

```
convert360 --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) |

-----

```
convert360 --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`).

----

```
convert360 --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.  
**Parameters**:
- `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.
**Parameters**:
- `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.  
**Parameters**:
- `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`.
    - Say that each face of the cube is in shape of `256 (width) x 256 (height)`
    - 'dice': a numpy array in shape of `1024 x 768` like below example
        <img src="assert/cube_dice.png" height="200">
    - 'horizon': a numpy array in shape of `1536 x 256` like below example
        <img src="assert/cube_horizon.png" height="100">
    - 'list': a `list` with 6 elements each of which is a numpy array in shape of `256 x 256`. It's just converted from 'horizon' format with one line of code: `np.split(cube_h, 6, axis=1)`.
    - 'dict': a `dict` with 6 elements with keys `'F', 'R', 'B', 'L', 'U', 'D'` each of which is a numpy array in shape of `256 x 256`.
    - Please refer to [the source code](https://github.com/sunset1995/py360convert/blob/master/py360convert/utils.py#L176) if you still have question about the conversion between formats.

**Example**:
```python
import numpy as np
from PIL import Image
import py360convert

cube_dice = np.array(Image.open('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)
```
Output:
```
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)
```
