# 📷 PyTorch 360° Image Conversion Toolkit
[](https://pypi.org/project/pytorch360convert/)
## Overview
This PyTorch-based library provides powerful and differentiable image transformation utilities for converting between different panoramic image formats:
- **Equirectangular (360°) Images**
- **Cubemap Representations**
- **Perspective Projections**
Built as an improved PyTorch implementation of the original [py360convert](https://github.com/sunset1995/py360convert) project, this library offers flexible, CPU & GPU-accelerated functions.
<div align="left">
<img src="https://github.com/ProGamerGov/pytorch360convert/raw/main/examples/basic_equirectangular.png?raw=true" width="710px">
</div>
* Equirectangular format
<div align="left">
<img src="https://github.com/ProGamerGov/pytorch360convert/raw/main/examples/basic_dice_cubemap.png?raw=true" width="710px">
</div>
* Cubemap 'dice' format
## 🔧 Requirements
- Python 3.7+
- [PyTorch](https://pytorch.org/)
## 📦 Installation
You can easily install the library using pip:
```bash
pip install pytorch360convert
```
Or you can install it from source like this:
```bash
pip install torch
```
Then clone the repository:
```bash
git clone https://github.com/ProGamerGov/pytorch360convert.git
cd pytorch360convert
pip install .
```
## 🚀 Key Features
- Lossless conversion between image formats.
- Supports different cubemap input formats (horizon, list, stack, dict, dice).
- Configurable sampling modes (bilinear, nearest).
- Supports different dtypes (float16, float32, float64).
- CPU support.
- GPU acceleration.
- Differentiable transformations for deep learning pipelines.
- [TorchScript](https://pytorch.org/docs/stable/jit.html) (JIT) support.
## 💡 Usage Examples
### Helper Functions
First we'll setup some helper functions:
```bash
pip install torchvision pillow
```
```python
import torch
from torchvision.transforms import ToTensor, ToPILImage
from PIL import Image
def load_image_to_tensor(image_path: str) -> torch.Tensor:
"""Load an image as a PyTorch tensor."""
return ToTensor()(Image.open(image_path).convert('RGB'))
def save_tensor_as_image(tensor: torch.Tensor, save_path: str) -> None:
"""Save a PyTorch tensor as an image."""
ToPILImage()(tensor).save(save_path)
```
### Equirectangular to Cubemap Conversion
Converting equirectangular images into cubemaps is easy. For simplicity, we'll use the 'dice' format, which places all cube faces into a single 4x3 grid image.
```python
from pytorch360convert import e2c
# Load equirectangular image (3, 1376, 2752)
equi_image = load_image_to_tensor("examples/example_world_map_equirectangular.png")
face_w = equi_image.shape[2] // 4 # 2752 / 4 = 688
# Convert to cubemap (dice format)
cubemap = e2c(
equi_image, # CHW format
face_w=face_w, # Width of each cube face
mode='bilinear', # Sampling interpolation
cube_format='dice' # Output cubemap layout
)
# Save cubemap faces
save_tensor_as_image(cubemap, "dice_cubemap.jpg")
```
| Equirectangular Input | Cubemap 'Dice' Output |
| :---: | :----: |
|  |  |
| Cubemap 'Horizon' Output |
| :---: |
|  |
### Cubemap to Equirectangular Conversion
We can also convert cubemaps into equirectangular images, like so.
```python
from pytorch360convert import c2e
# Load cubemap in 'dice' format
cubemap = load_image_to_tensor("dice_cubemap.jpg")
# Convert cubemap back to equirectangular
equirectangular = c2e(
cubemap, # Cubemap tensor(s)
mode='bilinear', # Sampling interpolation
cube_format='dice' # Input cubemap layout
)
save_tensor_as_image(equirectangular, "equirectangular.jpg")
```
### Equirectangular to Perspective Projection
```python
from pytorch360convert import e2p
# Load equirectangular input
equi_image = load_image_to_tensor("examples/example_world_map_equirectangular.png")
# Extract perspective view from equirectangular image
perspective_view = e2p(
equi_image, # Equirectangular image
fov_deg=(70, 60), # Horizontal and vertical FOV
h_deg=260, # Horizontal rotation
v_deg=50, # Vertical rotation
out_hw=(512, 768), # Output image dimensions
mode='bilinear' # Sampling interpolation
)
save_tensor_as_image(perspective_view, "perspective.jpg")
```
| Equirectangular Input | Perspective Output |
| :---: | :----: |
|  |  |
### Equirectangular to Equirectangular
```python
from pytorch360convert import e2e
# Load equirectangular input
equi_image = load_image_to_tensor("examples/example_world_map_equirectangular.png")
# Rotate an equirectangular image around one more axes
rotated_equi = e2e(
equi_image, # Equirectangular image
h_deg=90.0, # Vertical rotation/shift
v_deg=200.0, # Horizontal rotation/shift
roll=45.0, # Clockwise/counter clockwise rotation
mode='bilinear' # Sampling interpolation
)
save_tensor_as_image(rotated_equi, "rotated.jpg")
```
| Equirectangular Input | Rotated Output |
| :---: | :----: |
|  |  |
## 📚 Basic Functions
### `e2c(e_img, face_w=256, mode='bilinear', cube_format='dice')`
Converts an equirectangular image to a cubemap projection.
- **Parameters**:
- `e_img` (torch.Tensor): Equirectangular CHW image tensor.
- `face_w` (int, optional): Cube face width. Default: `256`.
- `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`
- `cube_format` (str, optional): The desired output cubemap format. Options are `dict`, `list`, `horizon`, `stack`, and `dice`. Default: `dice`
- `stack` (torch.Tensor): Stack of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `list` (list of torch.Tensor): List of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `dict` (dict of torch.Tensor): Dictionary with keys pointing to face tensors. Keys are: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `dice` (torch.Tensor): A cubemap in a 'dice' layout.
- `horizon` (torch.Tensor): A cubemap in a 'horizon' layout, a 1x6 grid in the order: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.
- **Returns**: Cubemap representation of the input image as a tensor, list of tensors, or dict or tensors.
### `c2e(cubemap, h, w, mode='bilinear', cube_format='dice')`
Converts a cubemap projection to an equirectangular image.
- **Parameters**:
- `cubemap` (torch.Tensor, list of torch.Tensor, or dict of torch.Tensor): Cubemap image tensor, list of tensors, or dict of tensors. Note that tensors should be in the shape of: `CHW`, except for when `cube_format = 'stack'`, in which case a batch dimension is present. Inputs should match the corresponding `cube_format`.
- `h` (int, optional): Output image height. If set to None, `<cube_face_width> * 2` will be used. Default: `None`.
- `w` (int, optional): Output image width. If set to None, `<cube_face_width> * 4` will be used. Default: `None`.
- `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`
- `cube_format` (str, optional): Input cubemap format. Options are `dict`, `list`, `horizon`, `stack`, and `dice`. Default: `dice`
- `stack` (torch.Tensor): Stack of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `list` (list of torch.Tensor): List of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `dict` (dict of torch.Tensor): Dictionary with keys pointing to face tensors. Keys are expected to be: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `dice` (torch.Tensor): A cubemap in a 'dice' layout.
- `horizon` (torch.Tensor): A cubemap in a 'horizon' layout, a 1x6 grid in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].
- `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.
- **Returns**: Equirectangular projection of the input cubemap as a tensor.
### `e2p(e_img, fov_deg, h_deg, v_deg, out_hw, in_rot_deg=0, mode='bilinear')`
Extracts a perspective view from an equirectangular image.
- **Parameters**:
- `e_img` (torch.Tensor): Equirectangular CHW or NCHW image tensor.
- `fov_deg` (float or tuple of float): Field of view in degrees. If a single value is provided, it will be used for both horizontal and vertical degrees. If using a tuple, values are expected to be in following format: (h_fov_deg, v_fov_deg).
- `h_deg` (float, optional): Horizontal viewing angle in range [-pi, pi]. (-Left/+Right). Default: `0.0`
- `v_deg` (float, optional): Vertical viewing angle in range [-pi/2, pi/2]. (-Down/+Up). Default: `0.0`
- `out_hw` (float or tuple of float, optional): Output image dimensions in the shape of '(height, width)'. Default: `(512, 512)`
- `in_rot_deg` (float, optional): Inplane rotation angle. Default: `0`
- `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`
- `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.
- **Returns**: Perspective view of the equirectangular image as a tensor.
### `e2e(e_img, h_deg, v_deg, roll=0, mode='bilinear')`
Rotate an equirectangular image along one or more axes (roll, pitch, and yaw) to produce a horizontal shift, vertical shift, or to roll the image.
- **Parameters**:
- `e_img` (torch.Tensor): Equirectangular CHW or NCHW image tensor.
- `roll` (float, optional): Roll angle in degrees (-Counter_Clockwise/+Clockwise). Rotates the image along the x-axis. Default: `0.0`
- `h_deg` (float, optional): Yaw angle in degrees (-Left/+Right). Rotates the image along the z-axis to produce a horizontal shift. Default: `0.0`
- `v_deg` (float, optional): Pitch angle in degrees (-Down/+Up). Rotates the image along the y-axis to produce a vertical shift. Default: `0.0`
- `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`
- `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.
- **Returns**: A modified equirectangular image tensor.
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 🔬 Citation
If you use this library in your research or project, please refer to the included [CITATION.cff](CITATION.cff) file or cite it as follows:
### BibTeX
```bibtex
@misc{egan2024pytorch360convert,
title={PyTorch 360° Image Conversion Toolkit},
author={Egan, Ben},
year={2024},
publisher={GitHub},
howpublished={\url{https://github.com/ProGamerGov/pytorch360convert}}
}
```
### APA Style
```
Egan, B. (2024). PyTorch 360° Image Conversion Toolkit [Computer software]. GitHub. https://github.com/ProGamerGov/pytorch360convert
```
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"description": "# \ud83d\udcf7 PyTorch 360\u00b0 Image Conversion Toolkit\n\n[](https://pypi.org/project/pytorch360convert/)\n\n\n## Overview\n\nThis PyTorch-based library provides powerful and differentiable image transformation utilities for converting between different panoramic image formats:\n\n- **Equirectangular (360\u00b0) Images** \n- **Cubemap Representations**\n- **Perspective Projections**\n\nBuilt as an improved PyTorch implementation of the original [py360convert](https://github.com/sunset1995/py360convert) project, this library offers flexible, CPU & GPU-accelerated functions.\n\n\n<div align=\"left\">\n <img src=\"https://github.com/ProGamerGov/pytorch360convert/raw/main/examples/basic_equirectangular.png?raw=true\" width=\"710px\">\n</div>\n\n* Equirectangular format\n\n\n<div align=\"left\">\n <img src=\"https://github.com/ProGamerGov/pytorch360convert/raw/main/examples/basic_dice_cubemap.png?raw=true\" width=\"710px\">\n</div>\n\n* Cubemap 'dice' format\n\n\n## \ud83d\udd27 Requirements\n\n- Python 3.7+\n- [PyTorch](https://pytorch.org/)\n\n\n## \ud83d\udce6 Installation\n\nYou can easily install the library using pip:\n\n```bash\npip install pytorch360convert\n```\n\nOr you can install it from source like this:\n\n```bash\npip install torch\n```\n\nThen clone the repository:\n\n```bash\ngit clone https://github.com/ProGamerGov/pytorch360convert.git\ncd pytorch360convert\npip install .\n```\n\n\n## \ud83d\ude80 Key Features\n\n- Lossless conversion between image formats.\n- Supports different cubemap input formats (horizon, list, stack, dict, dice).\n- Configurable sampling modes (bilinear, nearest).\n- Supports different dtypes (float16, float32, float64).\n- CPU support.\n- GPU acceleration.\n- Differentiable transformations for deep learning pipelines.\n- [TorchScript](https://pytorch.org/docs/stable/jit.html) (JIT) support.\n\n\n## \ud83d\udca1 Usage Examples\n\n\n### Helper Functions\n\nFirst we'll setup some helper functions:\n\n```bash\npip install torchvision pillow\n```\n\n\n```python\nimport torch\nfrom torchvision.transforms import ToTensor, ToPILImage\nfrom PIL import Image\n\ndef load_image_to_tensor(image_path: str) -> torch.Tensor:\n \"\"\"Load an image as a PyTorch tensor.\"\"\"\n return ToTensor()(Image.open(image_path).convert('RGB'))\n\ndef save_tensor_as_image(tensor: torch.Tensor, save_path: str) -> None:\n \"\"\"Save a PyTorch tensor as an image.\"\"\"\n ToPILImage()(tensor).save(save_path)\n\n```\n\n### Equirectangular to Cubemap Conversion\n\nConverting equirectangular images into cubemaps is easy. For simplicity, we'll use the 'dice' format, which places all cube faces into a single 4x3 grid image.\n\n```python\nfrom pytorch360convert import e2c\n\n# Load equirectangular image (3, 1376, 2752)\nequi_image = load_image_to_tensor(\"examples/example_world_map_equirectangular.png\")\nface_w = equi_image.shape[2] // 4 # 2752 / 4 = 688\n\n# Convert to cubemap (dice format)\ncubemap = e2c(\n equi_image, # CHW format\n face_w=face_w, # Width of each cube face\n mode='bilinear', # Sampling interpolation\n cube_format='dice' # Output cubemap layout\n)\n\n# Save cubemap faces\nsave_tensor_as_image(cubemap, \"dice_cubemap.jpg\")\n```\n\n| Equirectangular Input | Cubemap 'Dice' Output |\n| :---: | :----: |\n|  |  |\n\n| Cubemap 'Horizon' Output |\n| :---: |\n|  |\n\n### Cubemap to Equirectangular Conversion\n\nWe can also convert cubemaps into equirectangular images, like so.\n\n```python\nfrom pytorch360convert import c2e\n\n# Load cubemap in 'dice' format\ncubemap = load_image_to_tensor(\"dice_cubemap.jpg\")\n\n# Convert cubemap back to equirectangular\nequirectangular = c2e(\n cubemap, # Cubemap tensor(s)\n mode='bilinear', # Sampling interpolation\n cube_format='dice' # Input cubemap layout\n)\n\nsave_tensor_as_image(equirectangular, \"equirectangular.jpg\")\n```\n\n### Equirectangular to Perspective Projection\n\n```python\nfrom pytorch360convert import e2p\n\n# Load equirectangular input\nequi_image = load_image_to_tensor(\"examples/example_world_map_equirectangular.png\")\n\n# Extract perspective view from equirectangular image\nperspective_view = e2p(\n equi_image, # Equirectangular image\n fov_deg=(70, 60), # Horizontal and vertical FOV\n h_deg=260, # Horizontal rotation\n v_deg=50, # Vertical rotation\n out_hw=(512, 768), # Output image dimensions\n mode='bilinear' # Sampling interpolation\n)\n\nsave_tensor_as_image(perspective_view, \"perspective.jpg\")\n```\n\n| Equirectangular Input | Perspective Output |\n| :---: | :----: |\n|  |  |\n\n\n\n### Equirectangular to Equirectangular\n\n```python\nfrom pytorch360convert import e2e\n\n# Load equirectangular input\nequi_image = load_image_to_tensor(\"examples/example_world_map_equirectangular.png\")\n\n# Rotate an equirectangular image around one more axes\nrotated_equi = e2e(\n equi_image, # Equirectangular image\n h_deg=90.0, # Vertical rotation/shift\n v_deg=200.0, # Horizontal rotation/shift\n roll=45.0, # Clockwise/counter clockwise rotation\n mode='bilinear' # Sampling interpolation\n)\n\nsave_tensor_as_image(rotated_equi, \"rotated.jpg\")\n```\n\n| Equirectangular Input | Rotated Output |\n| :---: | :----: |\n|  |  |\n\n\n## \ud83d\udcda Basic Functions\n\n### `e2c(e_img, face_w=256, mode='bilinear', cube_format='dice')`\nConverts an equirectangular image to a cubemap projection.\n\n- **Parameters**:\n - `e_img` (torch.Tensor): Equirectangular CHW image tensor.\n - `face_w` (int, optional): Cube face width. Default: `256`.\n - `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`\n - `cube_format` (str, optional): The desired output cubemap format. Options are `dict`, `list`, `horizon`, `stack`, and `dice`. Default: `dice`\n - `stack` (torch.Tensor): Stack of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `list` (list of torch.Tensor): List of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `dict` (dict of torch.Tensor): Dictionary with keys pointing to face tensors. Keys are: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `dice` (torch.Tensor): A cubemap in a 'dice' layout.\n - `horizon` (torch.Tensor): A cubemap in a 'horizon' layout, a 1x6 grid in the order: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.\n\n- **Returns**: Cubemap representation of the input image as a tensor, list of tensors, or dict or tensors.\n\n### `c2e(cubemap, h, w, mode='bilinear', cube_format='dice')`\nConverts a cubemap projection to an equirectangular image.\n\n- **Parameters**:\n - `cubemap` (torch.Tensor, list of torch.Tensor, or dict of torch.Tensor): Cubemap image tensor, list of tensors, or dict of tensors. Note that tensors should be in the shape of: `CHW`, except for when `cube_format = 'stack'`, in which case a batch dimension is present. Inputs should match the corresponding `cube_format`.\n - `h` (int, optional): Output image height. If set to None, `<cube_face_width> * 2` will be used. Default: `None`.\n - `w` (int, optional): Output image width. If set to None, `<cube_face_width> * 4` will be used. Default: `None`.\n - `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`\n - `cube_format` (str, optional): Input cubemap format. Options are `dict`, `list`, `horizon`, `stack`, and `dice`. Default: `dice`\n - `stack` (torch.Tensor): Stack of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `list` (list of torch.Tensor): List of 6 faces, in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `dict` (dict of torch.Tensor): Dictionary with keys pointing to face tensors. Keys are expected to be: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `dice` (torch.Tensor): A cubemap in a 'dice' layout.\n - `horizon` (torch.Tensor): A cubemap in a 'horizon' layout, a 1x6 grid in the order of: ['Front', 'Right', 'Back', 'Left', 'Up', 'Down'].\n - `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.\n \n- **Returns**: Equirectangular projection of the input cubemap as a tensor.\n\n### `e2p(e_img, fov_deg, h_deg, v_deg, out_hw, in_rot_deg=0, mode='bilinear')`\nExtracts a perspective view from an equirectangular image.\n\n- **Parameters**:\n - `e_img` (torch.Tensor): Equirectangular CHW or NCHW image tensor.\n - `fov_deg` (float or tuple of float): Field of view in degrees. If a single value is provided, it will be used for both horizontal and vertical degrees. If using a tuple, values are expected to be in following format: (h_fov_deg, v_fov_deg).\n - `h_deg` (float, optional): Horizontal viewing angle in range [-pi, pi]. (-Left/+Right). Default: `0.0`\n - `v_deg` (float, optional): Vertical viewing angle in range [-pi/2, pi/2]. (-Down/+Up). Default: `0.0`\n - `out_hw` (float or tuple of float, optional): Output image dimensions in the shape of '(height, width)'. Default: `(512, 512)`\n - `in_rot_deg` (float, optional): Inplane rotation angle. Default: `0`\n - `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`\n - `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.\n\n- **Returns**: Perspective view of the equirectangular image as a tensor.\n\n### `e2e(e_img, h_deg, v_deg, roll=0, mode='bilinear')`\n\nRotate an equirectangular image along one or more axes (roll, pitch, and yaw) to produce a horizontal shift, vertical shift, or to roll the image.\n\n- **Parameters**:\n - `e_img` (torch.Tensor): Equirectangular CHW or NCHW image tensor.\n - `roll` (float, optional): Roll angle in degrees (-Counter_Clockwise/+Clockwise). Rotates the image along the x-axis. Default: `0.0`\n - `h_deg` (float, optional): Yaw angle in degrees (-Left/+Right). Rotates the image along the z-axis to produce a horizontal shift. Default: `0.0`\n - `v_deg` (float, optional): Pitch angle in degrees (-Down/+Up). Rotates the image along the y-axis to produce a vertical shift. Default: `0.0` \n - `mode` (str, optional): Sampling interpolation mode. Options are `bilinear`, `bicubic`, and `nearest`. Default: `bilinear`\n - `channels_first` (bool, optional): Input cubemap channel format (CHW or HWC). Defaults to the PyTorch CHW standard of `True`.\n\n- **Returns**: A modified equirectangular image tensor.\n\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n\n## \ud83d\udd2c Citation\n\nIf you use this library in your research or project, please refer to the included [CITATION.cff](CITATION.cff) file or cite it as follows:\n\n### BibTeX\n```bibtex\n@misc{egan2024pytorch360convert,\n title={PyTorch 360\u00b0 Image Conversion Toolkit},\n author={Egan, Ben},\n year={2024},\n publisher={GitHub},\n howpublished={\\url{https://github.com/ProGamerGov/pytorch360convert}}\n}\n```\n\n### APA Style\n```\nEgan, B. (2024). PyTorch 360\u00b0 Image Conversion Toolkit [Computer software]. GitHub. https://github.com/ProGamerGov/pytorch360convert\n```\n",
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