📷 PyTorch 360° Image Conversion Toolkit

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 project, this library offers flexible, CPU & GPU-accelerated functions.

• Equirectangular format

• Cubemap 'dice' format

🔧 Requirements

• Python 3.7+
• PyTorch

📦 Installation

You can easily install the library using pip:

pip install pytorch360convert

Or you can install it from source like this:

pip install torch

Then clone the repository:

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, dict, dice).
• Configurable sampling modes (bilinear, nearest).
• Supports different dtypes (float16, float32, float64).
• CPU support.
• GPU acceleration.
• Differentiable transformations for deep learning pipelines.
• TorchScript (JIT) support.

💡 Usage Examples

Helper Functions

First we'll setup some helper functions:

pip install torchvision pillow

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.

from pytorch360convert import e2c

# Load equirectangular image (3, 1376, 2752)
equi_image = load_image_to_tensor("examples/example_world_map_equirectangular.jpg")
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.

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

from pytorch360convert import e2p

# Load equirectangular input
equi_image = load_image_to_tensor("examples/example_world_map_equirectangular.jpg")

# 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
    w_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

📚 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, u_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 image tensor.
  • fov_deg (float or tuple): 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): Horizontal viewing angle in range [-pi, pi]. (- Left / + Right).
  • w_deg (float): Vertical viewing angle in range [-pi/2, pi/2]. (- Down/ + Up).
  • out_hw (tuple): Output image dimensions in the shape of '(height, width)'.
  • 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.

🤝 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 file or cite it as follows:

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