Welcome to VR180 image converter documentation!¶

Installation & Usage

Project Info

VR180 image converter¶

License

Documentation: https://vr180-convert.readthedocs.io

Source Code: https://github.com/34j/vr180-convert

Simple VR180 image converter on top of OpenCV and NumPy.

Installation¶

Install this via pip (or your favourite package manager):

pipx install vr180-convert

Usage¶

Simply run the following command to convert 2 fisheye images to a SBS equirectangular VR180 image:

v1c lr left.jpg right.jpg

left.jpg	right.jpg	Output

If left and right image paths are the same, the image is divided into two halves (left and right, SBS) and processed as if they were separate images.

Advanced usage¶

Automatic image search¶

If one of left or right image path is a directory, the program will search for the closest image (in terms of creation time) in the other directory.

v1c lr left.jpg right_dir
v1c lr left_dir right.jpg

It is recommended to synchronize the clocks of the cameras before shooting. However, it can be adjusted by specifying -ac option.

v1c lr left.jpg right_dir -ac 1 # the clock of the right camera is 1 second faster / ahead
v1c lr left_dir right.jpg -ac 1 # the clock of the right camera is 1 second faster / ahead

Radius estimation¶

The radius of the non-black area of the input image is assumed by counting black pixels by default, but it would be better to specify it manually to get stable results:

v1c lr left.jpg right.jpg --radius 1000
v1c lr left.jpg right.jpg --radius max # min(width, height) / 2

Calibration¶

Rotation matching using the least-squares method can be performed by clicking corresponding points that can be regarded as infinitely far away from the camera.

v1c lr left.jpg right.jpg --automatch gui

You can also specify the corresponding points manually:

v1c lr left.jpg right.jpg --automatch "0,0;0,0;1,1;1,1" # left_x1,left_y1;right_x1,right_y1;...

$$ a_k, b_k \in \mathbb{R}^3, \min_{R \in SO(3)} \sum_k ||R a_k - b_k||^2 $$

Anaglyph¶

--merge option (which exports as anaglyph image) can be used to check if the calibration is successful by checking if the infinitely far points are overlapped.

v1c lr left.jpg right.jpg --automatch gui --merge

Swap¶

If the camera is mounted upside down, you can simply use the --swap option without changing the transformer or other parameters:

v1c lr left.jpg right.jpg --swap

Custom conversion model¶

You can also specify the conversion model by adding Python code directly to the --transformer option:

v1c lr left.jpg right.jpg --transformer 'EquirectangularEncoder() * Euclidean3DRotator(from_rotation_vector([0, np.pi / 4, 0])) * FisheyeDecoder("equidistant")'

If tuple, the first transformer is applied to the left image and the second transformer is applied to the right image. If a single transformer is given, it is applied to both images.

Please refer to the API documentation for the available transformers and their parameters. For from_rotation_vector, please refer to the numpy-quaternion documentation.

Single image conversion¶

To convert a single image, use v1c s instead.

Help¶

For more information, please refer to the help or API documentation:

v1c --help

Usage as a library¶

For more complex transformations, it is recommended to create your own Transformer.

Note that the transformation is applied in inverse order (new[(x, y)] = old[transform(x, y)], e.g. to decode orthographic fisheye images, transform_polar should be arcsin(theta), not sin(theta).)

from vr180_convert import PolarRollTransformer, apply_lr

class MyTransformer(PolarRollTransformer):
    def transform_polar(
        self, theta: NDArray, roll: NDArray, **kwargs: Any
    ) -> tuple[NDArray, NDArray]:
        return theta**0.98 + theta**1.01, roll

transformer = EquirectangularEncoder() * MyTransformer() * FisheyeDecoder("equidistant")
apply_lr(transformer, left_path="left.jpg", right_path="right.jpg", out_path="output.jpg")

Tips¶

How to determine which image is left or right¶

	Left	Right
Subject Orientation	Right	Left
Film Color	${\color{red}\text{Red}}$	${\color{blue}\text{Blue}}$
Anaglyph Color	${\color{blue}\text{Blue}}$	${\color{red}\text{Red}}$

In a SBS image, the subject is oriented toward the center.

How to edit images¶

This program cannot read RAW files. To deal with white-outs, etc., it is required to process each image with a program such as Photoshop, Lightroom, RawTherapee, Darktable, etc.

However, this is so exhaustive, so it is recommended to take the images with jpeg format, being careful not to overexpose the images, and convert them with this program, then use Lightroom, RawTherapee, Darktable or other software to adjust colors and exposure, etc.

Example of processing in Photoshop (Exquisite editing)¶

Open one of the images just for specifying the canvas size.
Add each image as Smart Objects (LRaw, RRaw) and make minimal corrections to match the exposure using Camera Raw Filter.
Make each Smart Object into Smart Objects (L, R) again and do any image-dependent processing, such as removing the background.
Make both images into a single Smart Object (P) and process them as a whole.
Delete the background image created in step 1.
Export as a PNG file.
Hide the other Smart Object (L or R) (created in step 3) in the Smart Object P (created in step 4) and save the Smart Object P, then export as a PNG file.

Contributors ✨¶

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!

Credits¶

This package was created with Copier and the browniebroke/pypackage-template project template.