photoncube 0.3.14

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Overview

Command line utility and python/rust library to preview a photon cube as a video or a series of frames and/or convert it to a different format.

Features:

• Zero runtime dependencies, except for FFmpeg, which will be automatically downloaded if not found on path.
• Multi-threaded and (despite very unoptimized code) blazing fast, usually an order of magnitude or two faster than equivalent cpu-based numpy+numba code.
• Supports a collection of .bin or .npy file. Assumes bitpacking in the width axis in all cases.
• Demosaicing, inpainting, tonemapping, inverting SPAD response, and transforms (rotate/flip) are also supported.

Limitations:

• Not all features are exposed to python yet.

Getting Started

To compile it locally, simply clone the repository, cd into it and run:

pip install -v . 

Ensure you have an up-to-date pip, and an adequate rust toolchain installed (install from here), else this might fail. This should work for python >= 3.8 (tested with 3.8 and py 3.12). You might have to update your rust toolchain with rustup update (MSRV: 1.74.0).

This should pull in any rust dependencies and compile bindings that are compatible with your machine's env. It will create both a CLI and package.
If this is your first time compiling a rust project, this may take a few minutes.

CLI Usage:

Three main functions are available via the CLI: preview, process, and convert.

$ photoncube -h

Convert a photon cube (npy file/directory of bin files) between formats or to a video preview (mp4) by naively averaging frames

Usage: photoncube <COMMAND>

Commands:
  convert  Convert photoncube from a collection of .bin files to a .npy file
  preview  Extract and preview virtual exposures from a photoncube
  process  Apply corrections directly to every bitplane and save results as new .npy file
  help     Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help
  -V, --version  Print version

For more info, run photoncube <COMMAND> --help.

Preview

To preview a photoncube as a video or series of frames (either averaged or single bitplanes), the photon cube needs to be saved as a npy file. See convert for how to convert a folder of .bin files to an .npy file.

Here we preview a photoncube as a video and apply a rotation by 270 degrees then flip frames left-right:

photoncube preview -i binary.npy -t=rot270 -t=flip-lr -o video.mp4

Note: Transforms can be composed arbitrarily, and order matters. Valid transforms are enumerated here and are serialized in kebab-case, i.e: Transform::FlipLR is "flip-lr" and so on.

You can annotate frames and correct for cfa arrays and inpaint like so:

photoncube preview -i binary.npy -a --cfa-path=rgbw_oh_bn_color_ss2_corrected.png --inpaint-path=colorspad_inpaint_mask.npy -o video.mp4

Note: Multiple inpaint-paths can be specified. These masks will be OR'd together then used.

Individual frames can be saved using the --img-dir option, and a range of frames, as well as the window over which we aggregate frames, can be specified:

photoncube preview -i binary.npy --img-dir=frames/ --start=20000 --end=50000 --burst-size=100

Note: You can, of course, save a video and frames at the same time!

In particular, if you wish to view individual bitplanes, you can set burst-size to one, and use the step argument to skip over frames as to not create a huge video. For a photoncube captured at 15kHz, a realtime playback of individual bitplanes can be shown:

photoncube preview -i binary.npy --batch-size=1 --step=625 -o video.mp4

Note: Here we set step=625 because the default playback speed is 24 fps (i.e: 15k/24 == 625), but the fps can also be changed using --fps, although using a very large or small fps will cause ffmpeg issues.

Process

Many of the operations detailed above can be applied directly to a photoncube at the bitplane level, notably all transforms, slicing operations (start/end) and inpainting/cfa-correction can be applied. The later will use a dithering-like approach to perform stochastic inpainting. Again, only numpy files are supported.

Here we process a photoncube by applying cfa corrections and a few transforms:

photoncube process -i binary.npy --cfa-path=rgbw_oh_bn_color_ss2_corrected.png --inpaint-path=colorspad_inpaint_mask.npy --start=0 --end=100000 -t flip-lr -o processed.npy

Convert

You can convert from a collection of .bin files to a single .npy file like so:

photoncube convert -i <DIR OF BINS> -o <OUTPUT>

By default this assumes half array frames (i.e: 256x512), you can specify --full-array for the whole array.

Library Usage (Python)

The python API mirrors the rust and CLI functionality, for more info on how to use it please see the above section.

from photoncube import PhotonCube, Transform

PhotonCube.convert_to_npy(
    # Directory containing `.bin` files
    "full-array/binary/16kHz/", 
    "test.npy", 
    is_full_array=True, 
    message="Converting..."
)

# Open cube 
pc = PhotonCube.open("test.npy")

# Get a frame
frame = pc[10]

# Load inpainting masks, both .png and .npy supported.
pc.load_mask("dead_pixels.png")
pc.load_mask("hot_pixels.npy")

# Inpainting masks will be OR'ed together
print(pc.inpaint_mask)

# If using colorspad, you can specify the color filter array 
# and all non-white pixels will be interpolated out
pc.load_cfa("cfa.png")

# Generate 100 preview images with bit-depth of 256 
pc.set_range(0, 25600, 256)
pc.save_images(
    "tmp/", 
    invert_response=False,
    tonemap2srgb=False,
    colorspad_fix=False,
    grayspad_fix=False,
    annotate_frames=False,
    message="Saving Images..."  # If not None, a progress bar will be drawn
)

# Make video preview instead, but transform frames first, and invert the SPAD
# response, and normalize to a 95% quantile, for better low-light performance
pc.set_transforms([Transform.Rot90, Transform.FlipUD])
pc.set_quantile(0.95)
pc.save_video(
    "output.mp4", fps=24, 
    # If specified, images are also saved
    img_dir=None,
    invert_response=True,
    # Options from `save_images` can be used here too:
    message="Making video..." 
) 

# Save a new photoncube that has been processed with any transforms,
# cfas, masks or color/grayspad fixes  such as cropping dead regions or column swapping.
pc.process_cube("processed.npy")

For the full python API and up-to-date typing, see photoncube.pyi.

Development

We use maturin as a build system, which enables this package to be built as a python extension using pyo3 bindings. Some other tools are needed for development work, which can be installed using pip install -v .[dev].

There are both rust tests, and python ones, run them with:

cargo test && pytest . 

Code Quality

We use rustfmt to format the codebase, we use some customizations (i.e for import sorting) which require nightly. First ensure you have the nightly toolchain installed with:

rustup toolchain install nightly

Then you can format the code using:

cargo +nightly fmt 

Similarly we use black to format the python parts of the project.

To keep the project lean, it's recommended to check for unused dependencies using this tool, or this one, like so:

cargo +nightly udeps
cargo machete --with-metadata