This is a selection of tools to map LEDs into 2D and 3D space using only your webcam!

After downloading this repository and installing Python, run pip install -r requirements.txt

Run python scripts/check_camera.py to ensure your camera is compatible with MariMapper, or check the list below:

Test LED identification by turning down the lights and holding a torch or led up to the camera This should start with few warnings, no errors and produce a very dark image with a single crosshair on centered on your LED:

Your LEDs are as unique as you are, so the fastest way to connect MariMapper to your system is to fill in the blanks in backends/custom/custom_backend.py:

# import some_led_library

class Backend:

    def __init__(self):
        # Remove the following line after you have implemented the set_led function!
        raise NotImplementedError("Could not load backend as it has not been implemented, go implement it!")

    def get_led_count(self):
        # return the number of LEDs in your system here
        return 0

    def set_led(self, led_index: int, on: bool):
        # Write your code for controlling your LEDs here
        # It should turn on or off the led at the led_index depending on the "on" variable
        # For example:
        # if on:
        #     some_led_library.set_led(led_index, (255, 255, 255))
        # else:
        #     some_led_library.set_led(led_index, (0, 0, 0))
        pass

You can test your backend with python scripts/check_backend.py

MariMapper also support the following pre-made backends. This can be selected in the following steps using the --backend argument.

• Fadecandy / OPC
• WLED
• FC Mega
• LCM

Set up your LEDs in front of your camera and run python scripts/capture_sequence.py my_scan --backend fadecandy

Change --backend to whatever backend you're using

This will produce a timestamped CSV file in the my_scan folder with led index, u and v values.

Run python scripts/visualise.py <filename> to visualise 2D or 3D map files.

It's time to thunderize!

To create a 3D map, run capture_sequence multiple times from different views of your LEDs, this can either be done by moving your webcam around your LEDs or rotating your LEDs.

Once you have a selection of 2D maps captured with the capture_sequence.py script, run python scripts/reconstruct.py my_scan

This may take a while, however once complete will generate reconstruction.csv in the my_scan folder.

Here is an example reconstruction of Highbeam's body LEDs

If you have a high enough density 3d map, you can use the remesh tool to create a 3D mesh based on your leds!

Run python scripts/remesh.py reconstruction.csv my_mesh.ply

This will generate a ply file which you can open and look at with your eyes

I would really love to hear what you think and if you have any bugs or improvements, please raise them here or drop me a line on Telegram.

If you implement a backend that you think others might use, please raise a pull request or just drop me a message on Telegram!

If you want a super speed PR, run flake8, flake8-bugbear and black before submitting changes!