In the past few days I've been working on finding away to approximate the wavelengths of lights in photos. This isn't technically possible to do with complete accuracy, but I was able to write a script that can approximate the wavelength with some exceptions (mentioned later in the post).
Using data from a chart that has the XYZ colorspace of all monochromatic wavelengths of visible light, I was able to convert my RGB image to XYZ colorspace and find the closest wavelength value to the XYZ values of the pixel. I've produced a histogram of this script in action on an entire image, however I also produced a smaller version of the script that can be used as a function and convert one pixel at a time from an RGB value to wavelength value.
Here is the histogram of wavelengths of light found in the photo:
Here is a histogram of the hues present in the same photo:
As you can see, there are a few issues with the wavelength approximations. Some color hues (most notably pinks and purples) cannot be produced using a single wavelength. My script matches the RGB value of the pixel to the closest monochromatic wavelength, and therefore colors that cannot be created with one wavelength are approximated to the closest component wave, which can explain why there are larger amounts of blue and red light shown in the wavelength histogram.
This behavior seems fairly consistent across images, but in the future I might have to figure out a way to approximate these more complex colors if we find that more accuracy is needed. For now this script gives us an accurate enough idea of the wavelengths of light produced in the image in order to begin using holographic light diffraction grating equations to approximate other information such as the angle of the incoming wavelength before the diffraction occurred.