Performance Issue with custom annotator

[Cutuchiqueno]

Basically because HSV is not derived from a color awareness model like CIE-Lab is but in comparison to CIE-Lab defines more meaningful axis I wanted to build a custom annotator that turns the input RGB (it is RGB, right? Not openCVs BGR?) into CIE-LCH (Luminance, Chroma, Hue), a color awareness appropriate version of HSV. (Instead of a histogramm I calculate the median value for each channel, but that is not important here). openCV, unfortunately, does not provide a conversion to CIE-LCH, but scikit-image does. In principle everything works out, BUT for a 110 minute movie such as Il Divo with this custom annotator dvt requires 7h30m!!! Considering, that the demo project, on my computer, requires just 30-45 minutes to annotate everything it can (the color annotator and the dominant colors annotator included), I wondered if I got something completely wrong.

My annotator looks like this:

class LchAnnotator(FrameAnnotator):

    name = 'lch'

    def annotate(self, batch):

        img = batch.get_batch()
        fnames = batch.get_frame_names()

        luminance = list()
        chroma = list()
        hue = list()

        for i in img:

            img_lab = rgb2lab(i)
            img_lch = lab2lch(img_lab)

            lch = img_lch.reshape(-1, 3)

            luminance.append(median(lch[:, 0]))
            chroma.append(median(lch[:, 1]))
            hue.append(median(lch[:, 2]))

        output = {'frame': fnames,
                  'luminance': luminance,
                  'chroma': chroma,
                  'hue': hue}

        return output