import numpy as np
from colour import *
from colour.difference import delta_E_CIE1976
from colour.models import RGB_COLOURSPACES
from colour.recovery import RGB_to_sd_Jakob2019, Jakob2019Interpolator
from gsoc_common import *

colourspace = RGB_COLOURSPACES["ACES2065-1"]


def stats(table, prefix):
    table = np.array(table)
    print("%s min/max/avg/std: %g/%g/%g/%g" % (prefix, np.min(table),
           np.max(table), np.mean(table), np.std(table)))


if __name__ == "__main__":
    interp = Jakob2019Interpolator()
    interp.from_file("data/aces2065_1.coeff")

    errors = []
    errors_interp = []
    rel_deltas = []
    for i in range(100):
        RGB = np.random.random(3)

        for use_feedback in [False, True]:
            recovered_sd, error = RGB_to_sd_Jakob2019(
                RGB,
                colourspace,
                return_error=True,
                use_feedback=use_feedback
            )

            if error < 1e-3:
                break

        XYZ = RGB_to_XYZ(
            RGB,
            colourspace.whitepoint,
            D65_xy,
            colourspace.RGB_to_XYZ_matrix,
        )

        Lab = XYZ_to_Lab(XYZ, D65_xy)

        sd_interp = interp.RGB_to_sd(RGB)
        XYZ_interp = sd_to_XYZ(sd_interp, illuminant=D65) / 100
        Lab_interp = XYZ_to_Lab(XYZ_interp, D65_xy)
        error_interp = delta_E_CIE1976(Lab, Lab_interp)

        XYZ_recovered = sd_to_XYZ(recovered_sd, illuminant=D65) / 100
        Lab_recovered = XYZ_to_Lab(XYZ_recovered, D65_xy)
        rel_delta = error / error_interp

        print("%.5f %.5f %.5f  E%10.6f  I%7.3f  RΔ%.6f"
              % (*RGB, error, error_interp, rel_delta))

        errors.append(error)
        errors_interp.append(error_interp)
        rel_deltas.append(rel_delta)

    print(colourspace.name)
    stats(errors, "Colour")
    stats(errors_interp, "Interpolator")
    stats(rel_deltas, "Rel. delta")