deltae

#!/usr/bin/python3

# Requires:
#   python3-scipy >= 1.1.0
#   python3-six
#   python3-pillow (or python3-Pillow, or python3-pil)
#   python3-imageio
#     or install the latest from PyPi if not provided as system package:
#       pip3 install imageio
#   python3-colour-science >= 0.3.14
#     or install the latest from PyPi if not provided as system package:
#       pip3 install colour-science

# Maximum delta allowed, above this value the difference can be detected
MAX_DELTA_E = 2.3
MAX_AVG_DELTA_E = MAX_DELTA_E / 3.

from packaging import version
import colour
import numpy
import sys

if "delta_E" not in dir(colour):
    print("  Unable to compute delta-E, please upgrade python3-colour-science")
    exit(0)

expected = sys.argv[1]
output = sys.argv[2]

expected_rgb = colour.read_image(expected, method="Imageio")
output_rgb = colour.read_image(output, method="Imageio")

if version.parse(colour.__version__) > version.parse("0.3"):
    expected_lab = colour.XYZ_to_Lab(colour.sRGB_to_XYZ(expected_rgb,
                                                        chromatic_adaptation_transform="Bradford"))
    output_lab = colour.XYZ_to_Lab(colour.sRGB_to_XYZ(output_rgb,
                                                      chromatic_adaptation_transform="Bradford"))
else:
    expected_lab = colour.XYZ_to_Lab(colour.sRGB_to_XYZ(expected_rgb,
                                                        chromatic_adaptation_method="Bradford"))
    output_lab = colour.XYZ_to_Lab(colour.sRGB_to_XYZ(output_rgb,
                                                      chromatic_adaptation_method="Bradford"))

delta_E = colour.delta_E(expected_lab, output_lab, method="CIE 2000")

num_elem = delta_E.size

max_dE = numpy.amax(delta_E)
mean_dE = numpy.mean(delta_E)
std_dE = numpy.std(delta_E)

print("      ----------------------------------")
print("      Max dE                   : %.5f" % max_dE)
print("      Avg dE                   : %.5f" % mean_dE)
print("      Std dE                   : %.5f" % std_dE)
print("      ----------------------------------")

count_below_avg = numpy.sum(delta_E <= mean_dE)
print("      Pixels below avg + 0 std : %.2f %%" % (count_below_avg / num_elem * 100.))

count_below_avg = numpy.sum(delta_E <= mean_dE + std_dE)
print("      Pixels below avg + 1 std : %.2f %%" % (count_below_avg / num_elem * 100.))

count_below_avg = numpy.sum(delta_E <= mean_dE + 3. * std_dE)
print("      Pixels below avg + 3 std : %.2f %%" % (count_below_avg / num_elem * 100.))

count_below_avg = numpy.sum(delta_E <= mean_dE + 6. * std_dE)
print("      Pixels below avg + 6 std : %.2f %%" % (count_below_avg / num_elem * 100.))

count_below_avg = numpy.sum(delta_E <= mean_dE + 9. * std_dE)
print("      Pixels below avg + 9 std : %.2f %%" % (count_below_avg / num_elem * 100.))

print("      ----------------------------------")

count_above = numpy.sum(delta_E >= MAX_DELTA_E)
print("      Pixels above tolerance   : %.2f %%" % (count_above / num_elem * 100.))

if(max_dE > MAX_DELTA_E or mean_dE > MAX_AVG_DELTA_E):
    exit(2)
elif(max_dE < 0.01):
    exit(0)
else:
    exit(1)