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utils: raspberrypi: ctt: Code tidying

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Altered the way that some lines are laid out, made functions
more attractive to look at, and tidied up messy areas.

Signed-off-by: Ben Benson <ben.benson@raspberrypi.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
Reviewed-by: Naushir Patuck <naush@raspberrypi.com>
Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
This commit is contained in:
Ben Benson 2023-07-07 04:17:02 +01:00 • committed by Naushir Patuck
parent 6213ecb859
commit 09dd65442b
1 changed files with 27 additions and 34 deletions
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61
utils/raspberrypi/ctt/ctt_ccm.py
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@ -47,11 +47,8 @@ def degamma(x):
def gamma(x):
# return (x * * (1 / 2.4) * 1.055 - 0.055)
e = []
for i in range(len(x)):
e.append(((x[i] / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255)
return e
# Take 3 long array of color values and gamma them
return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x]
"""
@ -96,10 +93,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
"""
m_srgb = degamma(m_rgb) # now in 16 bit color.
m_lab = []
for col in m_srgb:
m_lab.append(colors.RGB_to_LAB(col / 256))
# This produces matrix of LAB values for ideal color chart)
# Produce array of LAB values for ideal color chart
m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb]
"""
reorder reference values to match how patches are ordered
@ -168,7 +163,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
sumde = 0
ccm = do_ccm(r, g, b, m_srgb)
# This is the initial guess that our optimisation code works with.
original_ccm = ccm
r1 = ccm[0]
r2 = ccm[1]
g1 = ccm[3]
@ -188,7 +183,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
We use our old CCM as the initial guess for the program to find the
optimised matrix
'''
result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.0000000001)
result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.01)
'''
This produces a color matrix which has the lowest delta E possible,
based off the input data. Note it is impossible for this to reach
@ -199,12 +194,13 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
[r1, r2, g1, g2, b1, b2] = result.x
# The new, optimised color correction matrix values
optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)]
# This is the optimised Color Matrix (preserving greys by summing rows up to 1)
Cam.log += str(optimised_ccm)
Cam.log += "\n Old Color Correction Matrix Below \n"
Cam.log += str(ccm)
formatted_ccm = np.array(ccm).reshape((3, 3))
formatted_ccm = np.array(original_ccm).reshape((3, 3))
'''
below is a whole load of code that then applies the latest color
@ -213,22 +209,21 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
'''
optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB
optimised_ccm_lab = []
for w in range(24):
RGB = np.array([r[w], g[w], b[w]])
ccm_applied_rgb = np.dot(formatted_ccm, (RGB / 256))
formatted_optimised_ccm = np.array(optimised_ccm).reshape((3, 3))
after_gamma_rgb = []
after_gamma_lab = []
for RGB in zip(r, g, b):
ccm_applied_rgb = np.dot(formatted_ccm, (np.array(RGB) / 256))
optimised_ccm_rgb.append(gamma(ccm_applied_rgb))
optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))
formatted_optimised_ccm = np.array(ccm).reshape((3, 3))
after_gamma_rgb = []
after_gamma_lab = []
for w in range(24):
RGB = np.array([r[w], g[w], b[w]])
optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, RGB / 256)
optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, np.array(RGB) / 256)
after_gamma_rgb.append(gamma(optimised_ccm_applied_rgb))
after_gamma_lab.append(colors.RGB_to_LAB(optimised_ccm_applied_rgb))
'''
Gamma After RGB / LAB
Gamma After RGB / LAB - not used in calculations, only used for visualisation
We now want to spit out some data that shows
how the optimisation has improved the color matrices
'''
@ -303,9 +298,8 @@ def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback f
def transform_and_evaluate(ccm, r, g, b, m_lab): # Transforms colors to LAB and applies the correction matrix
# create list of matrix changed colors
realrgb = []
for i in range(len(r)):
RGB = np.array([r[i], g[i], b[i]])
rgb_post_ccm = np.dot(ccm, RGB) # This is RGB values after the color correction matrix has been applied
for RGB in zip(r, g, b):
rgb_post_ccm = np.dot(ccm, np.array(RGB) / 256) # This is RGB values after the color correction matrix has been applied
realrgb.append(colors.RGB_to_LAB(rgb_post_ccm))
# now compare that with m_lab and return numeric result, averaged for each patch
return (sumde(realrgb, m_lab) / 24) # returns an average result of delta E
@ -315,12 +309,12 @@ def sumde(listA, listB):
global typenum, test_patches
sumde = 0
maxde = 0
patchde = []
for i in range(len(listA)):
if maxde < (deltae(listA[i], listB[i])):
maxde = deltae(listA[i], listB[i])
patchde.append(deltae(listA[i], listB[i]))
sumde += deltae(listA[i], listB[i])
patchde = [] # Create array of the delta E values for each patch. useful for optimisation of certain patches
for listA_item, listB_item in zip(listA, listB):
if maxde < (deltae(listA_item, listB_item)):
maxde = deltae(listA_item, listB_item)
patchde.append(deltae(listA_item, listB_item))
sumde += deltae(listA_item, listB_item)
'''
The different options specified at the start allow for
the maximum to be returned, average or specific patches
@ -330,9 +324,8 @@ def sumde(listA, listB):
if typenum == 1:
return maxde
if typenum == 2:
output = 0
for y in range(len(test_patches)):
output += patchde[test_patches[y]] # grabs the specific patches (no need for averaging here)
output = sum([patchde[test_patch] for test_patch in test_patches])
# Selects only certain patches and returns the output for them
return output