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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# camera tuning tool for CCM (colour correction matrix)
+
+from ctt_image_load import *
+from ctt_awb import get_alsc_patches
+import colors
+from scipy.optimize import minimize
+from ctt_visualise import visualise_macbeth_chart
+import numpy as np
+"""
+takes 8-bit macbeth chart values, degammas and returns 16 bit
+"""
+
+'''
+This program has many options from which to derive the color matrix from.
+The first is average. This minimises the average delta E across all patches of
+the macbeth chart. Testing across all cameras yeilded this as the most color
+accurate and vivid. Other options are avalible however.
+Maximum minimises the maximum Delta E of the patches. It iterates through till
+a minimum maximum is found (so that there is
+not one patch that deviates wildly.)
+This yields generally good results but overall the colors are less accurate
+Have a fiddle with maximum and see what you think.
+The final option allows you to select the patches for which to average across.
+This means that you can bias certain patches, for instance if you want the
+reds to be more accurate.
+'''
+
+matrix_selection_types = ["average", "maximum", "patches"]
+typenum = 0 # select from array above, 0 = average, 1 = maximum, 2 = patches
+test_patches = [1, 2, 5, 8, 9, 12, 14]
+
+'''
+Enter patches to test for. Can also be entered twice if you
+would like twice as much bias on one patch.
+'''
+
+
+def degamma(x):
+ x = x / ((2 ** 8) - 1) # takes 255 and scales it down to one
+ x = np.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4)
+ x = x * ((2 ** 16) - 1) # takes one and scales up to 65535, 16 bit color
+ return x
+
+
+def gamma(x):
+ # 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]
+
+
+"""
+FInds colour correction matrices for list of images
+"""
+
+
+def ccm(Cam, cal_cr_list, cal_cb_list):
+ global matrix_selection_types, typenum
+ imgs = Cam.imgs
+ """
+ standard macbeth chart colour values
+ """
+ m_rgb = np.array([ # these are in RGB
+ [116, 81, 67], # dark skin
+ [199, 147, 129], # light skin
+ [91, 122, 156], # blue sky
+ [90, 108, 64], # foliage
+ [130, 128, 176], # blue flower
+ [92, 190, 172], # bluish green
+ [224, 124, 47], # orange
+ [68, 91, 170], # purplish blue
+ [198, 82, 97], # moderate red
+ [94, 58, 106], # purple
+ [159, 189, 63], # yellow green
+ [230, 162, 39], # orange yellow
+ [35, 63, 147], # blue
+ [67, 149, 74], # green
+ [180, 49, 57], # red
+ [238, 198, 20], # yellow
+ [193, 84, 151], # magenta
+ [0, 136, 170], # cyan (goes out of gamut)
+ [245, 245, 243], # white 9.5
+ [200, 202, 202], # neutral 8
+ [161, 163, 163], # neutral 6.5
+ [121, 121, 122], # neutral 5
+ [82, 84, 86], # neutral 3.5
+ [49, 49, 51] # black 2
+ ])
+ """
+ convert reference colours from srgb to rgb
+ """
+ m_srgb = degamma(m_rgb) # now in 16 bit color.
+
+ # 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
+ """
+ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3))
+ m_lab = np.array([m_lab[i::6] for i in range(6)]).reshape((24, 3))
+ m_rgb = np.array([m_rgb[i::6] for i in range(6)]).reshape((24, 3))
+ """
+ reformat alsc correction tables or set colour_cals to None if alsc is
+ deactivated
+ """
+ if cal_cr_list is None:
+ colour_cals = None
+ else:
+ colour_cals = {}
+ for cr, cb in zip(cal_cr_list, cal_cb_list):
+ cr_tab = cr['table']
+ cb_tab = cb['table']
+ """
+ normalise tables so min value is 1
+ """
+ cr_tab = cr_tab / np.min(cr_tab)
+ cb_tab = cb_tab / np.min(cb_tab)
+ colour_cals[cr['ct']] = [cr_tab, cb_tab]
+
+ """
+ for each image, perform awb and alsc corrections.
+ Then calculate the colour correction matrix for that image, recording the
+ ccm and the colour tempertaure.
+ """
+ ccm_tab = {}
+ for Img in imgs:
+ Cam.log += '\nProcessing image: ' + Img.name
+ """
+ get macbeth patches with alsc applied if alsc enabled.
+ Note: if alsc is disabled then colour_cals will be set to None and no
+ the function will simply return the macbeth patches
+ """
+ r, b, g = get_alsc_patches(Img, colour_cals, grey=False)
+ # 256 values for each patch of sRGB values
+
+ """
+ do awb
+ Note: awb is done by measuring the macbeth chart in the image, rather
+ than from the awb calibration. This is done so the awb will be perfect
+ and the ccm matrices will be more accurate.
+ """
+ r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4]
+ r_g = np.mean(r_greys / g_greys)
+ b_g = np.mean(b_greys / g_greys)
+ r = r / r_g
+ b = b / b_g
+ """
+ normalise brightness wrt reference macbeth colours and then average
+ each channel for each patch
+ """
+ gain = np.mean(m_srgb) / np.mean((r, g, b))
+ Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain)
+ r = np.mean(gain * r, axis=1)
+ b = np.mean(gain * b, axis=1)
+ g = np.mean(gain * g, axis=1)
+ """
+ calculate ccm matrix
+ """
+ # ==== All of below should in sRGB ===##
+ 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]
+ g2 = ccm[4]
+ b1 = ccm[6]
+ b2 = ccm[7]
+ '''
+ COLOR MATRIX LOOKS AS BELOW
+ R1 R2 R3 Rval Outr
+ G1 G2 G3 * Gval = G
+ B1 B2 B3 Bval B
+ Will be optimising 6 elements and working out the third element using 1-r1-r2 = r3
+ '''
+
+ x0 = [r1, r2, g1, g2, b1, b2]
+ '''
+ 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=0.01)
+ '''
+ 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
+ zero since the input data is imperfect
+ '''
+
+ Cam.log += ("\n \n Optimised Matrix Below: \n \n")
+ [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(original_ccm).reshape((3, 3))
+
+ '''
+ below is a whole load of code that then applies the latest color
+ matrix, and returns LAB values for color. This can then be used
+ to calculate the final delta E
+ '''
+ optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB
+ optimised_ccm_lab = []
+
+ 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))
+
+ 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 - 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
+ '''
+ Cam.log += "Here are the Improvements"
+
+ # CALCULATE WORST CASE delta e
+ old_worst_delta_e = 0
+ before_average = transform_and_evaluate(formatted_ccm, r, g, b, m_lab)
+ new_worst_delta_e = 0
+ after_average = transform_and_evaluate(formatted_optimised_ccm, r, g, b, m_lab)
+ for i in range(24):
+ old_delta_e = deltae(optimised_ccm_lab[i], m_lab[i]) # Current Old Delta E
+ new_delta_e = deltae(after_gamma_lab[i], m_lab[i]) # Current New Delta E
+ if old_delta_e > old_worst_delta_e:
+ old_worst_delta_e = old_delta_e
+ if new_delta_e > new_worst_delta_e:
+ new_worst_delta_e = new_delta_e
+
+ Cam.log += "Before color correction matrix was optimised, we got an average delta E of " + str(before_average) + " and a maximum delta E of " + str(old_worst_delta_e)
+ Cam.log += "After color correction matrix was optimised, we got an average delta E of " + str(after_average) + " and a maximum delta E of " + str(new_worst_delta_e)
+
+ visualise_macbeth_chart(m_rgb, optimised_ccm_rgb, after_gamma_rgb, str(Img.col) + str(matrix_selection_types[typenum]))
+ '''
+ The program will also save some visualisations of improvements.
+ Very pretty to look at. Top rectangle is ideal, Left square is
+ before optimisation, right square is after.
+ '''
+
+ """
+ if a ccm has already been calculated for that temperature then don't
+ overwrite but save both. They will then be averaged later on
+ """ # Now going to use optimised color matrix, optimised_ccm
+ if Img.col in ccm_tab.keys():
+ ccm_tab[Img.col].append(optimised_ccm)
+ else:
+ ccm_tab[Img.col] = [optimised_ccm]
+ Cam.log += '\n'
+
+ Cam.log += '\nFinished processing images'
+ """
+ average any ccms that share a colour temperature
+ """
+ for k, v in ccm_tab.items():
+ tab = np.mean(v, axis=0)
+ tab = np.where((10000 * tab) % 1 <= 0.05, tab + 0.00001, tab)
+ tab = np.where((10000 * tab) % 1 >= 0.95, tab - 0.00001, tab)
+ ccm_tab[k] = list(np.round(tab, 5))
+ Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k)
+
+ """
+ return all ccms with respective colour temperature in the correct format,
+ sorted by their colour temperature
+ """
+ sorted_ccms = sorted(ccm_tab.items(), key=lambda kv: kv[0])
+ ccms = []
+ for i in sorted_ccms:
+ ccms.append({
+ 'ct': i[0],
+ 'ccm': i[1]
+ })
+ return ccms
+
+
+def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback for the optimisation code
+ [r1, r2, g1, g2, b1, b2] = x0
+ ccm = np.array([r1, r2, (1 - r1 - r2),
+ g1, g2, (1 - g1 - g2),
+ b1, b2, (1 - b1 - b2)]).reshape((3, 3)) # format the matrix correctly
+ return transform_and_evaluate(ccm, r, g, b, m_lab)
+
+
+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 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
+
+
+def sumde(listA, listB):
+ global typenum, test_patches
+ sumde = 0
+ maxde = 0
+ 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
+ '''
+ if typenum == 0:
+ return sumde
+ if typenum == 1:
+ return maxde
+ if typenum == 2:
+ output = sum([patchde[test_patch] for test_patch in test_patches])
+ # Selects only certain patches and returns the output for them
+ return output
+
+
+"""
+calculates the ccm for an individual image.
+ccms are calculated in rgb space, and are fit by hand. Although it is a 3x3
+matrix, each row must add up to 1 in order to conserve greyness, simplifying
+calculation.
+The initial CCM is calculated in RGB, and then optimised in LAB color space
+This simplifies the initial calculation but then gets us the accuracy of
+using LAB color space.
+"""
+
+
+def do_ccm(r, g, b, m_srgb):
+ rb = r-b
+ gb = g-b
+ rb_2s = (rb * rb)
+ rb_gbs = (rb * gb)
+ gb_2s = (gb * gb)
+
+ r_rbs = rb * (m_srgb[..., 0] - b)
+ r_gbs = gb * (m_srgb[..., 0] - b)
+ g_rbs = rb * (m_srgb[..., 1] - b)
+ g_gbs = gb * (m_srgb[..., 1] - b)
+ b_rbs = rb * (m_srgb[..., 2] - b)
+ b_gbs = gb * (m_srgb[..., 2] - b)
+
+ """
+ Obtain least squares fit
+ """
+ rb_2 = np.sum(rb_2s)
+ gb_2 = np.sum(gb_2s)
+ rb_gb = np.sum(rb_gbs)
+ r_rb = np.sum(r_rbs)
+ r_gb = np.sum(r_gbs)
+ g_rb = np.sum(g_rbs)
+ g_gb = np.sum(g_gbs)
+ b_rb = np.sum(b_rbs)
+ b_gb = np.sum(b_gbs)
+
+ det = rb_2 * gb_2 - rb_gb * rb_gb
+
+ """
+ Raise error if matrix is singular...
+ This shouldn't really happen with real data but if it does just take new
+ pictures and try again, not much else to be done unfortunately...
+ """
+ if det < 0.001:
+ raise ArithmeticError
+
+ r_a = (gb_2 * r_rb - rb_gb * r_gb) / det
+ r_b = (rb_2 * r_gb - rb_gb * r_rb) / det
+ """
+ Last row can be calculated by knowing the sum must be 1
+ """
+ r_c = 1 - r_a - r_b
+
+ g_a = (gb_2 * g_rb - rb_gb * g_gb) / det
+ g_b = (rb_2 * g_gb - rb_gb * g_rb) / det
+ g_c = 1 - g_a - g_b
+
+ b_a = (gb_2 * b_rb - rb_gb * b_gb) / det
+ b_b = (rb_2 * b_gb - rb_gb * b_rb) / det
+ b_c = 1 - b_a - b_b
+
+ """
+ format ccm
+ """
+ ccm = [r_a, r_b, r_c, g_a, g_b, g_c, b_a, b_b, b_c]
+
+ return ccm
+
+
+def deltae(colorA, colorB):
+ return ((colorA[0] - colorB[0]) ** 2 + (colorA[1] - colorB[1]) ** 2 + (colorA[2] - colorB[2]) ** 2) ** 0.5
+ # return ((colorA[1]-colorB[1]) * * 2 + (colorA[2]-colorB[2]) * * 2) * * 0.5
+ # UNCOMMENT IF YOU WANT TO NEGLECT LUMINANCE FROM CALCULATION OF DELTA E