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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_ccm.py - camera tuning tool for CCM (colour correction matrix)
+
+from ctt_image_load import *
+from ctt_awb import get_alsc_patches
+
+"""
+takes 8-bit macbeth chart values, degammas and returns 16 bit
+"""
+def degamma(x):
+ x = x / ((2**8)-1)
+ x = np.where(x < 0.04045, x/12.92, ((x+0.055)/1.055)**2.4)
+ x = x * ((2**16)-1)
+ return x
+
+"""
+FInds colour correction matrices for list of images
+"""
+def ccm(Cam,cal_cr_list,cal_cb_list):
+ imgs = Cam.imgs
+ """
+ standard macbeth chart colour values
+ """
+ m_rgb = np.array([ # these are in sRGB
+ [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)
+ """
+ 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))
+
+ """
+ reformat alsc correction tables or set colour_cals to None if alsc is
+ deactivated
+ """
+ if cal_cr_list == 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)
+ """
+ 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
+ """
+ ccm = do_ccm(r,g,b,m_srgb)
+
+ """
+ if a ccm has already been calculated for that temperature then don't
+ overwrite but save both. They will then be averaged later on
+ """
+ if Img.col in ccm_tab.keys():
+ ccm_tab[Img.col].append(ccm)
+ else:
+ ccm_tab[Img.col] = [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
+
+"""
+calculates the ccm for an individual image.
+ccms are calculate 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.
+Should you want to fit them in another space (e.g. LAB) we wish you the best of
+luck and send us the code when you are done! :-)
+"""
+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