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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# camera tuning tool for AWB
+
+from ctt_image_load import *
+import matplotlib.pyplot as plt
+from bisect import bisect_left
+from scipy.optimize import fmin
+
+
+"""
+obtain piecewise linear approximation for colour curve
+"""
+def awb(Cam, cal_cr_list, cal_cb_list, plot):
+ imgs = Cam.imgs
+ """
+ condense alsc calibration tables into one dictionary
+ """
+ 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]
+ """
+ obtain data from greyscale macbeth patches
+ """
+ rb_raw = []
+ rbs_hat = []
+ for Img in imgs:
+ Cam.log += '\nProcessing '+Img.name
+ """
+ get greyscale patches with alsc applied if alsc enabled.
+ Note: if alsc is disabled then colour_cals will be set to None and the
+ function will just return the greyscale patches
+ """
+ r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)
+ """
+ calculate ratio of r, b to g
+ """
+ r_g = np.mean(r_patchs/g_patchs)
+ b_g = np.mean(b_patchs/g_patchs)
+ Cam.log += '\n r : {:.4f} b : {:.4f}'.format(r_g, b_g)
+ """
+ The curve tends to be better behaved in so-called hatspace.
+ R, B, G represent the individual channels. The colour curve is plotted in
+ r, b space, where:
+ r = R/G
+ b = B/G
+ This will be referred to as dehatspace... (sorry)
+ Hatspace is defined as:
+ r_hat = R/(R+B+G)
+ b_hat = B/(R+B+G)
+ To convert from dehatspace to hastpace (hat operation):
+ r_hat = r/(1+r+b)
+ b_hat = b/(1+r+b)
+ To convert from hatspace to dehatspace (dehat operation):
+ r = r_hat/(1-r_hat-b_hat)
+ b = b_hat/(1-r_hat-b_hat)
+ Proof is left as an excercise to the reader...
+ Throughout the code, r and b are sometimes referred to as r_g and b_g
+ as a reminder that they are ratios
+ """
+ r_g_hat = r_g/(1+r_g+b_g)
+ b_g_hat = b_g/(1+r_g+b_g)
+ Cam.log += '\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat)
+ rbs_hat.append((r_g_hat, b_g_hat, Img.col))
+ rb_raw.append((r_g, b_g))
+ Cam.log += '\n'
+
+ Cam.log += '\nFinished processing images'
+ """
+ sort all lits simultaneously by r_hat
+ """
+ rbs_zip = list(zip(rbs_hat, rb_raw))
+ rbs_zip.sort(key=lambda x: x[0][0])
+ rbs_hat, rb_raw = list(zip(*rbs_zip))
+ """
+ unzip tuples ready for processing
+ """
+ rbs_hat = list(zip(*rbs_hat))
+ rb_raw = list(zip(*rb_raw))
+ """
+ fit quadratic fit to r_g hat and b_g_hat
+ """
+ a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)
+ Cam.log += '\nFit quadratic curve in hatspace'
+ """
+ the algorithm now approximates the shortest distance from each point to the
+ curve in dehatspace. Since the fit is done in hatspace, it is easier to
+ find the actual shortest distance in hatspace and use the projection back
+ into dehatspace as an overestimate.
+ The distance will be used for two things:
+ 1) In the case that colour temperature does not strictly decrease with
+ increasing r/g, the closest point to the line will be chosen out of an
+ increasing pair of colours.
+
+ 2) To calculate transverse negative an dpositive, the maximum positive
+ and negative distance from the line are chosen. This benefits from the
+ overestimate as the transverse pos/neg are upper bound values.
+ """
+ """
+ define fit function
+ """
+ def f(x):
+ return a*x**2 + b*x + c
+ """
+ iterate over points (R, B are x and y coordinates of points) and calculate
+ distance to line in dehatspace
+ """
+ dists = []
+ for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])):
+ """
+ define function to minimise as square distance between datapoint and
+ point on curve. Squaring is monotonic so minimising radius squared is
+ equivalent to minimising radius
+ """
+ def f_min(x):
+ y = f(x)
+ return((x-R)**2+(y-B)**2)
+ """
+ perform optimisation with scipy.optmisie.fmin
+ """
+ x_hat = fmin(f_min, R, disp=0)[0]
+ y_hat = f(x_hat)
+ """
+ dehat
+ """
+ x = x_hat/(1-x_hat-y_hat)
+ y = y_hat/(1-x_hat-y_hat)
+ rr = R/(1-R-B)
+ bb = B/(1-R-B)
+ """
+ calculate euclidean distance in dehatspace
+ """
+ dist = ((x-rr)**2+(y-bb)**2)**0.5
+ """
+ return negative if point is below the fit curve
+ """
+ if (x+y) > (rr+bb):
+ dist *= -1
+ dists.append(dist)
+ Cam.log += '\nFound closest point on fit line to each point in dehatspace'
+ """
+ calculate wiggle factors in awb. 10% added since this is an upper bound
+ """
+ transverse_neg = - np.min(dists) * 1.1
+ transverse_pos = np.max(dists) * 1.1
+ Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
+ Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
+ """
+ set minimum transverse wiggles to 0.1 .
+ Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
+ is a suitable minimum that gives better results for lighting conditions not
+ within calibration dataset. Anything less will generalise poorly.
+ """
+ if transverse_pos < 0.01:
+ transverse_pos = 0.01
+ Cam.log += '\nForced transverse pos to 0.01'
+ if transverse_neg < 0.01:
+ transverse_neg = 0.01
+ Cam.log += '\nForced transverse neg to 0.01'
+
+ """
+ generate new b_hat values at each r_hat according to fit
+ """
+ r_hat_fit = np.array(rbs_hat[0])
+ b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c
+ """
+ transform from hatspace to dehatspace
+ """
+ r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)
+ b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)
+ c_fit = np.round(rbs_hat[2], 0)
+ """
+ round to 4dp
+ """
+ r_fit = np.where((1000*r_fit) % 1 <= 0.05, r_fit+0.0001, r_fit)
+ r_fit = np.where((1000*r_fit) % 1 >= 0.95, r_fit-0.0001, r_fit)
+ b_fit = np.where((1000*b_fit) % 1 <= 0.05, b_fit+0.0001, b_fit)
+ b_fit = np.where((1000*b_fit) % 1 >= 0.95, b_fit-0.0001, b_fit)
+ r_fit = np.round(r_fit, 4)
+ b_fit = np.round(b_fit, 4)
+ """
+ The following code ensures that colour temperature decreases with
+ increasing r/g
+ """
+ """
+ iterate backwards over list for easier indexing
+ """
+ i = len(c_fit) - 1
+ while i > 0:
+ if c_fit[i] > c_fit[i-1]:
+ Cam.log += '\nColour temperature increase found\n'
+ Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])
+ Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])
+ """
+ if colour temperature increases then discard point furthest from
+ the transformed fit (dehatspace)
+ """
+ error_1 = abs(dists[i-1])
+ error_2 = abs(dists[i])
+ Cam.log += '\nDistances from fit:\n'
+ Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)
+ Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)
+ """
+ find bad index
+ note that in python false = 0 and true = 1
+ """
+ bad = i - (error_1 < error_2)
+ Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad])
+ Cam.log += 'it is furthest from fit'
+ """
+ delete bad point
+ """
+ r_fit = np.delete(r_fit, bad)
+ b_fit = np.delete(b_fit, bad)
+ c_fit = np.delete(c_fit, bad).astype(np.uint16)
+ """
+ note that if a point has been discarded then the length has decreased
+ by one, meaning that decreasing the index by one will reassess the kept
+ point against the next point. It is therefore possible, in theory, for
+ two adjacent points to be discarded, although probably rare
+ """
+ i -= 1
+
+ """
+ return formatted ct curve, ordered by increasing colour temperature
+ """
+ ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]
+ Cam.log += '\nFinal CT curve:'
+ for i in range(len(ct_curve)//3):
+ j = 3*i
+ Cam.log += '\n ct: {} '.format(ct_curve[j])
+ Cam.log += ' r: {} '.format(ct_curve[j+1])
+ Cam.log += ' b: {} '.format(ct_curve[j+2])
+
+ """
+ plotting code for debug
+ """
+ if plot:
+ x = np.linspace(np.min(rbs_hat[0]), np.max(rbs_hat[0]), 100)
+ y = a*x**2 + b*x + c
+ plt.subplot(2, 1, 1)
+ plt.title('hatspace')
+ plt.plot(rbs_hat[0], rbs_hat[1], ls='--', color='blue')
+ plt.plot(x, y, color='green', ls='-')
+ plt.scatter(rbs_hat[0], rbs_hat[1], color='red')
+ for i, ct in enumerate(rbs_hat[2]):
+ plt.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i]))
+ plt.xlabel('$\\hat{r}$')
+ plt.ylabel('$\\hat{b}$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.grid()
+ plt.subplot(2, 1, 2)
+ plt.title('dehatspace - indoors?')
+ plt.plot(r_fit, b_fit, color='blue')
+ plt.scatter(rb_raw[0], rb_raw[1], color='green')
+ plt.scatter(r_fit, b_fit, color='red')
+ for i, ct in enumerate(c_fit):
+ plt.annotate(str(ct), (r_fit[i], b_fit[i]))
+ plt.xlabel('$r$')
+ plt.ylabel('$b$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.subplots_adjust(hspace=0.5)
+ plt.grid()
+ plt.show()
+ """
+ end of plotting code
+ """
+ return(ct_curve, np.round(transverse_pos, 5), np.round(transverse_neg, 5))
+
+
+"""
+obtain greyscale patches and perform alsc colour correction
+"""
+def get_alsc_patches(Img, colour_cals, grey=True):
+ """
+ get patch centre coordinates, image colour and the actual
+ patches for each channel, remembering to subtract blacklevel
+ If grey then only greyscale patches considered
+ """
+ if grey:
+ cen_coords = Img.cen_coords[3::4]
+ col = Img.col
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0][3::4] - Img.blacklevel_16
+ b_patchs = patches[3][3::4] - Img.blacklevel_16
+ """
+ note two green channels are averages
+ """
+ g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16
+ else:
+ cen_coords = Img.cen_coords
+ col = Img.col
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0] - Img.blacklevel_16
+ b_patchs = patches[3] - Img.blacklevel_16
+ g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16
+
+ if colour_cals is None:
+ return r_patchs, b_patchs, g_patchs
+ """
+ find where image colour fits in alsc colour calibration tables
+ """
+ cts = list(colour_cals.keys())
+ pos = bisect_left(cts, col)
+ """
+ if img colour is below minimum or above maximum alsc calibration colour, simply
+ pick extreme closest to img colour
+ """
+ if pos % len(cts) == 0:
+ """
+ this works because -0 = 0 = first and -1 = last index
+ """
+ col_tabs = np.array(colour_cals[cts[-pos//len(cts)]])
+ """
+ else, perform linear interpolation between existing alsc colour
+ calibration tables
+ """
+ else:
+ bef = cts[pos-1]
+ aft = cts[pos]
+ da = col-bef
+ db = aft-col
+ bef_tabs = np.array(colour_cals[bef])
+ aft_tabs = np.array(colour_cals[aft])
+ col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db)
+ col_tabs = np.reshape(col_tabs, (2, 12, 16))
+ """
+ calculate dx, dy used to calculate alsc table
+ """
+ w, h = Img.w/2, Img.h/2
+ dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))
+ """
+ make list of pairs of gains for each patch by selecting the correct value
+ in alsc colour calibration table
+ """
+ patch_gains = []
+ for cen in cen_coords:
+ x, y = cen[0]//dx, cen[1]//dy
+ # We could probably do with some better spatial interpolation here?
+ col_gains = (col_tabs[0][y][x], col_tabs[1][y][x])
+ patch_gains.append(col_gains)
+
+ """
+ multiply the r and b channels in each patch by the respective gain, finally
+ performing the alsc colour correction
+ """
+ for i, gains in enumerate(patch_gains):
+ r_patchs[i] = r_patchs[i] * gains[0]
+ b_patchs[i] = b_patchs[i] * gains[1]
+
+ """
+ return greyscale patches, g channel and correct r, b channels
+ """
+ return r_patchs, b_patchs, g_patchs
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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
new file mode 100644
@@ -0,0 +1,30 @@
+# Program to convert from RGB to LAB color space
+def RGB_to_LAB(RGB): # where RGB is a 1x3 array. e.g RGB = [100, 255, 230]
+ num = 0
+ XYZ = [0, 0, 0]
+ # converted all the three R, G, B to X, Y, Z
+ X = RGB[0] * 0.4124 + RGB[1] * 0.3576 + RGB[2] * 0.1805
+ Y = RGB[0] * 0.2126 + RGB[1] * 0.7152 + RGB[2] * 0.0722
+ Z = RGB[0] * 0.0193 + RGB[1] * 0.1192 + RGB[2] * 0.9505
+
+ XYZ[0] = X / 255 * 100
+ XYZ[1] = Y / 255 * 100 # XYZ Must be in range 0 -> 100, so scale down from 255
+ XYZ[2] = Z / 255 * 100
+ XYZ[0] = XYZ[0] / 95.047 # ref_X = 95.047 Observer= 2°, Illuminant= D65
+ XYZ[1] = XYZ[1] / 100.0 # ref_Y = 100.000
+ XYZ[2] = XYZ[2] / 108.883 # ref_Z = 108.883
+ num = 0
+ for value in XYZ:
+ if value > 0.008856:
+ value = value ** (0.3333333333333333)
+ else:
+ value = (7.787 * value) + (16 / 116)
+ XYZ[num] = value
+ num = num + 1
+
+ # L, A, B, values calculated below
+ L = (116 * XYZ[1]) - 16
+ a = 500 * (XYZ[0] - XYZ[1])
+ b = 200 * (XYZ[1] - XYZ[2])
+
+ return [L, a, b]
new file mode 100644
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# camera tuning tool RANSAC selector for Macbeth chart locator
+
+import numpy as np
+
+scale = 2
+
+
+"""
+constructs normalised macbeth chart corners for ransac algorithm
+"""
+def get_square_verts(c_err=0.05, scale=scale):
+ """
+ define macbeth chart corners
+ """
+ b_bord_x, b_bord_y = scale*8.5, scale*13
+ s_bord = 6*scale
+ side = 41*scale
+ x_max = side*6 + 5*s_bord + 2*b_bord_x
+ y_max = side*4 + 3*s_bord + 2*b_bord_y
+ c1 = (0, 0)
+ c2 = (0, y_max)
+ c3 = (x_max, y_max)
+ c4 = (x_max, 0)
+ mac_norm = np.array((c1, c2, c3, c4), np.float32)
+ mac_norm = np.array([mac_norm])
+
+ square_verts = []
+ square_0 = np.array(((0, 0), (0, side),
+ (side, side), (side, 0)), np.float32)
+ offset_0 = np.array((b_bord_x, b_bord_y), np.float32)
+ c_off = side * c_err
+ offset_cont = np.array(((c_off, c_off), (c_off, -c_off),
+ (-c_off, -c_off), (-c_off, c_off)), np.float32)
+ square_0 += offset_0
+ square_0 += offset_cont
+ """
+ define macbeth square corners
+ """
+ for i in range(6):
+ shift_i = np.array(((i*side, 0), (i*side, 0),
+ (i*side, 0), (i*side, 0)), np.float32)
+ shift_bord = np.array(((i*s_bord, 0), (i*s_bord, 0),
+ (i*s_bord, 0), (i*s_bord, 0)), np.float32)
+ square_i = square_0 + shift_i + shift_bord
+ for j in range(4):
+ shift_j = np.array(((0, j*side), (0, j*side),
+ (0, j*side), (0, j*side)), np.float32)
+ shift_bord = np.array(((0, j*s_bord),
+ (0, j*s_bord), (0, j*s_bord),
+ (0, j*s_bord)), np.float32)
+ square_j = square_i + shift_j + shift_bord
+ square_verts.append(square_j)
+ # print('square_verts')
+ # print(square_verts)
+ return np.array(square_verts, np.float32), mac_norm
+
+
+def get_square_centres(c_err=0.05, scale=scale):
+ """
+ define macbeth square centres
+ """
+ verts, mac_norm = get_square_verts(c_err, scale=scale)
+
+ centres = np.mean(verts, axis=1)
+ # print('centres')
+ # print(centres)
+ return np.array(centres, np.float32)
Copy ctt_{awb,ccm,colors,ransac} from the raspberrypi tuning scripts as basis for the libcamera implementation. color.py was renamed to ctt_colors.py to better express the origin. The files were taken from commit 66479605baca ("utils: raspberrypi: ctt: Improve the Macbeth Chart search reliability"). Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com> Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Acked-by: Paul Elder <paul.elder@ideasonboard.com> Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> --- utils/tuning/libtuning/ctt_awb.py | 376 +++++++++++++++++++++++++ utils/tuning/libtuning/ctt_ccm.py | 406 +++++++++++++++++++++++++++ utils/tuning/libtuning/ctt_colors.py | 30 ++ utils/tuning/libtuning/ctt_ransac.py | 71 +++++ 4 files changed, 883 insertions(+) create mode 100644 utils/tuning/libtuning/ctt_awb.py create mode 100644 utils/tuning/libtuning/ctt_ccm.py create mode 100644 utils/tuning/libtuning/ctt_colors.py create mode 100644 utils/tuning/libtuning/ctt_ransac.py