diff options
Diffstat (limited to 'utils/tuning/libtuning/ctt_ccm.py')
| -rw-r--r-- | utils/tuning/libtuning/ctt_ccm.py | 408 |
1 files changed, 408 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/ctt_ccm.py b/utils/tuning/libtuning/ctt_ccm.py new file mode 100644 index 00000000..2e87a667 --- /dev/null +++ b/utils/tuning/libtuning/ctt_ccm.py @@ -0,0 +1,408 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# camera tuning tool for CCM (colour correction matrix) + +import logging + +import numpy as np +from scipy.optimize import minimize + +from . import ctt_colors as colors +from .image import Image +from .ctt_awb import get_alsc_patches +from .utils import visualise_macbeth_chart + +logger = logging.getLogger(__name__) + +""" +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(imgs, cal_cr_list, cal_cb_list): + global matrix_selection_types, typenum + """ + 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: + logger.info('Processing 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)) + logger.info(f'Gain with respect to standard colours: {gain:.3f}') + 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 + ''' + + [r1, r2, g1, g2, b1, b2] = result.x + # The new, optimised color correction matrix values + # This is the optimised Color Matrix (preserving greys by summing rows up to 1) + optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)] + + logger.info(f'Optimized Matrix: {np.round(optimised_ccm, 4)}') + logger.info(f'Old Matrix: {np.round(ccm, 4)}') + + 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 + ''' + logger.info("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 + + logger.info(f'delta E optimized: average: {after_average:.2f} max:{new_worst_delta_e:.2f}') + logger.info(f'delta E old: average: {before_average:.2f} max:{old_worst_delta_e:.2f}') + + visualise_macbeth_chart(m_rgb, optimised_ccm_rgb, after_gamma_rgb, str(Img.color) + 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.color in ccm_tab.keys(): + ccm_tab[Img.color].append(optimised_ccm) + else: + ccm_tab[Img.color] = [optimised_ccm] + + logger.info('Finished 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)) + logger.info(f'Matrix calculated for colour temperature of {k} 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 |