diff options
Diffstat (limited to 'utils/tuning/libtuning')
| -rw-r--r-- | utils/tuning/libtuning/ctt_awb.py | 376 | ||||
| -rw-r--r-- | utils/tuning/libtuning/ctt_ccm.py | 406 | ||||
| -rw-r--r-- | utils/tuning/libtuning/ctt_colors.py | 30 | ||||
| -rw-r--r-- | utils/tuning/libtuning/ctt_ransac.py | 71 |
4 files changed, 883 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/ctt_awb.py b/utils/tuning/libtuning/ctt_awb.py new file mode 100644 index 00000000..5ba6f978 --- /dev/null +++ b/utils/tuning/libtuning/ctt_awb.py @@ -0,0 +1,376 @@ +# 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 diff --git a/utils/tuning/libtuning/ctt_ccm.py b/utils/tuning/libtuning/ctt_ccm.py new file mode 100644 index 00000000..59753e33 --- /dev/null +++ b/utils/tuning/libtuning/ctt_ccm.py @@ -0,0 +1,406 @@ +# 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 diff --git a/utils/tuning/libtuning/ctt_colors.py b/utils/tuning/libtuning/ctt_colors.py new file mode 100644 index 00000000..cb4d236b --- /dev/null +++ b/utils/tuning/libtuning/ctt_colors.py @@ -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] diff --git a/utils/tuning/libtuning/ctt_ransac.py b/utils/tuning/libtuning/ctt_ransac.py new file mode 100644 index 00000000..01bba302 --- /dev/null +++ b/utils/tuning/libtuning/ctt_ransac.py @@ -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) |