diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_ccm.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_ccm.py | 260 |
1 files changed, 221 insertions, 39 deletions
diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py index cebecfc2..a09bfd09 100644 --- a/utils/raspberrypi/ctt/ctt_ccm.py +++ b/utils/raspberrypi/ctt/ctt_ccm.py @@ -1,32 +1,68 @@ # SPDX-License-Identifier: BSD-2-Clause # -# Copyright (C) 2019, Raspberry Pi (Trading) Limited +# Copyright (C) 2019, Raspberry Pi Ltd # # ctt_ccm.py - 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) - x = np.where(x < 0.04045, x/12.92, ((x+0.055)/1.055)**2.4) - x = x * ((2**16)-1) + 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 sRGB + m_rgb = np.array([ # these are in RGB [116, 81, 67], # dark skin [199, 147, 129], # light skin [91, 122, 156], # blue sky @@ -34,7 +70,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): [130, 128, 176], # blue flower [92, 190, 172], # bluish green [224, 124, 47], # orange - [68, 91, 170], # purplish blue + [68, 91, 170], # purplish blue [198, 82, 97], # moderate red [94, 58, 106], # purple [159, 189, 63], # yellow green @@ -52,16 +88,20 @@ def ccm(Cam, cal_cr_list, cal_cb_list): [82, 84, 86], # neutral 3.5 [49, 49, 51] # black 2 ]) - """ convert reference colours from srgb to rgb """ - m_srgb = degamma(m_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 @@ -76,8 +116,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list): """ normalise tables so min value is 1 """ - cr_tab = cr_tab/np.min(cr_tab) - cb_tab = cb_tab/np.min(cb_tab) + cr_tab = cr_tab / np.min(cr_tab) + cb_tab = cb_tab / np.min(cb_tab) colour_cals[cr['ct']] = [cr_tab, cb_tab] """ @@ -94,6 +134,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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 @@ -101,34 +143,123 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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_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)) + 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) - + 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(ccm) + ccm_tab[Img.col].append(optimised_ccm) else: - ccm_tab[Img.col] = [ccm] + ccm_tab[Img.col] = [optimised_ccm] Cam.log += '\n' Cam.log += '\nFinished processing images' @@ -137,8 +268,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list): """ 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) + 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) @@ -156,20 +287,65 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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 calculate in rgb space, and are fit by hand. Although it is a 3x3 +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. -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! :-) +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) + 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) @@ -191,7 +367,7 @@ def do_ccm(r, g, b, m_srgb): b_rb = np.sum(b_rbs) b_gb = np.sum(b_gbs) - det = rb_2*gb_2 - rb_gb*rb_gb + det = rb_2 * gb_2 - rb_gb * rb_gb """ Raise error if matrix is singular... @@ -201,19 +377,19 @@ def do_ccm(r, g, b, m_srgb): 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 + 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_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_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 """ @@ -222,3 +398,9 @@ def do_ccm(r, g, b, m_srgb): 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 |