# 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 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) """ 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 ef='#n109'>109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351