diff options
Diffstat (limited to 'utils/tuning/libtuning/ctt_ccm.py')
| -rw-r--r-- | utils/tuning/libtuning/ctt_ccm.py | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/utils/tuning/libtuning/ctt_ccm.py b/utils/tuning/libtuning/ctt_ccm.py index f37adaf4..c4362756 100644 --- a/utils/tuning/libtuning/ctt_ccm.py +++ b/utils/tuning/libtuning/ctt_ccm.py @@ -4,6 +4,8 @@ # # camera tuning tool for CCM (colour correction matrix) +import logging + import numpy as np from scipy.optimize import minimize @@ -12,6 +14,8 @@ 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 """ @@ -129,7 +133,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): """ ccm_tab = {} for Img in imgs: - Cam.log += '\nProcessing image: ' + Img.name + 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 @@ -154,7 +158,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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) + 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) @@ -192,15 +196,13 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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 + # 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)] - # 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) + 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)) @@ -229,7 +231,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): We now want to spit out some data that shows how the optimisation has improved the color matrices ''' - Cam.log += "Here are the Improvements" + logger.info("Here are the Improvements") # CALCULATE WORST CASE delta e old_worst_delta_e = 0 @@ -244,8 +246,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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) + 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.col) + str(matrix_selection_types[typenum])) ''' @@ -262,9 +264,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list): ccm_tab[Img.col].append(optimised_ccm) else: ccm_tab[Img.col] = [optimised_ccm] - Cam.log += '\n' - Cam.log += '\nFinished processing images' + logger.info('Finished processing images') """ average any ccms that share a colour temperature """ @@ -273,7 +274,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): 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) + logger.info(f'Matrix calculated for colour temperature of {k} K') """ return all ccms with respective colour temperature in the correct format, |