utils: libtuning: modules: alsc: Add raspberrypi ALSC module
Add an ALSC module for Raspberry Pi. Signed-off-by: Paul Elder <paul.elder@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
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Paul Elder
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# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
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from libtuning.modules.lsc.lsc import LSC
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from libtuning.modules.lsc.raspberrypi import ALSCRaspberryPi
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246
utils/tuning/libtuning/modules/lsc/raspberrypi.py
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246
utils/tuning/libtuning/modules/lsc/raspberrypi.py
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# SPDX-License-Identifier: BSD-2-Clause
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#
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# Copyright (C) 2019, Raspberry Pi Ltd
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# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
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#
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# raspberrypi.py - ALSC module for tuning Raspberry Pi
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from .lsc import LSC
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import libtuning as lt
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import libtuning.utils as utils
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from numbers import Number
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import numpy as np
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class ALSCRaspberryPi(LSC):
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# Override the type name so that the parser can match the entry in the
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# config file.
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type = 'alsc'
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hr_name = 'ALSC (Raspberry Pi)'
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out_name = 'rpi.alsc'
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compatible = ['raspberrypi']
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def __init__(self, *,
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do_color: lt.Param,
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luminance_strength: lt.Param,
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**kwargs):
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super().__init__(**kwargs)
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self.do_color = do_color
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self.luminance_strength = luminance_strength
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self.output_range = (0, 3.999)
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def validate_config(self, config: dict) -> bool:
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if self not in config:
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utils.eprint(f'{self.type} not in config')
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return False
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valid = True
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conf = config[self]
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lum_key = self.luminance_strength.name
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color_key = self.do_color.name
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if lum_key not in conf and self.luminance_strength.required:
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utils.eprint(f'{lum_key} is not in config')
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valid = False
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if lum_key in conf and (conf[lum_key] < 0 or conf[lum_key] > 1):
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utils.eprint(f'Warning: {lum_key} is not in range [0, 1]; defaulting to 0.5')
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if color_key not in conf and self.do_color.required:
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utils.eprint(f'{color_key} is not in config')
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valid = False
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return valid
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# @return Image color temperature, flattened array of red calibration table
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# (containing {sector size} elements), flattened array of blue
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# calibration table, flattened array of green calibration
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# table
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def _do_single_alsc(self, image: lt.Image, do_alsc_colour: bool):
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average_green = np.mean((image.channels[lt.Color.GR:lt.Color.GB + 1]), axis=0)
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cg, g = self._lsc_single_channel(average_green, image)
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if not do_alsc_colour:
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return image.color, None, None, cg.flatten()
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cr, _ = self._lsc_single_channel(image.channels[lt.Color.R], image, g)
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cb, _ = self._lsc_single_channel(image.channels[lt.Color.B], image, g)
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# \todo implement debug
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return image.color, cr.flatten(), cb.flatten(), cg.flatten()
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# @return Red shading table, Blue shading table, Green shading table,
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# number of images processed
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def _do_all_alsc(self, images: list, do_alsc_colour: bool, general_conf: dict) -> (list, list, list, Number, int):
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# List of colour temperatures
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list_col = []
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# Associated calibration tables
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list_cr = []
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list_cb = []
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list_cg = []
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count = 0
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for image in self._enumerate_lsc_images(images):
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col, cr, cb, cg = self._do_single_alsc(image, do_alsc_colour)
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list_col.append(col)
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list_cr.append(cr)
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list_cb.append(cb)
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list_cg.append(cg)
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count += 1
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# Convert to numpy array for data manipulation
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list_col = np.array(list_col)
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list_cr = np.array(list_cr)
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list_cb = np.array(list_cb)
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list_cg = np.array(list_cg)
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cal_cr_list = []
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cal_cb_list = []
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# Note: Calculation of average corners and center of the shading tables
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# has been removed (which ctt had, as it was unused)
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# Average all values for luminance shading and return one table for all temperatures
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lum_lut = list(np.round(np.mean(list_cg, axis=0), 3))
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if not do_alsc_colour:
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return None, None, lum_lut, count
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for ct in sorted(set(list_col)):
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# Average tables for the same colour temperature
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indices = np.where(list_col == ct)
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ct = int(ct)
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t_r = np.round(np.mean(list_cr[indices], axis=0), 3)
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t_b = np.round(np.mean(list_cb[indices], axis=0), 3)
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cr_dict = {
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'ct': ct,
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'table': list(t_r)
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}
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cb_dict = {
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'ct': ct,
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'table': list(t_b)
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}
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cal_cr_list.append(cr_dict)
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cal_cb_list.append(cb_dict)
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return cal_cr_list, cal_cb_list, lum_lut, count
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# @brief Calculate sigma from two adjacent gain tables
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def _calcSigma(self, g1, g2):
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g1 = np.reshape(g1, self.sector_shape[::-1])
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g2 = np.reshape(g2, self.sector_shape[::-1])
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# Apply gains to gain table
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gg = g1 / g2
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if np.mean(gg) < 1:
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gg = 1 / gg
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# For each internal patch, compute average difference between it and
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# its 4 neighbours, then append to list
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diffs = []
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for i in range(self.sector_shape[1] - 2):
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for j in range(self.sector_shape[0] - 2):
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# Indexing is incremented by 1 since all patches on borders are
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# not counted
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diff = np.abs(gg[i + 1][j + 1] - gg[i][j + 1])
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diff += np.abs(gg[i + 1][j + 1] - gg[i + 2][j + 1])
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diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j])
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diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j + 2])
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diffs.append(diff / 4)
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mean_diff = np.mean(diffs)
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return np.round(mean_diff, 5)
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# @brief Obtains sigmas for red and blue, effectively a measure of the
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# 'error'
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def _get_sigma(self, cal_cr_list, cal_cb_list):
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# Provided colour alsc tables were generated for two different colour
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# temperatures sigma is calculated by comparing two calibration temperatures
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# adjacent in colour space
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color_temps = [cal['ct'] for cal in cal_cr_list]
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# Calculate sigmas for each adjacent color_temps and return worst one
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sigma_rs = []
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sigma_bs = []
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for i in range(len(color_temps) - 1):
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sigma_rs.append(self._calcSigma(cal_cr_list[i]['table'], cal_cr_list[i + 1]['table']))
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sigma_bs.append(self._calcSigma(cal_cb_list[i]['table'], cal_cb_list[i + 1]['table']))
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# Return maximum sigmas, not necessarily from the same colour
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# temperature interval
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sigma_r = max(sigma_rs) if sigma_rs else 0.005
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sigma_b = max(sigma_bs) if sigma_bs else 0.005
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return sigma_r, sigma_b
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def process(self, config: dict, images: list, outputs: dict) -> dict:
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output = {
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'omega': 1.3,
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'n_iter': 100,
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'luminance_strength': 0.7
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}
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conf = config[self]
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general_conf = config['general']
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do_alsc_colour = self.do_color.get_value(conf)
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# \todo I have no idea where this input parameter is used
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luminance_strength = self.luminance_strength.get_value(conf)
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if luminance_strength < 0 or luminance_strength > 1:
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luminance_strength = 0.5
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output['luminance_strength'] = luminance_strength
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# \todo Validate images from greyscale camera and force grescale mode
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# \todo Debug functionality
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alsc_out = self._do_all_alsc(images, do_alsc_colour, general_conf)
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# \todo Handle the second green lut
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cal_cr_list, cal_cb_list, luminance_lut, count = alsc_out
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if not do_alsc_colour:
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output['luminance_lut'] = luminance_lut
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output['n_iter'] = 0
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return output
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output['calibrations_Cr'] = cal_cr_list
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output['calibrations_Cb'] = cal_cb_list
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output['luminance_lut'] = luminance_lut
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# The sigmas determine the strength of the adaptive algorithm, that
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# cleans up any lens shading that has slipped through the alsc. These
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# are determined by measuring a 'worst-case' difference between two
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# alsc tables that are adjacent in colour space. If, however, only one
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# colour temperature has been provided, then this difference can not be
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# computed as only one table is available.
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# To determine the sigmas you would have to estimate the error of an
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# alsc table with only the image it was taken on as a check. To avoid
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# circularity, dfault exaggerated sigmas are used, which can result in
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# too much alsc and is therefore not advised.
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# In general, just take another alsc picture at another colour
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# temperature!
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if count == 1:
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output['sigma'] = 0.005
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output['sigma_Cb'] = 0.005
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utils.eprint('Warning: Only one alsc calibration found; standard sigmas used for adaptive algorithm.')
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return output
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# Obtain worst-case scenario residual sigmas
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sigma_r, sigma_b = self._get_sigma(cal_cr_list, cal_cb_list)
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output['sigma'] = np.round(sigma_r, 5)
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output['sigma_Cb'] = np.round(sigma_b, 5)
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return output
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