@@ -3,3 +3,4 @@
# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
from libtuning.modules.lsc.lsc import LSC
+from libtuning.modules.lsc.raspberrypi import ALSCRaspberryPi
new file mode 100644
@@ -0,0 +1,246 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# raspberrypi.py - ALSC module for tuning Raspberry Pi
+
+from .lsc import LSC
+
+import libtuning as lt
+import libtuning.utils as utils
+
+from numbers import Number
+import numpy as np
+
+
+class ALSCRaspberryPi(LSC):
+ # Override the type name so that the parser can match the entry in the
+ # config file.
+ type = 'alsc'
+ hr_name = 'ALSC (Raspberry Pi)'
+ out_name = 'rpi.alsc'
+ compatible = ['raspberrypi']
+
+ def __init__(self, *,
+ do_color: lt.Param,
+ luminance_strength: lt.Param,
+ **kwargs):
+ super().__init__(**kwargs)
+
+ self.do_color = do_color
+ self.luminance_strength = luminance_strength
+
+ self.output_range = (0, 3.999)
+
+ def validate_config(self, config: dict) -> bool:
+ if self not in config:
+ utils.eprint(f'{self.type} not in config')
+ return False
+
+ valid = True
+
+ conf = config[self]
+
+ lum_key = self.luminance_strength.name
+ color_key = self.do_color.name
+
+ if lum_key not in conf and self.luminance_strength.required:
+ utils.eprint(f'{lum_key} is not in config')
+ valid = False
+
+ if lum_key in conf and (conf[lum_key] < 0 or conf[lum_key] > 1):
+ utils.eprint(f'Warning: {lum_key} is not in range [0, 1]; defaulting to 0.5')
+
+ if color_key not in conf and self.do_color.required:
+ utils.eprint(f'{color_key} is not in config')
+ valid = False
+
+ return valid
+
+ # @return Image color temperature, flattened array of red calibration table
+ # (containing {sector size} elements), flattened array of blue
+ # calibration table, flattened array of green calibration
+ # table
+
+ def _do_single_alsc(self, image: lt.Image, do_alsc_colour: bool):
+ average_green = np.mean((image.channels[lt.Color.GR:lt.Color.GB + 1]), axis=0)
+
+ cg, g = self._lsc_single_channel(average_green, image)
+
+ if not do_alsc_colour:
+ return image.color, None, None, cg.flatten()
+
+ cr, _ = self._lsc_single_channel(image.channels[lt.Color.R], image, g)
+ cb, _ = self._lsc_single_channel(image.channels[lt.Color.B], image, g)
+
+ # \todo implement debug
+
+ return image.color, cr.flatten(), cb.flatten(), cg.flatten()
+
+ # @return Red shading table, Blue shading table, Green shading table,
+ # number of images processed
+
+ def _do_all_alsc(self, images: list, do_alsc_colour: bool, general_conf: dict) -> (list, list, list, Number, int):
+ # List of colour temperatures
+ list_col = []
+ # Associated calibration tables
+ list_cr = []
+ list_cb = []
+ list_cg = []
+ count = 0
+ for image in self._enumerate_lsc_images(images):
+ col, cr, cb, cg = self._do_single_alsc(image, do_alsc_colour)
+ list_col.append(col)
+ list_cr.append(cr)
+ list_cb.append(cb)
+ list_cg.append(cg)
+ count += 1
+
+ # Convert to numpy array for data manipulation
+ list_col = np.array(list_col)
+ list_cr = np.array(list_cr)
+ list_cb = np.array(list_cb)
+ list_cg = np.array(list_cg)
+
+ cal_cr_list = []
+ cal_cb_list = []
+
+ # Note: Calculation of average corners and center of the shading tables
+ # has been removed (which ctt had, as it was unused)
+
+ # Average all values for luminance shading and return one table for all temperatures
+ lum_lut = list(np.round(np.mean(list_cg, axis=0), 3))
+
+ if not do_alsc_colour:
+ return None, None, lum_lut, count
+
+ for ct in sorted(set(list_col)):
+ # Average tables for the same colour temperature
+ indices = np.where(list_col == ct)
+ ct = int(ct)
+ t_r = np.round(np.mean(list_cr[indices], axis=0), 3)
+ t_b = np.round(np.mean(list_cb[indices], axis=0), 3)
+
+ cr_dict = {
+ 'ct': ct,
+ 'table': list(t_r)
+ }
+ cb_dict = {
+ 'ct': ct,
+ 'table': list(t_b)
+ }
+ cal_cr_list.append(cr_dict)
+ cal_cb_list.append(cb_dict)
+
+ return cal_cr_list, cal_cb_list, lum_lut, count
+
+ # @brief Calculate sigma from two adjacent gain tables
+ def _calcSigma(self, g1, g2):
+ g1 = np.reshape(g1, self.sector_shape[::-1])
+ g2 = np.reshape(g2, self.sector_shape[::-1])
+
+ # Apply gains to gain table
+ gg = g1 / g2
+ if np.mean(gg) < 1:
+ gg = 1 / gg
+
+ # For each internal patch, compute average difference between it and
+ # its 4 neighbours, then append to list
+ diffs = []
+ for i in range(self.sector_shape[1] - 2):
+ for j in range(self.sector_shape[0] - 2):
+ # Indexing is incremented by 1 since all patches on borders are
+ # not counted
+ diff = np.abs(gg[i + 1][j + 1] - gg[i][j + 1])
+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 2][j + 1])
+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j])
+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j + 2])
+ diffs.append(diff / 4)
+
+ mean_diff = np.mean(diffs)
+ return np.round(mean_diff, 5)
+
+ # @brief Obtains sigmas for red and blue, effectively a measure of the
+ # 'error'
+ def _get_sigma(self, cal_cr_list, cal_cb_list):
+ # Provided colour alsc tables were generated for two different colour
+ # temperatures sigma is calculated by comparing two calibration temperatures
+ # adjacent in colour space
+
+ color_temps = [cal['ct'] for cal in cal_cr_list]
+
+ # Calculate sigmas for each adjacent color_temps and return worst one
+ sigma_rs = []
+ sigma_bs = []
+ for i in range(len(color_temps) - 1):
+ sigma_rs.append(self._calcSigma(cal_cr_list[i]['table'], cal_cr_list[i + 1]['table']))
+ sigma_bs.append(self._calcSigma(cal_cb_list[i]['table'], cal_cb_list[i + 1]['table']))
+
+ # Return maximum sigmas, not necessarily from the same colour
+ # temperature interval
+ sigma_r = max(sigma_rs) if sigma_rs else 0.005
+ sigma_b = max(sigma_bs) if sigma_bs else 0.005
+
+ return sigma_r, sigma_b
+
+ def process(self, config: dict, images: list, outputs: dict) -> dict:
+ output = {
+ 'omega': 1.3,
+ 'n_iter': 100,
+ 'luminance_strength': 0.7
+ }
+
+ conf = config[self]
+ general_conf = config['general']
+
+ do_alsc_colour = self.do_color.get_value(conf)
+
+ # \todo I have no idea where this input parameter is used
+ luminance_strength = self.luminance_strength.get_value(conf)
+ if luminance_strength < 0 or luminance_strength > 1:
+ luminance_strength = 0.5
+
+ output['luminance_strength'] = luminance_strength
+
+ # \todo Validate images from greyscale camera and force grescale mode
+ # \todo Debug functionality
+
+ alsc_out = self._do_all_alsc(images, do_alsc_colour, general_conf)
+ # \todo Handle the second green lut
+ cal_cr_list, cal_cb_list, luminance_lut, count = alsc_out
+
+ if not do_alsc_colour:
+ output['luminance_lut'] = luminance_lut
+ output['n_iter'] = 0
+ return output
+
+ output['calibrations_Cr'] = cal_cr_list
+ output['calibrations_Cb'] = cal_cb_list
+ output['luminance_lut'] = luminance_lut
+
+ # The sigmas determine the strength of the adaptive algorithm, that
+ # cleans up any lens shading that has slipped through the alsc. These
+ # are determined by measuring a 'worst-case' difference between two
+ # alsc tables that are adjacent in colour space. If, however, only one
+ # colour temperature has been provided, then this difference can not be
+ # computed as only one table is available.
+ # To determine the sigmas you would have to estimate the error of an
+ # alsc table with only the image it was taken on as a check. To avoid
+ # circularity, dfault exaggerated sigmas are used, which can result in
+ # too much alsc and is therefore not advised.
+ # In general, just take another alsc picture at another colour
+ # temperature!
+
+ if count == 1:
+ output['sigma'] = 0.005
+ output['sigma_Cb'] = 0.005
+ utils.eprint('Warning: Only one alsc calibration found; standard sigmas used for adaptive algorithm.')
+ return output
+
+ # Obtain worst-case scenario residual sigmas
+ sigma_r, sigma_b = self._get_sigma(cal_cr_list, cal_cb_list)
+ output['sigma'] = np.round(sigma_r, 5)
+ output['sigma_Cb'] = np.round(sigma_b, 5)
+
+ return output