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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
#
# rkisp1.py - LSC module for tuning rkisp1
from .lsc import LSC
import libtuning as lt
import libtuning.utils as utils
from numbers import Number
import numpy as np
class LSCRkISP1(LSC):
hr_name = 'LSC (RkISP1)'
out_name = 'LensShadingCorrection'
# \todo Not sure if this is useful. Probably will remove later.
compatible = ['rkisp1']
def __init__(self, *args, **kwargs):
super().__init__(**kwargs)
# We don't actually need anything from the config file
def validate_config(self, config: dict) -> bool:
return True
# @return Image color temperature, flattened array of red calibration table
# (containing {sector size} elements), flattened array of blue
# calibration table, flattened array of (red's) green calibration
# table, flattened array of (blue's) green calibration table
def _do_single_lsc(self, image: lt.Image):
cgr, gr = self._lsc_single_channel(image.channels[lt.Color.GR], image)
cgb, gb = self._lsc_single_channel(image.channels[lt.Color.GB], image)
# \todo Should these ratio against the average of both greens or just
# each green like we've done here?
cr, _ = self._lsc_single_channel(image.channels[lt.Color.R], image, gr)
cb, _ = self._lsc_single_channel(image.channels[lt.Color.B], image, gb)
return image.color, cr.flatten(), cb.flatten(), cgr.flatten(), cgb.flatten()
# @return List of dictionaries of color temperature, red table, red's green
# table, blue's green table, and blue table
def _do_all_lsc(self, images: list) -> list:
output_list = []
output_map_func = lt.gradient.Linear().map
# List of colour temperatures
list_col = []
# Associated calibration tables
list_cr = []
list_cb = []
list_cgr = []
list_cgb = []
for image in self._enumerate_lsc_images(images):
col, cr, cb, cgr, cgb = self._do_single_lsc(image)
list_col.append(col)
list_cr.append(cr)
list_cb.append(cb)
list_cgr.append(cgr)
list_cgb.append(cgb)
# 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_cgr = np.array(list_cgr)
list_cgb = np.array(list_cgb)
for color_temperature in sorted(set(list_col)):
# Average tables for the same colour temperature
indices = np.where(list_col == color_temperature)
color_temperature = int(color_temperature)
tables = []
for lis in [list_cr, list_cgr, list_cgb, list_cb]:
table = np.mean(lis[indices], axis=0)
table = output_map_func((1, 3.999), (1024, 4095), table)
table = np.round(table).astype('int32').tolist()
tables.append(table)
entry = {
'ct': color_temperature,
'r': tables[0],
'gr': tables[1],
'gb': tables[2],
'b': tables[3],
}
output_list.append(entry)
return output_list
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