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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>
from ..module import Module
import libtuning as lt
import libtuning.utils as utils
import numpy as np
class LSC(Module):
type = 'lsc'
hr_name = 'LSC (Base)'
out_name = 'GenericLSC'
def __init__(self, *,
debug: list,
sector_shape: tuple,
sector_x_gradient: lt.Gradient,
sector_y_gradient: lt.Gradient,
sector_average_function: lt.Average,
smoothing_function: lt.Smoothing):
super().__init__()
self.debug = debug
self.sector_shape = sector_shape
self.sector_x_gradient = sector_x_gradient
self.sector_y_gradient = sector_y_gradient
self.sector_average_function = sector_average_function
self.smoothing_function = smoothing_function
def _enumerate_lsc_images(self, images):
for image in images:
if image.lsc_only:
yield image
def _get_grid(self, channel, img_w, img_h):
# List of number of pixels in each sector
sectors_x = self.sector_x_gradient.distribute(img_w / 2, self.sector_shape[0])
sectors_y = self.sector_y_gradient.distribute(img_h / 2, self.sector_shape[1])
grid = []
r = 0
for y in sectors_y:
c = 0
for x in sectors_x:
grid.append(self.sector_average_function.average(channel[r:r + y, c:c + x]))
c += x
r += y
return np.array(grid)
def _lsc_single_channel(self, channel: np.array,
image: lt.Image, green_grid: np.array = None):
grid = self._get_grid(channel, image.w, image.h)
# Clamp the values to a small positive, so that the following 1/grid
# doesn't produce negative results.
grid = np.maximum(grid - image.blacklevel_16, 0.1)
if green_grid is None:
table = np.reshape(1 / grid, self.sector_shape[::-1])
else:
table = np.reshape(green_grid / grid, self.sector_shape[::-1])
table = self.smoothing_function.smoothing(table)
if green_grid is None:
table = table / np.min(table)
return table, grid
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