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