d5db46232e
In cases where the calibration image contains super dark areas, or when an invalid blacklevel was supplied, the grid might get close to zero or negative. This would have bad effects on the 1/grid later. So clamp the values to a small positive number. Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Paul Elder <paul.elder@ideasonboard.com>
75 lines
2.3 KiB
Python
75 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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