summaryrefslogtreecommitdiff
path: root/utils/tuning/libtuning/modules/lsc
diff options
context:
space:
mode:
Diffstat (limited to 'utils/tuning/libtuning/modules/lsc')
-rw-r--r--utils/tuning/libtuning/modules/lsc/__init__.py5
-rw-r--r--utils/tuning/libtuning/modules/lsc/lsc.py72
2 files changed, 77 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/modules/lsc/__init__.py b/utils/tuning/libtuning/modules/lsc/__init__.py
new file mode 100644
index 00000000..47d9b846
--- /dev/null
+++ b/utils/tuning/libtuning/modules/lsc/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+
+from libtuning.modules.lsc.lsc import LSC
diff --git a/utils/tuning/libtuning/modules/lsc/lsc.py b/utils/tuning/libtuning/modules/lsc/lsc.py
new file mode 100644
index 00000000..344a07a3
--- /dev/null
+++ b/utils/tuning/libtuning/modules/lsc/lsc.py
@@ -0,0 +1,72 @@
+# 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)
+ grid -= image.blacklevel_16
+ 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