summaryrefslogtreecommitdiff
path: root/utils/tuning/libtuning/modules
diff options
context:
space:
mode:
Diffstat (limited to 'utils/tuning/libtuning/modules')
-rw-r--r--utils/tuning/libtuning/modules/lsc/__init__.py1
-rw-r--r--utils/tuning/libtuning/modules/lsc/rkisp1.py112
2 files changed, 113 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/modules/lsc/__init__.py b/utils/tuning/libtuning/modules/lsc/__init__.py
index 7cdecdb8..0ba4411b 100644
--- a/utils/tuning/libtuning/modules/lsc/__init__.py
+++ b/utils/tuning/libtuning/modules/lsc/__init__.py
@@ -4,3 +4,4 @@
from libtuning.modules.lsc.lsc import LSC
from libtuning.modules.lsc.raspberrypi import ALSCRaspberryPi
+from libtuning.modules.lsc.rkisp1 import LSCRkISP1
diff --git a/utils/tuning/libtuning/modules/lsc/rkisp1.py b/utils/tuning/libtuning/modules/lsc/rkisp1.py
new file mode 100644
index 00000000..5701ae0a
--- /dev/null
+++ b/utils/tuning/libtuning/modules/lsc/rkisp1.py
@@ -0,0 +1,112 @@
+# 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
+
+ def process(self, config: dict, images: list, outputs: dict) -> dict:
+ output = {}
+
+ # \todo This should actually come from self.sector_{x,y}_gradient
+ size_gradient = lt.gradient.Linear(lt.Remainder.Float)
+ output['x-size'] = size_gradient.distribute(0.5, 8)
+ output['y-size'] = size_gradient.distribute(0.5, 8)
+
+ output['sets'] = self._do_all_lsc(images)
+
+ # \todo Validate images from greyscale camera and force grescale mode
+ # \todo Debug functionality
+
+ return output