{"id":17871,"url":"https://patchwork.libcamera.org/api/patches/17871/?format=json","web_url":"https://patchwork.libcamera.org/patch/17871/","project":{"id":1,"url":"https://patchwork.libcamera.org/api/projects/1/?format=json","name":"libcamera","link_name":"libcamera","list_id":"libcamera_core","list_email":"libcamera-devel@lists.libcamera.org","web_url":"","scm_url":"","webscm_url":""},"msgid":"<20221124113550.2182342-6-paul.elder@ideasonboard.com>","date":"2022-11-24T11:35:43","name":"[libcamera-devel,v4,05/12] utils: libtuning: modules: alsc: Add raspberrypi ALSC module","commit_ref":null,"pull_url":null,"state":"accepted","archived":false,"hash":"2c8b4445b26147a4445625072b5fe4577347d35f","submitter":{"id":17,"url":"https://patchwork.libcamera.org/api/people/17/?format=json","name":"Paul Elder","email":"paul.elder@ideasonboard.com"},"delegate":null,"mbox":"https://patchwork.libcamera.org/patch/17871/mbox/","series":[{"id":3636,"url":"https://patchwork.libcamera.org/api/series/3636/?format=json","web_url":"https://patchwork.libcamera.org/project/libcamera/list/?series=3636","date":"2022-11-24T11:35:38","name":"utils: tuning: Add a new tuning infrastructure","version":4,"mbox":"https://patchwork.libcamera.org/series/3636/mbox/"}],"comments":"https://patchwork.libcamera.org/api/patches/17871/comments/","check":"pending","checks":"https://patchwork.libcamera.org/api/patches/17871/checks/","tags":{},"headers":{"Return-Path":"","X-Original-To":"parsemail@patchwork.libcamera.org","Delivered-To":"parsemail@patchwork.libcamera.org","Received":["from lancelot.ideasonboard.com (lancelot.ideasonboard.com\n\t[92.243.16.209])\n\tby patchwork.libcamera.org (Postfix) with ESMTPS id B3553BDE6B\n\tfor ;\n\tThu, 24 Nov 2022 11:36:12 +0000 (UTC)","from lancelot.ideasonboard.com (localhost 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t=1669289770;\n\tbh=cDCq7JJNR2UWkIvSoj8eSeAuK3jO7tRfJNCRiiTdAog=;\n\th=From:To:Cc:Subject:Date:In-Reply-To:References:From;\n\tb=qyVO3hxnGpO09hHszj6r+4m6TT4ouhqRAYR1htyFPfCcOQbkmoghHY8gSj6cl4lZd\n\tSGkvSmjnGr6Rvviqf5NCCIvoHAlxkdFOXEF817gvm5m7fLxMVpxawsOb2XWHxF9u3J\n\tIuJ2TO3/4kaw7qGR+lqI1JQ6+Efr59zOuNyxczKg="],"Authentication-Results":"lancelot.ideasonboard.com; dkim=pass (1024-bit key; \n\tunprotected) header.d=ideasonboard.com\n\theader.i=@ideasonboard.com\n\theader.b=\"qyVO3hxn\"; dkim-atps=neutral","To":"libcamera-devel@lists.libcamera.org","Date":"Thu, 24 Nov 2022 20:35:43 +0900","Message-Id":"<20221124113550.2182342-6-paul.elder@ideasonboard.com>","X-Mailer":"git-send-email 2.35.1","In-Reply-To":"<20221124113550.2182342-1-paul.elder@ideasonboard.com>","References":"<20221124113550.2182342-1-paul.elder@ideasonboard.com>","MIME-Version":"1.0","Content-Transfer-Encoding":"8bit","Subject":"[libcamera-devel] [PATCH v4 05/12] utils: libtuning: modules: alsc:\n\tAdd raspberrypi ALSC module","X-BeenThere":"libcamera-devel@lists.libcamera.org","X-Mailman-Version":"2.1.29","Precedence":"list","List-Id":"","List-Unsubscribe":",\n\t","List-Archive":"","List-Post":"","List-Help":"","List-Subscribe":",\n\t","From":"Paul Elder via libcamera-devel ","Reply-To":"Paul Elder ","Errors-To":"libcamera-devel-bounces@lists.libcamera.org","Sender":"\"libcamera-devel\" "},"content":"Add an ALSC module for Raspberry Pi.\n\nSigned-off-by: Paul Elder \nReviewed-by: Laurent Pinchart \n\n---\nChanges in v4:\n- minor style fix\n- remove cli args from module.process parameters\n- replace special rounding with standard rounding as it has higher\n accuracy\n\nChanges in v3:\n- add file description\n- add comment about type name change\n- override the type name to alsc now that the base type name is lsc\n- use Param's getter\n\nNew in v2\n---\n .../tuning/libtuning/modules/lsc/__init__.py | 1 +\n .../libtuning/modules/lsc/raspberrypi.py | 246 ++++++++++++++++++\n 2 files changed, 247 insertions(+)\n create mode 100644 utils/tuning/libtuning/modules/lsc/raspberrypi.py","diff":"diff --git a/utils/tuning/libtuning/modules/lsc/__init__.py b/utils/tuning/libtuning/modules/lsc/__init__.py\nindex 47d9b846..7cdecdb8 100644\n--- a/utils/tuning/libtuning/modules/lsc/__init__.py\n+++ b/utils/tuning/libtuning/modules/lsc/__init__.py\n@@ -3,3 +3,4 @@\n # Copyright (C) 2022, Paul Elder \n \n from libtuning.modules.lsc.lsc import LSC\n+from libtuning.modules.lsc.raspberrypi import ALSCRaspberryPi\ndiff --git a/utils/tuning/libtuning/modules/lsc/raspberrypi.py b/utils/tuning/libtuning/modules/lsc/raspberrypi.py\nnew file mode 100644\nindex 00000000..58f5000d\n--- /dev/null\n+++ b/utils/tuning/libtuning/modules/lsc/raspberrypi.py\n@@ -0,0 +1,246 @@\n+# SPDX-License-Identifier: BSD-2-Clause\n+#\n+# Copyright (C) 2019, Raspberry Pi Ltd\n+# Copyright (C) 2022, Paul Elder \n+#\n+# raspberrypi.py - ALSC module for tuning Raspberry Pi\n+\n+from .lsc import LSC\n+\n+import libtuning as lt\n+import libtuning.utils as utils\n+\n+from numbers import Number\n+import numpy as np\n+\n+\n+class ALSCRaspberryPi(LSC):\n+ # Override the type name so that the parser can match the entry in the\n+ # config file.\n+ type = 'alsc'\n+ hr_name = 'ALSC (Raspberry Pi)'\n+ out_name = 'rpi.alsc'\n+ compatible = ['raspberrypi']\n+\n+ def __init__(self, *,\n+ do_color: lt.Param,\n+ luminance_strength: lt.Param,\n+ **kwargs):\n+ super().__init__(**kwargs)\n+\n+ self.do_color = do_color\n+ self.luminance_strength = luminance_strength\n+\n+ self.output_range = (0, 3.999)\n+\n+ def validate_config(self, config: dict) -> bool:\n+ if self not in config:\n+ utils.eprint(f'{self.type} not in config')\n+ return False\n+\n+ valid = True\n+\n+ conf = config[self]\n+\n+ lum_key = self.luminance_strength.name\n+ color_key = self.do_color.name\n+\n+ if lum_key not in conf and self.luminance_strength.required:\n+ utils.eprint(f'{lum_key} is not in config')\n+ valid = False\n+\n+ if lum_key in conf and (conf[lum_key] < 0 or conf[lum_key] > 1):\n+ utils.eprint(f'Warning: {lum_key} is not in range [0, 1]; defaulting to 0.5')\n+\n+ if color_key not in conf and self.do_color.required:\n+ utils.eprint(f'{color_key} is not in config')\n+ valid = False\n+\n+ return valid\n+\n+ # @return Image color temperature, flattened array of red calibration table\n+ # (containing {sector size} elements), flattened array of blue\n+ # calibration table, flattened array of green calibration\n+ # table\n+\n+ def _do_single_alsc(self, image: lt.Image, do_alsc_colour: bool):\n+ average_green = np.mean((image.channels[lt.Color.GR:lt.Color.GB + 1]), axis=0)\n+\n+ cg, g = self._lsc_single_channel(average_green, image)\n+\n+ if not do_alsc_colour:\n+ return image.color, None, None, cg.flatten()\n+\n+ cr, _ = self._lsc_single_channel(image.channels[lt.Color.R], image, g)\n+ cb, _ = self._lsc_single_channel(image.channels[lt.Color.B], image, g)\n+\n+ # \\todo implement debug\n+\n+ return image.color, cr.flatten(), cb.flatten(), cg.flatten()\n+\n+ # @return Red shading table, Blue shading table, Green shading table,\n+ # number of images processed\n+\n+ def _do_all_alsc(self, images: list, do_alsc_colour: bool, general_conf: dict) -> (list, list, list, Number, int):\n+ # List of colour temperatures\n+ list_col = []\n+ # Associated calibration tables\n+ list_cr = []\n+ list_cb = []\n+ list_cg = []\n+ count = 0\n+ for image in self._enumerate_lsc_images(images):\n+ col, cr, cb, cg = self._do_single_alsc(image, do_alsc_colour)\n+ list_col.append(col)\n+ list_cr.append(cr)\n+ list_cb.append(cb)\n+ list_cg.append(cg)\n+ count += 1\n+\n+ # Convert to numpy array for data manipulation\n+ list_col = np.array(list_col)\n+ list_cr = np.array(list_cr)\n+ list_cb = np.array(list_cb)\n+ list_cg = np.array(list_cg)\n+\n+ cal_cr_list = []\n+ cal_cb_list = []\n+\n+ # Note: Calculation of average corners and center of the shading tables\n+ # has been removed (which ctt had, as it was unused)\n+\n+ # Average all values for luminance shading and return one table for all temperatures\n+ lum_lut = list(np.round(np.mean(list_cg, axis=0), 3))\n+\n+ if not do_alsc_colour:\n+ return None, None, lum_lut, count\n+\n+ for ct in sorted(set(list_col)):\n+ # Average tables for the same colour temperature\n+ indices = np.where(list_col == ct)\n+ ct = int(ct)\n+ t_r = np.round(np.mean(list_cr[indices], axis=0), 3)\n+ t_b = np.round(np.mean(list_cb[indices], axis=0), 3)\n+\n+ cr_dict = {\n+ 'ct': ct,\n+ 'table': list(t_r)\n+ }\n+ cb_dict = {\n+ 'ct': ct,\n+ 'table': list(t_b)\n+ }\n+ cal_cr_list.append(cr_dict)\n+ cal_cb_list.append(cb_dict)\n+\n+ return cal_cr_list, cal_cb_list, lum_lut, count\n+\n+ # @brief Calculate sigma from two adjacent gain tables\n+ def _calcSigma(self, g1, g2):\n+ g1 = np.reshape(g1, self.sector_shape[::-1])\n+ g2 = np.reshape(g2, self.sector_shape[::-1])\n+\n+ # Apply gains to gain table\n+ gg = g1 / g2\n+ if np.mean(gg) < 1:\n+ gg = 1 / gg\n+\n+ # For each internal patch, compute average difference between it and\n+ # its 4 neighbours, then append to list\n+ diffs = []\n+ for i in range(self.sector_shape[1] - 2):\n+ for j in range(self.sector_shape[0] - 2):\n+ # Indexing is incremented by 1 since all patches on borders are\n+ # not counted\n+ diff = np.abs(gg[i + 1][j + 1] - gg[i][j + 1])\n+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 2][j + 1])\n+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j])\n+ diff += np.abs(gg[i + 1][j + 1] - gg[i + 1][j + 2])\n+ diffs.append(diff / 4)\n+\n+ mean_diff = np.mean(diffs)\n+ return np.round(mean_diff, 5)\n+\n+ # @brief Obtains sigmas for red and blue, effectively a measure of the\n+ # 'error'\n+ def _get_sigma(self, cal_cr_list, cal_cb_list):\n+ # Provided colour alsc tables were generated for two different colour\n+ # temperatures sigma is calculated by comparing two calibration temperatures\n+ # adjacent in colour space\n+\n+ color_temps = [cal['ct'] for cal in cal_cr_list]\n+\n+ # Calculate sigmas for each adjacent color_temps and return worst one\n+ sigma_rs = []\n+ sigma_bs = []\n+ for i in range(len(color_temps) - 1):\n+ sigma_rs.append(self._calcSigma(cal_cr_list[i]['table'], cal_cr_list[i + 1]['table']))\n+ sigma_bs.append(self._calcSigma(cal_cb_list[i]['table'], cal_cb_list[i + 1]['table']))\n+\n+ # Return maximum sigmas, not necessarily from the same colour\n+ # temperature interval\n+ sigma_r = max(sigma_rs) if sigma_rs else 0.005\n+ sigma_b = max(sigma_bs) if sigma_bs else 0.005\n+\n+ return sigma_r, sigma_b\n+\n+ def process(self, config: dict, images: list, outputs: dict) -> dict:\n+ output = {\n+ 'omega': 1.3,\n+ 'n_iter': 100,\n+ 'luminance_strength': 0.7\n+ }\n+\n+ conf = config[self]\n+ general_conf = config['general']\n+\n+ do_alsc_colour = self.do_color.get_value(conf)\n+\n+ # \\todo I have no idea where this input parameter is used\n+ luminance_strength = self.luminance_strength.get_value(conf)\n+ if luminance_strength < 0 or luminance_strength > 1:\n+ luminance_strength = 0.5\n+\n+ output['luminance_strength'] = luminance_strength\n+\n+ # \\todo Validate images from greyscale camera and force grescale mode\n+ # \\todo Debug functionality\n+\n+ alsc_out = self._do_all_alsc(images, do_alsc_colour, general_conf)\n+ # \\todo Handle the second green lut\n+ cal_cr_list, cal_cb_list, luminance_lut, count = alsc_out\n+\n+ if not do_alsc_colour:\n+ output['luminance_lut'] = luminance_lut\n+ output['n_iter'] = 0\n+ return output\n+\n+ output['calibrations_Cr'] = cal_cr_list\n+ output['calibrations_Cb'] = cal_cb_list\n+ output['luminance_lut'] = luminance_lut\n+\n+ # The sigmas determine the strength of the adaptive algorithm, that\n+ # cleans up any lens shading that has slipped through the alsc. These\n+ # are determined by measuring a 'worst-case' difference between two\n+ # alsc tables that are adjacent in colour space. If, however, only one\n+ # colour temperature has been provided, then this difference can not be\n+ # computed as only one table is available.\n+ # To determine the sigmas you would have to estimate the error of an\n+ # alsc table with only the image it was taken on as a check. To avoid\n+ # circularity, dfault exaggerated sigmas are used, which can result in\n+ # too much alsc and is therefore not advised.\n+ # In general, just take another alsc picture at another colour\n+ # temperature!\n+\n+ if count == 1:\n+ output['sigma'] = 0.005\n+ output['sigma_Cb'] = 0.005\n+ utils.eprint('Warning: Only one alsc calibration found; standard sigmas used for adaptive algorithm.')\n+ return output\n+\n+ # Obtain worst-case scenario residual sigmas\n+ sigma_r, sigma_b = self._get_sigma(cal_cr_list, cal_cb_list)\n+ output['sigma'] = np.round(sigma_r, 5)\n+ output['sigma_Cb'] = np.round(sigma_b, 5)\n+\n+ return output\n","prefixes":["libcamera-devel","v4","05/12"]}