[{"id":30933,"web_url":"https://patchwork.libcamera.org/comment/30933/","msgid":"","date":"2024-08-28T07:16:31","subject":"Re: [PATCH v4 4/6] libtuning: Modify ctt_awb.awb() so that it can\n\trun in our context","submitter":{"id":17,"url":"https://patchwork.libcamera.org/api/people/17/","name":"Paul Elder","email":"paul.elder@ideasonboard.com"},"content":"On Tue, Aug 13, 2024 at 10:44:21AM +0200, Stefan Klug wrote:\n> Modify the awb function that was copied from the Raspberry Pi tuning\n> scripts in a way that it can easily be called from the libtuning code.\n> In essence the logging was replaced by calls to a python logger and the\n> need for the Cam object was removed by providing a list of images to the\n> function.\n> \n> Signed-off-by: Stefan Klug \n> Reviewed-by: Kieran Bingham \n\nReviewed-by: Paul Elder \n\n> ---\n> utils/tuning/libtuning/ctt_awb.py | 55 ++++++++++++++++---------------\n> 1 file changed, 28 insertions(+), 27 deletions(-)\n> \n> diff --git a/utils/tuning/libtuning/ctt_awb.py b/utils/tuning/libtuning/ctt_awb.py\n> index abf22321a0ea..73a1bc1a840c 100644\n> --- a/utils/tuning/libtuning/ctt_awb.py\n> +++ b/utils/tuning/libtuning/ctt_awb.py\n> @@ -4,6 +4,8 @@\n> #\n> # camera tuning tool for AWB\n> \n> +import logging\n> +\n> import matplotlib.pyplot as plt\n> from bisect import bisect_left\n> from scipy.optimize import fmin\n> @@ -11,12 +13,12 @@ import numpy as np\n> \n> from .image import Image\n> \n> +logger = logging.getLogger(__name__)\n> \n> \"\"\"\n> obtain piecewise linear approximation for colour curve\n> \"\"\"\n> -def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> - imgs = Cam.imgs\n> +def awb(imgs, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> condense alsc calibration tables into one dictionary\n> \"\"\"\n> @@ -39,7 +41,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> rb_raw = []\n> rbs_hat = []\n> for Img in imgs:\n> - Cam.log += '\\nProcessing '+Img.name\n> + logger.info(f'Processing {Img.name}')\n> \"\"\"\n> get greyscale patches with alsc applied if alsc enabled.\n> Note: if alsc is disabled then colour_cals will be set to None and the\n> @@ -51,7 +53,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> r_g = np.mean(r_patchs/g_patchs)\n> b_g = np.mean(b_patchs/g_patchs)\n> - Cam.log += '\\n r : {:.4f} b : {:.4f}'.format(r_g, b_g)\n> + logger.info(' r : {:.4f} b : {:.4f}'.format(r_g, b_g))\n> \"\"\"\n> The curve tends to be better behaved in so-called hatspace.\n> R, B, G represent the individual channels. The colour curve is plotted in\n> @@ -74,12 +76,11 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> r_g_hat = r_g/(1+r_g+b_g)\n> b_g_hat = b_g/(1+r_g+b_g)\n> - Cam.log += '\\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat)\n> - rbs_hat.append((r_g_hat, b_g_hat, Img.col))\n> + logger.info('\\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat))\n> + rbs_hat.append((r_g_hat, b_g_hat, Img.color))\n> rb_raw.append((r_g, b_g))\n> - Cam.log += '\\n'\n> \n> - Cam.log += '\\nFinished processing images'\n> + logger.info('Finished processing images')\n> \"\"\"\n> sort all lits simultaneously by r_hat\n> \"\"\"\n> @@ -95,7 +96,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> fit quadratic fit to r_g hat and b_g_hat\n> \"\"\"\n> a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)\n> - Cam.log += '\\nFit quadratic curve in hatspace'\n> + logger.info('Fit quadratic curve in hatspace')\n> \"\"\"\n> the algorithm now approximates the shortest distance from each point to the\n> curve in dehatspace. Since the fit is done in hatspace, it is easier to\n> @@ -151,14 +152,14 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> if (x+y) > (rr+bb):\n> dist *= -1\n> dists.append(dist)\n> - Cam.log += '\\nFound closest point on fit line to each point in dehatspace'\n> + logger.info('Found closest point on fit line to each point in dehatspace')\n> \"\"\"\n> calculate wiggle factors in awb. 10% added since this is an upper bound\n> \"\"\"\n> transverse_neg = - np.min(dists) * 1.1\n> transverse_pos = np.max(dists) * 1.1\n> - Cam.log += '\\nTransverse pos : {:.5f}'.format(transverse_pos)\n> - Cam.log += '\\nTransverse neg : {:.5f}'.format(transverse_neg)\n> + logger.info('Transverse pos : {:.5f}'.format(transverse_pos))\n> + logger.info('Transverse neg : {:.5f}'.format(transverse_neg))\n> \"\"\"\n> set minimum transverse wiggles to 0.1 .\n> Wiggle factors dictate how far off of the curve the algorithm searches. 0.1\n> @@ -167,10 +168,10 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> if transverse_pos < 0.01:\n> transverse_pos = 0.01\n> - Cam.log += '\\nForced transverse pos to 0.01'\n> + logger.info('Forced transverse pos to 0.01')\n> if transverse_neg < 0.01:\n> transverse_neg = 0.01\n> - Cam.log += '\\nForced transverse neg to 0.01'\n> + logger.info('Forced transverse neg to 0.01')\n> \n> \"\"\"\n> generate new b_hat values at each r_hat according to fit\n> @@ -202,25 +203,25 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> i = len(c_fit) - 1\n> while i > 0:\n> if c_fit[i] > c_fit[i-1]:\n> - Cam.log += '\\nColour temperature increase found\\n'\n> - Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])\n> - Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])\n> + logger.info('Colour temperature increase found')\n> + logger.info('{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1]))\n> + logger.info('{} K at r = {}'.format(c_fit[i], r_fit[i]))\n> \"\"\"\n> if colour temperature increases then discard point furthest from\n> the transformed fit (dehatspace)\n> \"\"\"\n> error_1 = abs(dists[i-1])\n> error_2 = abs(dists[i])\n> - Cam.log += '\\nDistances from fit:\\n'\n> - Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)\n> - Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)\n> + logger.info('Distances from fit:')\n> + logger.info('{} K : {:.5f} , '.format(c_fit[i], error_1))\n> + logger.info('{} K : {:.5f}'.format(c_fit[i-1], error_2))\n> \"\"\"\n> find bad index\n> note that in python false = 0 and true = 1\n> \"\"\"\n> bad = i - (error_1 < error_2)\n> - Cam.log += '\\nPoint at {} K deleted as '.format(c_fit[bad])\n> - Cam.log += 'it is furthest from fit'\n> + logger.info('Point at {} K deleted as '.format(c_fit[bad]))\n> + logger.info('it is furthest from fit')\n> \"\"\"\n> delete bad point\n> \"\"\"\n> @@ -239,12 +240,12 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> return formatted ct curve, ordered by increasing colour temperature\n> \"\"\"\n> ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]\n> - Cam.log += '\\nFinal CT curve:'\n> + logger.info('Final CT curve:')\n> for i in range(len(ct_curve)//3):\n> j = 3*i\n> - Cam.log += '\\n ct: {} '.format(ct_curve[j])\n> - Cam.log += ' r: {} '.format(ct_curve[j+1])\n> - Cam.log += ' b: {} '.format(ct_curve[j+2])\n> + logger.info(' ct: {} '.format(ct_curve[j]))\n> + logger.info(' r: {} '.format(ct_curve[j+1]))\n> + logger.info(' b: {} '.format(ct_curve[j+2]))\n> \n> \"\"\"\n> plotting code for debug\n> @@ -303,7 +304,7 @@ def get_alsc_patches(Img, colour_cals, grey=True):\n> \"\"\"\n> if grey:\n> cen_coords = Img.cen_coords[3::4]\n> - col = Img.col\n> + col = Img.color\n> patches = [np.array(Img.patches[i]) for i in Img.order]\n> r_patchs = patches[0][3::4] - Img.blacklevel_16\n> b_patchs = patches[3][3::4] - Img.blacklevel_16\n> -- \n> 2.43.0\n>","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 3E328C323E\n\tfor ;\n\tWed, 28 Aug 2024 07:16:40 +0000 (UTC)","from lancelot.ideasonboard.com (localhost [IPv6:::1])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTP id C1C0263460;\n\tWed, 28 Aug 2024 09:16:39 +0200 (CEST)","from perceval.ideasonboard.com (perceval.ideasonboard.com\n\t[213.167.242.64])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTPS id 0476061903\n\tfor ;\n\tWed, 28 Aug 2024 09:16:38 +0200 (CEST)","from pyrite.rasen.tech (unknown\n\t[IPv6:2404:7a81:160:2100:632b:90c4:b4b9:9e22])\n\tby perceval.ideasonboard.com (Postfix) with ESMTPSA id 6F1AF7E4;\n\tWed, 28 Aug 2024 09:15:29 +0200 (CEST)"],"Authentication-Results":"lancelot.ideasonboard.com; dkim=pass (1024-bit key;\n\tunprotected) header.d=ideasonboard.com header.i=@ideasonboard.com\n\theader.b=\"UKcaIuW0\"; dkim-atps=neutral","DKIM-Signature":"v=1; a=rsa-sha256; c=relaxed/simple; d=ideasonboard.com;\n\ts=mail; t=1724829330;\n\tbh=Ij50kO3zJRFRVvrHvI4reLe5asfoR5WhTaJlnNDowfY=;\n\th=Date:From:To:Cc:Subject:References:In-Reply-To:From;\n\tb=UKcaIuW0cWwTXqmu84WY8cOTK+/a60XolUyRbaS4BNnF1bQL9FU9YslVjnVUBG42M\n\trlvnD1GlduZ3CIUlnor0+5a45Gu05FAxumAGNWr2hss1Uta9WLEKRZaXx90AJ01+88\n\t5Fj/9wHWlc6wZ9PoZNcHTQM3/qrwmfbw3ikCKi8k=","Date":"Wed, 28 Aug 2024 16:16:31 +0900","From":"Paul Elder ","To":"Stefan Klug ","Cc":"libcamera-devel@lists.libcamera.org,\n\tKieran Bingham ","Subject":"Re: [PATCH v4 4/6] libtuning: Modify ctt_awb.awb() so that it can\n\trun in our context","Message-ID":"","References":"<20240813084451.44099-1-stefan.klug@ideasonboard.com>\n\t<20240813084451.44099-5-stefan.klug@ideasonboard.com>","MIME-Version":"1.0","Content-Type":"text/plain; charset=us-ascii","Content-Disposition":"inline","In-Reply-To":"<20240813084451.44099-5-stefan.klug@ideasonboard.com>","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","Errors-To":"libcamera-devel-bounces@lists.libcamera.org","Sender":"\"libcamera-devel\" "}},{"id":30972,"web_url":"https://patchwork.libcamera.org/comment/30972/","msgid":"<20240829231640.GA26181@pendragon.ideasonboard.com>","date":"2024-08-29T23:16:40","subject":"Re: [PATCH v4 4/6] libtuning: Modify ctt_awb.awb() so that it can\n\trun in our context","submitter":{"id":2,"url":"https://patchwork.libcamera.org/api/people/2/","name":"Laurent Pinchart","email":"laurent.pinchart@ideasonboard.com"},"content":"Hi Stefan,\n\nThank you for the patch.\n\nOn Tue, Aug 13, 2024 at 10:44:21AM +0200, Stefan Klug wrote:\n> Modify the awb function that was copied from the Raspberry Pi tuning\n> scripts in a way that it can easily be called from the libtuning code.\n> In essence the logging was replaced by calls to a python logger and the\n\ns/was/is/\n\n> need for the Cam object was removed by providing a list of images to the\n\ns/was/is/\n\nThe commit message should use the imperative mood though.\n\n> function.\n\nThat sounds like two changes, calling for two patches.\n\n> Signed-off-by: Stefan Klug \n> Reviewed-by: Kieran Bingham \n> ---\n> utils/tuning/libtuning/ctt_awb.py | 55 ++++++++++++++++---------------\n> 1 file changed, 28 insertions(+), 27 deletions(-)\n> \n> diff --git a/utils/tuning/libtuning/ctt_awb.py b/utils/tuning/libtuning/ctt_awb.py\n> index abf22321a0ea..73a1bc1a840c 100644\n> --- a/utils/tuning/libtuning/ctt_awb.py\n> +++ b/utils/tuning/libtuning/ctt_awb.py\n> @@ -4,6 +4,8 @@\n> #\n> # camera tuning tool for AWB\n> \n> +import logging\n> +\n> import matplotlib.pyplot as plt\n> from bisect import bisect_left\n> from scipy.optimize import fmin\n> @@ -11,12 +13,12 @@ import numpy as np\n> \n> from .image import Image\n> \n> +logger = logging.getLogger(__name__)\n> \n> \"\"\"\n> obtain piecewise linear approximation for colour curve\n> \"\"\"\n> -def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> - imgs = Cam.imgs\n> +def awb(imgs, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> condense alsc calibration tables into one dictionary\n> \"\"\"\n> @@ -39,7 +41,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> rb_raw = []\n> rbs_hat = []\n> for Img in imgs:\n> - Cam.log += '\\nProcessing '+Img.name\n> + logger.info(f'Processing {Img.name}')\n> \"\"\"\n> get greyscale patches with alsc applied if alsc enabled.\n> Note: if alsc is disabled then colour_cals will be set to None and the\n> @@ -51,7 +53,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> r_g = np.mean(r_patchs/g_patchs)\n> b_g = np.mean(b_patchs/g_patchs)\n> - Cam.log += '\\n r : {:.4f} b : {:.4f}'.format(r_g, b_g)\n> + logger.info(' r : {:.4f} b : {:.4f}'.format(r_g, b_g))\n\nAs you use an f-string above, I think you can write this\n\n logger.info(f' r : {r_g:.4f} b : {b_g:.4f}'\n\nSame below.\n\n> \"\"\"\n> The curve tends to be better behaved in so-called hatspace.\n> R, B, G represent the individual channels. The colour curve is plotted in\n> @@ -74,12 +76,11 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> r_g_hat = r_g/(1+r_g+b_g)\n> b_g_hat = b_g/(1+r_g+b_g)\n> - Cam.log += '\\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat)\n> - rbs_hat.append((r_g_hat, b_g_hat, Img.col))\n> + logger.info('\\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat))\n> + rbs_hat.append((r_g_hat, b_g_hat, Img.color))\n> rb_raw.append((r_g, b_g))\n> - Cam.log += '\\n'\n> \n> - Cam.log += '\\nFinished processing images'\n> + logger.info('Finished processing images')\n> \"\"\"\n> sort all lits simultaneously by r_hat\n> \"\"\"\n> @@ -95,7 +96,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> fit quadratic fit to r_g hat and b_g_hat\n> \"\"\"\n> a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)\n> - Cam.log += '\\nFit quadratic curve in hatspace'\n> + logger.info('Fit quadratic curve in hatspace')\n> \"\"\"\n> the algorithm now approximates the shortest distance from each point to the\n> curve in dehatspace. Since the fit is done in hatspace, it is easier to\n> @@ -151,14 +152,14 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> if (x+y) > (rr+bb):\n> dist *= -1\n> dists.append(dist)\n> - Cam.log += '\\nFound closest point on fit line to each point in dehatspace'\n> + logger.info('Found closest point on fit line to each point in dehatspace')\n> \"\"\"\n> calculate wiggle factors in awb. 10% added since this is an upper bound\n> \"\"\"\n> transverse_neg = - np.min(dists) * 1.1\n> transverse_pos = np.max(dists) * 1.1\n> - Cam.log += '\\nTransverse pos : {:.5f}'.format(transverse_pos)\n> - Cam.log += '\\nTransverse neg : {:.5f}'.format(transverse_neg)\n> + logger.info('Transverse pos : {:.5f}'.format(transverse_pos))\n> + logger.info('Transverse neg : {:.5f}'.format(transverse_neg))\n> \"\"\"\n> set minimum transverse wiggles to 0.1 .\n> Wiggle factors dictate how far off of the curve the algorithm searches. 0.1\n> @@ -167,10 +168,10 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> \"\"\"\n> if transverse_pos < 0.01:\n> transverse_pos = 0.01\n> - Cam.log += '\\nForced transverse pos to 0.01'\n> + logger.info('Forced transverse pos to 0.01')\n> if transverse_neg < 0.01:\n> transverse_neg = 0.01\n> - Cam.log += '\\nForced transverse neg to 0.01'\n> + logger.info('Forced transverse neg to 0.01')\n> \n> \"\"\"\n> generate new b_hat values at each r_hat according to fit\n> @@ -202,25 +203,25 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> i = len(c_fit) - 1\n> while i > 0:\n> if c_fit[i] > c_fit[i-1]:\n> - Cam.log += '\\nColour temperature increase found\\n'\n> - Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])\n> - Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])\n> + logger.info('Colour temperature increase found')\n> + logger.info('{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1]))\n> + logger.info('{} K at r = {}'.format(c_fit[i], r_fit[i]))\n> \"\"\"\n> if colour temperature increases then discard point furthest from\n> the transformed fit (dehatspace)\n> \"\"\"\n> error_1 = abs(dists[i-1])\n> error_2 = abs(dists[i])\n> - Cam.log += '\\nDistances from fit:\\n'\n> - Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)\n> - Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)\n> + logger.info('Distances from fit:')\n> + logger.info('{} K : {:.5f} , '.format(c_fit[i], error_1))\n> + logger.info('{} K : {:.5f}'.format(c_fit[i-1], error_2))\n> \"\"\"\n> find bad index\n> note that in python false = 0 and true = 1\n> \"\"\"\n> bad = i - (error_1 < error_2)\n> - Cam.log += '\\nPoint at {} K deleted as '.format(c_fit[bad])\n> - Cam.log += 'it is furthest from fit'\n> + logger.info('Point at {} K deleted as '.format(c_fit[bad]))\n> + logger.info('it is furthest from fit')\n> \"\"\"\n> delete bad point\n> \"\"\"\n> @@ -239,12 +240,12 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot):\n> return formatted ct curve, ordered by increasing colour temperature\n> \"\"\"\n> ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]\n> - Cam.log += '\\nFinal CT curve:'\n> + logger.info('Final CT curve:')\n> for i in range(len(ct_curve)//3):\n> j = 3*i\n> - Cam.log += '\\n ct: {} '.format(ct_curve[j])\n> - Cam.log += ' r: {} '.format(ct_curve[j+1])\n> - Cam.log += ' b: {} '.format(ct_curve[j+2])\n> + logger.info(' ct: {} '.format(ct_curve[j]))\n> + logger.info(' r: {} '.format(ct_curve[j+1]))\n> + logger.info(' b: {} '.format(ct_curve[j+2]))\n> \n> \"\"\"\n> plotting code for debug\n> @@ -303,7 +304,7 @@ def get_alsc_patches(Img, colour_cals, grey=True):\n> \"\"\"\n> if grey:\n> cen_coords = Img.cen_coords[3::4]\n> - col = Img.col\n> + col = Img.color\n> patches = [np.array(Img.patches[i]) for i in Img.order]\n> r_patchs = patches[0][3::4] - Img.blacklevel_16\n> b_patchs = patches[3][3::4] - Img.blacklevel_16","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 DCC06C323E\n\tfor ;\n\tThu, 29 Aug 2024 23:17:14 +0000 (UTC)","from lancelot.ideasonboard.com (localhost [IPv6:::1])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTP id 9CEBD63458;\n\tFri, 30 Aug 2024 01:17:13 +0200 (CEST)","from perceval.ideasonboard.com (perceval.ideasonboard.com\n\t[213.167.242.64])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTPS id 9BDB56002E\n\tfor ;\n\tFri, 30 Aug 2024 01:17:11 +0200 (CEST)","from pendragon.ideasonboard.com (81-175-209-231.bb.dnainternet.fi\n\t[81.175.209.231])\n\tby perceval.ideasonboard.com (Postfix) with ESMTPSA id 9761D735;\n\tFri, 30 Aug 2024 01:16:02 +0200 (CEST)"],"Authentication-Results":"lancelot.ideasonboard.com; dkim=pass (1024-bit key;\n\tunprotected) header.d=ideasonboard.com header.i=@ideasonboard.com\n\theader.b=\"u3Us7GNt\"; dkim-atps=neutral","DKIM-Signature":"v=1; a=rsa-sha256; c=relaxed/simple; d=ideasonboard.com;\n\ts=mail; t=1724973362;\n\tbh=bmqJF9EvmKvGCU3ZuHYdi+eJsayRp71cy9+cDL5Xs0Q=;\n\th=Date:From:To:Cc:Subject:References:In-Reply-To:From;\n\tb=u3Us7GNtVUXFUjzSyeMrip+06D+EwVLfbA3TRidELDmTWX7yn8Jco/WclW4wGYTaJ\n\thqmYGvS8l3Ls2+KwSYaUabVaUwVOl/Zqo1iZAkmCKdKEksR7C+A8Vf6m/sSZY7Z9nW\n\tnBzlbhc0vg0kXr8kVskrxDEaLkdYY12o3WFPW3Ck=","Date":"Fri, 30 Aug 2024 02:16:40 +0300","From":"Laurent Pinchart ","To":"Stefan Klug ","Cc":"libcamera-devel@lists.libcamera.org,\n\tKieran Bingham ","Subject":"Re: [PATCH v4 4/6] libtuning: Modify ctt_awb.awb() so that it can\n\trun in our context","Message-ID":"<20240829231640.GA26181@pendragon.ideasonboard.com>","References":"<20240813084451.44099-1-stefan.klug@ideasonboard.com>\n\t<20240813084451.44099-5-stefan.klug@ideasonboard.com>","MIME-Version":"1.0","Content-Type":"text/plain; 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