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{ "id": 20462, "url": "https://patchwork.libcamera.org/api/patches/20462/?format=api", "web_url": "https://patchwork.libcamera.org/patch/20462/", "project": { "id": 1, "url": "https://patchwork.libcamera.org/api/projects/1/?format=api", "name": "libcamera", "link_name": "libcamera", "list_id": "libcamera_core", "list_email": "libcamera-devel@lists.libcamera.org", "web_url": "", "scm_url": "", "webscm_url": "" }, "msgid": "<20240628104828.2928109-4-stefan.klug@ideasonboard.com>", "date": "2024-06-28T10:46:56", "name": "[v2,03/25] libtuning: Copy files from raspberrypi", "commit_ref": null, "pull_url": null, "state": "superseded", "archived": false, "hash": "2923aae613a5600034a312237863aab4f3549e19", "submitter": { "id": 184, "url": "https://patchwork.libcamera.org/api/people/184/?format=api", "name": "Stefan Klug", "email": "stefan.klug@ideasonboard.com" }, "delegate": null, "mbox": "https://patchwork.libcamera.org/patch/20462/mbox/", "series": [ { "id": 4430, "url": "https://patchwork.libcamera.org/api/series/4430/?format=api", "web_url": "https://patchwork.libcamera.org/project/libcamera/list/?series=4430", "date": "2024-06-28T10:46:53", "name": "Add ccm calibration to libtuning", "version": 2, "mbox": "https://patchwork.libcamera.org/series/4430/mbox/" } ], "comments": "https://patchwork.libcamera.org/api/patches/20462/comments/", "check": "pending", "checks": "https://patchwork.libcamera.org/api/patches/20462/checks/", "tags": {}, "headers": { "Return-Path": "<libcamera-devel-bounces@lists.libcamera.org>", "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 1D55CBD87C\n\tfor <parsemail@patchwork.libcamera.org>;\n\tFri, 28 Jun 2024 10:48:50 +0000 (UTC)", "from lancelot.ideasonboard.com (localhost [IPv6:::1])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTP id 8D46E62E1C;\n\tFri, 28 Jun 2024 12:48:49 +0200 (CEST)", "from perceval.ideasonboard.com (perceval.ideasonboard.com\n\t[IPv6:2001:4b98:dc2:55:216:3eff:fef7:d647])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTPS id C4BF062C97\n\tfor <libcamera-devel@lists.libcamera.org>;\n\tFri, 28 Jun 2024 12:48:47 +0200 (CEST)", "from ideasonboard.com (unknown\n\t[IPv6:2a00:6020:448c:6c00:82ab:924:d918:cd24])\n\tby perceval.ideasonboard.com (Postfix) with ESMTPSA id 3DE14735;\n\tFri, 28 Jun 2024 12:48:23 +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=\"u1JdTbQK\"; dkim-atps=neutral", "DKIM-Signature": "v=1; a=rsa-sha256; c=relaxed/simple; d=ideasonboard.com;\n\ts=mail; t=1719571703;\n\tbh=KfwR1Zlw7EhbHUh3w6JkbMol3iTRIulaDUREP34UOHs=;\n\th=From:To:Cc:Subject:Date:In-Reply-To:References:From;\n\tb=u1JdTbQKxsHDOyE1yK1MIEjPKk5UtXGYA12ALAFLTyd4wctytJ2QXgw0SH86/cdp6\n\tap+xzUuTApqnDHPE191rhLHAiwy05l0psFCSohhiE6nahs/haSgmjCIPLwVHBx82in\n\tJ/6sTncaQPFgTTKhusOAhNiMrYrFUxpo01VpIcY8=", "From": "Stefan Klug <stefan.klug@ideasonboard.com>", "To": "libcamera-devel@lists.libcamera.org", "Cc": "Stefan Klug <stefan.klug@ideasonboard.com>", "Subject": "[PATCH v2 03/25] libtuning: Copy files from raspberrypi", "Date": "Fri, 28 Jun 2024 12:46:56 +0200", "Message-ID": "<20240628104828.2928109-4-stefan.klug@ideasonboard.com>", "X-Mailer": "git-send-email 2.43.0", "In-Reply-To": "<20240628104828.2928109-1-stefan.klug@ideasonboard.com>", "References": "<20240628104828.2928109-1-stefan.klug@ideasonboard.com>", "MIME-Version": "1.0", "Content-Type": "text/plain; charset=UTF-8", "Content-Transfer-Encoding": "8bit", "X-BeenThere": "libcamera-devel@lists.libcamera.org", "X-Mailman-Version": "2.1.29", "Precedence": "list", "List-Id": "<libcamera-devel.lists.libcamera.org>", "List-Unsubscribe": "<https://lists.libcamera.org/options/libcamera-devel>,\n\t<mailto:libcamera-devel-request@lists.libcamera.org?subject=unsubscribe>", "List-Archive": "<https://lists.libcamera.org/pipermail/libcamera-devel/>", "List-Post": "<mailto:libcamera-devel@lists.libcamera.org>", "List-Help": "<mailto:libcamera-devel-request@lists.libcamera.org?subject=help>", "List-Subscribe": "<https://lists.libcamera.org/listinfo/libcamera-devel>,\n\t<mailto:libcamera-devel-request@lists.libcamera.org?subject=subscribe>", "Errors-To": "libcamera-devel-bounces@lists.libcamera.org", "Sender": "\"libcamera-devel\" <libcamera-devel-bounces@lists.libcamera.org>" }, "content": "Copy ctt_{awb,ccm,colors,ransac} from the raspberrypi tuning scripts as\nbasis for the libcamera implementation. color.py was renamed to\nctt_colors.py to better express the origin.\n\nThe files were taken from commit 66479605baca4a22e2b\n\nSigned-off-by: Stefan Klug <stefan.klug@ideasonboard.com>\n---\n utils/tuning/libtuning/ctt_awb.py | 376 +++++++++++++++++++++++++\n utils/tuning/libtuning/ctt_ccm.py | 406 +++++++++++++++++++++++++++\n utils/tuning/libtuning/ctt_colors.py | 30 ++\n utils/tuning/libtuning/ctt_ransac.py | 71 +++++\n 4 files changed, 883 insertions(+)\n create mode 100644 utils/tuning/libtuning/ctt_awb.py\n create mode 100644 utils/tuning/libtuning/ctt_ccm.py\n create mode 100644 utils/tuning/libtuning/ctt_colors.py\n create mode 100644 utils/tuning/libtuning/ctt_ransac.py", "diff": "diff --git a/utils/tuning/libtuning/ctt_awb.py b/utils/tuning/libtuning/ctt_awb.py\nnew file mode 100644\nindex 000000000000..5ba6f978a228\n--- /dev/null\n+++ b/utils/tuning/libtuning/ctt_awb.py\n@@ -0,0 +1,376 @@\n+# SPDX-License-Identifier: BSD-2-Clause\n+#\n+# Copyright (C) 2019, Raspberry Pi Ltd\n+#\n+# camera tuning tool for AWB\n+\n+from ctt_image_load import *\n+import matplotlib.pyplot as plt\n+from bisect import bisect_left\n+from scipy.optimize import fmin\n+\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+ \"\"\"\n+ condense alsc calibration tables into one dictionary\n+ \"\"\"\n+ if cal_cr_list is None:\n+ colour_cals = None\n+ else:\n+ colour_cals = {}\n+ for cr, cb in zip(cal_cr_list, cal_cb_list):\n+ cr_tab = cr['table']\n+ cb_tab = cb['table']\n+ \"\"\"\n+ normalise tables so min value is 1\n+ \"\"\"\n+ cr_tab = cr_tab/np.min(cr_tab)\n+ cb_tab = cb_tab/np.min(cb_tab)\n+ colour_cals[cr['ct']] = [cr_tab, cb_tab]\n+ \"\"\"\n+ obtain data from greyscale macbeth patches\n+ \"\"\"\n+ rb_raw = []\n+ rbs_hat = []\n+ for Img in imgs:\n+ Cam.log += '\\nProcessing '+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+ function will just return the greyscale patches\n+ \"\"\"\n+ r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)\n+ \"\"\"\n+ calculate ratio of r, b to g\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+ \"\"\"\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+ r, b space, where:\n+ r = R/G\n+ b = B/G\n+ This will be referred to as dehatspace... (sorry)\n+ Hatspace is defined as:\n+ r_hat = R/(R+B+G)\n+ b_hat = B/(R+B+G)\n+ To convert from dehatspace to hastpace (hat operation):\n+ r_hat = r/(1+r+b)\n+ b_hat = b/(1+r+b)\n+ To convert from hatspace to dehatspace (dehat operation):\n+ r = r_hat/(1-r_hat-b_hat)\n+ b = b_hat/(1-r_hat-b_hat)\n+ Proof is left as an excercise to the reader...\n+ Throughout the code, r and b are sometimes referred to as r_g and b_g\n+ as a reminder that they are ratios\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+ rb_raw.append((r_g, b_g))\n+ Cam.log += '\\n'\n+\n+ Cam.log += '\\nFinished processing images'\n+ \"\"\"\n+ sort all lits simultaneously by r_hat\n+ \"\"\"\n+ rbs_zip = list(zip(rbs_hat, rb_raw))\n+ rbs_zip.sort(key=lambda x: x[0][0])\n+ rbs_hat, rb_raw = list(zip(*rbs_zip))\n+ \"\"\"\n+ unzip tuples ready for processing\n+ \"\"\"\n+ rbs_hat = list(zip(*rbs_hat))\n+ rb_raw = list(zip(*rb_raw))\n+ \"\"\"\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+ \"\"\"\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+ find the actual shortest distance in hatspace and use the projection back\n+ into dehatspace as an overestimate.\n+ The distance will be used for two things:\n+ 1) In the case that colour temperature does not strictly decrease with\n+ increasing r/g, the closest point to the line will be chosen out of an\n+ increasing pair of colours.\n+\n+ 2) To calculate transverse negative an dpositive, the maximum positive\n+ and negative distance from the line are chosen. This benefits from the\n+ overestimate as the transverse pos/neg are upper bound values.\n+ \"\"\"\n+ \"\"\"\n+ define fit function\n+ \"\"\"\n+ def f(x):\n+ return a*x**2 + b*x + c\n+ \"\"\"\n+ iterate over points (R, B are x and y coordinates of points) and calculate\n+ distance to line in dehatspace\n+ \"\"\"\n+ dists = []\n+ for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])):\n+ \"\"\"\n+ define function to minimise as square distance between datapoint and\n+ point on curve. Squaring is monotonic so minimising radius squared is\n+ equivalent to minimising radius\n+ \"\"\"\n+ def f_min(x):\n+ y = f(x)\n+ return((x-R)**2+(y-B)**2)\n+ \"\"\"\n+ perform optimisation with scipy.optmisie.fmin\n+ \"\"\"\n+ x_hat = fmin(f_min, R, disp=0)[0]\n+ y_hat = f(x_hat)\n+ \"\"\"\n+ dehat\n+ \"\"\"\n+ x = x_hat/(1-x_hat-y_hat)\n+ y = y_hat/(1-x_hat-y_hat)\n+ rr = R/(1-R-B)\n+ bb = B/(1-R-B)\n+ \"\"\"\n+ calculate euclidean distance in dehatspace\n+ \"\"\"\n+ dist = ((x-rr)**2+(y-bb)**2)**0.5\n+ \"\"\"\n+ return negative if point is below the fit curve\n+ \"\"\"\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+ \"\"\"\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+ \"\"\"\n+ set minimum transverse wiggles to 0.1 .\n+ Wiggle factors dictate how far off of the curve the algorithm searches. 0.1\n+ is a suitable minimum that gives better results for lighting conditions not\n+ within calibration dataset. Anything less will generalise poorly.\n+ \"\"\"\n+ if transverse_pos < 0.01:\n+ transverse_pos = 0.01\n+ Cam.log += '\\nForced 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+\n+ \"\"\"\n+ generate new b_hat values at each r_hat according to fit\n+ \"\"\"\n+ r_hat_fit = np.array(rbs_hat[0])\n+ b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c\n+ \"\"\"\n+ transform from hatspace to dehatspace\n+ \"\"\"\n+ r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)\n+ b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)\n+ c_fit = np.round(rbs_hat[2], 0)\n+ \"\"\"\n+ round to 4dp\n+ \"\"\"\n+ r_fit = np.where((1000*r_fit) % 1 <= 0.05, r_fit+0.0001, r_fit)\n+ r_fit = np.where((1000*r_fit) % 1 >= 0.95, r_fit-0.0001, r_fit)\n+ b_fit = np.where((1000*b_fit) % 1 <= 0.05, b_fit+0.0001, b_fit)\n+ b_fit = np.where((1000*b_fit) % 1 >= 0.95, b_fit-0.0001, b_fit)\n+ r_fit = np.round(r_fit, 4)\n+ b_fit = np.round(b_fit, 4)\n+ \"\"\"\n+ The following code ensures that colour temperature decreases with\n+ increasing r/g\n+ \"\"\"\n+ \"\"\"\n+ iterate backwards over list for easier indexing\n+ \"\"\"\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+ \"\"\"\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+ \"\"\"\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+ \"\"\"\n+ delete bad point\n+ \"\"\"\n+ r_fit = np.delete(r_fit, bad)\n+ b_fit = np.delete(b_fit, bad)\n+ c_fit = np.delete(c_fit, bad).astype(np.uint16)\n+ \"\"\"\n+ note that if a point has been discarded then the length has decreased\n+ by one, meaning that decreasing the index by one will reassess the kept\n+ point against the next point. It is therefore possible, in theory, for\n+ two adjacent points to be discarded, although probably rare\n+ \"\"\"\n+ i -= 1\n+\n+ \"\"\"\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+ 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+\n+ \"\"\"\n+ plotting code for debug\n+ \"\"\"\n+ if plot:\n+ x = np.linspace(np.min(rbs_hat[0]), np.max(rbs_hat[0]), 100)\n+ y = a*x**2 + b*x + c\n+ plt.subplot(2, 1, 1)\n+ plt.title('hatspace')\n+ plt.plot(rbs_hat[0], rbs_hat[1], ls='--', color='blue')\n+ plt.plot(x, y, color='green', ls='-')\n+ plt.scatter(rbs_hat[0], rbs_hat[1], color='red')\n+ for i, ct in enumerate(rbs_hat[2]):\n+ plt.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i]))\n+ plt.xlabel('$\\\\hat{r}$')\n+ plt.ylabel('$\\\\hat{b}$')\n+ \"\"\"\n+ optional set axes equal to shortest distance so line really does\n+ looks perpendicular and everybody is happy\n+ \"\"\"\n+ # ax = plt.gca()\n+ # ax.set_aspect('equal')\n+ plt.grid()\n+ plt.subplot(2, 1, 2)\n+ plt.title('dehatspace - indoors?')\n+ plt.plot(r_fit, b_fit, color='blue')\n+ plt.scatter(rb_raw[0], rb_raw[1], color='green')\n+ plt.scatter(r_fit, b_fit, color='red')\n+ for i, ct in enumerate(c_fit):\n+ plt.annotate(str(ct), (r_fit[i], b_fit[i]))\n+ plt.xlabel('$r$')\n+ plt.ylabel('$b$')\n+ \"\"\"\n+ optional set axes equal to shortest distance so line really does\n+ looks perpendicular and everybody is happy\n+ \"\"\"\n+ # ax = plt.gca()\n+ # ax.set_aspect('equal')\n+ plt.subplots_adjust(hspace=0.5)\n+ plt.grid()\n+ plt.show()\n+ \"\"\"\n+ end of plotting code\n+ \"\"\"\n+ return(ct_curve, np.round(transverse_pos, 5), np.round(transverse_neg, 5))\n+\n+\n+\"\"\"\n+obtain greyscale patches and perform alsc colour correction\n+\"\"\"\n+def get_alsc_patches(Img, colour_cals, grey=True):\n+ \"\"\"\n+ get patch centre coordinates, image colour and the actual\n+ patches for each channel, remembering to subtract blacklevel\n+ If grey then only greyscale patches considered\n+ \"\"\"\n+ if grey:\n+ cen_coords = Img.cen_coords[3::4]\n+ col = Img.col\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+ note two green channels are averages\n+ \"\"\"\n+ g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16\n+ else:\n+ cen_coords = Img.cen_coords\n+ col = Img.col\n+ patches = [np.array(Img.patches[i]) for i in Img.order]\n+ r_patchs = patches[0] - Img.blacklevel_16\n+ b_patchs = patches[3] - Img.blacklevel_16\n+ g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16\n+\n+ if colour_cals is None:\n+ return r_patchs, b_patchs, g_patchs\n+ \"\"\"\n+ find where image colour fits in alsc colour calibration tables\n+ \"\"\"\n+ cts = list(colour_cals.keys())\n+ pos = bisect_left(cts, col)\n+ \"\"\"\n+ if img colour is below minimum or above maximum alsc calibration colour, simply\n+ pick extreme closest to img colour\n+ \"\"\"\n+ if pos % len(cts) == 0:\n+ \"\"\"\n+ this works because -0 = 0 = first and -1 = last index\n+ \"\"\"\n+ col_tabs = np.array(colour_cals[cts[-pos//len(cts)]])\n+ \"\"\"\n+ else, perform linear interpolation between existing alsc colour\n+ calibration tables\n+ \"\"\"\n+ else:\n+ bef = cts[pos-1]\n+ aft = cts[pos]\n+ da = col-bef\n+ db = aft-col\n+ bef_tabs = np.array(colour_cals[bef])\n+ aft_tabs = np.array(colour_cals[aft])\n+ col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db)\n+ col_tabs = np.reshape(col_tabs, (2, 12, 16))\n+ \"\"\"\n+ calculate dx, dy used to calculate alsc table\n+ \"\"\"\n+ w, h = Img.w/2, Img.h/2\n+ dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))\n+ \"\"\"\n+ make list of pairs of gains for each patch by selecting the correct value\n+ in alsc colour calibration table\n+ \"\"\"\n+ patch_gains = []\n+ for cen in cen_coords:\n+ x, y = cen[0]//dx, cen[1]//dy\n+ # We could probably do with some better spatial interpolation here?\n+ col_gains = (col_tabs[0][y][x], col_tabs[1][y][x])\n+ patch_gains.append(col_gains)\n+\n+ \"\"\"\n+ multiply the r and b channels in each patch by the respective gain, finally\n+ performing the alsc colour correction\n+ \"\"\"\n+ for i, gains in enumerate(patch_gains):\n+ r_patchs[i] = r_patchs[i] * gains[0]\n+ b_patchs[i] = b_patchs[i] * gains[1]\n+\n+ \"\"\"\n+ return greyscale patches, g channel and correct r, b channels\n+ \"\"\"\n+ return r_patchs, b_patchs, g_patchs\ndiff --git a/utils/tuning/libtuning/ctt_ccm.py b/utils/tuning/libtuning/ctt_ccm.py\nnew file mode 100644\nindex 000000000000..59753e332ee9\n--- /dev/null\n+++ b/utils/tuning/libtuning/ctt_ccm.py\n@@ -0,0 +1,406 @@\n+# SPDX-License-Identifier: BSD-2-Clause\n+#\n+# Copyright (C) 2019, Raspberry Pi Ltd\n+#\n+# camera tuning tool for CCM (colour correction matrix)\n+\n+from ctt_image_load import *\n+from ctt_awb import get_alsc_patches\n+import colors\n+from scipy.optimize import minimize\n+from ctt_visualise import visualise_macbeth_chart\n+import numpy as np\n+\"\"\"\n+takes 8-bit macbeth chart values, degammas and returns 16 bit\n+\"\"\"\n+\n+'''\n+This program has many options from which to derive the color matrix from.\n+The first is average. This minimises the average delta E across all patches of\n+the macbeth chart. Testing across all cameras yeilded this as the most color\n+accurate and vivid. Other options are avalible however.\n+Maximum minimises the maximum Delta E of the patches. It iterates through till\n+a minimum maximum is found (so that there is\n+not one patch that deviates wildly.)\n+This yields generally good results but overall the colors are less accurate\n+Have a fiddle with maximum and see what you think.\n+The final option allows you to select the patches for which to average across.\n+This means that you can bias certain patches, for instance if you want the\n+reds to be more accurate.\n+'''\n+\n+matrix_selection_types = [\"average\", \"maximum\", \"patches\"]\n+typenum = 0 # select from array above, 0 = average, 1 = maximum, 2 = patches\n+test_patches = [1, 2, 5, 8, 9, 12, 14]\n+\n+'''\n+Enter patches to test for. Can also be entered twice if you\n+would like twice as much bias on one patch.\n+'''\n+\n+\n+def degamma(x):\n+ x = x / ((2 ** 8) - 1) # takes 255 and scales it down to one\n+ x = np.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4)\n+ x = x * ((2 ** 16) - 1) # takes one and scales up to 65535, 16 bit color\n+ return x\n+\n+\n+def gamma(x):\n+ # Take 3 long array of color values and gamma them\n+ return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x]\n+\n+\n+\"\"\"\n+FInds colour correction matrices for list of images\n+\"\"\"\n+\n+\n+def ccm(Cam, cal_cr_list, cal_cb_list):\n+ global matrix_selection_types, typenum\n+ imgs = Cam.imgs\n+ \"\"\"\n+ standard macbeth chart colour values\n+ \"\"\"\n+ m_rgb = np.array([ # these are in RGB\n+ [116, 81, 67], # dark skin\n+ [199, 147, 129], # light skin\n+ [91, 122, 156], # blue sky\n+ [90, 108, 64], # foliage\n+ [130, 128, 176], # blue flower\n+ [92, 190, 172], # bluish green\n+ [224, 124, 47], # orange\n+ [68, 91, 170], # purplish blue\n+ [198, 82, 97], # moderate red\n+ [94, 58, 106], # purple\n+ [159, 189, 63], # yellow green\n+ [230, 162, 39], # orange yellow\n+ [35, 63, 147], # blue\n+ [67, 149, 74], # green\n+ [180, 49, 57], # red\n+ [238, 198, 20], # yellow\n+ [193, 84, 151], # magenta\n+ [0, 136, 170], # cyan (goes out of gamut)\n+ [245, 245, 243], # white 9.5\n+ [200, 202, 202], # neutral 8\n+ [161, 163, 163], # neutral 6.5\n+ [121, 121, 122], # neutral 5\n+ [82, 84, 86], # neutral 3.5\n+ [49, 49, 51] # black 2\n+ ])\n+ \"\"\"\n+ convert reference colours from srgb to rgb\n+ \"\"\"\n+ m_srgb = degamma(m_rgb) # now in 16 bit color.\n+\n+ # Produce array of LAB values for ideal color chart\n+ m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb]\n+\n+ \"\"\"\n+ reorder reference values to match how patches are ordered\n+ \"\"\"\n+ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3))\n+ m_lab = np.array([m_lab[i::6] for i in range(6)]).reshape((24, 3))\n+ m_rgb = np.array([m_rgb[i::6] for i in range(6)]).reshape((24, 3))\n+ \"\"\"\n+ reformat alsc correction tables or set colour_cals to None if alsc is\n+ deactivated\n+ \"\"\"\n+ if cal_cr_list is None:\n+ colour_cals = None\n+ else:\n+ colour_cals = {}\n+ for cr, cb in zip(cal_cr_list, cal_cb_list):\n+ cr_tab = cr['table']\n+ cb_tab = cb['table']\n+ \"\"\"\n+ normalise tables so min value is 1\n+ \"\"\"\n+ cr_tab = cr_tab / np.min(cr_tab)\n+ cb_tab = cb_tab / np.min(cb_tab)\n+ colour_cals[cr['ct']] = [cr_tab, cb_tab]\n+\n+ \"\"\"\n+ for each image, perform awb and alsc corrections.\n+ Then calculate the colour correction matrix for that image, recording the\n+ ccm and the colour tempertaure.\n+ \"\"\"\n+ ccm_tab = {}\n+ for Img in imgs:\n+ Cam.log += '\\nProcessing image: ' + Img.name\n+ \"\"\"\n+ get macbeth patches with alsc applied if alsc enabled.\n+ Note: if alsc is disabled then colour_cals will be set to None and no\n+ the function will simply return the macbeth patches\n+ \"\"\"\n+ r, b, g = get_alsc_patches(Img, colour_cals, grey=False)\n+ # 256 values for each patch of sRGB values\n+\n+ \"\"\"\n+ do awb\n+ Note: awb is done by measuring the macbeth chart in the image, rather\n+ than from the awb calibration. This is done so the awb will be perfect\n+ and the ccm matrices will be more accurate.\n+ \"\"\"\n+ r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4]\n+ r_g = np.mean(r_greys / g_greys)\n+ b_g = np.mean(b_greys / g_greys)\n+ r = r / r_g\n+ b = b / b_g\n+ \"\"\"\n+ normalise brightness wrt reference macbeth colours and then average\n+ each channel for each patch\n+ \"\"\"\n+ gain = np.mean(m_srgb) / np.mean((r, g, b))\n+ Cam.log += '\\nGain with respect to standard colours: {:.3f}'.format(gain)\n+ r = np.mean(gain * r, axis=1)\n+ b = np.mean(gain * b, axis=1)\n+ g = np.mean(gain * g, axis=1)\n+ \"\"\"\n+ calculate ccm matrix\n+ \"\"\"\n+ # ==== All of below should in sRGB ===##\n+ sumde = 0\n+ ccm = do_ccm(r, g, b, m_srgb)\n+ # This is the initial guess that our optimisation code works with.\n+ original_ccm = ccm\n+ r1 = ccm[0]\n+ r2 = ccm[1]\n+ g1 = ccm[3]\n+ g2 = ccm[4]\n+ b1 = ccm[6]\n+ b2 = ccm[7]\n+ '''\n+ COLOR MATRIX LOOKS AS BELOW\n+ R1 R2 R3 Rval Outr\n+ G1 G2 G3 * Gval = G\n+ B1 B2 B3 Bval B\n+ Will be optimising 6 elements and working out the third element using 1-r1-r2 = r3\n+ '''\n+\n+ x0 = [r1, r2, g1, g2, b1, b2]\n+ '''\n+ We use our old CCM as the initial guess for the program to find the\n+ optimised matrix\n+ '''\n+ result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.01)\n+ '''\n+ This produces a color matrix which has the lowest delta E possible,\n+ based off the input data. Note it is impossible for this to reach\n+ zero since the input data is imperfect\n+ '''\n+\n+ Cam.log += (\"\\n \\n Optimised Matrix Below: \\n \\n\")\n+ [r1, r2, g1, g2, b1, b2] = result.x\n+ # The new, optimised color correction matrix values\n+ optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)]\n+\n+ # This is the optimised Color Matrix (preserving greys by summing rows up to 1)\n+ Cam.log += str(optimised_ccm)\n+ Cam.log += \"\\n Old Color Correction Matrix Below \\n\"\n+ Cam.log += str(ccm)\n+\n+ formatted_ccm = np.array(original_ccm).reshape((3, 3))\n+\n+ '''\n+ below is a whole load of code that then applies the latest color\n+ matrix, and returns LAB values for color. This can then be used\n+ to calculate the final delta E\n+ '''\n+ optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB\n+ optimised_ccm_lab = []\n+\n+ formatted_optimised_ccm = np.array(optimised_ccm).reshape((3, 3))\n+ after_gamma_rgb = []\n+ after_gamma_lab = []\n+\n+ for RGB in zip(r, g, b):\n+ ccm_applied_rgb = np.dot(formatted_ccm, (np.array(RGB) / 256))\n+ optimised_ccm_rgb.append(gamma(ccm_applied_rgb))\n+ optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))\n+\n+ optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, np.array(RGB) / 256)\n+ after_gamma_rgb.append(gamma(optimised_ccm_applied_rgb))\n+ after_gamma_lab.append(colors.RGB_to_LAB(optimised_ccm_applied_rgb))\n+ '''\n+ Gamma After RGB / LAB - not used in calculations, only used for visualisation\n+ We now want to spit out some data that shows\n+ how the optimisation has improved the color matrices\n+ '''\n+ Cam.log += \"Here are the Improvements\"\n+\n+ # CALCULATE WORST CASE delta e\n+ old_worst_delta_e = 0\n+ before_average = transform_and_evaluate(formatted_ccm, r, g, b, m_lab)\n+ new_worst_delta_e = 0\n+ after_average = transform_and_evaluate(formatted_optimised_ccm, r, g, b, m_lab)\n+ for i in range(24):\n+ old_delta_e = deltae(optimised_ccm_lab[i], m_lab[i]) # Current Old Delta E\n+ new_delta_e = deltae(after_gamma_lab[i], m_lab[i]) # Current New Delta E\n+ if old_delta_e > old_worst_delta_e:\n+ old_worst_delta_e = old_delta_e\n+ if new_delta_e > new_worst_delta_e:\n+ new_worst_delta_e = new_delta_e\n+\n+ Cam.log += \"Before color correction matrix was optimised, we got an average delta E of \" + str(before_average) + \" and a maximum delta E of \" + str(old_worst_delta_e)\n+ Cam.log += \"After color correction matrix was optimised, we got an average delta E of \" + str(after_average) + \" and a maximum delta E of \" + str(new_worst_delta_e)\n+\n+ visualise_macbeth_chart(m_rgb, optimised_ccm_rgb, after_gamma_rgb, str(Img.col) + str(matrix_selection_types[typenum]))\n+ '''\n+ The program will also save some visualisations of improvements.\n+ Very pretty to look at. Top rectangle is ideal, Left square is\n+ before optimisation, right square is after.\n+ '''\n+\n+ \"\"\"\n+ if a ccm has already been calculated for that temperature then don't\n+ overwrite but save both. They will then be averaged later on\n+ \"\"\" # Now going to use optimised color matrix, optimised_ccm\n+ if Img.col in ccm_tab.keys():\n+ ccm_tab[Img.col].append(optimised_ccm)\n+ else:\n+ ccm_tab[Img.col] = [optimised_ccm]\n+ Cam.log += '\\n'\n+\n+ Cam.log += '\\nFinished processing images'\n+ \"\"\"\n+ average any ccms that share a colour temperature\n+ \"\"\"\n+ for k, v in ccm_tab.items():\n+ tab = np.mean(v, axis=0)\n+ tab = np.where((10000 * tab) % 1 <= 0.05, tab + 0.00001, tab)\n+ tab = np.where((10000 * tab) % 1 >= 0.95, tab - 0.00001, tab)\n+ ccm_tab[k] = list(np.round(tab, 5))\n+ Cam.log += '\\nMatrix calculated for colour temperature of {} K'.format(k)\n+\n+ \"\"\"\n+ return all ccms with respective colour temperature in the correct format,\n+ sorted by their colour temperature\n+ \"\"\"\n+ sorted_ccms = sorted(ccm_tab.items(), key=lambda kv: kv[0])\n+ ccms = []\n+ for i in sorted_ccms:\n+ ccms.append({\n+ 'ct': i[0],\n+ 'ccm': i[1]\n+ })\n+ return ccms\n+\n+\n+def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback for the optimisation code\n+ [r1, r2, g1, g2, b1, b2] = x0\n+ ccm = np.array([r1, r2, (1 - r1 - r2),\n+ g1, g2, (1 - g1 - g2),\n+ b1, b2, (1 - b1 - b2)]).reshape((3, 3)) # format the matrix correctly\n+ return transform_and_evaluate(ccm, r, g, b, m_lab)\n+\n+\n+def transform_and_evaluate(ccm, r, g, b, m_lab): # Transforms colors to LAB and applies the correction matrix\n+ # create list of matrix changed colors\n+ realrgb = []\n+ for RGB in zip(r, g, b):\n+ rgb_post_ccm = np.dot(ccm, np.array(RGB) / 256) # This is RGB values after the color correction matrix has been applied\n+ realrgb.append(colors.RGB_to_LAB(rgb_post_ccm))\n+ # now compare that with m_lab and return numeric result, averaged for each patch\n+ return (sumde(realrgb, m_lab) / 24) # returns an average result of delta E\n+\n+\n+def sumde(listA, listB):\n+ global typenum, test_patches\n+ sumde = 0\n+ maxde = 0\n+ patchde = [] # Create array of the delta E values for each patch. useful for optimisation of certain patches\n+ for listA_item, listB_item in zip(listA, listB):\n+ if maxde < (deltae(listA_item, listB_item)):\n+ maxde = deltae(listA_item, listB_item)\n+ patchde.append(deltae(listA_item, listB_item))\n+ sumde += deltae(listA_item, listB_item)\n+ '''\n+ The different options specified at the start allow for\n+ the maximum to be returned, average or specific patches\n+ '''\n+ if typenum == 0:\n+ return sumde\n+ if typenum == 1:\n+ return maxde\n+ if typenum == 2:\n+ output = sum([patchde[test_patch] for test_patch in test_patches])\n+ # Selects only certain patches and returns the output for them\n+ return output\n+\n+\n+\"\"\"\n+calculates the ccm for an individual image.\n+ccms are calculated in rgb space, and are fit by hand. Although it is a 3x3\n+matrix, each row must add up to 1 in order to conserve greyness, simplifying\n+calculation.\n+The initial CCM is calculated in RGB, and then optimised in LAB color space\n+This simplifies the initial calculation but then gets us the accuracy of\n+using LAB color space.\n+\"\"\"\n+\n+\n+def do_ccm(r, g, b, m_srgb):\n+ rb = r-b\n+ gb = g-b\n+ rb_2s = (rb * rb)\n+ rb_gbs = (rb * gb)\n+ gb_2s = (gb * gb)\n+\n+ r_rbs = rb * (m_srgb[..., 0] - b)\n+ r_gbs = gb * (m_srgb[..., 0] - b)\n+ g_rbs = rb * (m_srgb[..., 1] - b)\n+ g_gbs = gb * (m_srgb[..., 1] - b)\n+ b_rbs = rb * (m_srgb[..., 2] - b)\n+ b_gbs = gb * (m_srgb[..., 2] - b)\n+\n+ \"\"\"\n+ Obtain least squares fit\n+ \"\"\"\n+ rb_2 = np.sum(rb_2s)\n+ gb_2 = np.sum(gb_2s)\n+ rb_gb = np.sum(rb_gbs)\n+ r_rb = np.sum(r_rbs)\n+ r_gb = np.sum(r_gbs)\n+ g_rb = np.sum(g_rbs)\n+ g_gb = np.sum(g_gbs)\n+ b_rb = np.sum(b_rbs)\n+ b_gb = np.sum(b_gbs)\n+\n+ det = rb_2 * gb_2 - rb_gb * rb_gb\n+\n+ \"\"\"\n+ Raise error if matrix is singular...\n+ This shouldn't really happen with real data but if it does just take new\n+ pictures and try again, not much else to be done unfortunately...\n+ \"\"\"\n+ if det < 0.001:\n+ raise ArithmeticError\n+\n+ r_a = (gb_2 * r_rb - rb_gb * r_gb) / det\n+ r_b = (rb_2 * r_gb - rb_gb * r_rb) / det\n+ \"\"\"\n+ Last row can be calculated by knowing the sum must be 1\n+ \"\"\"\n+ r_c = 1 - r_a - r_b\n+\n+ g_a = (gb_2 * g_rb - rb_gb * g_gb) / det\n+ g_b = (rb_2 * g_gb - rb_gb * g_rb) / det\n+ g_c = 1 - g_a - g_b\n+\n+ b_a = (gb_2 * b_rb - rb_gb * b_gb) / det\n+ b_b = (rb_2 * b_gb - rb_gb * b_rb) / det\n+ b_c = 1 - b_a - b_b\n+\n+ \"\"\"\n+ format ccm\n+ \"\"\"\n+ ccm = [r_a, r_b, r_c, g_a, g_b, g_c, b_a, b_b, b_c]\n+\n+ return ccm\n+\n+\n+def deltae(colorA, colorB):\n+ return ((colorA[0] - colorB[0]) ** 2 + (colorA[1] - colorB[1]) ** 2 + (colorA[2] - colorB[2]) ** 2) ** 0.5\n+ # return ((colorA[1]-colorB[1]) * * 2 + (colorA[2]-colorB[2]) * * 2) * * 0.5\n+ # UNCOMMENT IF YOU WANT TO NEGLECT LUMINANCE FROM CALCULATION OF DELTA E\ndiff --git a/utils/tuning/libtuning/ctt_colors.py b/utils/tuning/libtuning/ctt_colors.py\nnew file mode 100644\nindex 000000000000..cb4d236b04d7\n--- /dev/null\n+++ b/utils/tuning/libtuning/ctt_colors.py\n@@ -0,0 +1,30 @@\n+# Program to convert from RGB to LAB color space\n+def RGB_to_LAB(RGB): # where RGB is a 1x3 array. e.g RGB = [100, 255, 230]\n+ num = 0\n+ XYZ = [0, 0, 0]\n+ # converted all the three R, G, B to X, Y, Z\n+ X = RGB[0] * 0.4124 + RGB[1] * 0.3576 + RGB[2] * 0.1805\n+ Y = RGB[0] * 0.2126 + RGB[1] * 0.7152 + RGB[2] * 0.0722\n+ Z = RGB[0] * 0.0193 + RGB[1] * 0.1192 + RGB[2] * 0.9505\n+\n+ XYZ[0] = X / 255 * 100\n+ XYZ[1] = Y / 255 * 100 # XYZ Must be in range 0 -> 100, so scale down from 255\n+ XYZ[2] = Z / 255 * 100\n+ XYZ[0] = XYZ[0] / 95.047 # ref_X = 95.047 Observer= 2°, Illuminant= D65\n+ XYZ[1] = XYZ[1] / 100.0 # ref_Y = 100.000\n+ XYZ[2] = XYZ[2] / 108.883 # ref_Z = 108.883\n+ num = 0\n+ for value in XYZ:\n+ if value > 0.008856:\n+ value = value ** (0.3333333333333333)\n+ else:\n+ value = (7.787 * value) + (16 / 116)\n+ XYZ[num] = value\n+ num = num + 1\n+\n+ # L, A, B, values calculated below\n+ L = (116 * XYZ[1]) - 16\n+ a = 500 * (XYZ[0] - XYZ[1])\n+ b = 200 * (XYZ[1] - XYZ[2])\n+\n+ return [L, a, b]\ndiff --git a/utils/tuning/libtuning/ctt_ransac.py b/utils/tuning/libtuning/ctt_ransac.py\nnew file mode 100644\nindex 000000000000..01bba3022ef0\n--- /dev/null\n+++ b/utils/tuning/libtuning/ctt_ransac.py\n@@ -0,0 +1,71 @@\n+# SPDX-License-Identifier: BSD-2-Clause\n+#\n+# Copyright (C) 2019, Raspberry Pi Ltd\n+#\n+# camera tuning tool RANSAC selector for Macbeth chart locator\n+\n+import numpy as np\n+\n+scale = 2\n+\n+\n+\"\"\"\n+constructs normalised macbeth chart corners for ransac algorithm\n+\"\"\"\n+def get_square_verts(c_err=0.05, scale=scale):\n+ \"\"\"\n+ define macbeth chart corners\n+ \"\"\"\n+ b_bord_x, b_bord_y = scale*8.5, scale*13\n+ s_bord = 6*scale\n+ side = 41*scale\n+ x_max = side*6 + 5*s_bord + 2*b_bord_x\n+ y_max = side*4 + 3*s_bord + 2*b_bord_y\n+ c1 = (0, 0)\n+ c2 = (0, y_max)\n+ c3 = (x_max, y_max)\n+ c4 = (x_max, 0)\n+ mac_norm = np.array((c1, c2, c3, c4), np.float32)\n+ mac_norm = np.array([mac_norm])\n+\n+ square_verts = []\n+ square_0 = np.array(((0, 0), (0, side),\n+ (side, side), (side, 0)), np.float32)\n+ offset_0 = np.array((b_bord_x, b_bord_y), np.float32)\n+ c_off = side * c_err\n+ offset_cont = np.array(((c_off, c_off), (c_off, -c_off),\n+ (-c_off, -c_off), (-c_off, c_off)), np.float32)\n+ square_0 += offset_0\n+ square_0 += offset_cont\n+ \"\"\"\n+ define macbeth square corners\n+ \"\"\"\n+ for i in range(6):\n+ shift_i = np.array(((i*side, 0), (i*side, 0),\n+ (i*side, 0), (i*side, 0)), np.float32)\n+ shift_bord = np.array(((i*s_bord, 0), (i*s_bord, 0),\n+ (i*s_bord, 0), (i*s_bord, 0)), np.float32)\n+ square_i = square_0 + shift_i + shift_bord\n+ for j in range(4):\n+ shift_j = np.array(((0, j*side), (0, j*side),\n+ (0, j*side), (0, j*side)), np.float32)\n+ shift_bord = np.array(((0, j*s_bord),\n+ (0, j*s_bord), (0, j*s_bord),\n+ (0, j*s_bord)), np.float32)\n+ square_j = square_i + shift_j + shift_bord\n+ square_verts.append(square_j)\n+ # print('square_verts')\n+ # print(square_verts)\n+ return np.array(square_verts, np.float32), mac_norm\n+\n+\n+def get_square_centres(c_err=0.05, scale=scale):\n+ \"\"\"\n+ define macbeth square centres\n+ \"\"\"\n+ verts, mac_norm = get_square_verts(c_err, scale=scale)\n+\n+ centres = np.mean(verts, axis=1)\n+ # print('centres')\n+ # print(centres)\n+ return np.array(centres, np.float32)\n", "prefixes": [ "v2", "03/25" ] }