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{ "id": 18802, "url": "https://patchwork.libcamera.org/api/1.1/patches/18802/?format=api", "web_url": "https://patchwork.libcamera.org/patch/18802/", "project": { "id": 1, "url": "https://patchwork.libcamera.org/api/1.1/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": "<20230706013926.218131-4-ben.benson@raspberrypi.com>", "date": "2023-07-06T01:39:26", "name": "[libcamera-devel,3/3] utils: raspberrypi: ctt: Code tidying", "commit_ref": null, "pull_url": null, "state": "accepted", "archived": false, "hash": "35d9dd716c4d1ccc4f6e994f5600a681be382d9f", "submitter": { "id": 171, "url": "https://patchwork.libcamera.org/api/1.1/people/171/?format=api", "name": "Ben Benson", "email": "ben.benson@raspberrypi.com" }, "delegate": null, "mbox": "https://patchwork.libcamera.org/patch/18802/mbox/", "series": [ { "id": 3963, "url": "https://patchwork.libcamera.org/api/1.1/series/3963/?format=api", "web_url": "https://patchwork.libcamera.org/project/libcamera/list/?series=3963", "date": "2023-07-06T01:39:23", "name": "Raspberry Pi Color Matrix and Tuning Tool Improvements", "version": 1, "mbox": "https://patchwork.libcamera.org/series/3963/mbox/" } ], "comments": "https://patchwork.libcamera.org/api/patches/18802/comments/", "check": "pending", "checks": "https://patchwork.libcamera.org/api/patches/18802/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 C4146C32AA\n\tfor <parsemail@patchwork.libcamera.org>;\n\tFri, 7 Jul 2023 13:41:26 +0000 (UTC)", "from lancelot.ideasonboard.com (localhost [IPv6:::1])\n\tby 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2023 05:07:45 -0700 (PDT)", "To": "libcamera-devel@lists.libcamera.org", "Date": "Thu, 6 Jul 2023 02:39:26 +0100", "Message-Id": "<20230706013926.218131-4-ben.benson@raspberrypi.com>", "X-Mailer": "git-send-email 2.34.1", "In-Reply-To": "<20230706013926.218131-1-ben.benson@raspberrypi.com>", "References": "<20230706013926.218131-1-ben.benson@raspberrypi.com>", "MIME-Version": "1.0", "Content-Transfer-Encoding": "8bit", "X-Mailman-Approved-At": "Fri, 07 Jul 2023 15:41:21 +0200", "Subject": "[libcamera-devel] [PATCH 3/3] utils: raspberrypi: ctt: Code tidying", "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>", "From": "Ben Benson via libcamera-devel <libcamera-devel@lists.libcamera.org>", "Reply-To": "Ben Benson <ben.benson@raspberrypi.com>", "Cc": "Ben Benson <ben.benson@raspberrypi.com>", "Errors-To": "libcamera-devel-bounces@lists.libcamera.org", "Sender": "\"libcamera-devel\" <libcamera-devel-bounces@lists.libcamera.org>" }, "content": "Altered the way that some lines are laid out, made functions\nmore attractive to look at, and tidied up messy areas.\n\nSigned-off-by Ben Benson <ben.benson@raspberrypi.com>\n---\n utils/raspberrypi/ctt/ctt_ccm.py | 61 +++++++++++++++-----------------\n 1 file changed, 29 insertions(+), 32 deletions(-)", "diff": "diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py\nindex bd44b4d8..85ca6827 100644\n--- a/utils/raspberrypi/ctt/ctt_ccm.py\n+++ b/utils/raspberrypi/ctt/ctt_ccm.py\n@@ -47,11 +47,8 @@ def degamma(x):\n \n \n def gamma(x):\n- # return (x * * (1 / 2.4) * 1.055 - 0.055)\n- e = []\n- for i in range(len(x)):\n- e.append(((x[i] / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255)\n- return e\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@@ -96,10 +93,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list):\n \"\"\"\n m_srgb = degamma(m_rgb) # now in 16 bit color.\n \n- m_lab = []\n- for col in m_srgb:\n- m_lab.append(colors.RGB_to_LAB(col / 256))\n- # This produces matrix of LAB values for ideal color chart)\n+ m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb]\n+ # This produces array of LAB values for ideal color chart)\n \n \"\"\"\n reorder reference values to match how patches are ordered\n@@ -168,7 +163,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list):\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-\n+ original_ccm = ccm\n r1 = ccm[0]\n r2 = ccm[1]\n g1 = ccm[3]\n@@ -199,12 +194,13 @@ def ccm(Cam, cal_cr_list, cal_cb_list):\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(ccm).reshape((3, 3))\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@@ -213,22 +209,23 @@ def ccm(Cam, cal_cr_list, cal_cb_list):\n '''\n optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB\n optimised_ccm_lab = []\n- for w in range(24):\n- RGB = np.array([r[w], g[w], b[w]])\n- ccm_applied_rgb = np.dot(formatted_ccm, (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 formatted_optimised_ccm = np.array(ccm).reshape((3, 3))\n after_gamma_rgb = []\n after_gamma_lab = []\n- for w in range(24):\n- RGB = np.array([r[w], g[w], b[w]])\n+\n+ for red, green, blue in zip(r, g, b):\n+ RGB = np.array([red, green, blue])\n+ ccm_applied_rgb = np.dot(formatted_ccm, (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, 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 '''\n- Gamma After RGB / LAB\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 matricies\n '''\n@@ -303,8 +300,8 @@ def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback f\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 i in range(len(r)):\n- RGB = np.array([r[i], g[i], b[i]])\n+ for red, green, blue in zip(r, g, b):\n+ RGB = np.array([red, green, blue])\n rgb_post_ccm = np.dot(ccm, RGB) # 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@@ -315,12 +312,12 @@ def sumde(listA, listB):\n global typenum, test_patches\n sumde = 0\n maxde = 0\n- patchde = []\n- for i in range(len(listA)):\n- if maxde < (deltae(listA[i], listB[i])):\n- maxde = deltae(listA[i], listB[i])\n- patchde.append(deltae(listA[i], listB[i]))\n- sumde += deltae(listA[i], listB[i])\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@@ -330,9 +327,8 @@ def sumde(listA, listB):\n if typenum == 1:\n return maxde\n if typenum == 2:\n- output = 0\n- for y in range(len(test_patches)):\n- output += patchde[test_patches[y]] # grabs the specific patches (no need for averaging here)\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@@ -341,8 +337,9 @@ calculates the ccm for an individual image.\n ccms are calculate 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-Should you want to fit them in another space (e.g. LAB) we wish you the best of\n-luck and send us the code when you are done! :-)\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", "prefixes": [ "libcamera-devel", "3/3" ] }