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added threshold and distance limit calculations to research, threshold values are now based on SMPTE 2065-1 ColorChecker24 RGB values
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‎README.md‎

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@@ -29,7 +29,7 @@ The following implementations are available in the [model](model) directory:
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- Python Script for [Numpy](https://numpy.org/)
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## Default Parameter Values
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This [Google Colab notebook](https://colab.research.google.com/drive/1ZMSQhyhXtAYQXfop6qhifXudDiPu4eTV?usp=sharing) shows calculations for the threshold values needed to protect the colors of the ColorChecker24, as defined in Annexe B of [TB-2014-004](http://j.mp/TB-2014-004), and the distance limits needed to map the entirety of a set of common camera encoding gamuts into AP1.
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This [Google Colab notebook](https://colab.research.google.com/drive/1ZMSQhyhXtAYQXfop6qhifXudDiPu4eTV?usp=sharing) shows calculations for the threshold values needed to protect the colors of the ColorChecker24, as defined in table D.1 of SMPTE 2065-1, and the distance limits needed to map the entirety of a set of common camera encoding gamuts into AP1.
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## Research
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‎research/resources/notebooks/scharfenberg/gamut_compress_defaults_new_distance_limit_method.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "gamut_compress_defaults_new_distance_limit_method.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nP-x7mebWWOA",
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"colab_type": "text"
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},
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"source": [
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"# Set up Colour Science for Python with latest `develop` branch"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "UlCRUQswC3KF",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 748
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},
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"outputId": "a13e51c1-b797-47a1-b923-d8f01dee660d"
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},
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"source": [
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"!pip install -q colour-science\n",
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"\n",
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"!pip uninstall -y colour-science\n",
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"!if ! [ -d \"colour\" ]; then git clone https://github.com/colour-science/colour; fi\n",
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"!if [ -d \"colour\" ]; then cd colour && git fetch && git checkout develop && cd ..; fi\n",
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"\n",
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"import sys\n",
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"sys.path.append('colour')\n",
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"\n",
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"import colour\n",
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"colour.utilities.describe_environment()"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"\u001b[K |████████████████████████████████| 1.6MB 2.9MB/s \n",
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"\u001b[?25hUninstalling colour-science-0.3.15:\n",
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" Successfully uninstalled colour-science-0.3.15\n",
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"Cloning into 'colour'...\n",
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"remote: Enumerating objects: 270, done.\u001b[K\n",
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"remote: Counting objects: 100% (270/270), done.\u001b[K\n",
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"remote: Compressing objects: 100% (186/186), done.\u001b[K\n",
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"remote: Total 45133 (delta 170), reused 180 (delta 84), pack-reused 44863\u001b[K\n",
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"Receiving objects: 100% (45133/45133), 110.60 MiB | 32.13 MiB/s, done.\n",
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"Resolving deltas: 100% (34504/34504), done.\n",
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"Already on 'develop'\n",
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"Your branch is up to date with 'origin/develop'.\n",
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"===============================================================================\n",
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"* *\n",
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"* Interpreter : *\n",
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"* python : 3.6.9 (default, Jul 17 2020, 12:50:27) *\n",
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"* [GCC 8.4.0] *\n",
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"* *\n",
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"* colour-science.org : *\n",
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"* colour : v0.3.15-370-g1317e9df *\n",
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"* *\n",
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"* Runtime : *\n",
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"* imageio : 2.4.1 *\n",
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"* matplotlib : 3.2.2 *\n",
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"* networkx : 2.5 *\n",
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"* numpy : 1.18.5 *\n",
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"* pandas : 1.0.5 *\n",
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"* scipy : 1.4.1 *\n",
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"* six : 1.15.0 *\n",
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"* *\n",
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"===============================================================================\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"defaultdict(collections.OrderedDict,\n",
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" {'Interpreter': OrderedDict([('python',\n",
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" '3.6.9 (default, Jul 17 2020, 12:50:27) \\n[GCC 8.4.0]')]),\n",
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" 'Runtime': OrderedDict([('imageio', '2.4.1'),\n",
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" ('matplotlib', '3.2.2'),\n",
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" ('networkx', '2.5'),\n",
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" ('numpy', '1.18.5'),\n",
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" ('pandas', '1.0.5'),\n",
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" ('scipy', '1.4.1'),\n",
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" ('six', '1.15.0')]),\n",
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" 'colour-science.org': OrderedDict([('colour',\n",
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" 'v0.3.15-370-g1317e9df')])})"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 2
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AFjVUDozXPXM",
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"colab_type": "text"
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},
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"source": [
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"# Define Blackmagic Wide Gamut v4 colour space\n",
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"# (not currently included in Colour)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "XEdXherNWrXK",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"import colour\n",
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"from colour.models import (\n",
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" RGB_Colourspace,\n",
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" normalised_primary_matrix\n",
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" )\n",
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"from colour.colorimetry import CCS_ILLUMINANTS\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_PRIMARIES = np.array([\n",
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" [0.7177, 0.3171],\n",
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" [0.2280, 0.8616],\n",
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" [0.1006, -0.0820],\n",
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"])\n",
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"\"\"\"\n",
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"*BMD Wide Gamut V4* colourspace primaries.\n",
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"As displayed on DaVinci Resolve Chromaticity scope.\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_PRIMARIES : ndarray, (3, 2)\n",
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"\"\"\"\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_WHITEPOINT_NAME = 'D65'\n",
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"\"\"\"\n",
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"*BMD Wide Gamut V4* colourspace whitepoint name.\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_WHITEPOINT : unicode\n",
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"\"\"\"\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_WHITEPOINT = (CCS_ILLUMINANTS[\n",
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" 'CIE 1931 2 Degree Standard Observer'][BMD_WIDE_GAMUT_V4_WHITEPOINT_NAME])\n",
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"\"\"\"\n",
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"*BMD Wide Gamut V4* colourspace whitepoint.\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_WHITEPOINT : ndarray\n",
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"\"\"\"\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_TO_XYZ_MATRIX = (normalised_primary_matrix(\n",
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" BMD_WIDE_GAMUT_V4_PRIMARIES, BMD_WIDE_GAMUT_V4_WHITEPOINT))\n",
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"\"\"\"\n",
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"*BMD Wide Gamut V4* colourspace to *CIE XYZ* tristimulus values matrix.\n",
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"\n",
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"BMD_WIDE_GAMUT_V4_TO_XYZ_MATRIX : array_like, (3, 3)\n",
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"\"\"\"\n",
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"\n",
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"XYZ_TO_BMD_WIDE_GAMUT_V4_MATRIX = (\n",
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" np.linalg.inv(BMD_WIDE_GAMUT_V4_TO_XYZ_MATRIX))\n",
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"\"\"\"\n",
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"*CIE XYZ* tristimulus values to *BMD Wide Gamut V4* colourspace matrix.\n",
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"\n",
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"XYZ_TO_BMD_WIDE_GAMUT_V4_MATRIX : array_like, (3, 3)\n",
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"\"\"\"\n",
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"\n",
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"RGB_COLOURSPACE_BMD_WIDE_GAMUT_V4 = RGB_Colourspace(\n",
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" 'BMD Wide Gamut V4',\n",
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" BMD_WIDE_GAMUT_V4_PRIMARIES,\n",
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" BMD_WIDE_GAMUT_V4_WHITEPOINT,\n",
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" BMD_WIDE_GAMUT_V4_WHITEPOINT_NAME,\n",
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" BMD_WIDE_GAMUT_V4_TO_XYZ_MATRIX,\n",
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" XYZ_TO_BMD_WIDE_GAMUT_V4_MATRIX,\n",
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")"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qo0_9YCJXOLw",
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"colab_type": "text"
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},
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"source": [
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"# Thresholds\n",
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"Calculate `thresholds` to protect ColorChecker 24"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bCukamO6DOsF",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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},
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"outputId": "8a1eff8c-1ae3-4701-d449-1f78df8c89eb"
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},
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"source": [
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"from __future__ import division\n",
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"\n",
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"import warnings\n",
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"\n",
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"from colour.models import (\n",
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" RGB_to_RGB,\n",
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" RGB_COLOURSPACE_ACES2065_1,\n",
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" RGB_COLOURSPACE_ACESCG,\n",
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" RGB_COLOURSPACE_ALEXA_WIDE_GAMUT,\n",
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" RGB_COLOURSPACE_CINEMA_GAMUT,\n",
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" RGB_COLOURSPACE_RED_WIDE_GAMUT_RGB,\n",
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" RGB_COLOURSPACE_S_GAMUT3,\n",
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" RGB_COLOURSPACE_VENICE_S_GAMUT3,\n",
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" RGB_COLOURSPACE_V_GAMUT\n",
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" )\n",
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"from colour.utilities import tsplit, tstack, dot_vector\n",
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"\n",
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"# ColorChecker 24 values as per SMPTE 2065-1\n",
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"CC24 = np.array([[0.11877, 0.08709, 0.05895],\n",
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" [0.40002, 0.31916, 0.23736],\n",
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" [0.18476, 0.20398, 0.31311],\n",
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" [0.10901, 0.13511, 0.06493],\n",
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" [0.26684, 0.24604, 0.40932],\n",
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" [0.32283, 0.46208, 0.40606],\n",
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" [0.38605, 0.22743, 0.05777],\n",
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" [0.13822, 0.13037, 0.33703],\n",
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" [0.30202, 0.13752, 0.12758],\n",
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" [0.09310, 0.06347, 0.13525],\n",
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" [0.34876, 0.43654, 0.10613],\n",
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" [0.48655, 0.36685, 0.08061],\n",
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" [0.08732, 0.07443, 0.27274],\n",
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" [0.15366, 0.25692, 0.09071],\n",
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" [0.21742, 0.07070, 0.05130],\n",
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" [0.58919, 0.53943, 0.09157],\n",
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" [0.30904, 0.14818, 0.27426],\n",
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" [0.14901, 0.23378, 0.35939]])\n",
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"\n",
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"CC24_AP1 = RGB_to_RGB(CC24, RGB_COLOURSPACE_ACES2065_1, RGB_COLOURSPACE_ACESCG)\n",
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"\n",
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"ach = np.max(CC24_AP1, axis=-1)[..., np.newaxis]\n",
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"dist = ((ach - CC24_AP1) / ach)\n",
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"c, m, y = tsplit(dist)\n",
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"\n",
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"print('Thresholds:')\n",
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"print(np.max(c), np.max(m), np.max(y))"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Thresholds:\n",
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"0.814546172832 0.802945634483 0.87963185306\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Rok6XJ-ZY_Xt",
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"colab_type": "text"
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},
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"source": [
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"# Distance Limits\n",
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"Calculate `distances` to map common camera encoding spaces to within AP1"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "2-4jmmBKZJg8",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"outputId": "ca3d9312-8f21-40fe-a4de-31e38a185573"
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},
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"source": [
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"maxLimits = np.array([[-1.0, 1.0, 1.0],\n",
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" [ 1.0, -1.0, 1.0],\n",
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" [ 1.0, 1.0, -1.0]])\n",
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"\n",
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"spaces = [[RGB_COLOURSPACE_ALEXA_WIDE_GAMUT, 'CAT02'],\n",
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" [RGB_COLOURSPACE_BMD_WIDE_GAMUT_V4, 'Bradford'],\n",
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" [RGB_COLOURSPACE_CINEMA_GAMUT, 'CAT02'],\n",
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" [RGB_COLOURSPACE_RED_WIDE_GAMUT_RGB, 'Bradford'],\n",
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" [RGB_COLOURSPACE_S_GAMUT3, 'CAT02'],\n",
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" [RGB_COLOURSPACE_VENICE_S_GAMUT3, 'CAT02'],\n",
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" [RGB_COLOURSPACE_V_GAMUT, 'CAT02']]\n",
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"\n",
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"bounds = []\n",
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"\n",
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"\n",
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"for space, cat in spaces:\n",
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" vendorRGB = RGB_to_RGB(maxLimits,\n",
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" RGB_COLOURSPACE_ACESCG,\n",
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" space,\n",
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" chromatic_adaptation_transform=cat)\n",
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" vendorHSV = colour.RGB_to_HSV(vendorRGB)\n",
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"\n",
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" for hsv in vendorHSV:\n",
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" hsv[1] = min(1.0, hsv[1])\n",
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"\n",
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" vendorRGB = colour.HSV_to_RGB(vendorHSV)\n",
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"\n",
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" bounds.append(RGB_to_RGB(vendorRGB,\n",
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" space,\n",
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" RGB_COLOURSPACE_ACESCG,\n",
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" chromatic_adaptation_transform=cat))\n",
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"\n",
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"ach = np.max(bounds, axis=-1)[..., np.newaxis]\n",
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"dist = np.where(ach == 0.0, 0.0, (ach - bounds) / ach)\n",
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"\n",
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"c, m, y = tsplit(dist)\n",
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"\n",
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"print('\\nDistance Limits:')\n",
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"print(np.max(c) - 1, np.max(m) - 1, np.max(y) - 1)\n"
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],
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"\n",
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"Distance Limits:\n",
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"0.147282233491 0.264290310772 0.312377540725\n"
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],
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"name": "stdout"
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}
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]
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}
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]
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}

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