research/resources/notebooks/colour-science/mitsuba_images_processing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mitsuba_images_processing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHpCakGpLdcU",
        "colab_type": "text"
      },
      "source": [
        "# Mitsuba Image Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Gpr26VhLiHO",
        "colab_type": "text"
      },
      "source": [
        "An exploratory notebook about Mitsuba image processing.\n",
        "\n",
        "A companion Google Colab Notebook for spectral image rendering is available at this [url](https://colab.research.google.com/drive/1Z0BE49o9cbr7235kDs4VKijNwJl8jZsj).\n",
        "\n",
        "The final images are hosted at this [url](https://academy-vwg-gm-mitsuba-spectral-images.imfast.io).\n",
        "\n",
        "More information is available on [ACEScentral](https://acescentral.com/c/aces-development-acesnext/vwg-aces-gamut-mapping-working-group/).\n",
        "\n",
        "*TODO*:\n",
        "\n",
        "*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf3UYPdk8BPB",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Global Parameters\n",
        "\n",
        "ILLUMINANT = 'Daylight FL' #@param ['3-LED-1 (457/540/605)', '3-LED-2 (473/545/616)', '3-LED-2 Yellow', '3-LED-3 (465/546/614)', '3-LED-4 (455/547/623)', '4-LED No Yellow', '4-LED Yellow', '4-LED-1 (461/526/576/624)', '4-LED-2 (447/512/573/627)', '60 A/W (Soft White)', 'A', 'B', 'C', 'C100S54 (HPS)', 'C100S54C (HPS)', 'Cool White FL', 'D50', 'D55', 'D60', 'D65', 'D75', 'Daylight FL', 'E', 'F32T8/TL830 (Triphosphor)', 'F32T8/TL835 (Triphosphor)', 'F32T8/TL841 (Triphosphor)', 'F32T8/TL850 (Triphosphor)', 'F32T8/TL865 /PLUS (Triphosphor)', 'F34/CW/RS/EW (Cool White FL)', 'F34T12/LW/RS /EW', 'F34T12WW/RS /EW (Warm White FL)', 'F40/C50 (Broadband FL)', 'F40/C75 (Broadband FL)', 'F40/CWX (Broadband FL)', 'F40/DX (Broadband FL)', 'F40/DXTP (Delux FL)', 'F40/N (Natural FL)', 'FL1', 'FL10', 'FL11', 'FL12', 'FL2', 'FL3', 'FL3.1', 'FL3.10', 'FL3.11', 'FL3.12', 'FL3.13', 'FL3.14', 'FL3.15', 'FL3.2', 'FL3.3', 'FL3.4', 'FL3.5', 'FL3.6', 'FL3.7', 'FL3.8', 'FL3.9', 'FL4', 'FL5', 'FL6', 'FL7', 'FL8', 'FL9', 'H38HT-100 (Mercury)', 'H38JA-100/DX (Mercury DX)', 'HP1', 'HP2', 'HP3', 'HP4', 'HP5', 'HPS', 'ID50', 'ID65', 'Incandescent', 'Kinoton 75P', 'LED-B1', 'LED-B2', 'LED-B3', 'LED-B4', 'LED-B5', 'LED-BH1', 'LED-RGB1', 'LED-V1', 'LED-V2', 'LPS', 'Luxeon WW 2880', 'MHC100/U/MP /3K', 'MHC100/U/MP /4K', 'Mercury', 'Metal Halide', 'Natural', 'Neodimium Incandescent', 'PHOS-1', 'PHOS-2', 'PHOS-3', 'PHOS-4', 'Philips TL-84', 'Phosphor LED YAG', 'SA', 'SC', 'SDW-T 100W/LV (Super HPS)', 'Super HPS', 'T8 Luxline Plus White', 'T8 Polylux 3000', 'T8 Polylux 4000', 'Thorn Kolor-rite', 'Triphosphor FL']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkS4FMDOV6sI",
        "colab_type": "text"
      },
      "source": [
        "## Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwoZdf6Aw1w5",
        "colab_type": "code",
        "outputId": "f1e363b5-386a-4041-deae-3c084c2be58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxgCgFuXxJGy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "OUTPUT_DIRECTORY = '/content/drive/My Drive/Fast.io/academy-vwg-gm-mitsuba-spectral-images.imfast.io'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7GYrUbfUKQZ",
        "colab_type": "text"
      },
      "source": [
        "## colour-science Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HNVkiHyoITP",
        "colab_type": "code",
        "outputId": "fffd20e5-8c5b-4b38-b608-9326d341bd73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "!pip install colour-datasets\n",
        "\n",
        "!python -c \"import imageio;imageio.plugins.freeimage.download()\"\n",
        "\n",
        "!pip uninstall -y colour-science\n",
        "!if [ -d \"colour\" ]; then rm -rf colour; fi\n",
        "!if ! [ -d \"colour\" ]; then git clone https://github.com/colour-science/colour; fi\n",
        "!if [ -d \"colour\" ]; then cd colour && git fetch && git checkout feature/idt && git pull && cd ..; fi\n",
        "\n",
        "import sys\n",
        "sys.path.append('colour')\n",
        "\n",
        "!mkdir -p /content/downloads"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: colour-datasets in /usr/local/lib/python3.6/dist-packages (0.1.0)\n",
            "Collecting colour-science<0.4.0,>=0.3.14\n",
            "  Using cached https://files.pythonhosted.org/packages/2e/b8/a25564da5f41a2d349afb3e95f8c5d7007231450bacf433a92301b073070/colour_science-0.3.15-py2.py3-none-any.whl\n",
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.6/dist-packages (from colour-datasets) (1.1.0)\n",
            "Requirement already satisfied: cachetools in /usr/local/lib/python3.6/dist-packages (from colour-datasets) (3.1.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from colour-datasets) (4.38.0)\n",
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (from colour-science<0.4.0,>=0.3.14->colour-datasets) (2.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from colour-science<0.4.0,>=0.3.14->colour-datasets) (1.12.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from colour-science<0.4.0,>=0.3.14->colour-datasets) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from imageio->colour-science<0.4.0,>=0.3.14->colour-datasets) (1.18.2)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio->colour-science<0.4.0,>=0.3.14->colour-datasets) (7.0.0)\n",
            "Installing collected packages: colour-science\n",
            "Successfully installed colour-science-0.3.15\n",
            "Uninstalling colour-science-0.3.15:\n",
            "  Successfully uninstalled colour-science-0.3.15\n",
            "Cloning into 'colour'...\n",
            "remote: Enumerating objects: 49, done.\u001b[K\n",
            "remote: Counting objects: 100% (49/49), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 41769 (delta 17), reused 32 (delta 14), pack-reused 41720\u001b[K\n",
            "Receiving objects: 100% (41769/41769), 109.27 MiB | 37.61 MiB/s, done.\n",
            "Resolving deltas: 100% (31821/31821), done.\n",
            "Branch 'feature/idt' set up to track remote branch 'feature/idt' from 'origin'.\n",
            "Switched to a new branch 'feature/idt'\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkdwsUvzob6h",
        "colab_type": "code",
        "outputId": "e250fd40-c2f0-4f0f-90b3-f31593943fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "import colour\n",
        "import colour_datasets\n",
        "import colour.plotting\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import re\n",
        "import textwrap\n",
        "from IPython.display import HTML, display\n",
        "from copy import deepcopy\n",
        "\n",
        "colour.plotting.colour_style()\n",
        "\n",
        "plt.style.use({'figure.figsize': (19.20, 10.80)})\n",
        "\n",
        "colour.utilities.describe_environment();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===============================================================================\n",
            "*                                                                             *\n",
            "*   Interpreter :                                                             *\n",
            "*       python : 3.6.9 (default, Nov  7 2019, 10:44:02)                       *\n",
            "*                [GCC 8.3.0]                                                  *\n",
            "*                                                                             *\n",
            "*   colour-science.org :                                                      *\n",
            "*       colour : v0.3.15-68-ge2cab159                                         *\n",
            "*       colour-datasets : 0.1.0                                               *\n",
            "*                                                                             *\n",
            "*   Runtime :                                                                 *\n",
            "*       imageio : 2.4.1                                                       *\n",
            "*       matplotlib : 3.2.1                                                    *\n",
            "*       networkx : 2.4                                                        *\n",
            "*       numpy : 1.18.2                                                        *\n",
            "*       pandas : 1.0.3                                                        *\n",
            "*       scipy : 1.4.1                                                         *\n",
            "*       six : 1.12.0                                                          *\n",
            "*                                                                             *\n",
            "===============================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--PkkRRBCqhl",
        "colab_type": "text"
      },
      "source": [
        "## Utility Objects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MXUn8YdCqFl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def slugify(a):\n",
        "    return re.sub(r'\\s|-|\\.', '_',\n",
        "                  re.sub(r'(?u)[^-\\w.]', ' ',\n",
        "                         str(a).strip()).strip())\n",
        "\n",
        "\n",
        "def html_format_matrix(M, precision=10):\n",
        "    M = np.atleast_2d(colour.utilities.as_float_array(M))\n",
        "\n",
        "    html = '<table class=\"matrix-table\">'\n",
        "    shape = M.shape\n",
        "    for i in range(shape[0]):\n",
        "        html += '<tr class=\"matrix-row\">'\n",
        "        for j in range(shape[1]):\n",
        "            v = M[i][j]\n",
        "            pretty = '{{: 0.{}f}}'.format(precision).format(\n",
        "                v) if precision is not None else v\n",
        "            html += '<td class=\"matrix-column\">{0}</td>'.format(pretty)\n",
        "        html += '</tr>'\n",
        "    html += '</table>'\n",
        "\n",
        "    return html\n",
        "\n",
        "\n",
        "def set_css_in_cell_output():\n",
        "  display(HTML(\n",
        "\"\"\"\n",
        "<style>\n",
        "    body {\n",
        "        background-color: white !important;\n",
        "    }\n",
        "\n",
        "    .table, .matrix-table {\n",
        "        width: 100%\n",
        "    }\n",
        "\n",
        "    .table-row-header, .table-data-header {\n",
        "        background-color: #aaa !important;\n",
        "        color: #fff !important;\n",
        "        font-weight: bold !important;\n",
        "        text-align: center\n",
        "    }\n",
        "\n",
        "    .vertical-align-top {\n",
        "        vertical-align: top\n",
        "    }\n",
        "\n",
        "    .text-align-center {\n",
        "        text-align: center\n",
        "    }\n",
        "\n",
        "    .table-row-odd {\n",
        "        background-color: #eee !important;\n",
        "    }\n",
        "\n",
        "    .matrix-table, .matrix-row, .matrix-column {\n",
        "        border: none\n",
        "        text-align: center\n",
        "    }\n",
        "\n",
        "    .table, .table-canton, .table-row-header-group, .table-row-header, .table-data-header, .table-row, .table-data {\n",
        "        border-color: #fff\n",
        "    }\n",
        "</style>\n",
        "\"\"\"))\n",
        "\n",
        "\n",
        "get_ipython().events.register('pre_run_cell', set_css_in_cell_output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcB-qJ4n8aZR",
        "colab_type": "text"
      },
      "source": [
        "## Emitters Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmCFq2YC8brb",
        "colab_type": "code",
        "outputId": "b4c8566c-d5e7-47bd-9fa9-cca2bf8d81d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "EMITTER_DATABASE = deepcopy(colour.ILLUMINANT_SDS)\n",
        "EMITTER_DATABASE.update(colour.LIGHT_SOURCE_SDS)\n",
        "\n",
        "EMITTER_NAMES = sorted(EMITTER_DATABASE.keys())\n",
        "\n",
        "print(EMITTER_NAMES)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    body {\n",
              "        background-color: white !important;\n",
              "    }\n",
              "\n",
              "    .table, .matrix-table {\n",
              "        width: 100%\n",
              "    }\n",
              "\n",
              "    .table-row-header, .table-data-header {\n",
              "        background-color: #aaa !important;\n",
              "        color: #fff !important;\n",
              "        font-weight: bold !important;\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .vertical-align-top {\n",
              "        vertical-align: top\n",
              "    }\n",
              "\n",
              "    .text-align-center {\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table-row-odd {\n",
              "        background-color: #eee !important;\n",
              "    }\n",
              "\n",
              "    .matrix-table, .matrix-row, .matrix-column {\n",
              "        border: none\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table, .table-canton, .table-row-header-group, .table-row-header, .table-data-header, .table-row, .table-data {\n",
              "        border-color: #fff\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "['3-LED-1 (457/540/605)', '3-LED-2 (473/545/616)', '3-LED-2 Yellow', '3-LED-3 (465/546/614)', '3-LED-4 (455/547/623)', '4-LED No Yellow', '4-LED Yellow', '4-LED-1 (461/526/576/624)', '4-LED-2 (447/512/573/627)', '60 A/W (Soft White)', 'A', 'B', 'C', 'C100S54 (HPS)', 'C100S54C (HPS)', 'Cool White FL', 'D50', 'D55', 'D60', 'D65', 'D75', 'Daylight FL', 'E', 'F32T8/TL830 (Triphosphor)', 'F32T8/TL835 (Triphosphor)', 'F32T8/TL841 (Triphosphor)', 'F32T8/TL850 (Triphosphor)', 'F32T8/TL865 /PLUS (Triphosphor)', 'F34/CW/RS/EW (Cool White FL)', 'F34T12/LW/RS /EW', 'F34T12WW/RS /EW (Warm White FL)', 'F40/C50 (Broadband FL)', 'F40/C75 (Broadband FL)', 'F40/CWX (Broadband FL)', 'F40/DX (Broadband FL)', 'F40/DXTP (Delux FL)', 'F40/N (Natural FL)', 'FL1', 'FL10', 'FL11', 'FL12', 'FL2', 'FL3', 'FL3.1', 'FL3.10', 'FL3.11', 'FL3.12', 'FL3.13', 'FL3.14', 'FL3.15', 'FL3.2', 'FL3.3', 'FL3.4', 'FL3.5', 'FL3.6', 'FL3.7', 'FL3.8', 'FL3.9', 'FL4', 'FL5', 'FL6', 'FL7', 'FL8', 'FL9', 'H38HT-100 (Mercury)', 'H38JA-100/DX (Mercury DX)', 'HP1', 'HP2', 'HP3', 'HP4', 'HP5', 'HPS', 'ID50', 'ID65', 'ISO 7589 Photoflood', 'ISO 7589 Photographic Daylight', 'ISO 7589 Sensitometric Daylight', 'ISO 7589 Sensitometric Photoflood', 'ISO 7589 Sensitometric Printer', 'ISO 7589 Sensitometric Studio Tungsten', 'ISO 7589 Studio Tungsten', 'Incandescent', 'Kinoton 75P', 'LED-B1', 'LED-B2', 'LED-B3', 'LED-B4', 'LED-B5', 'LED-BH1', 'LED-RGB1', 'LED-V1', 'LED-V2', 'LPS', 'Luxeon WW 2880', 'MHC100/U/MP /3K', 'MHC100/U/MP /4K', 'Mercury', 'Metal Halide', 'Natural', 'Neodimium Incandescent', 'PHOS-1', 'PHOS-2', 'PHOS-3', 'PHOS-4', 'Philips TL-84', 'Phosphor LED YAG', 'SA', 'SC', 'SDW-T 100W/LV (Super HPS)', 'Super HPS', 'T8 Luxline Plus White', 'T8 Polylux 3000', 'T8 Polylux 4000', 'Thorn Kolor-rite', 'Triphosphor FL']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODRcL1wrVWpr",
        "colab_type": "text"
      },
      "source": [
        "## Cameras Sensitivites Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqqTE8w9qqu6",
        "colab_type": "code",
        "outputId": "3fc89a19-521f-4d00-edbd-f8e48600fa3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "print(colour_datasets.datasets())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    body {\n",
              "        background-color: white !important;\n",
              "    }\n",
              "\n",
              "    .table, .matrix-table {\n",
              "        width: 100%\n",
              "    }\n",
              "\n",
              "    .table-row-header, .table-data-header {\n",
              "        background-color: #aaa !important;\n",
              "        color: #fff !important;\n",
              "        font-weight: bold !important;\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .vertical-align-top {\n",
              "        vertical-align: top\n",
              "    }\n",
              "\n",
              "    .text-align-center {\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table-row-odd {\n",
              "        background-color: #eee !important;\n",
              "    }\n",
              "\n",
              "    .matrix-table, .matrix-row, .matrix-column {\n",
              "        border: none\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table, .table-canton, .table-row-header-group, .table-row-header, .table-data-header, .table-row, .table-data {\n",
              "        border-color: #fff\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "colour-science-datasets\n",
            "=======================\n",
            "\n",
            "Datasets : 16\n",
            "Synced   : 2\n",
            "URL      : https://zenodo.org/communities/colour-science-datasets/\n",
            "\n",
            "Datasets\n",
            "--------\n",
            "\n",
            "[ ] 3269926 : Agfa IT8.7/2 Set\n",
            "[x] 3245883 : Camera Spectral Sensitivity Database\n",
            "[ ] 3367463 : Constant Hue Loci Data\n",
            "[ ] 3362536 : Constant Perceived-Hue Data\n",
            "[ ] 3270903 : Corresponding-Colour Datasets\n",
            "[ ] 3269920 : Forest Colors\n",
            "[ ] 3245875 : Labsphere SRS-99-020\n",
            "[ ] 3269924 : Lumber Spectra\n",
            "[ ] 3269918 : Munsell Colors Glossy (All) (Spectrofotometer Measured)\n",
            "[ ] 3269916 : Munsell Colors Glossy (Spectrofotometer Measured)\n",
            "[ ] 3269914 : Munsell Colors Matt (AOTF Measured)\n",
            "[ ] 3269912 : Munsell Colors Matt (Spectrofotometer Measured)\n",
            "[ ] 3245895 : New Color Specifications for ColorChecker SG and Classic Charts\n",
            "[ ] 3252742 : Observer Function Database\n",
            "[ ] 3269922 : Paper Spectra\n",
            "[x] 3372171 : RAW to ACES Utility Data\n"
          ],
          "name": "stdout"
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "mhsT7KrIwP87",
        "colab_type": "code",
        "outputId": "3741edfe-25ec-402e-86d8-74f0e1b21a88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "# Fujifilm X-T2 sensitivities courtesy Prof. Dr. Jan Fröhlich.\n",
        "FUJIFILM_XT2_SENSITIVITIES = {\n",
        "    380: (-0.0000850511, 0.0000507283, 0.0001182680),\n",
        "    385: (0.0017391753, 0.0015664783, 0.0030663904),\n",
        "    390: (0.0018391639, 0.0013485245, 0.0056839334),\n",
        "    395: (0.0020047764, 0.0018898209, 0.0077782905),\n",
        "    400: (0.0024319499, 0.0023503233, 0.0152484840),\n",
        "    405: (0.0037214138, 0.0042017956, 0.0317564811),\n",
        "    410: (0.0040661443, 0.0059389581, 0.0461115021),\n",
        "    415: (0.0050188542, 0.0103283912, 0.0885883564),\n",
        "    420: (0.0141641162, 0.0446775540, 0.4678504332),\n",
        "    425: (0.0289368226, 0.1097150931, 1.2736648204),\n",
        "    430: (0.0229998057, 0.1218555473, 1.5379422148),\n",
        "    435: (0.0176522587, 0.1491990644, 1.7056632831),\n",
        "    440: (0.0144726504, 0.1867968184, 1.8400450934),\n",
        "    445: (0.0107464186, 0.2150155985, 1.9834635737),\n",
        "    450: (0.0088648711, 0.2162266003, 2.0580732061),\n",
        "    455: (0.0085491319, 0.2054437144, 2.0638934415),\n",
        "    460: (0.0086964157, 0.2537927934, 2.0888061940),\n",
        "    465: (0.0108394895, 0.4036712359, 2.1216650752),\n",
        "    470: (0.0135633997, 0.6631781839, 2.1256474514),\n",
        "    475: (0.0179550003, 1.0833786199, 2.1211476773),\n",
        "    480: (0.0237812140, 1.5477386938, 2.0495970465),\n",
        "    485: (0.0262252044, 1.7972822569, 1.8982666094),\n",
        "    490: (0.0294250769, 1.9944333531, 1.7210776896),\n",
        "    495: (0.0333635172, 2.1511102005, 1.5656151922),\n",
        "    500: (0.0396168375, 2.2832808494, 1.3762479591),\n",
        "    505: (0.0469268560, 2.4023283963, 1.1893304997),\n",
        "    510: (0.0572103232, 2.5511047707, 0.9961925946),\n",
        "    515: (0.0713868116, 2.6287493779, 0.8197455273),\n",
        "    520: (0.0836021073, 2.6545130596, 0.6435621628),\n",
        "    525: (0.0924338183, 2.6419193878, 0.5077499176),\n",
        "    530: (0.0963773110, 2.6712565485, 0.4116406265),\n",
        "    535: (0.0950553314, 2.6615841643, 0.3370802508),\n",
        "    540: (0.0931732510, 2.5982098046, 0.2759953966),\n",
        "    545: (0.0924634463, 2.5699888001, 0.2121870833),\n",
        "    550: (0.0944520009, 2.4783817927, 0.1535785534),\n",
        "    555: (0.1028024209, 2.3719940255, 0.1080085899),\n",
        "    560: (0.1167127364, 2.2914320681, 0.0759132103),\n",
        "    565: (0.1392658710, 2.1507480937, 0.0542478591),\n",
        "    570: (0.2036891001, 2.0062697844, 0.0403866321),\n",
        "    575: (0.4826071157, 1.8869681524, 0.0337911448),\n",
        "    580: (1.1421720720, 1.7063756982, 0.0290716955),\n",
        "    585: (1.7513548729, 1.5136504485, 0.0258337436),\n",
        "    590: (1.9912121898, 1.3231483525, 0.0218989884),\n",
        "    595: (2.0280314847, 1.1049710654, 0.0187962862),\n",
        "    600: (2.0285918274, 0.8849117960, 0.0160717365),\n",
        "    605: (1.9394389960, 0.6758881114, 0.0130994892),\n",
        "    610: (1.7930618254, 0.4994530474, 0.0110792449),\n",
        "    615: (1.6962768127, 0.3682860185, 0.0110615757),\n",
        "    620: (1.6297477439, 0.2807688931, 0.0112705333),\n",
        "    625: (1.5457506148, 0.2243510637, 0.0125268388),\n",
        "    630: (1.4590434578, 0.1858814155, 0.0136028671),\n",
        "    635: (1.3694020846, 0.1572222269, 0.0151973750),\n",
        "    640: (1.2590686946, 0.1319731733, 0.0168792374),\n",
        "    645: (1.1587083458, 0.1093771148, 0.0189046647),\n",
        "    650: (1.0968367497, 0.0925126597, 0.0215228256),\n",
        "    655: (1.0284329906, 0.0811408119, 0.0241990380),\n",
        "    660: (0.9199238968, 0.0751469182, 0.0263396877),\n",
        "    665: (0.8089567874, 0.0740249302, 0.0268489155),\n",
        "    670: (0.7410715904, 0.0794581154, 0.0272828488),\n",
        "    675: (0.6530452201, 0.0854697297, 0.0264080431),\n",
        "    680: (0.4691569675, 0.0762769868, 0.0200047025),\n",
        "    685: (0.2726319437, 0.0559628152, 0.0132758753),\n",
        "    690: (0.1474833504, 0.0361475626, 0.0073368266),\n",
        "    695: (0.0838719140, 0.0232583145, 0.0041905597),\n",
        "    700: (0.0495969245, 0.0148939268, 0.0023401688),\n",
        "    705: (0.0508611613, 0.0152692357, 0.0024024764),\n",
        "    710: (0.0275162807, 0.0086920249, 0.0011455218),\n",
        "    715: (0.0182886806, 0.0059500079, 0.0006282180),\n",
        "    720: (0.0141230590, 0.0041396701, -0.0001967192),\n",
        "    725: (0.0108287418, 0.0032726525, 0.0001009021),\n",
        "    730: (0.0070269151, 0.0016909734, -0.0004938016),\n",
        "    735: (0.0064099811, 0.0018393029, -0.0000322150),\n",
        "    740: (0.0059408997, 0.0013489824, -0.0005827746),\n",
        "    745: (0.0048495041, 0.0014616363, -0.0000900026),\n",
        "    750: (0.0126660573, 0.0040132707, -0.0004361908),\n",
        "    755: (0.0054878043, 0.0017572947, -0.0006635022),\n",
        "    760: (-0.0010269703, -0.0018653584, -0.0027123156),\n",
        "    765: (0.0026327462, 0.0007082691, -0.0007148143),\n",
        "    770: (-0.0003658680, -0.0016082711, -0.0028283335),\n",
        "    775: (0.0007704984, 0.0000952247, -0.0009148115),\n",
        "    780: (0.0002937840, -0.0000545454, -0.0008717080)\n",
        "}\n",
        "\n",
        "ADDITIONAL_CAMERA_SENSITIVITIES_DATABASE = {\n",
        "    'Fujifilm X-T2':\n",
        "    colour.characterisation.RGB_SpectralSensitivities(\n",
        "        FUJIFILM_XT2_SENSITIVITIES, name='Fujifilm X-T2'),\n",
        "    'ACES RICD':\n",
        "    colour.characterisation.ACES_RICD,\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "DXRwuHZarMwQ",
        "colab_type": "code",
        "outputId": "bc656be4-15ae-485d-f46b-77ed8ad65015",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "CAMERA_SENSITIVITIES_DATABASE = colour_datasets.load(\n",
        "    'Camera Spectral Sensitivity Database')\n",
        "AMPAS_CAMERA_SENSITIVITIES_DATABASE = colour_datasets.load(\n",
        "    'RAW to ACES Utility Data')\n",
        "\n",
        "TRAINING_DATA = AMPAS_CAMERA_SENSITIVITIES_DATABASE['training']['190-patch']\n",
        "\n",
        "COMPOSITE_CAMERA_SENSITIVITIES_DATABASE = deepcopy(\n",
        "    CAMERA_SENSITIVITIES_DATABASE)\n",
        "COMPOSITE_CAMERA_SENSITIVITIES_DATABASE.update(\n",
        "    deepcopy(AMPAS_CAMERA_SENSITIVITIES_DATABASE['camera']))\n",
        "COMPOSITE_CAMERA_SENSITIVITIES_DATABASE.update(\n",
        "    deepcopy(ADDITIONAL_CAMERA_SENSITIVITIES_DATABASE))\n",
        "\n",
        "CAMERA_NAMES = sorted(COMPOSITE_CAMERA_SENSITIVITIES_DATABASE.keys())\n",
        "\n",
        "print(CAMERA_NAMES)\n",
        "\n",
        "SLUGGIFIED_CAMERA_NAMES = {\n",
        "    slugify(camera_name): camera_name\n",
        "    for camera_name in CAMERA_NAMES\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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          "text": [
            "['ACES RICD', 'Canon 1DMarkIII', 'Canon 20D', 'Canon 300D', 'Canon 40D', 'Canon 500D', 'Canon 50D', 'Canon 5DMarkII', 'Canon 600D', 'Canon 60D', 'Fujifilm X-T2', 'Hasselblad H2', 'Nikon D200', 'Nikon D3', 'Nikon D300s', 'Nikon D3X', 'Nikon D40', 'Nikon D50', 'Nikon D5100', 'Nikon D700', 'Nikon D80', 'Nikon D90', 'Nokia N900', 'Olympus E-PL2', 'Pentax K-5', 'Pentax Q', 'Phase One', 'Point Grey Grasshopper 50S5C', 'Point Grey Grasshopper2 14S5C', 'SONY NEX-5N', 'arri d21', 'canon eos 5d mark ii', 'canon powershot s90', 'canon xti', 'nikon d200', 'nikon d5100', 'nikon d70', 'nikon d700', 'nikon d7000', 'sony ilce-7rm2', 'sony ilce-7sm2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epmEBrPVIZMM",
        "colab_type": "text"
      },
      "source": [
        "## Camera Sensitivities Normalisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaJ2k8V8SmP6",
        "colab_type": "code",
        "outputId": "d638301f-1dcc-4a7a-8242-4480e1374206",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "SHAPE = colour.SpectralShape(400, 700, 5)\n",
        "MITSUBA_SHAPE = colour.SpectralShape(360, 830, 5)\n",
        "\n",
        "\n",
        "def normalise_multi_signal(multi_signal):\n",
        "    multi_signal = multi_signal.copy()\n",
        "\n",
        "    f_n = colour.sd_to_XYZ(colour.sd_ones(), cmfs=multi_signal) / 100\n",
        "    print('\"{0}\": {1}'.format(multi_signal.name, f_n))\n",
        "    multi_signal.values = multi_signal.values / f_n\n",
        "\n",
        "    return multi_signal\n",
        "\n",
        "\n",
        "print('CMFS & Camera Sensitivities Normalisation Factors')\n",
        "INTERPOLATOR = colour.LinearInterpolator\n",
        "EXTRAPOLATOR_KWARGS = {'right': 0, 'left': 0, 'method': 'constant'}\n",
        "\n",
        "CMFS_NAME = 'CIE 1931 2 Degree Standard Observer'\n",
        "CMFS = normalise_multi_signal(colour.CMFS[CMFS_NAME].copy().align(MITSUBA_SHAPE))\n",
        "\n",
        "COMPOSITE_CAMERA_SENSITIVITIES_DATABASE = {\n",
        "    name: normalise_multi_signal(sensitivities.copy().align(\n",
        "        SHAPE).align(MITSUBA_SHAPE, extrapolator_kwargs=EXTRAPOLATOR_KWARGS))\n",
        "    for name, sensitivities in COMPOSITE_CAMERA_SENSITIVITIES_DATABASE.items()\n",
        "}\n",
        "\n",
        "colour.plotting.plot_multi_cmfs(\n",
        "    [CMFS, COMPOSITE_CAMERA_SENSITIVITIES_DATABASE[CAMERA_NAMES[0]]]);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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          "text": [
            "CMFS & Camera Sensitivities Normalisation Factors\n",
            "\"CIE 1931 2 Degree Standard Observer\": [ 1.00007817  1.          1.00034117]\n",
            "\"Canon 1DMarkIII\": [ 0.4643693  1.         0.7515508]\n",
            "\"Canon 20D\": [ 0.53492694  1.          0.65663821]\n",
            "\"Canon 300D\": [ 0.51365973  1.          0.94518052]\n",
            "\"Canon 40D\": [ 0.45873856  1.          0.76156072]\n",
            "\"Canon 500D\": [ 0.49424391  1.          0.79044306]\n",
            "\"Canon 50D\": [ 0.48288578  1.          0.81077445]\n",
            "\"Canon 5DMarkII\": [ 0.51226291  1.          0.63354197]\n",
            "\"Canon 600D\": [ 0.44912477  1.          0.6853175 ]\n",
            "\"Canon 60D\": [ 0.45252825  1.          0.71799655]\n",
            "\"Hasselblad H2\": [ 0.3939756   1.          1.00023554]\n",
            "\"Nikon D3X\": [ 0.4586101   1.          0.76026313]\n",
            "\"Nikon D200\": [ 0.58071958  1.          0.72940151]\n",
            "\"Nikon D3\": [ 0.55525235  1.          0.78156171]\n",
            "\"Nikon D300s\": [ 0.66096129  1.          0.83936663]\n",
            "\"Nikon D40\": [ 0.46573036  1.          0.78634668]\n",
            "\"Nikon D50\": [ 0.46014445  1.          0.77977596]\n",
            "\"Nikon D5100\": [ 0.50478011  1.          0.75262632]\n",
            "\"Nikon D700\": [ 0.56748599  1.          0.78400267]\n",
            "\"Nikon D80\": [ 0.54653664  1.          0.66837335]\n",
            "\"Nikon D90\": [ 0.54024525  1.          0.84795939]\n",
            "\"Nokia N900\": [ 0.80160036  1.          0.77910112]\n",
            "\"Olympus E-PL2\": [ 0.77409926  1.          0.76245943]\n",
            "\"Pentax K-5\": [ 0.4750127   1.          0.66313219]\n",
            "\"Pentax Q\": [ 0.67213258  1.          0.62923333]\n",
            "\"Point Grey Grasshopper 50S5C\": [ 0.95758598  1.          1.80665923]\n",
            "\"Point Grey Grasshopper2 14S5C\": [ 1.05356437  1.          1.73151742]\n",
            "\"Phase One\": [ 0.7133728   1.          0.72348132]\n",
            "\"SONY NEX-5N\": [ 0.39777003  1.          0.72351203]\n",
            "\"canon powershot s90\": [ 0.60126174  1.          0.52271898]\n",
            "\"canon xti\": [ 0.44746963  1.          0.7678    ]\n",
            "\"nikon d5100\": [ 0.66916294  1.          0.81563572]\n",
            "\"arri d21\": [ 1.11166188  1.          1.13991689]\n",
            "\"canon eos 5d mark ii\": [ 0.4450871   1.          0.70541399]\n",
            "\"nikon d70\": [ 0.43751051  1.          0.69604226]\n",
            "\"nikon d200\": [ 0.59336517  1.          0.74640156]\n",
            "\"nikon d700\": [ 0.57266989  1.          0.7843232 ]\n",
            "\"sony ilce-7rm2\": [ 0.38084481  1.          0.7270701 ]\n",
            "\"sony ilce-7sm2\": [ 0.37847283  1.          0.74889114]\n",
            "\"nikon d7000\": [ 0.50603195  1.          0.79027522]\n",
            "\"Fujifilm X-T2\": [ 0.52235152  1.          0.60917663]\n",
            "\"ACES RICD\": [ 0.99791399  1.          0.99625349]\n"
          ],
          "name": "stdout"
        },
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 1920x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXEgqb_dIlg2",
        "colab_type": "text"
      },
      "source": [
        "## IDT Matrices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uJCy4axEi0k",
        "colab_type": "code",
        "outputId": "dc805359-b0d5-4a43-939b-3dfc286233fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ILLUMINANT_SD = EMITTER_DATABASE[ILLUMINANT]\n",
        "\n",
        "M_IDT = {\n",
        "    camera: colour.idt_matrix(sensitivities, ILLUMINANT_SD, CMFS)\n",
        "    for camera, sensitivities in\n",
        "    COMPOSITE_CAMERA_SENSITIVITIES_DATABASE.items()\n",
        "}\n",
        "\n",
        "\n",
        "def html_format_pseudo_idt_matrices(M_IDT):\n",
        "    html = ('<table class=\"table\">'\n",
        "            '<thead class=\"table-header\">'\n",
        "            '<tr class=\"table-row-header-group\">'\n",
        "            '<th class=\"table-canton\"></th>'\n",
        "            '<th class=\"table-row-header\">Illuminant ({0})</th>'\n",
        "            '</tr>'\n",
        "            '</thead>'\n",
        "            '<tbody>'.format(ILLUMINANT))\n",
        "    for i, (camera_name, M) in enumerate(M_IDT.items()):\n",
        "        html += '<tr class=\"table-row {0}\">'.format('table-row-odd' if i %\n",
        "                                                    2 == 0 else '')\n",
        "        html += '<td class=\"table-data-header\">{0}</td>'.format(camera_name)\n",
        "        html += '<td class=\"table-data\">{0}</td>'.format(\n",
        "            html_format_matrix(M))\n",
        "        html += '</tr>'\n",
        "    html += ('<tbody>' '</table>')\n",
        "\n",
        "    return html\n",
        "\n",
        "\n",
        "HTML(\n",
        "    html_format_pseudo_idt_matrices(\n",
        "        {camera_name: M_IDT[camera_name]\n",
        "         for camera_name in CAMERA_NAMES}))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    body {\n",
              "        background-color: white !important;\n",
              "    }\n",
              "\n",
              "    .table, .matrix-table {\n",
              "        width: 100%\n",
              "    }\n",
              "\n",
              "    .table-row-header, .table-data-header {\n",
              "        background-color: #aaa !important;\n",
              "        color: #fff !important;\n",
              "        font-weight: bold !important;\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .vertical-align-top {\n",
              "        vertical-align: top\n",
              "    }\n",
              "\n",
              "    .text-align-center {\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table-row-odd {\n",
              "        background-color: #eee !important;\n",
              "    }\n",
              "\n",
              "    .matrix-table, .matrix-row, .matrix-column {\n",
              "        border: none\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table, .table-canton, .table-row-header-group, .table-row-header, .table-data-header, .table-row, .table-data {\n",
              "        border-color: #fff\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"table\"><thead class=\"table-header\"><tr class=\"table-row-header-group\"><th class=\"table-canton\"></th><th class=\"table-row-header\">Illuminant (Daylight FL)</th></tr></thead><tbody><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">ACES RICD</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 1.0062792728</td><td class=\"matrix-column\"> 0.0096138650</td><td class=\"matrix-column\">-0.0161755194</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0017333532</td><td class=\"matrix-column\"> 1.0042590712</td><td class=\"matrix-column\">-0.0059893194</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.0005735112</td><td class=\"matrix-column\">-0.0005785669</td><td class=\"matrix-column\"> 1.0003776822</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Canon 1DMarkIII</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6719438099</td><td class=\"matrix-column\"> 0.0412800644</td><td class=\"matrix-column\"> 0.1916185732</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1338667013</td><td class=\"matrix-column\"> 0.8278480705</td><td class=\"matrix-column\">-0.0625221665</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0941650471</td><td class=\"matrix-column\">-0.2201301584</td><td class=\"matrix-column\"> 1.0701190188</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Canon 20D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8116143848</td><td class=\"matrix-column\"> 0.0552354391</td><td class=\"matrix-column\"> 0.2136238818</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0988743178</td><td class=\"matrix-column\"> 1.0218031801</td><td class=\"matrix-column\">-0.0735794606</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1530998716</td><td class=\"matrix-column\">-0.3317615859</td><td class=\"matrix-column\"> 1.3026005035</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Canon 300D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5965274802</td><td class=\"matrix-column\"> 0.1513863581</td><td class=\"matrix-column\"> 0.1683798682</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1111317035</td><td class=\"matrix-column\"> 0.9437199685</td><td class=\"matrix-column\">-0.1580359041</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1064790460</td><td class=\"matrix-column\">-0.3059960251</td><td class=\"matrix-column\"> 1.2042636533</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Canon 40D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8256651930</td><td class=\"matrix-column\"> 0.0236005575</td><td class=\"matrix-column\"> 0.2351655592</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1009106176</td><td class=\"matrix-column\"> 1.0526390444</td><td class=\"matrix-column\">-0.0800214484</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1339072708</td><td class=\"matrix-column\">-0.3643597326</td><td class=\"matrix-column\"> 1.3322454502</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Canon 500D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8834321064</td><td class=\"matrix-column\">-0.2155212069</td><td class=\"matrix-column\"> 0.2569179806</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0680496377</td><td class=\"matrix-column\"> 1.0244112679</td><td class=\"matrix-column\">-0.1514384883</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1018458847</td><td class=\"matrix-column\">-0.5076344729</td><td class=\"matrix-column\"> 1.3702696006</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Canon 50D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8338243881</td><td class=\"matrix-column\">-0.1484094351</td><td class=\"matrix-column\"> 0.2317845892</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0670929140</td><td class=\"matrix-column\"> 0.9778962657</td><td class=\"matrix-column\">-0.1197245376</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0812259635</td><td class=\"matrix-column\">-0.4360510872</td><td class=\"matrix-column\"> 1.2919454147</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Canon 5DMarkII</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8830770958</td><td class=\"matrix-column\">-0.0735075099</td><td class=\"matrix-column\"> 0.2466713207</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0887792364</td><td class=\"matrix-column\"> 1.0635686349</td><td class=\"matrix-column\">-0.0895437441</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1485057253</td><td class=\"matrix-column\">-0.3619505611</td><td class=\"matrix-column\"> 1.3214605159</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Canon 600D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.7019860245</td><td class=\"matrix-column\">-0.0180375309</td><td class=\"matrix-column\"> 0.1985760189</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0612261250</td><td class=\"matrix-column\"> 0.9275709579</td><td class=\"matrix-column\">-0.1032236043</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1113281397</td><td class=\"matrix-column\">-0.3383931509</td><td class=\"matrix-column\"> 1.1339166130</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Canon 60D</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.7104762116</td><td class=\"matrix-column\">-0.0091737582</td><td class=\"matrix-column\"> 0.2013630972</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0664180331</td><td class=\"matrix-column\"> 0.9334725497</td><td class=\"matrix-column\">-0.0979284462</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1055477028</td><td class=\"matrix-column\">-0.3259989076</td><td class=\"matrix-column\"> 1.1526868777</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Fujifilm X-T2</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.4743778324</td><td class=\"matrix-column\"> 0.1930486342</td><td class=\"matrix-column\"> 0.1378331084</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0299731615</td><td class=\"matrix-column\"> 0.8086933626</td><td class=\"matrix-column\">-0.0720830774</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0965939470</td><td class=\"matrix-column\">-0.2508727581</td><td class=\"matrix-column\"> 0.9469187123</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Hasselblad H2</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6037147186</td><td class=\"matrix-column\"> 0.1046852927</td><td class=\"matrix-column\"> 0.1643859833</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0904093046</td><td class=\"matrix-column\"> 0.7328040755</td><td class=\"matrix-column\">-0.0405345676</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1470757254</td><td class=\"matrix-column\">-0.3855926701</td><td class=\"matrix-column\"> 1.1949771708</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nikon D200</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5849398156</td><td class=\"matrix-column\"> 0.1931236001</td><td class=\"matrix-column\"> 0.0981078384</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1156841285</td><td class=\"matrix-column\"> 0.8193785277</td><td class=\"matrix-column\">-0.0861823768</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0585156448</td><td class=\"matrix-column\">-0.1199593479</td><td class=\"matrix-column\"> 0.9354612748</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Nikon D3</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5971024299</td><td class=\"matrix-column\"> 0.1821982564</td><td class=\"matrix-column\"> 0.1069105963</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1192804302</td><td class=\"matrix-column\"> 0.8065056654</td><td class=\"matrix-column\">-0.0640765748</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0650421223</td><td class=\"matrix-column\">-0.1422626980</td><td class=\"matrix-column\"> 0.9674138476</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nikon D300s</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6465223395</td><td class=\"matrix-column\"> 0.1519924084</td><td class=\"matrix-column\"> 0.1160457843</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1112262282</td><td class=\"matrix-column\"> 0.8663836923</td><td class=\"matrix-column\">-0.1008973917</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1080507352</td><td class=\"matrix-column\">-0.2675916434</td><td class=\"matrix-column\"> 1.1105178914</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Nikon D3X</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5888317877</td><td class=\"matrix-column\"> 0.2020792857</td><td class=\"matrix-column\"> 0.1109958926</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0953139034</td><td class=\"matrix-column\"> 0.8922319240</td><td class=\"matrix-column\">-0.1226497169</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0795728172</td><td class=\"matrix-column\">-0.2087274789</td><td class=\"matrix-column\"> 1.0503309802</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nikon D40</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6115773203</td><td class=\"matrix-column\"> 0.1521131240</td><td class=\"matrix-column\"> 0.1421741758</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1295812568</td><td class=\"matrix-column\"> 0.8573077899</td><td class=\"matrix-column\">-0.1066494060</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0471246318</td><td class=\"matrix-column\">-0.1111493677</td><td class=\"matrix-column\"> 1.0118654738</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Nikon D50</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5773751186</td><td class=\"matrix-column\"> 0.1636656954</td><td class=\"matrix-column\"> 0.1385624453</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0983067258</td><td class=\"matrix-column\"> 0.8250994352</td><td class=\"matrix-column\">-0.0885100655</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0618863073</td><td class=\"matrix-column\">-0.1341973162</td><td class=\"matrix-column\"> 0.9936918603</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nikon D5100</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6235130285</td><td class=\"matrix-column\"> 0.1640970748</td><td class=\"matrix-column\"> 0.0948989841</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1028039462</td><td class=\"matrix-column\"> 0.9287991651</td><td class=\"matrix-column\">-0.1654912909</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0899397130</td><td class=\"matrix-column\">-0.2544392298</td><td class=\"matrix-column\"> 1.0517863093</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Nikon D700</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5949343863</td><td class=\"matrix-column\"> 0.1884284100</td><td class=\"matrix-column\"> 0.1075628923</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1079060516</td><td class=\"matrix-column\"> 0.8245812951</td><td class=\"matrix-column\">-0.0696685913</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0662074031</td><td class=\"matrix-column\">-0.1419493523</td><td class=\"matrix-column\"> 0.9734535891</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nikon D80</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5884122053</td><td class=\"matrix-column\"> 0.1811127694</td><td class=\"matrix-column\"> 0.1285182149</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1216905803</td><td class=\"matrix-column\"> 0.8169384809</td><td class=\"matrix-column\">-0.0740987889</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0789506907</td><td class=\"matrix-column\">-0.1578455936</td><td class=\"matrix-column\"> 1.0111878179</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Nikon D90</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6513606071</td><td class=\"matrix-column\"> 0.1687773344</td><td class=\"matrix-column\"> 0.0923227998</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1245722594</td><td class=\"matrix-column\"> 0.8699890330</td><td class=\"matrix-column\">-0.1177732845</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0925836546</td><td class=\"matrix-column\">-0.2356635232</td><td class=\"matrix-column\"> 1.0773057925</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Nokia N900</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5768341125</td><td class=\"matrix-column\"> 0.0903022798</td><td class=\"matrix-column\"> 0.2322609425</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.0093450042</td><td class=\"matrix-column\"> 0.8877960392</td><td class=\"matrix-column\">-0.0423238128</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.0048395969</td><td class=\"matrix-column\">-0.4521527840</td><td class=\"matrix-column\"> 1.3724180509</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Olympus E-PL2</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5086303502</td><td class=\"matrix-column\"> 0.2107805426</td><td class=\"matrix-column\"> 0.1661593117</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0488695334</td><td class=\"matrix-column\"> 0.8452086157</td><td class=\"matrix-column\">-0.0710872688</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0761125308</td><td class=\"matrix-column\">-0.2591771130</td><td class=\"matrix-column\"> 1.1053648107</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Pentax K-5</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6171183607</td><td class=\"matrix-column\"> 0.1735880915</td><td class=\"matrix-column\"> 0.0882142035</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0975491529</td><td class=\"matrix-column\"> 0.9232839629</td><td class=\"matrix-column\">-0.1699077400</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0807701138</td><td class=\"matrix-column\">-0.2254938376</td><td class=\"matrix-column\"> 1.0268876953</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Pentax Q</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5844699130</td><td class=\"matrix-column\"> 0.1576791445</td><td class=\"matrix-column\"> 0.1545019541</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.0095697958</td><td class=\"matrix-column\"> 0.9530091633</td><td class=\"matrix-column\">-0.0793293352</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0474683860</td><td class=\"matrix-column\">-0.3427375683</td><td class=\"matrix-column\"> 1.1624766142</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Phase One</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8115119252</td><td class=\"matrix-column\">-0.0622114200</td><td class=\"matrix-column\"> 0.2246762536</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.2134238816</td><td class=\"matrix-column\"> 0.8895055505</td><td class=\"matrix-column\">-0.0908985411</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0838744588</td><td class=\"matrix-column\">-0.5356426595</td><td class=\"matrix-column\"> 1.5501507395</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">Point Grey Grasshopper 50S5C</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.3573800551</td><td class=\"matrix-column\"> 0.1880649006</td><td class=\"matrix-column\"> 0.0862889767</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.1429233801</td><td class=\"matrix-column\"> 0.6030888526</td><td class=\"matrix-column\">-0.0240040252</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.2529296669</td><td class=\"matrix-column\">-0.3773249818</td><td class=\"matrix-column\"> 0.8164877464</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">Point Grey Grasshopper2 14S5C</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6492344690</td><td class=\"matrix-column\"> 0.2782016831</td><td class=\"matrix-column\"> 0.1796929880</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0478988060</td><td class=\"matrix-column\"> 1.0257713015</td><td class=\"matrix-column\">-0.1038102072</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1456900641</td><td class=\"matrix-column\">-0.2867941094</td><td class=\"matrix-column\"> 1.2915519401</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">SONY NEX-5N</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6794700797</td><td class=\"matrix-column\"> 0.1541866791</td><td class=\"matrix-column\"> 0.1000398818</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1308609208</td><td class=\"matrix-column\"> 0.9715369686</td><td class=\"matrix-column\">-0.1912980182</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0890254166</td><td class=\"matrix-column\">-0.2565809670</td><td class=\"matrix-column\"> 1.1492244816</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">arri d21</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 1.0456954667</td><td class=\"matrix-column\">-0.2394157576</td><td class=\"matrix-column\"> 0.2478664052</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.2094143002</td><td class=\"matrix-column\"> 0.9124337403</td><td class=\"matrix-column\">-0.0883655365</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0788209403</td><td class=\"matrix-column\">-0.6106989011</td><td class=\"matrix-column\"> 1.6601594393</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">canon eos 5d mark ii</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.9023062857</td><td class=\"matrix-column\">-0.0725253600</td><td class=\"matrix-column\"> 0.2208525212</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1416621818</td><td class=\"matrix-column\"> 1.0392375706</td><td class=\"matrix-column\">-0.1115240658</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1034981733</td><td class=\"matrix-column\">-0.2706191446</td><td class=\"matrix-column\"> 1.2642291369</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">canon powershot s90</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6907052996</td><td class=\"matrix-column\"> 0.2932836443</td><td class=\"matrix-column\"> 0.1409781297</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\">-0.0841316425</td><td class=\"matrix-column\"> 1.3022820492</td><td class=\"matrix-column\">-0.2014990342</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1667087936</td><td class=\"matrix-column\">-0.4010661490</td><td class=\"matrix-column\"> 1.3753631022</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">canon xti</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.7145301544</td><td class=\"matrix-column\"> 0.1929157862</td><td class=\"matrix-column\"> 0.1571135559</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1495740970</td><td class=\"matrix-column\"> 1.0980294471</td><td class=\"matrix-column\">-0.1781830581</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0660404284</td><td class=\"matrix-column\">-0.2041319747</td><td class=\"matrix-column\"> 1.2147851267</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">nikon d200</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.7145218394</td><td class=\"matrix-column\"> 0.2442607898</td><td class=\"matrix-column\"> 0.1407441098</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1246157540</td><td class=\"matrix-column\"> 1.0180864260</td><td class=\"matrix-column\">-0.0929698384</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0875770620</td><td class=\"matrix-column\">-0.1776710261</td><td class=\"matrix-column\"> 1.1763318170</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">nikon d5100</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6007124959</td><td class=\"matrix-column\"> 0.2006760019</td><td class=\"matrix-column\"> 0.0884612667</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0717465245</td><td class=\"matrix-column\"> 0.9488061363</td><td class=\"matrix-column\">-0.1673500428</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0922043662</td><td class=\"matrix-column\">-0.2535712805</td><td class=\"matrix-column\"> 1.0603489669</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">nikon d70</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5895918194</td><td class=\"matrix-column\"> 0.1587829059</td><td class=\"matrix-column\"> 0.1413395053</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1164078711</td><td class=\"matrix-column\"> 0.8322479371</td><td class=\"matrix-column\">-0.0914487140</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0602476487</td><td class=\"matrix-column\">-0.1376781354</td><td class=\"matrix-column\"> 0.9978566038</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">nikon d700</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.5811710791</td><td class=\"matrix-column\"> 0.1919254804</td><td class=\"matrix-column\"> 0.1049596418</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0907276384</td><td class=\"matrix-column\"> 0.8153602671</td><td class=\"matrix-column\">-0.0652199405</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0748630256</td><td class=\"matrix-column\">-0.1545261028</td><td class=\"matrix-column\"> 0.9631481322</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">nikon d7000</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6293295447</td><td class=\"matrix-column\"> 0.1803470253</td><td class=\"matrix-column\"> 0.0846422204</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0798945891</td><td class=\"matrix-column\"> 0.9528671834</td><td class=\"matrix-column\">-0.1736335974</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0896921224</td><td class=\"matrix-column\">-0.2431568959</td><td class=\"matrix-column\"> 1.0578755735</td></tr></table></td></tr><tr class=\"table-row \"><td class=\"table-data-header\">sony ilce-7rm2</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.6584412453</td><td class=\"matrix-column\"> 0.1669786165</td><td class=\"matrix-column\"> 0.0881972652</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0790753814</td><td class=\"matrix-column\"> 0.9638215581</td><td class=\"matrix-column\">-0.1695755022</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0964943565</td><td class=\"matrix-column\">-0.2540625732</td><td class=\"matrix-column\"> 1.0915577723</td></tr></table></td></tr><tr class=\"table-row table-row-odd\"><td class=\"table-data-header\">sony ilce-7sm2</td><td class=\"table-data\"><table class=\"matrix-table\"><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.8148259888</td><td class=\"matrix-column\"> 0.0932643034</td><td class=\"matrix-column\"> 0.0345570592</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.0099303171</td><td class=\"matrix-column\"> 1.1272612982</td><td class=\"matrix-column\">-0.2410027256</td></tr><tr class=\"matrix-row\"><td class=\"matrix-column\"> 0.1824977085</td><td class=\"matrix-column\">-0.4541278951</td><td class=\"matrix-column\"> 1.2587065361</td></tr></table></td></tr><tbody></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lupD8W_CwrIY",
        "colab_type": "text"
      },
      "source": [
        "## Camera Data Rendering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxKTUnNOwmYP",
        "colab_type": "code",
        "outputId": "155a1ead-0ff3-4e7e-eef3-223aaf8950f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "XYZ_IMAGES = sorted(glob.glob('{0}/*_XYZ.exr'.format(\n",
        "    '{0}/images'.format(OUTPUT_DIRECTORY))))\n",
        "\n",
        "\n",
        "XYZ_IMAGE_TO_CAMERA_NAME = {}\n",
        "for image in XYZ_IMAGES:\n",
        "    for sluggified_camera_name in SLUGGIFIED_CAMERA_NAMES:\n",
        "        if sluggified_camera_name in image:\n",
        "            XYZ_IMAGE_TO_CAMERA_NAME[image] = (\n",
        "                SLUGGIFIED_CAMERA_NAMES[sluggified_camera_name])\n",
        "            break\n",
        "\n",
        "\n",
        "def render_mitsuba_image(\n",
        "        XYZ_image,\n",
        "        title,\n",
        "        idt_matrix=None,\n",
        "        working_RGB_colourspace=colour.RGB_COLOURSPACES['ACES2065-1'],\n",
        "        display_RGB_colourspace=colour.RGB_COLOURSPACES['sRGB'],\n",
        "        chromaticity_diagram_RGB_colourspaces=['ACES2065-1', 'ACEScg'],\n",
        "        RGB_chromaticities_in_chromaticity_diagram_callable=(\n",
        "            colour.plotting.plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS),\n",
        "        normalisation_region=(slice(490, 522, 1), slice(530, 562, 1))):\n",
        "\n",
        "    XYZ = colour.read_image(XYZ_image)\n",
        "\n",
        "    if idt_matrix is not None:\n",
        "        RGB_W = colour.utilities.dot_vector(idt_matrix, XYZ)\n",
        "    else:\n",
        "        RGB_W = colour.XYZ_to_RGB(\n",
        "            XYZ,\n",
        "            working_RGB_colourspace.whitepoint,\n",
        "            working_RGB_colourspace.whitepoint,\n",
        "            working_RGB_colourspace.XYZ_to_RGB_matrix\n",
        "        )\n",
        "\n",
        "    RGB_N = np.mean(\n",
        "        RGB_W[normalisation_region].reshape([-1, 3]), axis=0)\n",
        "    Y = colour.RGB_luminance(\n",
        "        RGB_N,\n",
        "        working_RGB_colourspace.primaries,\n",
        "        working_RGB_colourspace.whitepoint)\n",
        "    \n",
        "    RGB_W /= Y\n",
        "    RGB_W *= 0.19\n",
        "\n",
        "    colour.write_image(\n",
        "        RGB_W,\n",
        "        XYZ_image.replace('_XYZ.exr', '_RGB_W.exr'))\n",
        "\n",
        "    RGB_D = colour.RGB_to_RGB(\n",
        "        RGB_W,\n",
        "        working_RGB_colourspace,\n",
        "        display_RGB_colourspace)\n",
        "    \n",
        "    colour.write_image(\n",
        "        RGB_D,\n",
        "        XYZ_image.replace('_XYZ.exr', '_RGB_D.exr'))\n",
        "\n",
        "    colour.plotting.plot_image(colour.cctf_encoding(RGB_D))\n",
        "    RGB_chromaticities_in_chromaticity_diagram_callable(\n",
        "        RGB_D,\n",
        "        colourspaces=chromaticity_diagram_RGB_colourspaces,\n",
        "        diagram_opacity=0.25,\n",
        "        title=title,\n",
        "        scatter_kwargs={'s': 15},\n",
        "        filename=XYZ_image.replace('_XYZ.exr', '_Diagram.png'))\n",
        "\n",
        "\n",
        "for image in XYZ_IMAGES:\n",
        "    if 'CIE_1931_2_Degree_Standard_Observer' in image:\n",
        "        render_mitsuba_image(image, CMFS.name)\n",
        "    else:\n",
        "        camera_name = XYZ_IMAGE_TO_CAMERA_NAME[image]\n",
        "        render_mitsuba_image(image, camera_name, M_IDT[camera_name])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6cpapdV103N",
        "colab_type": "text"
      },
      "source": [
        "## Served Content"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "samjgMow1mML",
        "colab_type": "code",
        "outputId": "241bc7da-1e3d-4ad3-82ae-5e93694431ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "JS_JERI_CAMERA_DATA_TEMPLATE = \"\"\"\n",
        "const cameraData = {{\n",
        "    title: 'root',\n",
        "    children: [{camera_data}\n",
        "    ]\n",
        "}};\"\"\"[1:]\n",
        "\n",
        "JS_JERI_CAMERA_TEMPLATE = \"\"\"\n",
        "{{\n",
        "    title: '{camera_name}',\n",
        "    children: [\n",
        "        {{\n",
        "            title: 'Render',\n",
        "            image: 'images/{camera_image}',\n",
        "        }},\n",
        "        {{\n",
        "            title: 'MRSE',\n",
        "            lossMap: {{\n",
        "                'function': 'MRSE',\n",
        "                imageA: 'images/{observer_image}',\n",
        "                imageB: 'images/{camera_image}'\n",
        "            }}\n",
        "        }},\n",
        "        {{\n",
        "            title: 'CIE 1976 UCS Chromaticity Diagram',\n",
        "            image: 'images/{diagram_image}',\n",
        "        }}\n",
        "    ]\n",
        "}},\"\"\"\n",
        "    \n",
        "\n",
        "def generate_js_camera_payload(XYZ_images, output_directory=OUTPUT_DIRECTORY):\n",
        "    observer_image = None\n",
        "    for image in XYZ_images:\n",
        "        if 'CIE_1931_2_Degree_Standard_Observer' in image:\n",
        "            observer_image = os.path.basename(image)\n",
        "            break\n",
        "\n",
        "    camera_data = textwrap.indent(JS_JERI_CAMERA_TEMPLATE.format(\n",
        "        **{\n",
        "            'camera_name': 'Observer',\n",
        "            'camera_image': observer_image.replace('_XYZ.exr', '_RGB_D.exr'),\n",
        "            'observer_image': observer_image,\n",
        "            'diagram_image': observer_image.replace('_XYZ.exr', '_Diagram.png'),\n",
        "        }), 8 * ' ')\n",
        "\n",
        "    for image in XYZ_images:\n",
        "        if 'CIE_1931_2_Degree_Standard_Observer' in image:\n",
        "            continue\n",
        "\n",
        "        camera_image = os.path.basename(image)\n",
        "        camera_data += textwrap.indent(JS_JERI_CAMERA_TEMPLATE.format(\n",
        "            **{\n",
        "                'camera_name': XYZ_IMAGE_TO_CAMERA_NAME[image],\n",
        "                'camera_image': camera_image.replace('_XYZ.exr', '_RGB_D.exr'),\n",
        "                'observer_image': observer_image,\n",
        "                'diagram_image': camera_image.replace('_XYZ.exr', '_Diagram.png'),\n",
        "            }), 8 * ' ')\n",
        "\n",
        "    with open('{0}/mitsuba-cornell-box-rigid-spheres.js'.format(\n",
        "        output_directory), 'w') as js_camera_payload:\n",
        "        js_camera_payload.write(\n",
        "            JS_JERI_CAMERA_DATA_TEMPLATE.format(\n",
        "                **{'camera_data': camera_data}))\n",
        "        \n",
        "generate_js_camera_payload(XYZ_IMAGES)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    body {\n",
              "        background-color: white !important;\n",
              "    }\n",
              "\n",
              "    .table, .matrix-table {\n",
              "        width: 100%\n",
              "    }\n",
              "\n",
              "    .table-row-header, .table-data-header {\n",
              "        background-color: #aaa !important;\n",
              "        color: #fff !important;\n",
              "        font-weight: bold !important;\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .vertical-align-top {\n",
              "        vertical-align: top\n",
              "    }\n",
              "\n",
              "    .text-align-center {\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table-row-odd {\n",
              "        background-color: #eee !important;\n",
              "    }\n",
              "\n",
              "    .matrix-table, .matrix-row, .matrix-column {\n",
              "        border: none\n",
              "        text-align: center\n",
              "    }\n",
              "\n",
              "    .table, .table-canton, .table-row-header-group, .table-row-header, .table-data-header, .table-row, .table-data {\n",
              "        border-color: #fff\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}