{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Colour Matching Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The colour matching functions are the tristimulus values of monochromatic stimuli of equal radiant power [1] and are at the hearth of the *CIE Colour System*, their creation history has been briefly introduced in the [Spectrum](spectrum.ipynb#Colour-Matching-Functions) notebook.\n",
"\n",
"[Colour](https://github.com/colour-science/colour/) provides the following spectral sensitivity and colour matching functions:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Smith & Pokorny 1975 Normal Trichromats',\n",
" 'Stockman & Sharpe 10 Degree Cone Fundamentals',\n",
" 'Stockman & Sharpe 2 Degree Cone Fundamentals']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import colour\n",
"\n",
"sorted(colour.colorimetry.LMS_CMFS.keys())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Stiles & Burch 1955 2 Degree RGB CMFs',\n",
" 'Stiles & Burch 1959 10 Degree RGB CMFs',\n",
" 'Wright & Guild 1931 2 Degree RGB CMFs']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(colour.colorimetry.RGB_CMFS.keys())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['CIE 1931 2 Degree Standard Observer',\n",
" 'CIE 1964 10 Degree Standard Observer',\n",
" 'CIE 2012 10 Degree Standard Observer',\n",
" 'CIE 2012 2 Degree Standard Observer',\n",
" 'cie_10_1964',\n",
" 'cie_2_1931']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(colour.colorimetry.STANDARD_OBSERVERS_CMFS.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Note: `'cie_2_1931'` and `'cie_10_1964'` are convenient aliases for respectively `'CIE 1931 2 Degree Standard Observer'` and `'CIE 1964 10 Degree Standard Observer'`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['CIE 1931 2 Degree Standard Observer',\n",
" 'CIE 1964 10 Degree Standard Observer',\n",
" 'CIE 2012 10 Degree Standard Observer',\n",
" 'CIE 2012 2 Degree Standard Observer',\n",
" 'Smith & Pokorny 1975 Normal Trichromats',\n",
" 'Stiles & Burch 1955 2 Degree RGB CMFs',\n",
" 'Stiles & Burch 1959 10 Degree RGB CMFs',\n",
" 'Stockman & Sharpe 10 Degree Cone Fundamentals',\n",
" 'Stockman & Sharpe 2 Degree Cone Fundamentals',\n",
" 'Wright & Guild 1931 2 Degree RGB CMFs',\n",
" 'cie_10_1964',\n",
" 'cie_2_1931']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(colour.CMFS.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most of them are sourced from the [*Colour & Vision Research Laboratory*](http://www.cvrl.org/) database. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tristismulus Space"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Colour stimuli can be represented by vectors into a three-dimensionl space called *tristimulus space*. They are usually denoted by boldface letters such as $\\textbf{Q}$, $\\textbf{R}$, $\\textbf{G}$ and $\\textbf{B}$ where $\\textbf{Q}$ represents a test colour stimulus and $\\textbf{R}$, $\\textbf{G}$ and $\\textbf{B}$ are reserved for the fixed primary stimuli chosen for the colour matching experiment. [2]\n",
"\n",
"A colour match between an test colour stimulus $\\textbf{Q}$ and the additive mixture of the three fixed primary stimuli $\\textbf{R}$, $\\textbf{G}$ and $\\textbf{B}$ is expressed by the following equation: [2]\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\textbf{Q}=R_Q\\textbf{R}+G_Q\\textbf{G}+B_Q\\textbf{B}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"where the multipliers $R_Q$,$G_Q$,$B_Q$ measured in terms of the assigned respective units of the $\\textbf{R}$, $\\textbf{G}$ and $\\textbf{B}$ primary stimuli are called the *tristimulus values* of $\\textbf{Q}$.\n",
"\n",
"A monochromatic stimulus $\\textbf{Q}_\\lambda$ of wavelength $\\lambda$ is the quantity $\\lbrace P_\\lambda d\\lambda\\rbrace$ that represents the radiant power in the wavelength interval of width $d\\lambda$ centered at the wavelength $\\lambda$ and can be expressed by the following equation: [2]\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\textbf{Q}_\\lambda=R_\\lambda\\textbf{R}+G_\\lambda\\textbf{G}+B_\\lambda\\textbf{B}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"where $R_\\lambda$,$G_\\lambda$,$B_\\lambda$ are called the *spectral tristimulus values* of $\\textbf{Q}_\\lambda$.\n",
"\n",
"The equation for a colour match involving a monochromatic constituent $\\textbf{E}_\\lambda$ of the equal energy stimulus $\\textbf{E}$ is:\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\textbf{E}_\\lambda=\\bar{r}(\\lambda)\\textbf{R}+\\bar{g}(\\lambda)\\textbf{G}+\\bar{b}(\\lambda)\\textbf{B}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"where $\\bar{r}(\\lambda)$,$\\bar{g}(\\lambda)$,$\\bar{b}(\\lambda)$ are called the *spectral tristimulus values* of $\\textbf{E}_\\lambda$. The lowercase notation with an overbar indictates a special set of tristimulus values and is the standard notation for colour matching functions."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from colour.plotting import *"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"colour_style();"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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