-
-
Save petrklus/b1f427accdf7438606a6 to your computer and use it in GitHub Desktop.
""" | |
Based on: http://www.tannerhelland.com/4435/convert-temperature-rgb-algorithm-code/ | |
Comments resceived: https://gist.github.com/petrklus/b1f427accdf7438606a6 | |
Original pseudo code: | |
Set Temperature = Temperature \ 100 | |
Calculate Red: | |
If Temperature <= 66 Then | |
Red = 255 | |
Else | |
Red = Temperature - 60 | |
Red = 329.698727446 * (Red ^ -0.1332047592) | |
If Red < 0 Then Red = 0 | |
If Red > 255 Then Red = 255 | |
End If | |
Calculate Green: | |
If Temperature <= 66 Then | |
Green = Temperature | |
Green = 99.4708025861 * Ln(Green) - 161.1195681661 | |
If Green < 0 Then Green = 0 | |
If Green > 255 Then Green = 255 | |
Else | |
Green = Temperature - 60 | |
Green = 288.1221695283 * (Green ^ -0.0755148492) | |
If Green < 0 Then Green = 0 | |
If Green > 255 Then Green = 255 | |
End If | |
Calculate Blue: | |
If Temperature >= 66 Then | |
Blue = 255 | |
Else | |
If Temperature <= 19 Then | |
Blue = 0 | |
Else | |
Blue = Temperature - 10 | |
Blue = 138.5177312231 * Ln(Blue) - 305.0447927307 | |
If Blue < 0 Then Blue = 0 | |
If Blue > 255 Then Blue = 255 | |
End If | |
End If | |
""" | |
import math | |
def convert_K_to_RGB(colour_temperature): | |
""" | |
Converts from K to RGB, algorithm courtesy of | |
http://www.tannerhelland.com/4435/convert-temperature-rgb-algorithm-code/ | |
""" | |
#range check | |
if colour_temperature < 1000: | |
colour_temperature = 1000 | |
elif colour_temperature > 40000: | |
colour_temperature = 40000 | |
tmp_internal = colour_temperature / 100.0 | |
# red | |
if tmp_internal <= 66: | |
red = 255 | |
else: | |
tmp_red = 329.698727446 * math.pow(tmp_internal - 60, -0.1332047592) | |
if tmp_red < 0: | |
red = 0 | |
elif tmp_red > 255: | |
red = 255 | |
else: | |
red = tmp_red | |
# green | |
if tmp_internal <=66: | |
tmp_green = 99.4708025861 * math.log(tmp_internal) - 161.1195681661 | |
if tmp_green < 0: | |
green = 0 | |
elif tmp_green > 255: | |
green = 255 | |
else: | |
green = tmp_green | |
else: | |
tmp_green = 288.1221695283 * math.pow(tmp_internal - 60, -0.0755148492) | |
if tmp_green < 0: | |
green = 0 | |
elif tmp_green > 255: | |
green = 255 | |
else: | |
green = tmp_green | |
# blue | |
if tmp_internal >=66: | |
blue = 255 | |
elif tmp_internal <= 19: | |
blue = 0 | |
else: | |
tmp_blue = 138.5177312231 * math.log(tmp_internal - 10) - 305.0447927307 | |
if tmp_blue < 0: | |
blue = 0 | |
elif tmp_blue > 255: | |
blue = 255 | |
else: | |
blue = tmp_blue | |
return red, green, blue | |
if __name__ == "__main__": | |
print("Preview requires matplotlib") | |
from matplotlib import pyplot as plt | |
step_size = 100 | |
for i in range(0, 15000, step_size): | |
color = list(map(lambda div: div/255.0, convert_K_to_RGB(i))) + [1] | |
print(color) | |
plt.plot((i, i), (0, 1), linewidth=step_size/2.0, linestyle="-", color=color) | |
plt.show() | |
If you are interested in the rgb_to_kelvin()
I just reversed the values, with a step size of 100K, find it below as a header file to run it in C.
// temperature.h
#include <stdint.h>
#define HEIGHT 150
#define WIDTH 4
const unsigned colorTable[HEIGHT][WIDTH] = {
{181,205,255,14900},
{182,205,255,14800},
{182,206,255,14500},
{182,206,255,14600},
{182,206,255,14700},
{183,206,255,14300},
{183,206,255,14400},
{183,207,255,14200},
{184,207,255,13900},
{184,207,255,14000},
{184,207,255,14100},
{185,207,255,13800},
{185,208,255,13600},
{185,208,255,13700},
{186,208,255,13300},
{186,208,255,13400},
{186,208,255,13500},
{187,209,255,13000},
{187,209,255,13100},
{187,209,255,13200},
{188,209,255,12900},
{188,210,255,12700},
{188,210,255,12800},
{189,210,255,12400},
{189,210,255,12500},
{189,210,255,12600},
{190,211,255,12200},
{190,211,255,12300},
{191,211,255,12000},
{191,211,255,12100},
{192,212,255,11700},
{192,212,255,11800},
{192,212,255,11900},
{193,213,255,11500},
{193,213,255,11600},
{194,213,255,11300},
{194,213,255,11400},
{195,214,255,11100},
{195,214,255,11200},
{196,214,255,11000},
{196,215,255,10900},
{197,215,255,10700},
{197,215,255,10800},
{198,216,255,10600},
{199,216,255,10500},
{199,217,255,10400},
{200,217,255,10200},
{200,217,255,10300},
{201,218,255,10100},
{202,218,255,9900},
{202,218,255,10000},
{203,219,255,9800},
{204,219,255,9700},
{205,220,255,9500},
{205,220,255,9600},
{206,221,255,9400},
{207,221,255,9300},
{208,222,255,9200},
{209,222,255,9100},
{210,223,255,9000},
{211,223,255,8900},
{212,224,255,8800},
{213,225,255,8700},
{214,225,255,8600},
{215,226,255,8500},
{216,227,255,8400},
{217,227,255,8300},
{218,228,255,8200},
{220,229,255,8100},
{221,230,255,8000},
{223,231,255,7900},
{224,232,255,7800},
{226,233,255,7700},
{228,234,255,7600},
{230,235,255,7500},
{232,236,255,7400},
{234,237,255,7300},
{237,239,255,7200},
{240,240,255,7100},
{243,242,255,7000},
{246,244,255,6900},
{250,246,255,6800},
{254,249,255,6700},
{255,68,0,0},
{255,68,0,100},
{255,68,0,200},
{255,68,0,300},
{255,68,0,400},
{255,68,0,500},
{255,68,0,600},
{255,68,0,700},
{255,68,0,800},
{255,68,0,900},
{255,68,0,1000},
{255,77,0,1100},
{255,86,0,1200},
{255,94,0,1300},
{255,101,0,1400},
{255,108,0,1500},
{255,115,0,1600},
{255,121,0,1700},
{255,126,0,1800},
{255,132,0,1900},
{255,137,14,2000},
{255,142,27,2100},
{255,146,39,2200},
{255,151,50,2300},
{255,155,61,2400},
{255,159,70,2500},
{255,163,79,2600},
{255,167,87,2700},
{255,170,95,2800},
{255,174,103,2900},
{255,177,110,3000},
{255,180,117,3100},
{255,184,123,3200},
{255,187,129,3300},
{255,190,135,3400},
{255,193,141,3500},
{255,195,146,3600},
{255,198,151,3700},
{255,201,157,3800},
{255,203,161,3900},
{255,206,166,4000},
{255,208,171,4100},
{255,211,175,4200},
{255,213,179,4300},
{255,215,183,4400},
{255,218,187,4500},
{255,220,191,4600},
{255,222,195,4700},
{255,224,199,4800},
{255,226,202,4900},
{255,228,206,5000},
{255,230,209,5100},
{255,232,213,5200},
{255,234,216,5300},
{255,236,219,5400},
{255,237,222,5500},
{255,239,225,5600},
{255,241,228,5700},
{255,243,231,5800},
{255,244,234,5900},
{255,246,237,6000},
{255,248,240,6100},
{255,249,242,6200},
{255,251,245,6300},
{255,253,248,6400},
{255,254,250,6500},
{255,255,255,6600}
};
// function that takes an RGB value,
// checks the lowest distance to the colorTable and returns the temperature
unsigned rgb_to_kelvin(unsigned r, unsigned g, unsigned b) {
unsigned minDist = 0xFFFFFF;
unsigned minTemp = 0;
for (unsigned i = 0; i < HEIGHT; i++) {
unsigned rT = colorTable[i][0];
unsigned gT = colorTable[i][1];
unsigned bT = colorTable[i][2];
unsigned dist = (r-rT)*(r-rT) + (g-gT)*(g-gT) + (b-bT)*(b-bT);
if (dist < minDist) {
minDist = dist;
minTemp = colorTable[i][3];
}
}
return minTemp;
}
With increasing temperature, output seems like red becoming less saturated, passing through white, and becoming more saturated blue. I expected red turning to orange, yellow, becoming less saturated and turning into white, then becoming more saturated blue. I don't see that yellow part here. It does match https://en.wikipedia.org/wiki/Color_temperature so I guess it is correct. But I was expecting something more like the heated metal colour discussed at: https://physics.stackexchange.com/questions/304299/how-heated-metal-colors-relate-to-black-body-color-at-the-same-temperature. The heated metal colour matches colour temperature of light bulbs more closely.
Edit: For using an RGB led to approximate colour temperature, https://github.com/carpdiem/Color-Match gave better results. Note that there you need to know the wavelength of LEDs to get accurate results.
Edit: Another source is https://github.com/jonls/redshift/blob/master/src/colorramp.c. It uses a lookup table and interpolation, and is much faster than Color-Match.
@dreamlayers Thx for all the links, especially the redhift one really helped be get this going for my ESPHome project. For anyone interested, see: https://github.com/markusressel/CustomRGBWLight/blob/master/components/ColorTempRGBWLight/ColorTempRGBWLight.h
I suggest using numpy for a much shorter code:
I have also changed your
main
so that we can zoom in infinitely without seeing empty space between the color bars.With 10 more minutes of work, we could make the function support polymorphism so that it works on a whole numpy array at once.