def plot_colors(values, parameters, ylabels):
#fig, ax = colour.plotting.plot_chromaticity_diagram_CIE1931(standalone=False)
fig, ax = colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931([], standalone=False)
mincct = values[0]['requested']
maxcct = values[-1]['requested']
ax.set_title(f'CCT measured for RGB LED, {int(mincct)} to {int(maxcct)}')
sRGB = colour.RGB_COLOURSPACES['sRGB']
plotcoloridx = 0
for value in values:
plotcolor = f'C{plotcoloridx}'
plotcoloridx += 1
cct_requested = value['requested']
# for ylabel in ylabels:
x_req, y_req = colour.CCT_to_xy(cct_requested)
rgb_xvalues = []
rgb_yvalues = []
cct_xvalues = []
cct_yvalues = []
for ylabel in ylabels:
r = value[f'rgb_r_{ylabel}']
g = value[f'rgb_g_{ylabel}']
b = value[f'rgb_b_{ylabel}']
RGB = np.array([r, g, b])
XYZ = colour.RGB_to_XYZ(RGB, sRGB.whitepoint, sRGB.whitepoint, sRGB.RGB_to_XYZ_matrix)
xy = colour.XYZ_to_xy(XYZ)
x, y = xy
rgb_xvalues.append(x)
rgb_yvalues.append(y)
cct_measured = value[f'rgb_cct_{ylabel}']
x, y = colour.CCT_to_xy(cct_measured)
cct_xvalues.append(x)
cct_yvalues.append(y)
matplotlib.pyplot.plot(rgb_xvalues, rgb_yvalues, color=plotcolor, marker='o', linestyle='', label=str(cct_requested))
matplotlib.pyplot.plot(cct_xvalues, cct_yvalues, color=plotcolor, marker='.', linestyle='')
matplotlib.pyplot.plot([x_req], [y_req], color=plotcolor, marker='X')
p_text = []
for k, v in parameters.items():
if k in ('measurement', 'interval'): continue
p_text.append(f'{k} = {v}')
ax.text(0.95, 0.05, '\n'.join(p_text), transform=ax.transAxes, bbox=dict(boxstyle='round'), multialignment='left', horizontalalignment='right', verticalalignment='bottom')
ax.legend()
# matplotlib.pyplot.annotate(str(cct_requested),
# xy=(x, y),
# xytext=(-50, 30),
# textcoords='offset points',
# arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))
matplotlib.pyplot.show()
def plot_colors_bycct(values, parameters, ylabels):
fig, ax = colour.plotting.plot_chromaticity_diagram_CIE1931(standalone=False)
mincct = values[0]['requested']
maxcct = values[-1]['requested']
ax.set_title(f'CCT measured for RGB LED, {int(mincct)} to {int(maxcct)}')
for ylabel in ylabels:
xvalues = []
yvalues = []
for value in values:
cct_requested = value['requested']
cct_measured = value[f'rgb_cct_{ylabel}']
x, y = colour.CCT_to_xy(cct_measured)
xvalues.append(x)
yvalues.append(y)
matplotlib.pyplot.plot(xvalues, yvalues, '.', label=ylabel)
p_text = []
for k, v in parameters.items():
if k in ('measurement', 'interval'): continue
p_text.append(f'{k} = {v}')
ax.text(0.95, 0.05, '\n'.join(p_text), transform=ax.transAxes, bbox=dict(boxstyle='round'), multialignment='left', horizontalalignment='right', verticalalignment='bottom')
ax.legend()
# matplotlib.pyplot.annotate(str(cct_requested),
# xy=(x, y),
# xytext=(-50, 30),
# textcoords='offset points',
# arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))
matplotlib.pyplot.show()
def cs_convert_K_to_RGB(colour_temperature):
xy = colour.CCT_to_xy(colour_temperature)
xyY = np.array([xy[0], xy[1], 1])
XYZ = colour.xyY_to_XYZ(xyY)
sRGB = colour.RGB_COLOURSPACES['sRGB']
RGB = colour.XYZ_to_RGB(XYZ, sRGB.whitepoint, sRGB.whitepoint,
sRGB.XYZ_to_RGB_matrix)
return RGB[0], RGB[1], RGB[2]
def temperatureChange(self):
value = self.temperatureSlider.value() +1
#range is 1100K to 11 000K
K = int(1000+ (value*120))
print("Kelvins: ", K)
xy = colour.CCT_to_xy(K)
#print("xy", xy)
xyz = colour.xy_to_XYZ(xy)
#print("xyz", xyz)
rgb = colour.XYZ_to_sRGB(xyz)
r = int(min(1,max(0,rgb[0]))*255)
g = int(min(1,max(0,rgb[1]))*255)
b = int(min(1,max(0,rgb[2]))*255)
self.color = [r, g, b]
print("rgb", self.color)
self.updateColorLabel()
def temperature_to_rgb(cct):
""" Convert temperature in Kelvin to sRGB.
First, we convert CCT (Kelvin) to chromaticity coordinates.
Second, we convert to tristimulus values.
Third, we convert to rgb
Parameters
----------
cct : Correlated color temperature in kelvin
Returns
-------
array : List/tuple of rgb values, e.g. [255.0, 235.0, 12.0]
"""
xy = colour.CCT_to_xy(cct)
xyz = colour.xy_to_XYZ(xy)
rgb = colour.XYZ_to_sRGB(xyz)
return rgb
message_box(('Converting to "CCT" from given "xy" chromaticity coordinates '
'using "McCamy (1992)" method:\n'
'\n\t{0}'.format(xy)))
print(colour.xy_to_CCT_McCamy1992(xy))
print(colour.xy_to_CCT(xy, method='McCamy 1992'))
print('\n')
message_box(('Converting to "CCT" from given "xy" chromaticity coordinates '
'using "Hernandez-Andres, Lee and Romero (1999)" method:\n'
'\n\t{0}'.format(xy)))
print(colour.xy_to_CCT_Hernandez1999(xy))
print(colour.xy_to_CCT(xy, method='Hernandez 1999'))
print('\n')
CCT = 6503.49254150
message_box(('Converting to "xy" chromaticity coordinates from given "CCT" '
'using "Kang, Moon, Hong, Lee, Cho and Kim (2002)" method:\n'
'\n\t{0}'.format(CCT)))
print(colour.CCT_to_xy_Kang2002(CCT))
print(colour.CCT_to_xy(CCT, method="Kang 2002"))
print('\n')
message_box(('Converting to "xy" chromaticity coordinates from given "CCT" '
'using "CIE Illuminant D Series" method:\n'
'\n\t{0}'.format(CCT)))
print(colour.CCT_to_xy_CIE_D(CCT))
print(colour.CCT_to_xy(CCT, method="CIE Illuminant D Series"))
print('\n')
xy = np.tile((0.31270, 0.32900), (6, 1))
message_box(('Definitions return value may lose a dimension with respect to '
'the parameter(s):\n'
'\n{0}'.format(xy)))
print(colour.xy_to_CCT(xy))
print('\n')
CCT = np.tile(6504.38938305, 6)
message_box(('Definitions return value may gain a dimension with respect to '
'the parameter(s):\n'
'\n{0}'.format(CCT)))
print(colour.CCT_to_xy(CCT))
print('\n')
message_box(('Definitions mixing "array_like" and "numeric" parameters '
'expect the "numeric" parameters to have a dimension less than '
'the "array_like" parameters.'))
XYZ_1 = np.array([28.00, 21.26, 5.27])
xy_o1 = np.array([0.4476, 0.4074])
xy_o2 = np.array([0.3127, 0.3290])
Y_o = 20
E_o1 = 1000
E_o2 = 1000
message_box(('Parameters:\n\n'
'XYZ_1:\n\n{0}\n\n'
'\nxy_o1:\n\n{1}\n\n'
'\n\t{0}'.format(xy)))
print(colour.xy_to_CCT(xy, method='McCamy 1992'))
print(colour.temperature.xy_to_CCT_McCamy1992(xy))
print('\n')
message_box(('Converting to "CCT" from given "CIE xy" chromaticity '
'coordinates using "Hernandez-Andres, Lee and Romero (1999)" '
'method:\n'
'\n\t{0}'.format(xy)))
print(colour.xy_to_CCT(xy, method='Hernandez 1999'))
print(colour.temperature.xy_to_CCT_Hernandez1999(xy))
print('\n')
CCT = 6503.49254150
message_box(('Converting to "CIE xy" chromaticity coordinates from given '
'"CCT" using "Kang, Moon, Hong, Lee, Cho and Kim (2002)" '
'method:\n'
'\n\t{0}'.format(CCT)))
print(colour.CCT_to_xy(CCT, method='Kang 2002'))
print(colour.temperature.CCT_to_xy_Kang2002(CCT))
print('\n')
message_box(('Converting to "CIE xy" chromaticity coordinates from given '
'"CCT" using "CIE Illuminant D Series" method:\n'
'\n\t{0}'.format(CCT)))
print(colour.CCT_to_xy(CCT, method='CIE Illuminant D Series'))
print(colour.temperature.CCT_to_xy_CIE_D(CCT))
#Attempt to find the color temp of the image and move it from the default
temp = find_temp(img)
if temp:
print(f"found photo temp: {temp}K")
Temperature = temp
#Resize the image to something more workable
r, c = img.size
img = img.resize(map(int, [r * scale, c * scale]))
#RGB = np.asarray(img)
#Convert to XYZ->xyY colorspace
XYZ = convert_to_XYZ(img) #colour.sRGB_to_XYZ(RGB / 255)
xy = colour.XYZ_to_xy(XYZ)
xy_n = colour.CCT_to_xy(Temperature)
#Calculate the dominant wavelength of the color
spectra_dom, spectra_com = domcom_spectra(xy, xy_n)
#Plot the CIExyY colorpsace and our pixels on it
CIExyY = plt.figure("CIExyY colorspace")
cmfs = colour.CMFS['CIE 1931 2 Degree Standard Observer']
i, j = colour.XYZ_to_xy(cmfs.values).T
RGB = d.chromaticity_diagram_colours_CIE1931()
x, y = xy.T
scatter = plt.scatter(i, j, marker='o', label='Wavelengths (nm))')
pixels = plt.plot(x.flatten(),
y.flatten(),
