def check_xyz_irgb(self, xyz0, verbose):
''' Check the direct conversions from xyz to irgb. '''
irgb0 = colormodels.irgb_from_rgb(colormodels.rgb_from_xyz(xyz0))
irgb1 = colormodels.irgb_from_xyz(xyz0)
self.assertEqual(irgb0[0], irgb1[0])
self.assertEqual(irgb0[1], irgb1[1])
self.assertEqual(irgb0[2], irgb1[2])
# The string should also match.
irgbs0 = colormodels.irgb_string_from_rgb(
colormodels.rgb_from_xyz(xyz0))
irgbs1 = colormodels.irgb_string_from_xyz(xyz0)
msg = 'irgb0: %s text0: %s irgb1: %s text1: %s' % (
str(irgb0), irgbs0, str(irgb1), irgbs1)
if verbose:
print(msg)
self.assertEqual(irgbs0, irgbs1)
def check_xyz_irgb(self, xyz0, verbose):
''' Check the direct conversions from xyz to irgb. '''
irgb0 = colormodels.irgb_from_rgb (
colormodels.rgb_from_xyz (xyz0))
irgb1 = colormodels.irgb_from_xyz (xyz0)
self.assertEqual(irgb0[0], irgb1[0])
self.assertEqual(irgb0[1], irgb1[1])
self.assertEqual(irgb0[2], irgb1[2])
# The string should also match.
irgbs0 = colormodels.irgb_string_from_rgb (
colormodels.rgb_from_xyz (xyz0))
irgbs1 = colormodels.irgb_string_from_xyz (xyz0)
msg = 'irgb0: %s text0: %s irgb1: %s text1: %s' % (
str(irgb0), irgbs0, str(irgb1), irgbs1)
if verbose:
print (msg)
self.assertEqual(irgbs0, irgbs1)
def test_xyz_irgb(verbose=1):
'''Test the direct conversions from xyz to irgb.'''
for i in xrange(0, 100):
x0 = 10.0 * random.random()
y0 = 10.0 * random.random()
z0 = 10.0 * random.random()
xyz0 = colormodels.xyz_color(x0, y0, z0)
irgb0 = colormodels.irgb_from_rgb(colormodels.rgb_from_xyz(xyz0))
irgb1 = colormodels.irgb_from_xyz(xyz0)
if (irgb0[0] != irgb1[0]) or (irgb0[1] != irgb1[1]) or (irgb0[2] !=
irgb1[2]):
raise ValueError
irgbs0 = colormodels.irgb_string_from_rgb(
colormodels.rgb_from_xyz(xyz0))
irgbs1 = colormodels.irgb_string_from_xyz(xyz0)
if irgbs0 != irgbs1:
raise ValueError
print('Passed test_xyz_irgb()')
def test_xyz_irgb (verbose=1):
'''Test the direct conversions from xyz to irgb.'''
for i in xrange (0, 100):
x0 = 10.0 * random.random()
y0 = 10.0 * random.random()
z0 = 10.0 * random.random()
xyz0 = colormodels.xyz_color (x0,y0,z0)
irgb0 = colormodels.irgb_from_rgb (
colormodels.rgb_from_xyz (xyz0))
irgb1 = colormodels.irgb_from_xyz (xyz0)
if (irgb0[0] != irgb1[0]) or (irgb0[1] != irgb1[1]) or (irgb0[2] != irgb1[2]):
raise ValueError
irgbs0 = colormodels.irgb_string_from_rgb (
colormodels.rgb_from_xyz (xyz0))
irgbs1 = colormodels.irgb_string_from_xyz (xyz0)
if irgbs0 != irgbs1:
raise ValueError
print 'Passed test_xyz_irgb()'
wavel_df = pd.read_pickle("wavel_df.pkl")
wavel_array = wavel_df.iloc[0:].values
inten_df = pd.read_csv(
'C:/Users/juanr/Documents/data_mediciones/simultaneidad/defectoColorinche/inten50x50cuadrado.dat',
header=None,
sep=',',
engine='python')
inten_array = inten_df.iloc[:, 0:].values
list_of_colors = []
for row in range(len(inten_array)):
spectra = np.column_stack((wavel_array, inten_array[row, :]))
xyz_color_vec = xyz_from_spectrum(spectra)
rgb_disp = irgb_from_xyz(xyz_color_vec)
list_of_colors.append(rgb_disp)
rgb_matrix = np.asarray(list_of_colors)
R_array = rgb_matrix[:, 0]
G_array = rgb_matrix[:, 1]
B_array = rgb_matrix[:, 2]
R = R_array.reshape(50, 50)
G = G_array.reshape(50, 50)
B = B_array.reshape(50, 50)
img = np.empty((50, 50, 3), dtype=np.uint16)
img[:, :, 0] = R
img[:, :, 1] = G
img[:, :, 2] = B
def perceptually_uniform_spectral_colors (
brightness = 1.0,
plot_name = 'PerceptuallyEqualColors',
plot_title = 'Perceptually (almost) Equally Spaced Pure Colors',
table_name = 'percep_equal_names.txt'):
'''Patch plot of (nearly) perceptually equally spaced colors, covering the pure spectral lines plus purples.'''
# TODO - This may or may not be quite right...
# get pure colors
# xyzs = ciexyz.get_normalized_spectral_line_colors (brightness=1.0, num_purples=200, dwl_angstroms=1)
(xyzs, names) = ciexyz.get_normalized_spectral_line_colors_annotated (brightness=brightness, num_purples=200, dwl_angstroms=1)
(num_colors, num_columns) = xyzs.shape
# pick these two functions for either Luv or Lab
uniform_from_xyz = colormodels.luv_from_xyz
xyz_from_uniform = colormodels.xyz_from_luv
#uniform_from_xyz = colormodels.lab_from_xyz
#xyz_from_uniform = colormodels.xyz_from_lab
# convert colors to a nearly perceptually uniform space
uniforms = numpy.empty ((num_colors, 3))
for i in range (0, num_colors):
uniforms [i] = uniform_from_xyz (xyzs [i])
# determine spacing
sum_ds = 0.0
dss = numpy.empty ((num_colors, 1))
for i in range (0, num_colors-1):
dri = uniforms [i+1] - uniforms [i]
dsi = math.sqrt (numpy.dot (dri, dri))
dss [i] = dsi
sum_ds += dsi
# last point closes the curve
dri = uniforms [0] - uniforms [num_colors - 1]
dsi = math.sqrt (numpy.dot (dri, dri))
dss [num_colors - 1] = dsi
sum_ds += dsi
# pick out subsamples as evenly spaced as possible
num_samples = 160
ds_avg = sum_ds / float (num_samples - 1)
E_indices = []
index = 0
count = 0
need = 0.0
while True:
while need > 1.0e-10:
need -= dss [index]
index += 1
E_indices.append (index)
need += ds_avg
count += 1
if count >= num_samples:
break
# patch plot and save names
xyz_list = []
fil = open (table_name, 'wt')
fil.write ('%s\n' % plot_title)
fil.write ('Name iRGB\n')
fil.write ('\n')
for index in E_indices:
uniform_color = uniforms [index]
uniform_xyz = xyz_from_uniform (uniform_color)
uniform_irgb = colormodels.irgb_from_xyz (uniform_xyz)
uniform_name = names [index]
xyz_list.append (uniform_xyz)
fil.write ('%s %s\n' % (uniform_name, str (uniform_irgb)))
fil.close ()
plots.xyz_patch_plot (
xyz_list, None, plot_title, plot_name, num_across=20)
def perceptually_uniform_spectral_colors(
brightness=1.0,
plot_name='PerceptuallyEqualColors',
plot_title='Perceptually (almost) Equally Spaced Pure Colors',
table_name='percep_equal_names.txt'):
'''Patch plot of (nearly) perceptually equally spaced colors, covering the pure spectral lines plus purples.'''
# TODO - This may or may not be quite right...
# get pure colors
# xyzs = ciexyz.get_normalized_spectral_line_colors (brightness=1.0, num_purples=200, dwl_angstroms=1)
(xyzs, names) = ciexyz.get_normalized_spectral_line_colors_annotated(
brightness=brightness, num_purples=200, dwl_angstroms=1)
(num_colors, num_columns) = xyzs.shape
# pick these two functions for either Luv or Lab
uniform_from_xyz = colormodels.luv_from_xyz
xyz_from_uniform = colormodels.xyz_from_luv
#uniform_from_xyz = colormodels.lab_from_xyz
#xyz_from_uniform = colormodels.xyz_from_lab
# convert colors to a nearly perceptually uniform space
uniforms = numpy.empty((num_colors, 3))
for i in range(0, num_colors):
uniforms[i] = uniform_from_xyz(xyzs[i])
# determine spacing
sum_ds = 0.0
dss = numpy.empty((num_colors, 1))
for i in range(0, num_colors - 1):
dri = uniforms[i + 1] - uniforms[i]
dsi = math.sqrt(numpy.dot(dri, dri))
dss[i] = dsi
sum_ds += dsi
# last point closes the curve
dri = uniforms[0] - uniforms[num_colors - 1]
dsi = math.sqrt(numpy.dot(dri, dri))
dss[num_colors - 1] = dsi
sum_ds += dsi
# pick out subsamples as evenly spaced as possible
num_samples = 160
ds_avg = sum_ds / float(num_samples - 1)
E_indices = []
index = 0
count = 0
need = 0.0
while True:
while need > 1.0e-10:
need -= dss[index]
index += 1
E_indices.append(index)
need += ds_avg
count += 1
if count >= num_samples:
break
# patch plot and save names
xyz_list = []
fil = open(table_name, 'wt')
fil.write('%s\n' % plot_title)
fil.write('Name iRGB\n')
fil.write('\n')
for index in E_indices:
uniform_color = uniforms[index]
uniform_xyz = xyz_from_uniform(uniform_color)
uniform_irgb = colormodels.irgb_from_xyz(uniform_xyz)
uniform_name = names[index]
xyz_list.append(uniform_xyz)
fil.write('%s %s\n' % (uniform_name, str(uniform_irgb)))
fil.close()
plots.xyz_patch_plot(xyz_list, None, plot_title, plot_name, num_across=20)
