def test_cmc():
data = load_ciede2000_data()
N = len(data)
lab1 = np.zeros((N, 3))
lab1[:, 0] = data['L1']
lab1[:, 1] = data['a1']
lab1[:, 2] = data['b1']
lab2 = np.zeros((N, 3))
lab2[:, 0] = data['L2']
lab2[:, 1] = data['a2']
lab2[:, 2] = data['b2']
dE2 = deltaE_cmc(lab1, lab2)
oracle = np.array([
1.73873611, 2.49660844, 3.30494501, 0.85735576, 0.88332927,
0.97822692, 3.50480874, 2.87930032, 6.5783807, 6.57838075,
6.5783808, 6.57838086, 6.67492321, 6.67492326, 6.67492331,
4.66852997, 42.10875485, 39.45889064, 38.36005919, 33.93663807,
1.14400168, 1.00600419, 1.11302547, 1.05335328, 1.42822951,
1.2548143, 1.76838061, 2.02583367, 3.08695508, 1.74893533,
1.90095165, 1.70258148, 1.80317207, 2.44934417
])
assert_allclose(dE2, oracle, rtol=1.e-8)
def __create_col__(self,i,j):
i_start,i_end,j_start,j_end = (i*self.n_col,(i+1)*self.n_col,j*self.m_col,(j+1)*self.m_col)
slze = self.img_in[i_start:i_end,j_start:j_end]
slze = color.rgb2lab(slze)
DISTS=[np.mean(color.deltaE_cmc(slze,img)) for img in self.col_images_array]#Calculate "differences"
best_col = np.nanargmin(DISTS)
self.img_out[i_start:i_end,j_start:j_end]=self.col_images_array[best_col]
def color_diff(rgb1, rgb2):
"""
Calculate distance between two RGB colors. See discussion:
http://stackoverflow.com/questions/8863810/python-find-similar-colors-best-way
- for basic / fast calculations, you can use dE76 but beware of its problems
- for graphics arts use we recommend dE94 and perhaps dE-CMC 2:1
- for textiles use dE-CMC
"""
rgb1 = np.array(rgb1, dtype="float64").reshape(1, 1, 3) / 255.0
rgb2 = np.array(rgb2, dtype="float64").reshape(1, 1, 3) / 255.0
lab1 = rgb2lab(rgb1)
lab2 = rgb2lab(rgb2)
return deltaE_cmc(lab1, lab2, kL=2, kC=1)[0, 0]
def color_diff(rgb1, rgb2):
"""
Calculate distance between two RGB colors. See discussion:
http://stackoverflow.com/questions/8863810/python-find-similar-colors-best-way
- for basic / fast calculations, you can use dE76 but beware of its problems
- for graphics arts use we recommend dE94 and perhaps dE-CMC 2:1
- for textiles use dE-CMC
"""
import numpy as np
from skimage.color import rgb2lab, deltaE_cmc
rgb1 = np.array(rgb1, dtype="float64").reshape(1, 1, 3) / 255.
rgb2 = np.array(rgb2, dtype="float64").reshape(1, 1, 3) / 255.
lab1 = rgb2lab(rgb1)
lab2 = rgb2lab(rgb2)
return deltaE_cmc(lab1, lab2, kL=2, kC=1)[0, 0]
def delta_filter(self, l, a, b):
interval = 3
s = l.size-1
t_row = np.zeros((1, s))
for j in range(1,interval):
c_int = j
for x in range(0, s):
r_row = np.zeros((1,s))
if x-c_int > 0:
i1 = x-c_int
else:
i1 = 0
if x+c_int > s:
i2 = s
else:
i2 = x+c_int
l1 = np.mean(l.take([i1, x]))
l2 = np.mean(l.take([x, i2]))
a1 = np.mean(a.take([i1, x]))
a2 = np.mean(a.take([x, i2]))
b1 = np.mean(b.take([i1, x]))
b2 = np.mean(b.take([x, i2]))
dE = color.deltaE_cmc([l1, a1, b1], [l2, a2, b2])
if not math.isnan(dE):
np.put(r_row, range(i1, i2), dE)
t_row = np.add(t_row, r_row)
return t_row
def test_cmc():
data = load_ciede2000_data()
N = len(data)
lab1 = np.zeros((N, 3))
lab1[:, 0] = data['L1']
lab1[:, 1] = data['a1']
lab1[:, 2] = data['b1']
lab2 = np.zeros((N, 3))
lab2[:, 0] = data['L2']
lab2[:, 1] = data['a2']
lab2[:, 2] = data['b2']
dE2 = deltaE_cmc(lab1, lab2)
oracle = np.array([
1.73873611, 2.49660844, 3.30494501, 0.85735576, 0.88332927, 0.97822692,
3.50480874, 2.87930032, 6.5783807, 6.57838075, 6.5783808, 6.57838086,
6.67492321, 6.67492326, 6.67492331, 4.66852997, 42.10875485,
39.45889064, 38.36005919, 33.93663807, 1.14400168, 1.00600419,
1.11302547, 1.05335328, 1.42822951, 1.2548143, 1.76838061, 2.02583367,
3.08695508, 1.74893533, 1.90095165, 1.70258148, 1.80317207, 2.44934417
])
assert_allclose(dE2, oracle, rtol=1.e-8)
def test_single_color_cmc():
lab1 = (0.5, 0.5, 0.5)
lab2 = (0.4, 0.4, 0.4)
deltaE_cmc(lab1, lab2)
def get_delta(pic):
print(color.deltaE_cmc(LabPic, pic)[0][0])
print(color.deltaE_ciede94(LabPic, pic)[0][0])
print(color.deltaE_ciede2000(LabPic, pic)[0][0])
def distance_cmc(src_lab, dst_lab):
return color.deltaE_cmc(src_lab, dst_lab)
def test_single_color_cmc():
lab1 = (0.5, 0.5, 0.5)
lab2 = (0.4, 0.4, 0.4)
deltaE_cmc(lab1, lab2)
# quadrant.
# <codecell>
from skimage import img_as_float
image = img_as_float(io.imread('../images/color-wheel.jpg'))
blue_lab = color.rgb2lab([[[0, 0, 1.]]])
light_blue_lab = color.rgb2lab([[[0, 1, 1.]]])
red_lab = color.rgb2lab([[[1, 0, 0.]]])
image_lab = color.rgb2lab(image)
distance_blue = color.deltaE_cmc(blue_lab, image_lab, kL=0.5, kC=0.5)
distance_light_blue = color.deltaE_cmc(light_blue_lab, image_lab, kL=0.5, kC=0.5)
distance_red = color.deltaE_cmc(red_lab, image_lab, kL=0.5, kC=0.5)
distance = distance_blue + distance_light_blue - distance_red
distance = exposure.rescale_intensity(distance)
image_blue = image.copy()
image_blue[distance > 0.3] = 0
f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(10, 5))
ax0.imshow(image)
ax1.imshow(distance, cmap='gray')
ax2.imshow(image_blue)
plt.show()
