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Zero
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from skimage import color
import numpy as np
import torch
class deltaEab():
def __init__(self, color_chart_area=0):
super().__init__()
self.color_chart_area = color_chart_area
def __call__(self, img1, img2):
""" Compute the deltaE76 between two numpy RGB images
From M. Afifi: https://github.com/mahmoudnafifi/WB_sRGB/blob/master/WB_sRGB_Python/evaluation/calc_deltaE.py
:param img1: numpy RGB image or pytorch tensor
:param img2: numpy RGB image or pytocrh tensor
:return: deltaE76
"""
if type(img1) == torch.Tensor:
assert img1.shape[0] == 1
img1 = img1.squeeze().permute(1, 2, 0).cpu().numpy()
if type(img2) == torch.Tensor:
assert img2.shape[0] == 1
img2 = img2.squeeze().permute(1, 2, 0).cpu().numpy()
# Convert to Lab
img1 = color.rgb2lab(img1)
img2 = color.rgb2lab(img2)
# reshape to 1D array
img1 = img1.reshape(-1, 3).astype(np.float32)
img2 = img2.reshape(-1, 3).astype(np.float32)
# compute deltaE76
de76 = np.sqrt(np.sum(np.power(img1 - img2, 2), 1))
return sum(de76) / (np.shape(de76)[0] - self.color_chart_area)
class deltaE00():
def __init__(self, color_chart_area=0):
super().__init__()
self.color_chart_area = color_chart_area
self.kl = 1
self.kc = 1
self.kh = 1
def __call__(self, img1, img2):
""" Compute the deltaE00 between two numpy RGB images
From M. Afifi: https://github.com/mahmoudnafifi/WB_sRGB/blob/master/WB_sRGB_Python/evaluation/calc_deltaE2000.py
:param img1: numpy RGB image or pytocrh tensor
:param img2: numpy RGB image or pytocrh tensor
:return: deltaE00
"""
if type(img1) == torch.Tensor:
assert img1.shape[0] == 1
img1 = img1.squeeze().permute(1, 2, 0).cpu().numpy()
if type(img2) == torch.Tensor:
assert img2.shape[0] == 1
img2 = img2.squeeze().permute(1, 2, 0).cpu().numpy()
# Convert to Lab
img1 = color.rgb2lab(img1)
img2 = color.rgb2lab(img2)
# reshape to 1D array
img1 = img1.reshape(-1, 3).astype(np.float32)
img2 = img2.reshape(-1, 3).astype(np.float32)
# compute deltaE00
Lstd = np.transpose(img1[:, 0])
astd = np.transpose(img1[:, 1])
bstd = np.transpose(img1[:, 2])
Cabstd = np.sqrt(np.power(astd, 2) + np.power(bstd, 2))
Lsample = np.transpose(img2[:, 0])
asample = np.transpose(img2[:, 1])
bsample = np.transpose(img2[:, 2])
Cabsample = np.sqrt(np.power(asample, 2) + np.power(bsample, 2))
Cabarithmean = (Cabstd + Cabsample) / 2
G = 0.5 * (1 - np.sqrt((np.power(Cabarithmean, 7)) / (np.power(
Cabarithmean, 7) + np.power(25, 7))))
apstd = (1 + G) * astd
apsample = (1 + G) * asample
Cpsample = np.sqrt(np.power(apsample, 2) + np.power(bsample, 2))
Cpstd = np.sqrt(np.power(apstd, 2) + np.power(bstd, 2))
Cpprod = (Cpsample * Cpstd)
zcidx = np.argwhere(Cpprod == 0)
hpstd = np.arctan2(bstd, apstd)
hpstd[np.argwhere((np.abs(apstd) + np.abs(bstd)) == 0)] = 0
hpsample = np.arctan2(bsample, apsample)
hpsample = hpsample + 2 * np.pi * (hpsample < 0)
hpsample[np.argwhere((np.abs(apsample) + np.abs(bsample)) == 0)] = 0
dL = (Lsample - Lstd)
dC = (Cpsample - Cpstd)
dhp = (hpsample - hpstd)
dhp = dhp - 2 * np.pi * (dhp > np.pi)
dhp = dhp + 2 * np.pi * (dhp < (-np.pi))
dhp[zcidx] = 0
dH = 2 * np.sqrt(Cpprod) * np.sin(dhp / 2)
Lp = (Lsample + Lstd) / 2
Cp = (Cpstd + Cpsample) / 2
hp = (hpstd + hpsample) / 2
hp = hp - (np.abs(hpstd - hpsample) > np.pi) * np.pi
hp = hp + (hp < 0) * 2 * np.pi
hp[zcidx] = hpsample[zcidx] + hpstd[zcidx]
Lpm502 = np.power((Lp - 50), 2)
Sl = 1 + 0.015 * Lpm502 / np.sqrt(20 + Lpm502)
Sc = 1 + 0.045 * Cp
T = 1 - 0.17 * np.cos(hp - np.pi / 6) + 0.24 * np.cos(2 * hp) + \
0.32 * np.cos(3 * hp + np.pi / 30) \
- 0.20 * np.cos(4 * hp - 63 * np.pi / 180)
Sh = 1 + 0.015 * Cp * T
delthetarad = (30 * np.pi / 180) * np.exp(
- np.power((180 / np.pi * hp - 275) / 25, 2))
Rc = 2 * np.sqrt((np.power(Cp, 7)) / (np.power(Cp, 7) + np.power(25, 7)))
RT = - np.sin(2 * delthetarad) * Rc
klSl = self.kl * Sl
kcSc = self.kc * Sc
khSh = self.kh * Sh
de00 = np.sqrt(np.power((dL / klSl), 2) + np.power((dC / kcSc), 2) +
np.power((dH / khSh), 2) + RT * (dC / kcSc) * (dH / khSh))
return np.sum(de00) / (np.shape(de00)[0] - self.color_chart_area)
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