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import numpy as np
import random
from scipy.stats import tukeylambda
camera_params = {
'Kmin': 0.2181895124454343,
'Kmax': 3.0,
'G_shape': np.array([0.15714286, 0.14285714, 0.08571429, 0.08571429, 0.2 ,
0.2 , 0.1 , 0.08571429, 0.05714286, 0.07142857,
0.02857143, 0.02857143, 0.01428571, 0.02857143, 0.08571429,
0.07142857, 0.11428571, 0.11428571]),
'Profile-1': {
'R_scale': {
'slope': 0.4712797750747537,
'bias': -0.8078958947116487,
'sigma': 0.2436176299944695
},
'g_scale': {
'slope': 0.6771267783987617,
'bias': 1.5121876510805845,
'sigma': 0.24641096601611254
},
'G_scale': {
'slope': 0.6558756156508007,
'bias': 1.09268679594838,
'sigma': 0.28604721742277756
}
},
'black_level': 2048,
'max_value': 16383
}
# photon shot noise
def addPStarNoise(img, K):
return np.random.poisson(img / K).astype(np.float32) * K
# read noise
# tukey lambda distribution
def addGStarNoise(img, K, G_shape, G_scale_param):
# sample a shape parameter [lambda] from histogram of samples
a, b = np.histogram(G_shape, bins=10, range=(-0.25, 0.25))
a, b = np.array(a), np.array(b)
a = a / a.sum()
rand_num = random.uniform(0, 1)
idx = np.sum(np.cumsum(a) < rand_num)
lam = random.uniform(b[idx], b[idx+1])
# calculate scale parameter [G_scale]
log_K = np.log(K)
log_G_scale = np.random.standard_normal() * G_scale_param['sigma'] * 1 +\
G_scale_param['slope'] * log_K + G_scale_param['bias']
G_scale = np.exp(log_G_scale)
# print(f'G_scale: {G_scale}')
return img + tukeylambda.rvs(lam, scale=G_scale, size=img.shape).astype(np.float32)
# row noise
# uniform distribution for each row
def addRowNoise(img, K, R_scale_param):
# calculate scale parameter [R_scale]
log_K = np.log(K)
log_R_scale = np.random.standard_normal() * R_scale_param['sigma'] * 1 +\
R_scale_param['slope'] * log_K + R_scale_param['bias']
R_scale = np.exp(log_R_scale)
# print(f'R_scale: {R_scale}')
row_noise = np.random.randn(img.shape[0], 1).astype(np.float32) * R_scale
return img + np.tile(row_noise, (1, img.shape[1]))
# quantization noise
# uniform distribution
def addQuantNoise(img, q):
return img + np.random.uniform(low=-0.5*q, high=0.5*q, size=img.shape)
def sampleK(Kmin, Kmax):
return np.exp(np.random.uniform(low=np.log(Kmin), high=np.log(Kmax)))
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