def get_images(sample, add_noise_img = False, sigma_noise = None, add_padding_noise=False, fixed_snr = False, SNR_target=None, seg_map=None):
"""1. To pad with noise, use 'add_padding_noise=True' + 'sigma_noise'
2. To simply add noise, use 'add_noise_img=True' + 'sigma_noise'
3. Otherwise, the function retrieves non noisy simulations by default"""
if add_noise_img:
#Add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(sample[i]['galsim_image'][0].array, sigma = sigma_noise)
return np.array([sample[obj]['galsim_image_noisy'] for obj in
range(sample.size)])
#Padding of 7 by 7 + noise
if add_padding_noise:
#Pad image
for i in range(len(sample)):
sample[i]['galsim_image_pad'] = pad2d(sample[i]['galsim_image'][0].array, (7, 7))
#Then add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(sample[i]['galsim_image_pad'], sigma = sigma_noise)
return np.array([sample[obj]['galsim_image_noisy'] for obj in
range(sample.size)])
#Get non noisy images
else:
return np.array([sample[obj]['galsim_image'][0].array for obj in
range(sample.size)])
def test_add_noise_gaussian(self):
"""Test add_noise with Gaussian noise."""
np.random.seed(1)
npt.assert_almost_equal(
noise.add_noise(self.data1),
self.data3,
err_msg='Incorrect noise: Gaussian',
)
np.random.seed(1)
npt.assert_almost_equal(
noise.add_noise(self.data1, sigma=(1, 1, 1)),
self.data3,
err_msg='Incorrect noise: Gaussian',
)
def get_images(sample,
add_noise_img=False,
sigma_noise=None,
add_padding_noise=False,
fixed_snr=False,
SNR_target=None,
seg_map=None):
if add_noise_img:
#Add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(
sample[i]['galsim_image'][0].array, sigma=sigma_noise)
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
if add_padding_noise:
#Pad image 7 by 7
for i in range(len(sample)):
sample[i]['galsim_image_pad'] = pad2d(
sample[i]['galsim_image'][0].array, (7, 7))
#Then add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(
sample[i]['galsim_image_pad'], sigma=sigma_noise)
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
# Add noise according to a fixed SNR level to reach
if fixed_snr:
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = noise_to_add(
img=sample[i]['galsim_image'][0].array,
SNR_=SNR_target,
map=seg_map[i])
#Normalise images
sample[i]['galsim_image_noisy'] /= sample[i][
'galsim_image_noisy'].max()
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
#Return only non noisy image
else:
return np.array([
sample[obj]['galsim_image'][0].array for obj in range(sample.size)
])
def noise_to_add(img=None, SNR_=None, map=None):
#Calculate std of central object signal
signal = np.sum(img[map == 1]**2)
# Calculate noise to add on image
std_noise = np.sqrt(signal) / SNR_
#add noise
return add_noise(img, sigma=std_noise)
def get_images(sample, add_noise_img = False, sigma_noise = None, add_padding_noise=False, fixed_snr = False, SNR_target=None, seg_map=None):
"""1. To pad with noise, use 'add_padding_noise=True' + 'sigma_noise'
2. To simply add noise, use 'add_noise_img=True' + 'sigma_noise'
3. To create datasets with fixed SNR according to signal use 'fixed_snr=True' + 'SNR_target'
4. Otherwise, the function retrieves non noisy simulations by default"""
if add_noise_img:
#Add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(sample[i]['galsim_image'][0].array, sigma = sigma_noise)
return np.array([sample[obj]['galsim_image_noisy'] for obj in
range(sample.size)])
#Padding of 7 by 7 + noise
if add_padding_noise:
#Pad image
for i in range(len(sample)):
sample[i]['galsim_image_pad'] = pad2d(sample[i]['galsim_image'][0].array, (7, 7))
#Then add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(sample[i]['galsim_image_pad'], sigma = sigma_noise)
return np.array([sample[obj]['galsim_image_noisy'] for obj in
range(sample.size)])
#Create images with fixed SNR
if fixed_snr:
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = noise_to_add(img=sample[i]['galsim_image'][0].array, SNR_=SNR_target, map=seg_map[i])
#Normalise images
sample[i]['galsim_image_noisy'] /= sample[i]['galsim_image_noisy'].max()
return np.array([sample[obj]['galsim_image_noisy'] for obj in
range(sample.size)])
#Get non noisy images
else:
return np.array([sample[obj]['galsim_image'][0].array for obj in
range(sample.size)])
def test_add_noise_poisson(self):
"""Test add_noise with Poisson noise."""
np.random.seed(1)
npt.assert_array_equal(
noise.add_noise(self.data1, noise_type='poisson'),
self.data2,
err_msg='Incorrect noise: Poisson',
)
npt.assert_raises(
ValueError,
noise.add_noise,
self.data1,
noise_type='bla',
)
npt.assert_raises(ValueError, noise.add_noise, self.data1, (1, 1))
"""
import numpy as np
from pysap import Image
from pysap.data import get_sample_data
from pysap.plugins.astro.deconvolve.deconvolve import sparse_deconv_condatvu
from modopt.signal.noise import add_noise
from modopt.math.convolve import convolve
# Load the example images
galaxy = get_sample_data('astro-galaxy')
psf = get_sample_data('astro-psf')
# Show the clean galaxy image
galaxy.show()
# Generate a noisy observed image
obs_data = add_noise(convolve(galaxy.data, psf.data), sigma=0.0005)
image_obs = Image(data=np.abs(obs_data))
image_obs.show()
# Deconvolve the observed image
deconv_data = sparse_deconv_condatvu(obs_data, psf.data, n_iter=3000)
image_rec = Image(data=np.abs(deconv_data))
image_rec.show()
# Show the residual
residual = Image(data=np.abs(galaxy.data - deconv_data))
residual.show()
def get_images(sample,
add_noise_img=False,
sigma_noise=None,
extra_padding=False,
add_pad_noise=False,
fixed_snr=False,
SNR_target=None,
seg_map=None,
get_noisy=False):
if add_noise_img:
#Add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(
sample[i]['galsim_image'][0].array, sigma=sigma_noise)
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
if extra_padding:
#Add extra padding to image and store in dict
for i in range(len(sample)):
sample[i]['galsim_image_pad'] = pad2d(
sample[i]['galsim_image'][0].array, (7, 7))
return np.array(
[sample[obj]['galsim_image_pad'] for obj in range(sample.size)])
if add_pad_noise:
#Pad image
for i in range(len(sample)):
sample[i]['galsim_image_pad'] = pad2d(
sample[i]['galsim_image'][0].array, (7, 7))
#Then add noise to image and store array in dict
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = add_noise(
sample[i]['galsim_image_pad'], sigma=sigma_noise)
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
if fixed_snr:
for i in range(len(sample)):
sample[i]['galsim_image_noisy'] = noise_to_add(
img=sample[i]['galsim_image'][0].array,
SNR_=SNR_target,
map=seg_map[i])
#Normalise images
sample[i]['galsim_image_noisy'] /= sample[i][
'galsim_image_noisy'].max()
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
if get_noisy:
return np.array(
[sample[obj]['galsim_image_noisy'] for obj in range(sample.size)])
else:
return np.array([
sample[obj]['galsim_image'][0].array for obj in range(sample.size)
])
galaxy = get_sample_data('astro-ngc2997')
#############################################################################
# Show the clean galaxy image
# galaxy.show()
#############################################################################
# Generate noisy observation
# --------------------------
#
# Convolve the image with a point spread function (PSF) using the `convolve`
# function. Then add random Gaussian noise with standard deviation 0.0005
# using the `add_noise` function.
obs_data = add_noise(galaxy.data, sigma=100)
#############################################################################
# Create a PySAP image object
image_obs = Image(data=np.abs(obs_data))
#############################################################################
# Show the noisy galaxy image
# image_obs.show()
#############################################################################
# Deconvolve
# ----------
#
