def _decon_shape(self, data, kernel):
# Apply blur to original shape and run restoration on blurred image
acq = fd_data.Acquisition(data=data, kernel=kernel, actual=data)
acq = tfv.reblur(acq, scale=.001) # Add low amount of noise
res = tfv.decon_tf(acq, 10, real_domain_fft=False)
# Binarize resulting image and validate that pixels match original exactly
bin_res = (res > res.mean()).astype(np.int64)
bin_tru = acq.actual.astype(np.int64)
return bin_res, bin_tru
def test_bars(self):
"""Test reconstruction of blurred "Hollow Bars" volume"""
n_iter = 10
thresholds = {
# Results should always improve on the original
'original': 1.,
# Results should also be better than DL2 and scikit-image most of the time
'sk': .75,
'dl2': .75 if dl2_enabled() else None
}
acq = fd_data.bars_25pct()
# Initialize list of acquisitions to deconvolve
acqs = [acq]
# Add translations to images and kernels to ensure that there aren't
# any issues supporting non-symmetric inputs
acqs += [
tfv.reblur(tfv.shift(acq, data_shift=(0, 10, 10))),
tfv.reblur(tfv.shift(acq, data_shift=(0, -10, -10))),
tfv.reblur(tfv.shift(acq, kern_shift=(0, 10, 10))),
tfv.reblur(tfv.shift(acq, kern_shift=(0, -10, -10))),
tfv.reblur(
tfv.shift(acq, data_shift=(-3, 5, -5), kern_shift=(-3, 5, -5)))
]
# Subset image and kernel to make sure that padding of inputs for fft is added
# and then cropped out correctly (tests asymmetries and non-power-of-2 dimensions)
acqs += [
tfv.reblur(
tfv.subset(acq,
kern_slice=[
slice(None, 24),
slice(None, 48),
slice(None, 48)
])),
]
# Validate that downsampling the original volume also causes no issues
acqs += [
tfv.reblur(tfv.downsample(acq, data_factor=.8, kern_factor=.4)),
tfv.reblur(tfv.downsample(acq, data_factor=.5, kern_factor=.5))
]
self._validate_restoration(acqs, n_iter, thresholds=thresholds)
def test_bars(self):
"""Test reconstruction of blurred "Hollow Bars" volume"""
n_iter = 10
thresholds = {
# Results should always improve on the original
'original': 1.0,
'dl2': .75 if dl2_enabled() else None
}
acq = fd_data.bars_25pct()
self.assertTrue(acq.data.shape == acq.kernel.shape == (32, 64, 64))
# shape: {'actual': (32, 64, 64), 'kernel': (32, 64, 64), 'data': (32, 64, 64)}
# Initialize list of acquisitions to deconvolve
acqs = [acq]
# Slice to give shape with odd dimensions as this is a better test of padding features
acq_slice = [slice(2, 29), slice(None, 57), slice(None, 57)]
acq = tfv.subset(acq, data_slice=acq_slice, kern_slice=acq_slice)
# shape: {'data': (27, 57, 57), 'actual': (27, 57, 57), 'kernel': (27, 57, 57)}
acqs += [acq]
# Add translations to images and kernels to ensure that there aren't
# any issues supporting non-symmetric inputs
acqs += [
tfv.reblur(tfv.shift(acq, data_shift=(0, 4, 4))),
tfv.reblur(tfv.shift(acq, data_shift=(0, -4, -4))),
tfv.reblur(tfv.shift(acq, kern_shift=(0, 4, 4))),
tfv.reblur(tfv.shift(acq, kern_shift=(0, -4, -4))),
tfv.reblur(
tfv.shift(acq, data_shift=(-3, 5, -5), kern_shift=(-3, 5, -5)))
]
# Validate that downsampling the original volume also causes no issues
acqs += [
tfv.reblur(tfv.downsample(acq, data_factor=.8, kern_factor=.4)),
tfv.reblur(tfv.downsample(acq, data_factor=.5, kern_factor=.5))
]
self._validate_restoration(acqs,
n_iter,
thresholds=thresholds,
dtype=np.uint16)