def test_background_substraction_precomputed(self):
"""
Test clean up before polar conversion
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = hdf5.read_data("substracted_background_image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_background_substraction_float_input(self):
"""
Tests for input of DataArray with float ndarray.
"""
data = self.data
data[0] = data[0].astype(numpy.float)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = hdf5.read_data("substracted_background_image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_background_substraction_int8_input(self):
"""
Tests for input of DataArray with int8 ndarray.
"""
data = self.data
# scipy.misc.bytescale(data)
data[0] = data[0].astype(numpy.int64)
data[0] = numpy.right_shift(data[0], 8)
data[0] = data[0].astype(numpy.int8)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = angleres.AngleResolved2Polar(data[0].astype(float),
201)
numpy.testing.assert_allclose(result, desired_output, rtol=1)
def _project2Polar(self, pos):
"""
Return the polar projection of the image at the given position.
pos (float, float, string or None): position (must be part of the ._pos)
returns DataArray: the polar projection
"""
# Note: Need a copy of the link to the dict. If self._polar is reset while
# still running this method, the dict might get new entries again, though it should be empty.
polar = self._polar
if pos in polar:
polard = polar[pos]
else:
# Compute the polar representation
data = self._pos[pos]
try:
# Get bg image, if existing. It must match the polarization (defaulting to MD_POL_NONE).
bg_image = self._getBackground(
data.metadata.get(MD_POL_MODE, MD_POL_NONE))
if bg_image is None:
# Simple version: remove the background value
data_bg_corr = angleres.ARBackgroundSubtract(data)
else:
data_bg_corr = img.Subtract(data,
bg_image) # metadata from data
if numpy.prod(data_bg_corr.shape) > (1280 * 1080):
# AR conversion fails with very large images due to too much
# memory consumed (> 2Gb). So, rescale + use a "degraded" type that
# uses less memory. As the display size is small (compared
# to the size of the input image, it shouldn't actually
# affect much the output.
logging.info(
"AR image is very large %s, will convert to "
"azimuthal projection in reduced precision.",
data_bg_corr.shape)
y, x = data_bg_corr.shape
if y > x:
small_shape = 1024, int(round(1024 * x / y))
else:
small_shape = int(round(1024 * y / x)), 1024
# resize
data_bg_corr = img.rescale_hq(data_bg_corr, small_shape)
# 2 x size of original image (on smallest axis) and at most
# the size of a full-screen canvas
size = min(min(data_bg_corr.shape) * 2, 1134)
# TODO: First compute quickly a low resolution and then
# compute a high resolution version.
# TODO: could use the size of the canvas that will display
# the image to save some computation time.
# Warning: allocates lot of memory, which will not be free'd until
# the current thread is terminated.
polard = angleres.AngleResolved2Polar(data_bg_corr,
size,
hole=False)
# TODO: don't hold too many of them in cache (eg, max 3 * 1134**2)
polar[pos] = polard
except Exception:
logging.exception("Failed to convert to azimuthal projection")
return data # display it raw as fallback
return polard