def find_axis_std(self, ctset, tmpdir, ax_range, p_width, search_row,
nviews):
indir = self.make_inpaths(ctset[0], ctset[1])
print indir[2]
image = read_image(get_filenames(indir[2])[0])
cmd = 'tofu lamino --absorptivity --fix-nan-and-inf --overall-angle 180'\
' --lamino-angle 90 --height 2'
cmd += ' --darks {} --flats {} --projections {}'.\
format(indir[0], indir[1], enquote(indir[2]))
cmd += ' --number {}'.format(nviews)
cmd += ' --angle {:0.5f}'.format(np.radians(180.0 / float(nviews)))
if ctset[1] == 4:
cmd += ' --flats2 {}'.format(indir[3])
out_pattern = os.path.join(tmpdir, "axis-search/slice")
cmd += ' --output {}'.format(enquote(out_pattern))
cmd += ' --x-region={},{},{}'.format(-p_width / 2, p_width / 2, 1)
cmd += ' --y-region={},{},{}'.format(-p_width / 2, p_width / 2, 1)
cmd += ' --y {} --height 2'.format(search_row)
cmd += ' --z-parameter x-center'
cmd += ' --region={}'.format(enquote(ax_range))
cmd += ' --z 0'
res = [float(num) for num in ax_range.split(',')]
cmd += ' --axis {},{}'.format((res[0] + res[1]) / 2.,
1.0) #middle of ax search range?
cmd += " --output-bytes-per-file 0"
# cmd += ' --delete-slice-dir'
print cmd
os.system(cmd)
points, maximum = evaluate_images_simp(out_pattern + '*.tif', "msag")
return res[0] + res[2] * maximum
def _read_next_file(self):
if self.index < len(self.filenames):
self._image = read_image(self.filenames[self.index])
self._image_index = 0
self.index += 1
if self._image.ndim == 2:
# Make sure the image is 3D so that grab can be simpler
self._image = self._image[np.newaxis, :]
else:
self._image = None
def RR(mws, odir, fname):
im = read_image(fname).astype(np.float32)
N = im.shape[0]
tmp = np.sum(im, 0) / N
tmp = medf(tmp, (mws, ))
tmp = np.array([
tmp,
] * N)
im -= tmp
filt_sin_name = os.path.join(odir, os.path.split(fname)[1])
write_tiff(filt_sin_name, (im).astype(np.float32))
def find_axis_corr(self, ctset, vcrop, y, height):
indir = self.make_inpaths(ctset[0], ctset[1])
"""Use correlation to estimate center of rotation for tomography."""
from scipy.signal import fftconvolve
def flat_correct(flat, radio):
nonzero = np.where(radio != 0)
result = np.zeros_like(radio)
result[nonzero] = flat[nonzero] / radio[nonzero]
# log(1) = 0
result[result <= 0] = 1
return np.log(result)
first = read_image(get_filenames(indir[2])[0]).astype(np.float)
last = read_image(get_filenames(indir[2])[-1]).astype(np.float)
dark = read_image(get_filenames(indir[0])[0]).astype(np.float)
flat1 = read_image(get_filenames(indir[1])[-1]) - dark
first = flat_correct(flat1, first - dark)
if ctset[1] == 4:
flat2 = read_image(get_filenames(indir[3])[-1]) - dark
last = flat_correct(flat2, last - dark)
else:
last = flat_correct(flat1, last - dark)
if vcrop:
y_region = slice(y, min(y + height, first.shape[0]), 1)
first = first[y_region, :]
last = last[y_region, :]
width = first.shape[1]
first = first - first.mean()
last = last - last.mean()
conv = fftconvolve(first, last[::-1, :], mode='same')
center = np.unravel_index(conv.argmax(), conv.shape)[1]
return (width / 2.0 + center) / 2.0
def _grab_real(self):
if self.index < len(self.filenames):
image = read_image(self.filenames[self.index])
y_region = self.roi_y0 + self.roi_height
x_region = self.roi_x0 + self.roi_width
result = image[self.roi_y0.magnitude:y_region.magnitude,
self.roi_x0.magnitude:x_region.magnitude]
self.index += 1
else:
result = None
return result
def _grab_real(self):
if self.index < len(self.filenames):
image = read_image(self.filenames[self.index])
y_region = self.roi_y0 + self.roi_height
x_region = self.roi_x0 + self.roi_width
result = image[self.roi_y0.magnitude:y_region.magnitude,
self.roi_x0.magnitude:x_region.magnitude]
self.index += 1
else:
result = None
return result
def __init__(self, directory, reset_on_start=True):
# Let users change the directory
self.directory = directory
super(FileCamera, self).__init__()
self.index = 0
self.reset_on_start = reset_on_start
self.filenames = [os.path.join(directory, file_name) for file_name in
sorted(os.listdir(directory))]
if not self.filenames:
raise base.CameraError("No files found")
image = read_image(self.filenames[0])
self._roi_width = image.shape[1] * q.pixel
self._roi_height = image.shape[0] * q.pixel
def __init__(self, directory, reset_on_start=True):
# Let users change the directory
self.directory = directory
super(FileCamera, self).__init__()
self.index = 0
self.reset_on_start = reset_on_start
self.filenames = [
os.path.join(directory, file_name)
for file_name in sorted(os.listdir(directory))
]
if not self.filenames:
raise base.CameraError("No files found")
image = read_image(self.filenames[0])
self._roi_width = image.shape[1] * q.pixel
self._roi_height = image.shape[0] * q.pixel
def _grab_real(self):
if self._recording is None:
raise CameraError("Camera hasn't been in recording mode yet")
if self._recording:
if self.trigger_mode == FileCamera.TRIGGER_SOFTWARE:
self._trigger.wait()
self._trigger.clear()
if self._get_index() == self._index:
# Wait for the next frame if the readout is too fast
time.sleep(1.0 / self.frame_rate)
self._index = self._get_index()
else:
if self.trigger_mode == FileCamera.TRIGGER_AUTO:
self._index += 1
else:
if self._trigger.is_set():
self._trigger.clear()
else:
# Camera hasn't been triggered, don't return anything
return None
self._index = self._get_index(self._trigger_time)
if self._index > self._get_index(self._stop_time) or \
self._index >= len(self._files):
return None
image = read_image(self._files[self._index])
if self.roi_height is None:
y_region = image.shape[0]
else:
y_region = self.roi_y + self.roi_height
if self.roi_width is None:
x_region = image.shape[1]
else:
x_region = self.roi_x + self.roi_width
return image[self.roi_y:y_region, self.roi_x:x_region]
def RR_sort(mws, odir, fname):
filt_sin_name = os.path.join(odir, os.path.split(fname)[1])
write_tiff(
filt_sin_name,
remove_stripe_based_sorting(read_image(fname).astype(np.float32),
mws).astype(np.float32))
def RR_wide_sort(mws, mws2, snr, odir, fname):
filt_sin_name = os.path.join(odir, os.path.split(fname)[1])
im = read_image(fname).astype(np.float32)
im = remove_large_stripe(im, snr, mws2)
im = remove_stripe_based_sorting(im, mws)
write_tiff(filt_sin_name, im.astype(np.float32))
