def test_subpixel(self):
anarr = np.zeros((4, 5))
anarr[2, 3] = 1
# The correspondence principle should hold
first_guess = (2, 3)
second_guess = utils._interpolate(anarr, first_guess, rad=1)
np.testing.assert_equal(second_guess, (2, 3))
# Now something more meaningful
anarr[2, 4] = 1
second_guess = utils._interpolate(anarr, first_guess, rad=1)
np.testing.assert_almost_equal(second_guess, (2, 3.5))
def test_subpixel_edge(self):
anarr = np.zeros((4, 5))
anarr[3, 0] = 1
anarr[3, 4] = 1
first_guess = (4, 0)
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_almost_equal(second_guess, (3, -0.5))
anarr[3, 0] += 1
anarr[0, 4] = 1
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_almost_equal(second_guess, (3.25, -0.5))
def test_subpixel_edge(self):
anarr = np.zeros((4, 5))
anarr[3, 0] = 1
anarr[3, 4] = 1
first_guess = (4, 0)
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_almost_equal(second_guess, (3, -0.5))
anarr[3, 0] += 1
anarr[0, 4] = 1
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_almost_equal(second_guess, (3.25, -0.5))
def test_subpixel(self):
anarr = np.zeros((4, 5))
anarr[2, 3] = 1
# The correspondence principle should hold
first_guess = (2, 3)
second_guess = utils._interpolate(anarr, first_guess, rad=1)
np.testing.assert_equal(second_guess, (2, 3))
# Now something more meaningful
anarr[2, 4] = 1
second_guess = utils._interpolate(anarr, first_guess, rad=1)
np.testing.assert_almost_equal(second_guess, (2, 3.5))
def compute_motion_shifts(scan, template, in_place=True, num_threads=8):
""" Compute shifts in y and x for rigid subpixel motion correction.
Returns the number of pixels that each image in the scan was to the right (x_shift)
or below (y_shift) the template. Negative shifts mean the image was to the left or
above the template.
:param np.array scan: 2 or 3-dimensional scan (image_height, image_width[, num_frames]).
:param np.array template: 2-d template image. Each frame in scan is aligned to this.
:param bool in_place: Whether the scan can be overwritten.
:param int num_threads: Number of threads used for the ffts.
:returns: (y_shifts, x_shifts) Two arrays (num_frames) with the y, x motion shifts.
..note:: Based in imreg_dft.translation().
"""
import pyfftw
from imreg_dft import utils
# Add third dimension if scan is a single image
if scan.ndim == 2:
scan = np.expand_dims(scan, -1)
# Get some params
image_height, image_width, num_frames = scan.shape
taper = np.outer(signal.tukey(image_height, 0.2), signal.tukey(image_width, 0.2))
# Prepare fftw
frame = pyfftw.empty_aligned((image_height, image_width), dtype='complex64')
fft = pyfftw.builders.fft2(frame, threads=num_threads, overwrite_input=in_place,
avoid_copy=True)
ifft = pyfftw.builders.ifft2(frame, threads=num_threads, overwrite_input=in_place,
avoid_copy=True)
# Get fourier transform of template
template_freq = fft(template * taper).conj() # we only need the conjugate
abs_template_freq = abs(template_freq)
eps = abs_template_freq.max() * 1e-15
# Compute subpixel shifts per image
y_shifts = np.empty(num_frames)
x_shifts = np.empty(num_frames)
for i in range(num_frames):
# Compute correlation via cross power spectrum
image_freq = fft(scan[:, :, i] * taper)
cross_power = (image_freq * template_freq) / (abs(image_freq) * abs_template_freq + eps)
shifted_cross_power = np.fft.fftshift(abs(ifft(cross_power)))
# Get best shift
shifts = np.unravel_index(np.argmax(shifted_cross_power), shifted_cross_power.shape)
shifts = utils._interpolate(shifted_cross_power, shifts, rad=3)
# Map back to deviations from center
y_shifts[i] = shifts[0] - image_height // 2
x_shifts[i] = shifts[1] - image_width // 2
return y_shifts, x_shifts
def compute_motion_shifts(scan, template, in_place=True, num_threads=8):
""" Compute shifts in y and x for rigid subpixel motion correction.
Returns the number of pixels that each image in the scan was to the right (x_shift)
or below (y_shift) the template. Negative shifts mean the image was to the left or
above the template.
:param np.array scan: 2 or 3-dimensional scan (image_height, image_width[, num_frames]).
:param np.array template: 2-d template image. Each frame in scan is aligned to this.
:param bool in_place: Whether the scan can be overwritten.
:param int num_threads: Number of threads used for the ffts.
:returns: (y_shifts, x_shifts) Two arrays (num_frames) with the y, x motion shifts.
..note:: Based in imreg_dft.translation().
"""
import pyfftw
from imreg_dft import utils
# Add third dimension if scan is a single image
if scan.ndim == 2:
scan = np.expand_dims(scan, -1)
# Get some params
image_height, image_width, num_frames = scan.shape
taper = np.outer(signal.tukey(image_height, 0.2), signal.tukey(image_width, 0.2))
# Prepare fftw
frame = pyfftw.empty_aligned((image_height, image_width), dtype='complex64')
fft = pyfftw.builders.fft2(frame, threads=num_threads, overwrite_input=in_place,
avoid_copy=True)
ifft = pyfftw.builders.ifft2(frame, threads=num_threads, overwrite_input=in_place,
avoid_copy=True)
# Get fourier transform of template
template_freq = fft(template * taper).conj() # we only need the conjugate
abs_template_freq = abs(template_freq)
eps = abs_template_freq.max() * 1e-15
# Compute subpixel shifts per image
y_shifts = np.empty(num_frames)
x_shifts = np.empty(num_frames)
for i in range(num_frames):
# Compute correlation via cross power spectrum
image_freq = fft(scan[:, :, i] * taper)
cross_power = (image_freq * template_freq) / (abs(image_freq) * abs_template_freq + eps)
shifted_cross_power = np.fft.fftshift(abs(ifft(cross_power)))
# Get best shift
shifts = np.unravel_index(np.argmax(shifted_cross_power), shifted_cross_power.shape)
shifts = utils._interpolate(shifted_cross_power, shifts, rad=3)
# Map back to deviations from center
y_shifts[i] = shifts[0] - image_height // 2
x_shifts[i] = shifts[1] - image_width // 2
return y_shifts, x_shifts
def test_subpixel_crazy(self):
anarr = np.zeros((4, 5))
first_guess = (0, 0)
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_array_less(second_guess, anarr.shape)
def test_subpixel_crazy(self):
anarr = np.zeros((4, 5))
first_guess = (0, 0)
second_guess = utils._interpolate(anarr, first_guess, rad=2)
np.testing.assert_array_less(second_guess, anarr.shape)
