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Incorporate fixes for complex images

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Michael Sarahan
2015-02-20 20:51:14 -08:00
parent 03dc489457
commit cf09b0c5ef
1 changed files with 10 additions and 11 deletions
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skimage/feature/register_translation.py
+10 -11
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@@ -183,15 +183,15 @@ def register_translation(src_image, target_image, upsample_factor=1,
# Whole-pixel shift - Compute cross-correlation by an IFFT
shape = src_freq.shape
image_product = src_freq * target_freq.conj()
cross_correlation = np.fft.fftshift(np.fft.ifftn(image_product))
cross_correlation = np.fft.ifftn(image_product)
# Locate maximum
maxima = np.unravel_index(np.argmax(cross_correlation),
maxima = np.unravel_index(np.argmax(np.abs(cross_correlation)),
cross_correlation.shape)
midpoints = np.array([np.fix(axis_size / 2) for axis_size in shape])
shifts = np.array(maxima, dtype=np.float64)
shifts -= midpoints
shifts[shifts>midpoints] -= np.array(shape)[shifts>midpoints]
if upsample_factor == 1:
src_amp = np.sum(np.abs(src_freq) ** 2) / src_freq.size
@@ -204,28 +204,27 @@ def register_translation(src_image, target_image, upsample_factor=1,
upsampled_region_size = np.ceil(upsample_factor * 1.5)
# Center of output array at dftshift + 1
dftshift = np.fix(upsampled_region_size / 2.0)
midpoint_product = np.product(midpoints)
normalization = (midpoint_product * upsample_factor ** 2)
upsample_factor = np.array(upsample_factor, dtype=np.float64)
normalization = (src_freq.size * upsample_factor ** 2)
# Matrix multiply DFT around the current shift estimate
sample_region_offset = shifts*upsample_factor + dftshift
cross_correlation = _upsampled_dft(image_product,
sample_region_offset = dftshift - shifts*upsample_factor
cross_correlation = _upsampled_dft(image_product.conj(),
upsampled_region_size,
upsample_factor,
sample_region_offset).conj()
cross_correlation /= normalization
# Locate maximum and map back to original pixel grid
maxima = np.array(np.unravel_index(np.argmax(cross_correlation),
maxima = np.array(np.unravel_index(np.argmax(np.abs(cross_correlation)),
cross_correlation.shape),
dtype=np.float64)
maxima -= dftshift
shifts = shifts - maxima / upsample_factor
shifts = shifts + maxima / upsample_factor
CCmax = cross_correlation.max()
src_amp = _upsampled_dft(src_freq * src_freq.conj(),
1, upsample_factor)[0, 0]
src_amp /= normalization
target_amp = _upsampled_dft(target_freq * target_freq.conj(),
1,
upsample_factor)[0, 0]
1, upsample_factor)[0, 0]
target_amp /= normalization
# If its only one row or column the shift along that dimension has no