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BFSReddyChetterji k Bug #590

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Merged
merged 2 commits into from
Mar 8, 2022
Merged

BFSReddyChetterji k Bug #590

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ed09404
Fix `k` bug in BFSReddyChetterji
garrettwrong Feb 24, 2022
a2fba0b
restrict corr to a disc
garrettwrong Feb 24, 2022
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22 changes: 11 additions & 11 deletions src/aspire/classification/averager2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -466,6 +466,8 @@ def __init__(
if self.alignment_src.dtype != src.dtype:
raise RuntimeError("Currently `alignment_src.dtype` must equal `src.dtype`")

self.mask = grid_2d(src.L, normalized=False)["r"] < src.L // 2

super().__init__(composite_basis, src, composite_basis, dtype=dtype)

def _phase_cross_correlation(self, img0, img1):
Expand Down Expand Up @@ -547,7 +549,7 @@ def _reddychatterji(self, images, class_k, reflection_k):
# Result arrays
M = len(images)
rotations_k = np.zeros(M, dtype=self.dtype)
correlations_k = np.zeros(M, dtype=self.dtype)
correlations_k = np.full(M, -np.inf, dtype=self.dtype)
shifts_k = np.zeros((M, 2), dtype=int)

# De-Mean, note images is mutated and should be a `copy`.
Expand Down Expand Up @@ -655,8 +657,8 @@ def _reddychatterji(self, images, class_k, reflection_k):
# Hack
regis_img_estimated = rotate(regis_img, r)
regis_img_rotated_p180 = rotate(regis_img, r + 180)
da = np.dot(fixed_img.flatten(), regis_img_estimated.flatten())
db = np.dot(fixed_img.flatten(), regis_img_rotated_p180.flatten())
da = np.dot(fixed_img[self.mask], regis_img_estimated[self.mask])
db = np.dot(fixed_img[self.mask], regis_img_rotated_p180[self.mask])
if db > da:
regis_img_estimated = regis_img_rotated_p180
r += 180
Expand Down Expand Up @@ -702,7 +704,7 @@ def _reddychatterji(self, images, class_k, reflection_k):
shift = None # For logger line

# Estimated `corr` metric
corr = np.dot(fixed_img.flatten(), regis_img_estimated.flatten())
corr = np.dot(fixed_img[self.mask], regis_img_estimated[self.mask])
correlations_k[m] = corr

logger.debug(
Expand Down Expand Up @@ -962,9 +964,7 @@ def align(self, classes, reflections, basis_coefficients):
L = self.alignment_src.L

# Instantiate matrices for inner loop, and best results.
_rotations = np.zeros(classes.shape, dtype=self.dtype)
rotations = np.zeros(classes.shape, dtype=self.dtype)
_correlations = np.zeros(classes.shape, dtype=self.dtype)
correlations = np.ones(classes.shape, dtype=self.dtype) * -np.inf
shifts = np.zeros((*classes.shape, 2), dtype=int)

Expand All @@ -986,16 +986,16 @@ def align(self, classes, reflections, basis_coefficients):
# Don't shift the base image
images[1:] = Image(unshifted_images[1:]).shift(s).asnumpy()

rotations[k], _, correlations[k] = self._reddychatterji(
_rotations, _, _correlations = self._reddychatterji(
images, classes[k], reflections[k]
)

# Where corr has improved
# update our rolling best results with this loop.
improved = _correlations > correlations
correlations = np.where(improved, _correlations, correlations)
rotations = np.where(improved, _rotations, rotations)
shifts = np.where(improved[..., np.newaxis], s, shifts)
improved = _correlations > correlations[k]
correlations[k] = np.where(improved, _correlations, correlations[k])
rotations[k] = np.where(improved, _rotations, rotations[k])
shifts[k] = np.where(improved[..., np.newaxis], s, shifts[k])
logger.debug(f"Shift {s} has improved {np.sum(improved)} results")

return rotations, shifts, correlations
Expand Down