def update(self, y0, y1):
if y0.dtype == np.uint8:
y0 = (y0 / 255.0).astype('float32')
y1 = (y1 / 255.0).astype('float32')
self.num_samples += 1
# init structures if not already
if not self.initialized():
self.init_structures(y0.shape)
# check shape didn't change
if self.shape != y0.shape:
msg = 'Shape changed from %s to %s.' % (self.shape, y0.shape)
raise ValueError(msg)
# remember last images
self.last_y0 = y0
self.last_y1 = y1
# The _update_scalar function is typically faster
check_times = False
ts = []
ts.append(time.time())
if check_times:
self._update_vectorial(y0, y1)
ts.append(time.time())
self._update_scalar(y0, y1)
ts.append(time.time())
if check_times:
delta = np.diff(ts)
msg = 'Update times: vect %5.3f scal %5.3f seconds' % (delta[0],
delta[1])
logger.info(msg)
def merge(self, other):
""" Merges the values obtained by "other" with ours. """
if not other.initialized():
# nothing to do
return
if not self.initialized() and other.initialized():
# Let's initialized like the other
self.init_structures(other.shape)
self.num_samples = other.num_samples
self.neig_esimmin_score = other.neig_esimmin_score
if self.inference_method == DiffeomorphismEstimatorFaster.Similarity:
self.neig_esim_score = other.neig_esim_score
if self.inference_method == DiffeomorphismEstimatorFaster.Order:
self.neig_eord_score = other.neig_eord_score
return
if self.inference_method != other.inference_method:
raise ValueError()
assert other.initialized(), "Can only merge initialized structures"
logger.info('merging %s + %s samples' %
(self.num_samples, other.num_samples))
self.num_samples += other.num_samples
self.neig_esimmin_score += other.neig_esimmin_score
if self.inference_method == DiffeomorphismEstimatorFaster.Order:
self.neig_eord_score += other.neig_eord_score
elif self.inference_method == DiffeomorphismEstimatorFaster.Similarity:
self.neig_esim_score += other.neig_esim_score
else:
assert False
def update(self, y0, y1):
if y0.dtype == np.uint8:
y0 = (y0 / 255.0).astype('float32')
y1 = (y1 / 255.0).astype('float32')
self.num_samples += 1
# init structures if not already
if not self.initialized():
self.init_structures(y0.shape)
# check shape didn't change
if self.shape != y0.shape:
msg = 'Shape changed from %s to %s.' % (self.shape, y0.shape)
raise ValueError(msg)
# remember last images
self.last_y0 = y0
self.last_y1 = y1
# The _update_scalar function is typically faster
check_times = False
ts = []
ts.append(time.time())
if check_times:
self._update_vectorial(y0, y1)
ts.append(time.time())
self._update_scalar(y0, y1)
ts.append(time.time())
if check_times:
delta = np.diff(ts)
msg = 'Update times: vect %5.3f scal %5.3f seconds' % (delta[0], delta[1])
logger.info(msg)
def merge(self, other):
""" Merges the values obtained by "other" with ours. """
if not other.initialized():
# nothing to do
return
if not self.initialized() and other.initialized():
# Let's initialized like the other
self.init_structures(other.shape)
self.num_samples = other.num_samples
self.neig_esimmin_score = other.neig_esimmin_score
if self.inference_method == DiffeomorphismEstimatorFaster.Similarity:
self.neig_esim_score = other.neig_esim_score
if self.inference_method == DiffeomorphismEstimatorFaster.Order:
self.neig_eord_score = other.neig_eord_score
return
if self.inference_method != other.inference_method:
raise ValueError()
assert other.initialized(), "Can only merge initialized structures"
logger.info('merging %s + %s samples' % (self.num_samples, other.num_samples))
self.num_samples += other.num_samples
self.neig_esimmin_score += other.neig_esimmin_score
if self.inference_method == DiffeomorphismEstimatorFaster.Order:
self.neig_eord_score += other.neig_eord_score
elif self.inference_method == DiffeomorphismEstimatorFaster.Similarity:
self.neig_esim_score += other.neig_esim_score
else:
assert False
def init_structures(self, shape):
self.shape = shape
self.nsensels = shape[0] * shape[1]
self.ydd = np.zeros(shape, dtype='float32')
# for each sensel, create an area
self.lengths = np.ceil(self.max_displ *
np.array(self.shape)).astype('int32')
# print(' Field Shape: %s' % str(self.shape))
# print(' Fraction: %s' % str(self.max_displ))
# print(' Search area: %s' % str(self.lengths))
self.neighbor_coords = [None] * self.nsensels
self.neighbor_indices = [None] * self.nsensels
self.neighbor_indices_flat = [None] * self.nsensels
self.neighbor_similarity_flat = [None] * self.nsensels
self.neighbor_similarity_best = np.zeros(self.nsensels,
dtype='float32')
self.neighbor_argsort_flat = [None] * self.nsensels
self.neighbor_num_bestmatch_flat = [None] * self.nsensels
self.flattening = Flattening.by_rows(shape)
logger.info('Creating structure shape %s lengths %s' %
(self.shape, self.lengths))
cmg = cmap(self.lengths)
for coord in coords_iterate(self.shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
cm[:, :, 0] += coord[0]
cm[:, :, 1] += coord[1]
cm[:, :, 0] = cm[:, :, 0] % self.shape[0]
cm[:, :, 1] = cm[:, :, 1] % self.shape[1]
self.neighbor_coords[k] = cm
indices = np.zeros(self.lengths, 'int32')
for a, b in coords_iterate(indices.shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
self.neighbor_indices[k] = indices
self.neighbor_indices_flat[k] = np.array(indices.flat)
self.neighbor_similarity_flat[k] = np.zeros(
indices.size, 'float32')
self.neighbor_argsort_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_num_bestmatch_flat[k] = np.zeros(
indices.size, 'uint')
def init_structures(self, shape):
self.shape = shape
self.nsensels = shape[0] * shape[1]
self.ydd = np.zeros(shape, dtype='float32')
# for each sensel, create an area
self.lengths = np.ceil(self.max_displ *
np.array(self.shape)).astype('int32')
# print(' Field Shape: %s' % str(self.shape))
# print(' Fraction: %s' % str(self.max_displ))
# print(' Search area: %s' % str(self.lengths))
self.neighbor_coords = [None] * self.nsensels
self.neighbor_indices = [None] * self.nsensels
self.neighbor_indices_flat = [None] * self.nsensels
self.neighbor_similarity_flat = [None] * self.nsensels
self.neighbor_similarity_best = np.zeros(self.nsensels, dtype='float32')
self.neighbor_argsort_flat = [None] * self.nsensels
self.neighbor_num_bestmatch_flat = [None] * self.nsensels
self.flattening = Flattening.by_rows(shape)
logger.info('Creating structure shape %s lengths %s' %
(self.shape, self.lengths))
cmg = cmap(self.lengths)
for coord in coords_iterate(self.shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
cm[:, :, 0] += coord[0]
cm[:, :, 1] += coord[1]
cm[:, :, 0] = cm[:, :, 0] % self.shape[0]
cm[:, :, 1] = cm[:, :, 1] % self.shape[1]
self.neighbor_coords[k] = cm
indices = np.zeros(self.lengths, 'int32')
for a, b in coords_iterate(indices.shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
self.neighbor_indices[k] = indices
self.neighbor_indices_flat[k] = np.array(indices.flat)
self.neighbor_similarity_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_argsort_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_num_bestmatch_flat[k] = np.zeros(indices.size, 'uint')
def get_value(self):
'''
Find maximum likelihood estimate for diffeomorphism looking
at each pixel singularly.
Returns a Diffeomorphism2D.
'''
if not self.initialized():
msg = 'No data seen yet'
raise Diffeo2dEstimatorInterface.NotReady(msg)
maximum_likelihood_index = np.zeros(self.shape, dtype='int32')
variance = np.zeros(self.shape, dtype='float32')
E2 = np.zeros(self.shape, dtype='float32')
E3 = np.zeros(self.shape, dtype='float32')
E4 = np.zeros(self.shape, dtype='float32')
num_problems = 0
i = 0
# for each coordinate
for c in coords_iterate(self.shape):
# find index in flat array
k = self.flattening.cell2index[c]
# Look at the average similarities of the neihgbors
sim = self.neighbor_similarity_flat[k]
sim_min = sim.min()
sim_max = sim.max()
if sim_max == sim_min:
# if all the neighbors have the same similarity
best_index = 0
variance[c] = 0 # minimum information
maximum_likelihood_index[c] = best_index
else:
best = np.argmin(sim)
best_index = self.neighbor_indices_flat[k][best]
# uncertainty ~= similarity of the best pixel
variance[c] = sim[best]
maximum_likelihood_index[c] = best_index
E2[c] = self.neighbor_similarity_best[k] / self.num_samples
# Best match error
E3[c] = np.min(
self.neighbor_num_bestmatch_flat[k]) / self.num_samples
E4[c] = np.min(self.neighbor_argsort_flat[k]) / self.num_samples
i += 1
d = self.flattening.flat2coords(maximum_likelihood_index)
if num_problems > 0:
logger.info('Warning, %d were not informative.' % num_problems)
pass
# normalization for this variance measure
vmin = variance.min()
variance = variance - vmin
vmax = variance.max()
if vmax > 0:
variance *= (1 / vmax)
# return maximum likelihood plus uncertainty measure
return Diffeomorphism2D(d, variance) # , E2, E3, E4)
def get_value(self):
'''
Find maximum likelihood estimate for diffeomorphism looking
at each pixel singularly.
Returns a Diffeomorphism2D.
'''
if not self.initialized():
msg = 'No data seen yet'
raise Diffeo2dEstimatorInterface.NotReady(msg)
maximum_likelihood_index = np.zeros(self.shape, dtype='int32')
variance = np.zeros(self.shape, dtype='float32')
E2 = np.zeros(self.shape, dtype='float32')
E3 = np.zeros(self.shape, dtype='float32')
E4 = np.zeros(self.shape, dtype='float32')
num_problems = 0
i = 0
# for each coordinate
for c in coords_iterate(self.shape):
# find index in flat array
k = self.flattening.cell2index[c]
# Look at the average similarities of the neihgbors
sim = self.neighbor_similarity_flat[k]
sim_min = sim.min()
sim_max = sim.max()
if sim_max == sim_min:
# if all the neighbors have the same similarity
best_index = 0
variance[c] = 0 # minimum information
maximum_likelihood_index[c] = best_index
else:
best = np.argmin(sim)
best_index = self.neighbor_indices_flat[k][best]
# uncertainty ~= similarity of the best pixel
variance[c] = sim[best]
maximum_likelihood_index[c] = best_index
E2[c] = self.neighbor_similarity_best[k] / self.num_samples
# Best match error
E3[c] = np.min(self.neighbor_num_bestmatch_flat[k]) / self.num_samples
E4[c] = np.min(self.neighbor_argsort_flat[k]) / self.num_samples
i += 1
d = self.flattening.flat2coords(maximum_likelihood_index)
if num_problems > 0:
logger.info('Warning, %d were not informative.' % num_problems)
pass
# normalization for this variance measure
vmin = variance.min()
variance = variance - vmin
vmax = variance.max()
if vmax > 0:
variance *= (1 / vmax)
# return maximum likelihood plus uncertainty measure
return Diffeomorphism2D(d, variance) # , E2, E3, E4)
