def _train(self,
images,
increment=False,
group=None,
bounding_box_group_glob=None,
verbose=False,
batch_size=None):
r"""
"""
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
image_batches = batch(images, batch_size)
else:
image_batches = [list(images)]
for k, image_batch in enumerate(image_batches):
if k == 0:
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn(
'No reference shape was provided. The '
'mean of the first batch will be the '
'reference shape. If the batch mean is '
'not representative of the true mean, '
'this may cause issues.', MenpoFitBuilderWarning)
self.reference_shape = compute_reference_shape(
[i.landmarks[group].lms for i in image_batch],
self.diagonal,
verbose=verbose)
# We set landmarks on the images to archive the perturbations, so
# when the default 'None' is used, we need to grab the actual
# label to sort out the ambiguity
if group is None:
group = image_batch[0].landmarks.group_labels[0]
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print('Computing batch {}'.format(k))
# Train each batch
self._train_batch(image_batch,
increment=increment,
group=group,
bounding_box_group_glob=bounding_box_group_glob,
verbose=verbose)
def _train(
self, template, shapes, increment=False, group=None, shape_forgetting_factor=1.0, verbose=False, batch_size=None
):
r"""
"""
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
shape_batches = batch(shapes, batch_size)
else:
shape_batches = [list(shapes)]
for k, shape_batch in enumerate(shape_batches):
if k == 0:
# Rescale the template the reference shape
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn(
"No reference shape was provided. The "
"mean of the first batch will be the "
"reference shape. If the batch mean is "
"not representative of the true mean, "
"this may cause issues.",
MenpoFitBuilderWarning,
)
checks.check_trilist(shape_batch[0], self.transform)
self.reference_shape = compute_reference_shape(shape_batch, self.diagonal, verbose=verbose)
# Rescale the template the reference shape
template = template.rescale_to_pointcloud(self.reference_shape, group=group)
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print("Computing batch {}".format(k))
# Train each batch
self._train_batch(
template,
shape_batch,
increment=increment,
group=group,
shape_forgetting_factor=shape_forgetting_factor,
verbose=verbose,
)
def _train(self, images, increment=False, group=None, bounding_box_group_glob=None, verbose=False, batch_size=None):
r"""
"""
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
image_batches = batch(images, batch_size)
else:
image_batches = [list(images)]
for k, image_batch in enumerate(image_batches):
if k == 0:
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn(
"No reference shape was provided. The "
"mean of the first batch will be the "
"reference shape. If the batch mean is "
"not representative of the true mean, "
"this may cause issues.",
MenpoFitBuilderWarning,
)
self.reference_shape = compute_reference_shape(
[i.landmarks[group].lms for i in image_batch], self.diagonal, verbose=verbose
)
# We set landmarks on the images to archive the perturbations, so
# when the default 'None' is used, we need to grab the actual
# label to sort out the ambiguity
if group is None:
group = image_batch[0].landmarks.group_labels[0]
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print("Computing batch {}".format(k))
# Train each batch
self._train_batch(
image_batch,
increment=increment,
group=group,
bounding_box_group_glob=bounding_box_group_glob,
verbose=verbose,
)
def _train(self,
images,
increment=False,
group=None,
verbose=False,
shape_forgetting_factor=1.0,
batch_size=None):
r"""
"""
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
image_batches = batch(images, batch_size)
else:
image_batches = [list(images)]
for k, image_batch in enumerate(image_batches):
if k == 0:
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn(
'No reference shape was provided. The '
'mean of the first batch will be the '
'reference shape. If the batch mean is '
'not representative of the true mean, '
'this may cause issues.', MenpoFitBuilderWarning)
self.reference_shape = compute_reference_shape(
[i.landmarks[group].lms for i in image_batch],
self.diagonal,
verbose=verbose)
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print('Computing batch {}'.format(k))
# Train each batch
self._train_batch(image_batch,
increment=increment,
group=group,
shape_forgetting_factor=shape_forgetting_factor,
verbose=verbose)
def _train(self, images, increment=False, group=None,
shape_forgetting_factor=1.0, appearance_forgetting_factor=1.0,
verbose=False, batch_size=None):
r"""
"""
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
image_batches = batch(images, batch_size)
else:
image_batches = [list(images)]
for k, image_batch in enumerate(image_batches):
if k == 0:
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn('No reference shape was provided. The '
'mean of the first batch will be the '
'reference shape. If the batch mean is '
'not representative of the true mean, '
'this may cause issues.',
MenpoFitBuilderWarning)
checks.check_landmark_trilist(image_batch[0],
self.transform, group=group)
self.reference_shape = compute_reference_shape(
[i.landmarks[group].lms for i in image_batch],
self.diagonal, verbose=verbose)
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print('Computing batch {}'.format(k))
# Train each batch
self._train_batch(
image_batch, increment=increment, group=group,
shape_forgetting_factor=shape_forgetting_factor,
appearance_forgetting_factor=appearance_forgetting_factor,
verbose=verbose)
def _train(self, template, shapes, increment=False, group=None,
shape_forgetting_factor=1.0, verbose=False, batch_size=None):
# If batch_size is not None, then we may have a generator, else we
# assume we have a list.
if batch_size is not None:
# Create a generator of fixed sized batches. Will still work even
# on an infinite list.
shape_batches = batch(shapes, batch_size)
else:
shape_batches = [list(shapes)]
for k, shape_batch in enumerate(shape_batches):
if k == 0:
# Rescale the template the reference shape
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first
# batch
if batch_size is not None:
warnings.warn('No reference shape was provided. The '
'mean of the first batch will be the '
'reference shape. If the batch mean is '
'not representative of the true mean, '
'this may cause issues.',
MenpoFitBuilderWarning)
checks.check_trilist(shape_batch[0], self.transform)
self.reference_shape = compute_reference_shape(
shape_batch, self.diagonal, verbose=verbose)
# Rescale the template the reference shape
template = template.rescale_to_pointcloud(
self.reference_shape, group=group)
# After the first batch, we are incrementing the model
if k > 0:
increment = True
if verbose:
print('Computing batch {}'.format(k))
# Train each batch
self._train_batch(template, shape_batch, increment=increment,
group=group,
shape_forgetting_factor=shape_forgetting_factor,
verbose=verbose)
