def _warp_images(self, images, shapes, reference_shape, scale_index,
prefix, verbose):
scaled_images = scale_images(images,
self.scales[scale_index],
prefix=prefix,
verbose=verbose)
warpped, landmarkmapping, timages = warp_images(
scaled_images, self.reference_frame, self.transforms,
self.scales[scale_index])
if self.scales[scale_index] >= 1.0:
self.mapping = [landmarkmapping, timages]
return warpped
def _train(self, images, group=None, verbose=False):
checks.check_landmark_trilist(images[0], self.transform, group=group)
self.reference_shape = compute_reference_shape(
[i.landmarks[group] for i in images],
self.diagonal,
verbose=verbose)
# normalize images
images = rescale_images_to_reference_shape(images,
group,
self.reference_shape,
verbose=verbose)
if self.sigma:
images = [fsmooth(i, self.sigma) for i in images]
# Build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = images
elif j == 0 or self.holistic_features[
j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(images,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images,
self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group] for i in scaled_images]
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_images = self._warp_images(scaled_images, scale_shapes,
scaled_reference_shape, j,
scale_prefix, verbose)
# obtain appearance model
if verbose:
print_dynamic(
'{}Building appearance model'.format(scale_prefix))
appearance_model = PCAModel(warped_images)
# trim appearance model if required
if self.max_appearance_components[j] is not None:
appearance_model.trim_components(
self.max_appearance_components[j])
# add appearance model to the list
self.appearance_models.append(appearance_model)
expert_ensemble = self.expert_ensemble_cls[j](
images=scaled_images,
shapes=scale_shapes,
patch_shape=self.patch_shape[j],
patch_normalisation=self.patch_normalisation,
cosine_mask=self.cosine_mask,
context_shape=self.context_shape[j],
sample_offsets=self.sample_offsets,
prefix=scale_prefix,
verbose=verbose)
self.expert_ensembles.append(expert_ensemble)
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
def _train_batch(
self, template, shape_batch, increment=False, group=None, shape_forgetting_factor=1.0, verbose=False
):
r"""
Builds an Active Template Model from a list of landmarked images.
"""
# build models at each scale
if verbose:
print_dynamic("- Building models\n")
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = " - Scale {}: ".format(j)
else:
scale_prefix = " - "
else:
scale_prefix = None
# Handle features
if j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(
[template], self.holistic_features[j], prefix=scale_prefix, verbose=verbose
)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j], prefix=scale_prefix, verbose=verbose)
# Extract potentially rescaled shapes
scale_transform = Scale(scale_factor=self.scales[j], n_dims=2)
scale_shapes = [scale_transform.apply(s) for s in shape_batch]
else:
scaled_images = feature_images
scale_shapes = shape_batch
# Build the shape model
if verbose:
print_dynamic("{}Building shape model".format(scale_prefix))
if not increment:
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
else:
self._increment_shape_model(scale_shapes, j, forgetting_factor=shape_forgetting_factor)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(self.reference_shape)
warped_template = self._warp_template(
scaled_images[0], group, scaled_reference_shape, j, scale_prefix, verbose
)
self.warped_templates.append(warped_template[0])
if verbose:
print_dynamic("{}Done\n".format(scale_prefix))
def _train_batch(self, image_batch, increment=False, group=None,
bounding_box_group_glob=None, verbose=False):
# Rescale images wrt the scale factor between the existing
# reference_shape and their ground truth (group) shapes
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape,
verbose=verbose)
# Create a callable that generates perturbations of the bounding boxes
# of the provided images.
generated_bb_func = generate_perturbations_from_gt(
image_batch, self.n_perturbations,
self._perturb_from_gt_bounding_box, gt_group=group,
bb_group_glob=bounding_box_group_glob, verbose=verbose)
# For each scale (low --> high)
for j in range(self.n_scales):
# Print progress if asked
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Extract features. Features are extracted only if we are at the
# first scale or if the features of the current scale are different
# than the ones extracted at the previous scale.
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif (j == 0 or
self.holistic_features[j] != self.holistic_features[j - 1]):
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# Rescale images according to scales. Note that scale_images is smart
# enough in order not to rescale the images if the current scale
# factor equals to 1.
scaled_images, scale_transforms = scale_images(
feature_images, self.scales[j], prefix=scale_prefix,
return_transforms=True, verbose=verbose)
# Extract scaled ground truth shapes for current scale
scaled_shapes = [i.landmarks[group] for i in scaled_images]
# Get shape estimations of current scale. If we are at the first
# scale, this is done by aligning the reference shape with the
# perturbed bounding boxes. If we are at the rest of the scales,
# then the current shapes are attached on the scaled_images with
# key '__sdm_current_shape_{}'.
current_shapes = []
if j == 0:
# At the first scale, the current shapes are created by aligning
# the reference shape to the perturbed bounding boxes.
msg = '{}Aligning reference shape with bounding boxes.'.format(
scale_prefix)
wrap = partial(print_progress, prefix=msg,
end_with_newline=False, verbose=verbose)
# Extract perturbations at the very bottom level
for ii in wrap(scaled_images):
c_shapes = []
for bbox in generated_bb_func(ii):
c_s = align_shape_with_bounding_box(
self.reference_shape, bbox)
c_shapes.append(c_s)
current_shapes.append(c_shapes)
else:
# At the rest of the scales, extract the current shapes that
# were attached to the images
msg = '{}Extracting shape estimations from previous ' \
'scale.'.format(scale_prefix)
wrap = partial(print_progress, prefix=msg,
end_with_newline=False, verbose=verbose)
for ii in wrap(scaled_images):
c_shapes = []
for k in list(range(self.n_perturbations)):
c_key = '__sdm_current_shape_{}'.format(k)
c_shapes.append(ii.landmarks[c_key])
current_shapes.append(c_shapes)
# Train supervised descent algorithm. This returns the shape
# estimations for the next scale.
if not increment:
current_shapes = self.algorithms[j].train(
scaled_images, scaled_shapes, current_shapes,
prefix=scale_prefix, verbose=verbose)
else:
current_shapes = self.algorithms[j].increment(
scaled_images, scaled_shapes, current_shapes,
prefix=scale_prefix, verbose=verbose)
# Scale the current shape estimations for the next level. This
# doesn't have to be done for the last scale. The only thing we need
# to do at the last scale is to remove any attached landmarks from
# the training images.
if j < (self.n_scales - 1):
if self.holistic_features[j + 1] != self.holistic_features[j]:
# Features will be extracted, thus attach current_shapes on
# the training images (image_batch)
for jj, image_shapes in enumerate(current_shapes):
for k, shape in enumerate(image_shapes):
c_key = '__sdm_current_shape_{}'.format(k)
image_batch[jj].landmarks[c_key] = \
scale_transforms[jj].apply(shape)
else:
# Features won't be extracted;. the same feature_images will
# be used for the next scale, thus attach current_shapes on
# them.
for jj, image_shapes in enumerate(current_shapes):
for k, shape in enumerate(image_shapes):
c_key = '__sdm_current_shape_{}'.format(k)
feature_images[jj].landmarks[c_key] = \
scale_transforms[jj].apply(shape)
else:
# Check if original training image (image_batch) got some current
# shape estimations attached. If yes, delete them.
if '__sdm_current_shape_0' in image_batch[0].landmarks:
for image in image_batch:
for k in list(range(self.n_perturbations)):
c_key = '__sdm_current_shape_{}'.format(k)
del image.landmarks[c_key]
def _train_batch(self,
image_batch,
increment=False,
group=None,
bounding_box_group_glob=None,
verbose=False):
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(image_batch,
group,
self.reference_shape,
verbose=verbose)
generated_bb_func = generate_perturbations_from_gt(
image_batch,
self.n_perturbations,
self._perturb_from_gt_bounding_box,
gt_group=group,
bb_group_glob=bounding_box_group_glob,
verbose=verbose)
# for each scale (low --> high)
current_shapes = []
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif j == 0 or self.holistic_features[
j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images,
self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract scaled ground truth shapes for current scale
scaled_shapes = [i.landmarks[group].lms for i in scaled_images]
if j == 0:
msg = '{}Aligning reference shape with bounding boxes.'.format(
scale_prefix)
wrap = partial(print_progress,
prefix=msg,
end_with_newline=False,
verbose=verbose)
# Extract perturbations at the very bottom level
for ii in wrap(scaled_images):
c_shapes = []
for bbox in generated_bb_func(ii):
c_s = align_shape_with_bounding_box(
self.reference_shape, bbox)
c_shapes.append(c_s)
current_shapes.append(c_shapes)
# train supervised descent algorithm
if not increment:
current_shapes = self.algorithms[j].train(scaled_images,
scaled_shapes,
current_shapes,
prefix=scale_prefix,
verbose=verbose)
else:
current_shapes = self.algorithms[j].increment(
scaled_images,
scaled_shapes,
current_shapes,
prefix=scale_prefix,
verbose=verbose)
# Scale current shapes to next resolution, don't bother
# scaling final level
if j != (self.n_scales - 1):
transform = Scale(self.scales[j + 1] / self.scales[j],
n_dims=2)
for image_shapes in current_shapes:
for k, shape in enumerate(image_shapes):
image_shapes[k] = transform.apply(shape)
def _train_batch(self, image_batch, increment=False, group=None,
shape_forgetting_factor=1.0, verbose=False):
# normalize images
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape, verbose=verbose)
# build models at each scale
if verbose:
print_dynamic('- Training models\n')
# for each level (low --> high)
for i in range(self.n_scales):
if verbose:
if self.n_scales > 1:
prefix = ' - Scale {}: '.format(i)
else:
prefix = ' - '
else:
prefix = None
# Handle holistic features
if i == 0 and self.holistic_features[i] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif i == 0 or self.holistic_features[i] is not self.holistic_features[i - 1]:
# compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[i],
prefix=prefix,
verbose=verbose)
# handle scales
if self.scales[i] != 1:
# scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images,
self.scales[i],
prefix=prefix,
verbose=verbose)
else:
scaled_images = feature_images
# extract scaled shapes
scaled_shapes = [image.landmarks[group]
for image in scaled_images]
# train shape model
if verbose:
print_dynamic('{}Training shape model'.format(prefix))
if not increment:
shape_model = self._build_shape_model(scaled_shapes, i)
self.shape_models.append(shape_model)
else:
self._increment_shape_model(
scaled_shapes, i, forgetting_factor=shape_forgetting_factor)
# train expert ensemble
if verbose:
print_dynamic('{}Training expert ensemble'.format(prefix))
if increment:
self.expert_ensembles[i].increment(scaled_images,
scaled_shapes,
prefix=prefix,
verbose=verbose)
else:
expert_ensemble = self.expert_ensemble_cls[i](
images=scaled_images, shapes=scaled_shapes,
patch_shape=self.patch_shape[i],
patch_normalisation=self.patch_normalisation,
cosine_mask=self.cosine_mask,
context_shape=self.context_shape[i],
sample_offsets=self.sample_offsets,
prefix=prefix, verbose=verbose)
self.expert_ensembles.append(expert_ensemble)
if verbose:
print_dynamic('{}Done\n'.format(prefix))
def _train(self, images, group=None, verbose=False):
checks.check_landmark_trilist(images[0], self.transform, group=group)
self.reference_shape = compute_reference_shape(
[i.landmarks[group] for i in images],
self.diagonal, verbose=verbose)
# normalize images
images = rescale_images_to_reference_shape(
images, group, self.reference_shape, verbose=verbose)
if self.sigma:
images = [fsmooth(i, self.sigma) for i in images]
# Build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = images
elif j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(images,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group] for i in scaled_images]
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_images = self._warp_images(scaled_images, scale_shapes,
scaled_reference_shape,
j, scale_prefix, verbose)
# obtain appearance model
if verbose:
print_dynamic('{}Building appearance model'.format(
scale_prefix))
appearance_model = PCAModel(warped_images)
# trim appearance model if required
if self.max_appearance_components[j] is not None:
appearance_model.trim_components(
self.max_appearance_components[j])
# add appearance model to the list
self.appearance_models.append(appearance_model)
expert_ensemble = self.expert_ensemble_cls[j](
images=scaled_images, shapes=scale_shapes,
patch_shape=self.patch_shape[j],
patch_normalisation=self.patch_normalisation,
cosine_mask=self.cosine_mask,
context_shape=self.context_shape[j],
sample_offsets=self.sample_offsets,
prefix=scale_prefix, verbose=verbose)
self.expert_ensembles.append(expert_ensemble)
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
def _train(self,
original_images,
group=None,
bounding_box_group_glob=None,
verbose=False):
# Dlib does not support incremental builds, so we must be passed a list
if not isinstance(original_images, list):
original_images = list(original_images)
# We use temporary landmark groups - so we need the group key to not be
# None
if group is None:
group = original_images[0].landmarks.group_labels[0]
# Temporarily store all the bounding boxes for rescaling
for i in original_images:
i.landmarks['__gt_bb'] = i.landmarks[group].lms.bounding_box()
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first batch
self._reference_shape = compute_reference_shape(
[i.landmarks['__gt_bb'].lms for i in original_images],
self.diagonal,
verbose=verbose)
# Rescale images wrt the scale factor between the existing
# reference_shape and their ground truth (group) bboxes
images = rescale_images_to_reference_shape(original_images,
'__gt_bb',
self.reference_shape,
verbose=verbose)
# Scaling is done - remove temporary gt bounding boxes
for i, i2 in zip(original_images, images):
del i.landmarks['__gt_bb']
del i2.landmarks['__gt_bb']
# Create a callable that generates perturbations of the bounding boxes
# of the provided images.
generated_bb_func = generate_perturbations_from_gt(
images,
self.n_perturbations,
self._perturb_from_gt_bounding_box,
gt_group=group,
bb_group_glob=bounding_box_group_glob,
verbose=verbose)
# For each scale (low --> high)
for j in range(self.n_scales):
# Print progress if asked
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Rescale images according to scales. Note that scale_images is smart
# enough in order not to rescale the images if the current scale
# factor equals to 1.
scaled_images, scale_transforms = scale_images(
images,
self.scales[j],
prefix=scale_prefix,
return_transforms=True,
verbose=verbose)
# Get bbox estimations of current scale. If we are at the first
# scale, this is done by using generated_bb_func. If we are at the
# rest of the scales, then the current bboxes are attached on the
# scaled_images with key '__ert_current_bbox_{}'.
current_bounding_boxes = []
if j == 0:
# At the first scale, the current bboxes are created by calling
# generated_bb_func.
current_bounding_boxes = [
generated_bb_func(im) for im in scaled_images
]
else:
# At the rest of the scales, extract the current bboxes that
# were attached to the images
msg = '{}Extracting bbox estimations from previous ' \
'scale.'.format(scale_prefix)
wrap = partial(print_progress,
prefix=msg,
end_with_newline=False,
verbose=verbose)
for ii in wrap(scaled_images):
c_bboxes = []
for k in list(range(self.n_perturbations)):
c_key = '__ert_current_bbox_{}'.format(k)
c_bboxes.append(ii.landmarks[c_key].lms)
current_bounding_boxes.append(c_bboxes)
# Extract scaled ground truth shapes for current scale
scaled_gt_shapes = [i.landmarks[group].lms for i in scaled_images]
# Train the Dlib model. This returns the bbox estimations for the
# next scale.
current_bounding_boxes = self.algorithms[j].train(
scaled_images,
scaled_gt_shapes,
current_bounding_boxes,
prefix=scale_prefix,
verbose=verbose)
# Scale the current bbox estimations for the next level. This
# doesn't have to be done for the last scale. The only thing we need
# to do at the last scale is to remove any attached landmarks from
# the training images.
if j < (self.n_scales - 1):
for jj, image_bboxes in enumerate(current_bounding_boxes):
for k, bbox in enumerate(image_bboxes):
c_key = '__ert_current_bbox_{}'.format(k)
images[jj].landmarks[c_key] = \
scale_transforms[jj].apply(bbox)
def _train(self,
original_images,
group=None,
bounding_box_group_glob=None,
verbose=False):
r"""
"""
# Dlib does not support incremental builds, so we must be passed a list
if not isinstance(original_images, list):
original_images = list(original_images)
# We use temporary landmark groups - so we need the group key to not be
# None
if group is None:
group = original_images[0].landmarks.group_labels[0]
# Temporarily store all the bounding boxes for rescaling
for i in original_images:
i.landmarks['__gt_bb'] = i.landmarks[group].lms.bounding_box()
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first batch
self.reference_shape = compute_reference_shape(
[i.landmarks['__gt_bb'].lms for i in original_images],
self.diagonal,
verbose=verbose)
# Rescale to existing reference shape
images = rescale_images_to_reference_shape(original_images,
'__gt_bb',
self.reference_shape,
verbose=verbose)
# Scaling is done - remove temporary gt bounding boxes
for i, i2 in zip(original_images, images):
del i.landmarks['__gt_bb']
del i2.landmarks['__gt_bb']
generated_bb_func = generate_perturbations_from_gt(
images,
self.n_perturbations,
self._perturb_from_gt_bounding_box,
gt_group=group,
bb_group_glob=bounding_box_group_glob,
verbose=verbose)
# for each scale (low --> high)
current_bounding_boxes = []
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(images,
self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = images
if j == 0:
current_bounding_boxes = [
generated_bb_func(im) for im in scaled_images
]
# Extract scaled ground truth shapes for current scale
scaled_gt_shapes = [i.landmarks[group].lms for i in scaled_images]
# Train the Dlib model
current_bounding_boxes = self.algorithms[j].train(
scaled_images,
scaled_gt_shapes,
current_bounding_boxes,
prefix=scale_prefix,
verbose=verbose)
# Scale current shapes to next resolution, don't bother
# scaling final level
if j != (self.n_scales - 1):
transform = Scale(self.scales[j + 1] / self.scales[j],
n_dims=2)
for bboxes in current_bounding_boxes:
for bb in enumerate(bboxes):
bboxes[k] = transform.apply(bb)
def _train(self, original_images, group=None, bounding_box_group_glob=None,
verbose=False):
# Dlib does not support incremental builds, so we must be passed a list
if not isinstance(original_images, list):
original_images = list(original_images)
# We use temporary landmark groups - so we need the group key to not be
# None
if group is None:
group = original_images[0].landmarks.group_labels[0]
# Temporarily store all the bounding boxes for rescaling
for i in original_images:
i.landmarks['__gt_bb'] = i.landmarks[group].bounding_box()
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first batch
self._reference_shape = compute_reference_shape(
[i.landmarks['__gt_bb'] for i in original_images],
self.diagonal, verbose=verbose)
# Rescale images wrt the scale factor between the existing
# reference_shape and their ground truth (group) bboxes
images = rescale_images_to_reference_shape(
original_images, '__gt_bb', self.reference_shape,
verbose=verbose)
# Scaling is done - remove temporary gt bounding boxes
for i, i2 in zip(original_images, images):
del i.landmarks['__gt_bb']
del i2.landmarks['__gt_bb']
# Create a callable that generates perturbations of the bounding boxes
# of the provided images.
generated_bb_func = generate_perturbations_from_gt(
images, self.n_perturbations, self._perturb_from_gt_bounding_box,
gt_group=group, bb_group_glob=bounding_box_group_glob,
verbose=verbose)
# For each scale (low --> high)
for j in range(self.n_scales):
# Print progress if asked
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Rescale images according to scales. Note that scale_images is smart
# enough in order not to rescale the images if the current scale
# factor equals to 1.
scaled_images, scale_transforms = scale_images(
images, self.scales[j], prefix=scale_prefix,
return_transforms=True, verbose=verbose)
# Get bbox estimations of current scale. If we are at the first
# scale, this is done by using generated_bb_func. If we are at the
# rest of the scales, then the current bboxes are attached on the
# scaled_images with key '__ert_current_bbox_{}'.
current_bounding_boxes = []
if j == 0:
# At the first scale, the current bboxes are created by calling
# generated_bb_func.
current_bounding_boxes = [generated_bb_func(im)
for im in scaled_images]
else:
# At the rest of the scales, extract the current bboxes that
# were attached to the images
msg = '{}Extracting bbox estimations from previous ' \
'scale.'.format(scale_prefix)
wrap = partial(print_progress, prefix=msg,
end_with_newline=False, verbose=verbose)
for ii in wrap(scaled_images):
c_bboxes = []
for k in list(range(self.n_perturbations)):
c_key = '__ert_current_bbox_{}'.format(k)
c_bboxes.append(ii.landmarks[c_key])
current_bounding_boxes.append(c_bboxes)
# Extract scaled ground truth shapes for current scale
scaled_gt_shapes = [i.landmarks[group] for i in scaled_images]
# Train the Dlib model. This returns the bbox estimations for the
# next scale.
current_bounding_boxes = self.algorithms[j].train(
scaled_images, scaled_gt_shapes, current_bounding_boxes,
prefix=scale_prefix, verbose=verbose)
# Scale the current bbox estimations for the next level. This
# doesn't have to be done for the last scale. The only thing we need
# to do at the last scale is to remove any attached landmarks from
# the training images.
if j < (self.n_scales - 1):
for jj, image_bboxes in enumerate(current_bounding_boxes):
for k, bbox in enumerate(image_bboxes):
c_key = '__ert_current_bbox_{}'.format(k)
images[jj].landmarks[c_key] = \
scale_transforms[jj].apply(bbox)
def _train_batch(self, image_batch, increment=False, group=None,
verbose=False, shape_forgetting_factor=1.0,
appearance_forgetting_factor=1.0):
r"""
Builds an Active Appearance Model from a list of landmarked images.
Parameters
----------
images : list of :map:`MaskedImage`
The set of landmarked images from which to build the AAM.
group : `string`, optional
The key of the landmark set that should be used. If ``None``,
and if there is only one set of landmarks, this set will be used.
verbose : `boolean`, optional
Flag that controls information and progress printing.
Returns
-------
aam : :map:`AAM`
The AAM object. Shape and appearance models are stored from
lowest to highest scale
"""
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape,
verbose=verbose)
# build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group].lms for i in scaled_images]
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
else:
self._increment_shape_model(
scale_shapes, j, forgetting_factor=shape_forgetting_factor)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_images = self._warp_images(scaled_images, scale_shapes,
scaled_reference_shape,
j, scale_prefix, verbose)
# obtain appearance model
if verbose:
print_dynamic('{}Building appearance model'.format(
scale_prefix))
if not increment:
appearance_model = PCAModel(warped_images)
# trim appearance model if required
if self.max_appearance_components is not None:
appearance_model.trim_components(
self.max_appearance_components[j])
# add appearance model to the list
self.appearance_models.append(appearance_model)
else:
# increment appearance model
self.appearance_models[j].increment(
warped_images,
forgetting_factor=appearance_forgetting_factor)
# trim appearance model if required
if self.max_appearance_components is not None:
self.appearance_models[j].trim_components(
self.max_appearance_components[j])
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
def _train_batch(self,
image_batch,
increment=False,
group=None,
verbose=False):
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(image_batch,
group,
self.reference_shape,
verbose=verbose)
# If the deformation graph was not provided (None given), then compute
# the MST
if None in self.deformation_graph:
graph_shapes = [i.landmarks[group].lms for i in image_batch]
deformation_mst = _compute_minimum_spanning_tree(graph_shapes,
root_vertex=0,
prefix='- ',
verbose=verbose)
self.deformation_graph = [
deformation_mst if g is None else g
for g in self.deformation_graph
]
# Build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif (j == 0 or self.holistic_features[j]
is not self.holistic_features[j - 1]):
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images,
self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group].lms for i in scaled_images]
# Apply procrustes to align the shapes
aligned_shapes = align_shapes(scale_shapes)
# Build the shape model using the aligned shapes
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
self.shape_models.append(
self._build_shape_model(aligned_shapes,
self.shape_graph[j],
self.max_shape_components[j],
verbose=verbose))
else:
self.shape_models[j].increment(aligned_shapes, verbose=verbose)
# Build the deformation model
if verbose:
print_dynamic(
'{}Building deformation model'.format(scale_prefix))
if self.use_procrustes:
deformation_shapes = aligned_shapes
else:
deformation_shapes = scale_shapes
if not increment:
self.deformation_models.append(
self._build_deformation_model(deformation_shapes,
self.deformation_graph[j],
verbose=verbose))
else:
self.deformation_models[j].increment(deformation_shapes,
verbose=verbose)
# Obtain warped images
warped_images = self._warp_images(scaled_images, scale_shapes, j,
scale_prefix, verbose)
# Build the appearance model
if verbose:
print_dynamic(
'{}Building appearance model'.format(scale_prefix))
if not increment:
self.appearance_models.append(
self._build_appearance_model(
warped_images,
self.appearance_graph[j],
self.n_appearance_components[j],
verbose=verbose))
else:
self._increment_appearance_model(warped_images,
self.appearance_graph[j],
self.appearance_models[j],
verbose=verbose)
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
def _train_batch(self, image_batch, increment=False, group=None,
verbose=False):
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape, verbose=verbose)
# If the deformation graph was not provided (None given), then compute
# the MST
if None in self.deformation_graph:
graph_shapes = [i.landmarks[group] for i in image_batch]
deformation_mst = _compute_minimum_spanning_tree(
graph_shapes, root_vertex=0, prefix='- ', verbose=verbose)
self.deformation_graph = [deformation_mst if g is None else g
for g in self.deformation_graph]
# Build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif (j == 0 or self.holistic_features[j] is not
self.holistic_features[j - 1]):
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group] for i in scaled_images]
# Apply procrustes to align the shapes
aligned_shapes = align_shapes(scale_shapes)
# Build the shape model using the aligned shapes
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
self.shape_models.append(self._build_shape_model(
aligned_shapes, self.shape_graph[j],
self.max_shape_components[j], verbose=verbose))
else:
self.shape_models[j].increment(aligned_shapes, verbose=verbose)
# Build the deformation model
if verbose:
print_dynamic('{}Building deformation model'.format(
scale_prefix))
if self.use_procrustes:
deformation_shapes = aligned_shapes
else:
deformation_shapes = scale_shapes
if not increment:
self.deformation_models.append(self._build_deformation_model(
deformation_shapes, self.deformation_graph[j],
verbose=verbose))
else:
self.deformation_models[j].increment(deformation_shapes,
verbose=verbose)
# Obtain warped images
warped_images = self._warp_images(scaled_images, scale_shapes,
j, scale_prefix, verbose)
# Build the appearance model
if verbose:
print_dynamic('{}Building appearance model'.format(
scale_prefix))
if not increment:
self.appearance_models.append(self._build_appearance_model(
warped_images, self.appearance_graph[j],
self.n_appearance_components[j], verbose=verbose))
else:
self._increment_appearance_model(
warped_images, self.appearance_graph[j],
self.appearance_models[j], verbose=verbose)
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
def _train_batch(self, template, shape_batch, increment=False, group=None,
shape_forgetting_factor=1.0, verbose=False):
r"""
Builds an Active Template Model from a list of landmarked images.
"""
# build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle features
if j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features([template],
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
# Extract potentially rescaled shapes
scale_transform = Scale(scale_factor=self.scales[j],
n_dims=2)
scale_shapes = [scale_transform.apply(s)
for s in shape_batch]
else:
scaled_images = feature_images
scale_shapes = shape_batch
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
if j == 0:
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
else:
self.shape_models.append(deepcopy(shape_model))
else:
self._increment_shape_model(
scale_shapes, self.shape_models[j],
forgetting_factor=shape_forgetting_factor)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_template = self._warp_template(scaled_images[0], group,
scaled_reference_shape,
j, scale_prefix, verbose)
self.warped_templates.append(warped_template[0])
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
# Because we just copy the shape model, we need to wait to trim
# it after building each model. This ensures we can have a different
# number of components per level
for j, sm in enumerate(self.shape_models):
max_sc = self.max_shape_components[j]
if max_sc is not None:
sm.trim_components(max_sc)
def _train_batch(self, image_batch, increment=False, group=None,
verbose=False, shape_forgetting_factor=1.0,
appearance_forgetting_factor=1.0):
r"""
Builds an Active Appearance Model from a list of landmarked images.
Parameters
----------
images : list of :map:`MaskedImage`
The set of landmarked images from which to build the AAM.
group : `string`, optional
The key of the landmark set that should be used. If ``None``,
and if there is only one set of landmarks, this set will be used.
verbose : `boolean`, optional
Flag that controls information and progress printing.
Returns
-------
aam : :map:`AAM`
The AAM object. Shape and appearance models are stored from
lowest to highest scale
"""
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape,
verbose=verbose)
# build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract potentially rescaled shapes
scale_shapes = [i.landmarks[group].lms for i in scaled_images]
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
if j == 0:
shape_model = self._build_shape_model(
scale_shapes, j)
self.shape_models.append(shape_model)
else:
self.shape_models.append(deepcopy(shape_model))
else:
self._increment_shape_model(
scale_shapes, self.shape_models[j],
forgetting_factor=shape_forgetting_factor)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_images = self._warp_images(scaled_images, scale_shapes,
scaled_reference_shape,
j, scale_prefix, verbose)
# obtain appearance model
if verbose:
print_dynamic('{}Building appearance model'.format(
scale_prefix))
if not increment:
appearance_model = PCAModel(warped_images)
# trim appearance model if required
if self.max_appearance_components is not None:
appearance_model.trim_components(
self.max_appearance_components[j])
# add appearance model to the list
self.appearance_models.append(appearance_model)
else:
# increment appearance model
self.appearance_models[j].increment(
warped_images,
forgetting_factor=appearance_forgetting_factor)
# trim appearance model if required
if self.max_appearance_components is not None:
self.appearance_models[j].trim_components(
self.max_appearance_components[j])
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
# Because we just copy the shape model, we need to wait to trim
# it after building each model. This ensures we can have a different
# number of components per level
for j, sm in enumerate(self.shape_models):
max_sc = self.max_shape_components[j]
if max_sc is not None:
sm.trim_components(max_sc)
def _train(self, original_images, group=None, bounding_box_group_glob=None,
verbose=False):
r"""
"""
# Dlib does not support incremental builds, so we must be passed a list
if not isinstance(original_images, list):
original_images = list(original_images)
# We use temporary landmark groups - so we need the group key to not be
# None
if group is None:
group = original_images[0].landmarks.group_labels[0]
# Temporarily store all the bounding boxes for rescaling
for i in original_images:
i.landmarks['__gt_bb'] = i.landmarks[group].lms.bounding_box()
if self.reference_shape is None:
# If no reference shape was given, use the mean of the first batch
self.reference_shape = compute_reference_shape(
[i.landmarks['__gt_bb'].lms for i in original_images],
self.diagonal, verbose=verbose)
# Rescale to existing reference shape
images = rescale_images_to_reference_shape(
original_images, '__gt_bb', self.reference_shape,
verbose=verbose)
# Scaling is done - remove temporary gt bounding boxes
for i, i2 in zip(original_images, images):
del i.landmarks['__gt_bb']
del i2.landmarks['__gt_bb']
generated_bb_func = generate_perturbations_from_gt(
images, self.n_perturbations, self._perturb_from_gt_bounding_box,
gt_group=group, bb_group_glob=bounding_box_group_glob,
verbose=verbose)
# for each scale (low --> high)
current_bounding_boxes = []
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = images
if j == 0:
current_bounding_boxes = [generated_bb_func(im)
for im in scaled_images]
# Extract scaled ground truth shapes for current scale
scaled_gt_shapes = [i.landmarks[group].lms for i in scaled_images]
# Train the Dlib model
current_bounding_boxes = self.algorithms[j].train(
scaled_images, scaled_gt_shapes, current_bounding_boxes,
prefix=scale_prefix, verbose=verbose)
# Scale current shapes to next resolution, don't bother
# scaling final level
if j != (self.n_scales - 1):
transform = Scale(self.scales[j + 1] / self.scales[j],
n_dims=2)
for bboxes in current_bounding_boxes:
for bb in bboxes:
transform.apply_inplace(bb)
def _train_batch(self, image_batch, increment=False, group=None,
bounding_box_group_glob=None, verbose=False):
# Rescale to existing reference shape
image_batch = rescale_images_to_reference_shape(
image_batch, group, self.reference_shape,
verbose=verbose)
generated_bb_func = generate_perturbations_from_gt(
image_batch, self.n_perturbations,
self._perturb_from_gt_bounding_box, gt_group=group,
bb_group_glob=bounding_box_group_glob, verbose=verbose)
# for each scale (low --> high)
current_shapes = []
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle holistic features
if j == 0 and self.holistic_features[j] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
else:
scaled_images = feature_images
# Extract scaled ground truth shapes for current scale
scaled_shapes = [i.landmarks[group].lms for i in scaled_images]
if j == 0:
msg = '{}Aligning reference shape with bounding boxes.'.format(
scale_prefix)
wrap = partial(print_progress, prefix=msg,
end_with_newline=False, verbose=verbose)
# Extract perturbations at the very bottom level
for ii in wrap(scaled_images):
c_shapes = []
for bbox in generated_bb_func(ii):
c_s = align_shape_with_bounding_box(
self.reference_shape, bbox)
c_shapes.append(c_s)
current_shapes.append(c_shapes)
# train supervised descent algorithm
if not increment:
current_shapes = self.algorithms[j].train(
scaled_images, scaled_shapes, current_shapes,
prefix=scale_prefix, verbose=verbose)
else:
current_shapes = self.algorithms[j].increment(
scaled_images, scaled_shapes, current_shapes,
prefix=scale_prefix, verbose=verbose)
# Scale current shapes to next resolution, don't bother
# scaling final level
if j != (self.n_scales - 1):
transform = Scale(self.scales[j + 1] / self.scales[j],
n_dims=2)
for image_shapes in current_shapes:
for shape in image_shapes:
transform.apply_inplace(shape)
def _train_batch(self,
image_batch,
increment=False,
group=None,
shape_forgetting_factor=1.0,
verbose=False):
# normalize images
image_batch = rescale_images_to_reference_shape(image_batch,
group,
self.reference_shape,
verbose=verbose)
# build models at each scale
if verbose:
print_dynamic('- Training models\n')
# for each level (low --> high)
for i in range(self.n_scales):
if verbose:
if self.n_scales > 1:
prefix = ' - Scale {}: '.format(i)
else:
prefix = ' - '
else:
prefix = None
# Handle holistic features
if i == 0 and self.holistic_features[i] == no_op:
# Saves a lot of memory
feature_images = image_batch
elif i == 0 or self.holistic_features[
i] is not self.holistic_features[i - 1]:
# compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features(image_batch,
self.holistic_features[i],
prefix=prefix,
verbose=verbose)
# handle scales
if self.scales[i] != 1:
# scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images,
self.scales[i],
prefix=prefix,
verbose=verbose)
else:
scaled_images = feature_images
# extract scaled shapes
scaled_shapes = [image.landmarks[group] for image in scaled_images]
# train shape model
if verbose:
print_dynamic('{}Training shape model'.format(prefix))
if not increment:
shape_model = self._build_shape_model(scaled_shapes, i)
self.shape_models.append(shape_model)
else:
self._increment_shape_model(
scaled_shapes,
i,
forgetting_factor=shape_forgetting_factor)
# train expert ensemble
if verbose:
print_dynamic('{}Training expert ensemble'.format(prefix))
if increment:
self.expert_ensembles[i].increment(scaled_images,
scaled_shapes,
prefix=prefix,
verbose=verbose)
else:
expert_ensemble = self.expert_ensemble_cls[i](
images=scaled_images,
shapes=scaled_shapes,
patch_shape=self.patch_shape[i],
patch_normalisation=self.patch_normalisation,
cosine_mask=self.cosine_mask,
context_shape=self.context_shape[i],
sample_offsets=self.sample_offsets,
prefix=prefix,
verbose=verbose)
self.expert_ensembles.append(expert_ensemble)
if verbose:
print_dynamic('{}Done\n'.format(prefix))
def _train_batch(self, template, shape_batch, increment=False, group=None,
shape_forgetting_factor=1.0, verbose=False):
# build models at each scale
if verbose:
print_dynamic('- Building models\n')
feature_images = []
# for each scale (low --> high)
for j in range(self.n_scales):
if verbose:
if len(self.scales) > 1:
scale_prefix = ' - Scale {}: '.format(j)
else:
scale_prefix = ' - '
else:
scale_prefix = None
# Handle features
if j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:
# Compute features only if this is the first pass through
# the loop or the features at this scale are different from
# the features at the previous scale
feature_images = compute_features([template],
self.holistic_features[j],
prefix=scale_prefix,
verbose=verbose)
# handle scales
if self.scales[j] != 1:
# Scale feature images only if scale is different than 1
scaled_images = scale_images(feature_images, self.scales[j],
prefix=scale_prefix,
verbose=verbose)
# Extract potentially rescaled shapes
scale_transform = Scale(scale_factor=self.scales[j],
n_dims=2)
scale_shapes = [scale_transform.apply(s)
for s in shape_batch]
else:
scaled_images = feature_images
scale_shapes = shape_batch
# Build the shape model
if verbose:
print_dynamic('{}Building shape model'.format(scale_prefix))
if not increment:
shape_model = self._build_shape_model(scale_shapes, j)
self.shape_models.append(shape_model)
else:
self._increment_shape_model(
scale_shapes, j, forgetting_factor=shape_forgetting_factor)
# Obtain warped images - we use a scaled version of the
# reference shape, computed here. This is because the mean
# moves when we are incrementing, and we need a consistent
# reference frame.
scaled_reference_shape = Scale(self.scales[j], n_dims=2).apply(
self.reference_shape)
warped_template = self._warp_template(scaled_images[0], group,
scaled_reference_shape,
j, scale_prefix, verbose)
self.warped_templates.append(warped_template[0])
if verbose:
print_dynamic('{}Done\n'.format(scale_prefix))
