def crop_image(img, center, scale, res, base=384):
h = base * scale
t = Translation(
[
res[0] * (-center[0] / h + .5),
res[1] * (-center[1] / h + .5)
]) \
.compose_after(
Scale(
(res[0] / h,
res[1] / h)
)).pseudoinverse()
# Upper left point of original image
ul = np.floor(t.apply([0, 0]))
# Bottom right point of original image
br = np.ceil(t.apply(res).astype(np.int))
# crop and rescale
cimg, trans = img.warp_to_shape(br - ul,
Translation(-(br - ul) / 2 +
(br + ul) / 2),
return_transform=True)
c_scale = np.min(cimg.shape) / np.mean(res)
new_img = cimg.rescale(1 / c_scale).resize(res)
return new_img, trans, c_scale
def test_align_2d_translation():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source and target
estimate = AlignmentTranslation(source, target)
# check the estimates is correct
assert_allclose(translation.h_matrix, estimate.h_matrix)
def test_align_2d_translation_set_h_matrix_raises_notimplemented_error():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and change the target.
estimate.set_h_matrix(translation.h_matrix)
def test_align_2d_translation_set_h_matrix_raises_notimplemented_error():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and change the target.
estimate.set_h_matrix(translation.h_matrix)
def test_align_2d_translation():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source and target
estimate = AlignmentTranslation(source, target)
# check the estimates is correct
assert_allclose(translation.h_matrix, estimate.h_matrix)
def test_align_2d_translation_from_vector_inplace():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and update from_vector
estimate.from_vector_inplace(t_vec)
# check the estimates is correct
assert_allclose(target.points, estimate.target.points)
def test_align_2d_translation_from_vector_inplace():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and update from_vector
estimate._from_vector_inplace(t_vec)
# check the estimates is correct
assert_allclose(target.points, estimate.target.points)
def mask_pc(pc):
t = Translation(-pc.centre())
p = t.apply(pc)
(y1, x1), (y2, x2) = p.bounds()
a, b = np.meshgrid(np.arange(np.floor(y1), np.ceil(y2)),
np.arange(np.floor(x1), np.ceil(x2)))
mask = np.vstack([a.flatten(), b.flatten()]).T
return PointCloud(t.pseudoinverse().apply(mask[matpath(
p.points).contains_points(mask)]).astype(int))
def chain_compose_before_tps_test():
a = PointCloud(np.random.random([10, 2]))
b = PointCloud(np.random.random([10, 2]))
tps = ThinPlateSplines(a, b)
t = Translation([3, 4])
s = Scale([4, 2])
chain = TransformChain([t, s])
chain_mod = chain.compose_before(tps)
points = PointCloud(np.random.random([10, 2]))
manual_res = tps.apply(s.apply(t.apply(points)))
chain_res = chain_mod.apply(points)
assert(np.all(manual_res.points == chain_res.points))
def test_chain_compose_after_inplace_tps():
a = PointCloud(np.random.random([10, 2]))
b = PointCloud(np.random.random([10, 2]))
tps = ThinPlateSplines(a, b)
t = Translation([3, 4])
s = Scale([4, 2])
chain = TransformChain([t, s])
chain.compose_after_inplace(tps)
points = PointCloud(np.random.random([10, 2]))
manual_res = s.apply(t.apply(tps.apply(points)))
chain_res = chain.apply(points)
assert (np.all(manual_res.points == chain_res.points))
def test_align_2d_translation_from_vector():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and update from_vector
new_est = estimate.from_vector(t_vec)
# check the original is unchanged
assert_allclose(estimate.source.points, source.points)
assert_allclose(estimate.target.points, source.points)
# check the new estimate has the source and target correct
assert_allclose(new_est.source.points, source.points)
assert_allclose(new_est.target.points, target.points)
def test_align_2d_translation_from_vector():
t_vec = np.array([1, 2])
translation = Translation(t_vec)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = translation.apply(source)
# estimate the transform from source to source..
estimate = AlignmentTranslation(source, source)
# and update from_vector
new_est = estimate.from_vector(t_vec)
# check the original is unchanged
assert_allclose(estimate.source.points, source.points)
assert_allclose(estimate.target.points, source.points)
# check the new estimate has the source and target correct
assert_allclose(new_est.source.points, source.points)
assert_allclose(new_est.target.points, target.points)
def test_init_from_pointcloud_return_transform():
correct_tr = Translation([5, 5])
pc = correct_tr.apply(PointCloud.init_2d_grid((10, 10)))
im, tr = Image.init_from_pointcloud(pc, return_transform=True)
assert im.shape == (9, 9)
assert_allclose(tr.as_vector(), -correct_tr.as_vector())
def test_translation():
t_vec = np.array([1, 2, 3])
starting_vector = np.random.rand(10, 3)
transform = Translation(t_vec)
transformed = transform.apply(starting_vector)
assert_allclose(starting_vector + t_vec, transformed)
def test_init_from_pointcloud_return_transform():
correct_tr = Translation([5, 5])
pc = correct_tr.apply(PointCloud.init_2d_grid((10, 10)))
im, tr = Image.init_from_pointcloud(pc, return_transform=True)
assert im.shape == (9, 9)
assert_allclose(tr.as_vector(), -correct_tr.as_vector())
def rescale_images_to_reference_shape(images,
group,
reference_shape,
tight_mask=True,
sd=svs_shape,
target_group=None,
verbose=False):
r"""
"""
_has_lms_align = False
_n_align_points = None
_is_mc = False
group_align = group
_db_path = images[0].path.parent
reference_align_shape = reference_shape
n_landmarks = reference_shape.n_points
# Normalize the scaling of all images wrt the reference_shape size
for i in images:
if 'LMS' in i.landmarks.keys():
_has_lms_align = True
i.landmarks['align'] = i.landmarks['LMS']
if not _n_align_points:
_n_align_points = i.landmarks['align'].lms.n_points
if _has_lms_align:
group_align = 'align'
reference_align_shape = PointCloud(
reference_shape.points[:_n_align_points])
reference_shape = PointCloud(reference_shape.points[_n_align_points:])
else:
group_align = '_nicp'
for i in images:
source_shape = TriMesh(reference_shape.points)
_, points_corr = nicp(source_shape, i.landmarks[group].lms)
i.landmarks[group_align] = PointCloud(
i.landmarks[group].lms.points[points_corr])
print(' - Normalising')
normalized_images = [
i.rescale_to_pointcloud(reference_align_shape, group=group_align)
for i in images
]
# Global Parameters
alpha = 30
pdm = 0
lms_shapes = [i.landmarks[group_align].lms for i in normalized_images]
shapes = [i.landmarks[group].lms for i in normalized_images]
n_shapes = len(shapes)
# Align Shapes Using ICP
aligned_shapes, target_shape, _removed_transform, _icp_transform, _icp\
= align_shapes(shapes, reference_shape, lms_shapes=lms_shapes, align_target=reference_align_shape)
# Build Reference Frame from Aligned Shapes
bound_list = []
for s in [reference_shape] + aligned_shapes.tolist():
bmin, bmax = s.bounds()
bound_list.append(bmin)
bound_list.append(bmax)
bound_list.append(np.array([bmin[0], bmax[1]]))
bound_list.append(np.array([bmax[0], bmin[1]]))
bound_list = PointCloud(np.array(bound_list))
scales = np.max(bound_list.points, axis=0) - np.min(bound_list.points,
axis=0)
max_scale = np.max(scales)
bound_list = PointCloud(
np.array([[max_scale, max_scale], [max_scale, 0], [0, max_scale],
[0, 0]]))
reference_frame = build_reference_frame(bound_list, boundary=15)
# Translation between reference shape and aliened shapes
align_centre = target_shape.centre_of_bounds()
align_t = Translation(reference_frame.centre() - align_centre)
_rf_align = Translation(align_centre - reference_frame.centre())
# Set All True Pixels for Mask
reference_frame.mask.pixels = np.ones(reference_frame.mask.pixels.shape,
dtype=np.bool)
# Create Cache Directory
home_dir = os.getcwd()
dir_hex = uuid.uuid1()
sd_path_in = '{}/shape_discriptor'.format(
_db_path) if _db_path else '{}/.cache/{}/sd_training'.format(
home_dir, dir_hex)
sd_path_out = sd_path_in
matE = MatlabExecuter()
mat_code_path = '/vol/atlas/homes/yz4009/gitdev/mfsfdev'
# Skip building svs is path specified
_build_shape_desc(sd_path_in,
normalized_images,
reference_shape,
aligned_shapes,
align_t,
reference_frame,
_icp_transform,
_is_mc=_is_mc,
group=group,
target_align_shape=reference_align_shape,
_shape_desc=sd,
align_group=group_align,
target_group=target_group)
# self._build_trajectory_basis(sample_groups, target_shape,
# aligned_shapes, dense_reference_shape, align_t)
# Call Matlab to Build Flows
if not isfile('{}/result.mat'.format(sd_path_in)):
print(' - Building Shape Flow')
matE.cd(mat_code_path)
ext = 'gif'
isLms = _has_lms_align + 0
isBasis = 0
fstr = 'gpuDevice(1);' \
'addpath(\'{0}/{1}\');' \
'addpath(\'{0}/{2}\');' \
'build_flow(\'{3}\', \'{4}\', \'{5}\', {6}, {7}, ' \
'{8}, \'{3}/{9}\', {10}, {11}, {14}, {15}, {12}, \'{13}\')'.format(
mat_code_path, 'cudafiles', 'tools',
sd_path_in, sd_path_out, 'sd_%04d.{}'.format(ext),
0,
1, n_shapes, 'bas.mat',
alpha, pdm, 30, 'sd_%04d_lms.pts', isBasis, isLms
)
sys.stderr.write(fstr)
sys.stderr.write(fstr.replace('build_flow', 'build_flow_test'))
p = matE.run_function(fstr)
p.wait()
else:
sd_path_out = sd_path_in
# Retrieve Results
mat = sio.loadmat('{}/result.mat'.format(sd_path_out))
_u, _v = mat['u'], mat['v']
# Build Transforms
print(" - Build Transform")
transforms = []
for i in range(n_shapes):
transforms.append(OpticalFlowTransform(_u[:, :, i], _v[:, :, i]))
# build dense shapes
print(" - Build Dense Shapes")
testing_points = reference_frame.mask.true_indices()
ref_sparse_lms = align_t.apply(reference_shape)
close_mask = BooleanImage(
matpath(ref_sparse_lms.points).contains_points(testing_points).reshape(
reference_frame.mask.mask.shape))
if tight_mask:
reference_frame.mask = close_mask
else:
reference_frame.landmarks['sparse'] = ref_sparse_lms
reference_frame.constrain_mask_to_landmarks(group='sparse')
# Get Dense Shape from Masked Image
dense_reference_shape = PointCloud(
np.vstack((align_t.apply(reference_align_shape).points,
align_t.apply(reference_shape).points,
reference_frame.mask.true_indices())))
# Set Dense Shape as Reference Landmarks
reference_frame.landmarks['source'] = dense_reference_shape
dense_shapes = []
for i, t in enumerate(transforms):
warped_points = t.apply(dense_reference_shape)
dense_shape = warped_points
dense_shapes.append(dense_shape)
ni = []
for i, ds, t in zip(normalized_images, dense_shapes, _removed_transform):
img = i.warp_to_shape(reference_frame.shape,
_rf_align.compose_before(t),
warp_landmarks=True)
img.landmarks[group] = ds
ni.append(img)
return ni, transforms, reference_frame, n_landmarks, _n_align_points, _removed_transform, normalized_images, _rf_align, reference_shape, [
align_t
# _rf_align, _removed_transform, aligned_shapes, target_shape,
# reference_frame, dense_reference_shape, testing_points,
# align_t, normalized_images, shapes, lms_shapes,
# reference_shape, reference_align_shape
]
def non_rigid_icp_generator(source, target, eps=1e-3,
stiffness_weights=None, data_weights=None,
landmark_group=None, landmark_weights=None,
v_i_update_func=None, verbose=False):
r"""
Deforms the source trimesh to align with to optimally the target.
"""
# If landmarks are provided, we should always start with a simple
# AlignmentSimilarity between the landmarks to initialize optimally.
if landmark_group is not None:
if verbose:
print("'{}' landmarks will be used as "
"a landmark constraint.".format(landmark_group))
print("performing similarity alignment using landmarks")
lm_align = AlignmentSimilarity(source.landmarks[landmark_group],
target.landmarks[landmark_group]).as_non_alignment()
source = lm_align.apply(source)
# Scale factors completely change the behavior of the algorithm - always
# rescale the source down to a sensible size (so it fits inside box of
# diagonal 1) and is centred on the origin. We'll undo this after the fit
# so the user can use whatever scale they prefer.
#tr = Translation(-1 * source.centre())
#sc = UniformScale(1.0 / np.sqrt(np.sum(source.range() ** 2)), 3)
#tr_t = Translation(-1 * target.centre())
#sc_t = UniformScale(1.0 / np.sqrt(np.sum(target.range() ** 2)), 3)
#tr = Translation([0, 0, 0])
#sc = UniformScale(1.0, 3)
#prepare = tr.compose_before(sc)
#prepare_t = tr_t.compose_before(sc_t)
#source = prepare.apply(source)
#target = prepare_t.apply(target)
#m3io.export_mesh(source, '/data/tmp/source.obj', overwrite = True)
#m3io.export_mesh(target, '/data/tmp/target.obj', overwrite = True)
#t = AlignmentSimilarity(source.landmarks['LJSON'], target.landmarks['LJSON'])
#source = t.apply(source)
# store how to undo the similarity transform
# restore = prepare.pseudoinverse()
# restore source to target scale
#restore = prepare_t.pseudoinverse()
restore = Translation([0, 0, 0])
n_dims = source.n_dims
# Homogeneous dimension (1 extra for translation effects)
h_dims = n_dims + 1
points, trilist = source.points, source.trilist
n = points.shape[0] # record number of points
# ========================================================================
edge_tris = target.boundary_tri_index() # SOURCE???
# ========================================================================
M_s, unique_edge_pairs = node_arc_incidence_matrix(source)
#print('M_s {}'.format(M_s.shape))
# weight matrix
G = np.identity(n_dims + 1)
M_kron_G_s = sp.kron(M_s, G)
#print('M_kron_G_s {}'.format(M_kron_G_s.shape))
# build octree for finding closest points on target.
target_vtk = trimesh_to_vtk(target)
closest_points_on_target = VTKClosestPointLocator(target_vtk)
# save out the target normals. We need them for the weight matrix.
target_tri_normals = target.tri_normals()
# init transformation
X_prev = np.tile(np.zeros((n_dims, h_dims)), n).T
v_i = points
if stiffness_weights is not None:
if verbose:
print('using user-defined stiffness_weights')
validate_weights('stiffness_weights', stiffness_weights,
source.n_points, verbose=verbose)
else:
# these values have been empirically found to perform well for well
# rigidly aligned facial meshes
stiffness_weights = [50, 20, 5, 2, 0.8, 0.5, 0.35, 0.2]
if verbose:
print('using default '
'stiffness_weights: {}'.format(stiffness_weights))
n_iterations = len(stiffness_weights)
if landmark_weights is not None:
if verbose:
print('using user defined '
'landmark_weights: {}'.format(landmark_weights))
elif landmark_group is not None:
# these values have been empirically found to perform well for well
# rigidly aligned facial meshes
landmark_weights = [5, 2, .5, 0, 0, 0, 0, 0]
if verbose:
print('using default '
'landmark_weights: {}'.format(landmark_weights))
else:
# no landmark_weights provided - no landmark_group in use. We still
# need a landmark group for the iterator
landmark_weights = [None] * n_iterations
# We should definitely have some landmark weights set now - check the
# number is correct.
# Note we say verbose=False, as we have done custom reporting above, and
# per-vertex landmarks are not supported.
validate_weights('landmark_weights', landmark_weights, source.n_points,
n_iterations=n_iterations, verbose=False)
if data_weights is not None:
if verbose:
print('using user-defined data_weights')
validate_weights('data_weights', data_weights,
source.n_points, n_iterations=n_iterations,
verbose=verbose)
else:
data_weights = [None] * n_iterations
if verbose:
print('Not customising data_weights')
# we need to prepare some indices for efficient construction of the D
# sparse matrix.
row = np.hstack((np.repeat(np.arange(n)[:, None], n_dims, axis=1).ravel(),
np.arange(n)))
x = np.arange(n * h_dims).reshape((n, h_dims))
col = np.hstack((x[:, :n_dims].ravel(),
x[:, n_dims]))
o = np.ones(n)
if landmark_group is not None:
source_lm_index = source.distance_to(
source.landmarks[landmark_group]).argmin(axis=0)
target_lms = target.landmarks[landmark_group]
U_L = target_lms.points
n_landmarks = target_lms.n_points
lm_mask = np.in1d(row, source_lm_index)
col_lm = col[lm_mask]
# pull out the rows for the lms - but the values are
# all wrong! need to map them back to the order of the landmarks
row_lm_to_fix = row[lm_mask]
source_lm_index_l = list(source_lm_index)
row_lm = np.array([source_lm_index_l.index(r) for r in row_lm_to_fix])
for i, (alpha, beta, gamma) in enumerate(zip(stiffness_weights,
landmark_weights,
data_weights), 1):
alpha_is_per_vertex = isinstance(alpha, np.ndarray)
if alpha_is_per_vertex:
# stiffness is provided per-vertex
if alpha.shape[0] != source.n_points:
raise ValueError()
alpha_per_edge = alpha[unique_edge_pairs].mean(axis=1)
alpha_M_s = sp.diags(alpha_per_edge).dot(M_s)
alpha_M_kron_G_s = sp.kron(alpha_M_s, G)
else:
# stiffness is global - just a scalar multiply. Note that here
# we don't have to recalculate M_kron_G_s
alpha_M_kron_G_s = alpha * M_kron_G_s
if verbose:
a_str = (alpha if not alpha_is_per_vertex
else 'min: {:.2f}, max: {:.2f}'.format(alpha.min(),
alpha.max()))
i_str = '{}/{}: stiffness: {}'.format(i, len(stiffness_weights), a_str)
if landmark_group is not None:
i_str += ' lm_weight: {}'.format(beta)
print(i_str)
j = 0
while True: # iterate until convergence
j += 1 # track the iterations for this alpha/landmark weight
# find nearest neighbour and the normals
U, tri_indices = closest_points_on_target(v_i)
# ---- WEIGHTS ----
# 1. Edges
# Are any of the corresponding tris on the edge of the target?
# Where they are we return a false weight (we *don't* want to
# include these points in the solve)
w_i_e = np.in1d(tri_indices, edge_tris, invert=True)
# 2. Normals
# Calculate the normals of the current v_i
v_i_tm = TriMesh(v_i, trilist=trilist)
v_i_n = v_i_tm.vertex_normals()
# Extract the corresponding normals from the target
u_i_n = target_tri_normals[tri_indices]
# If the dot of the normals is lt 0.9 don't contrib to deformation
w_i_n = (u_i_n * v_i_n).sum(axis=1) > 0.9
# 3. Self-intersection
# This adds approximately 12% to the running cost and doesn't seem
# to be very critical in helping mesh fitting performance so for
# now it's removed. Revisit later.
# # Build an intersector for the current deformed target
# intersect = build_intersector(to_vtk(v_i_tm))
# # budge the source points 1% closer to the target
# source = v_i + ((U - v_i) * 0.5)
# # if the vector from source to target intersects the deformed
# # template we don't want to include it in the optimisation.
# problematic = [i for i, (s, t) in enumerate(zip(source, U))
# if len(intersect(s, t)[0]) > 0]
# print(len(problematic) * 1.0 / n)
# w_i_i = np.ones(v_i_tm.n_points, dtype=np.bool)
# w_i_i[problematic] = False
# Form the overall w_i from the normals, edge case
# for now disable the edge constraint (it was noisy anyway)
#w_i = w_i_n
w_i = np.logical_and(w_i_n, w_i_e).astype(np.float)
# we could add self intersection at a later date too...
# w_i = np.logical_and(np.logical_and(w_i_n,
# w_i_e,
# w_i_i).astype(np.float))
# ===========================
prop_w_i = (n - w_i.sum() * 1.0) / n
prop_w_i_n = (n - w_i_n.sum() * 1.0) / n
prop_w_i_e = (n - w_i_e.sum() * 1.0) / n
if gamma is not None:
w_i = w_i * gamma
# Build the sparse diagonal weight matrix
W_s = sp.diags(w_i.astype(np.float)[None, :], [0])
#print('W_s {}'.format(W_s.shape))
data = np.hstack((v_i.ravel(), o))
D_s = sp.coo_matrix((data, (row, col)))
to_stack_A = [alpha_M_kron_G_s, W_s.dot(D_s)]
to_stack_B = [np.zeros((alpha_M_kron_G_s.shape[0], n_dims)),
U * w_i[:, None]] # nullify nearest points by w_i
if landmark_group is not None:
D_L = sp.coo_matrix((data[lm_mask], (row_lm, col_lm)),
shape=(n_landmarks, D_s.shape[1]))
to_stack_A.append(beta * D_L)
to_stack_B.append(beta * U_L)
A_s = sp.vstack(to_stack_A).tocsr()
#print('A_s {}'.format(A_s.shape))
B_s = sp.vstack(to_stack_B).tocsr()
#try:
X = spsolve(A_s, B_s)
#except CholmodError:
# m3io.export_mesh(v_i_tm, 'problematic.obj', overwrite = True)
# exit(0)
# deform template
v_i_prev = v_i
v_i = D_s.dot(X)
delta_v_i = v_i - v_i_prev
if v_i_update_func:
# custom logic is provided to update the current template
# deformation. This is typically used by Active NICP.
# take the v_i points matrix and convert back to a TriMesh in
# the original space
def_template = restore.apply(source.from_vector(v_i.ravel()))
# perform the update
updated_def_template = v_i_update_func(def_template)
# convert back to points in the NICP space
v_i = prepare.apply(updated_def_template.points)
err = np.linalg.norm(X_prev - X, ord='fro')
stop_criterion = err / np.sqrt(np.size(X_prev))
if landmark_group is not None:
src_lms = v_i[source_lm_index]
lm_err = np.sqrt((src_lms - U_L) ** 2).sum(axis=1).mean()
if verbose:
v_str = (' - {} stop crit: {:.3f} '
'total: {:.0%} norms: {:.0%} '
'edges: {:.0%}'.format(j, stop_criterion,
prop_w_i, prop_w_i_n,
prop_w_i_e))
if landmark_group is not None:
v_str += ' lm_err: {:.4f}'.format(lm_err)
print(v_str)
X_prev = X
# track the progress of the algorithm per-iteration
info_dict = {
'alpha': alpha,
'iteration': j,
'prop_omitted': prop_w_i,
'prop_omitted_norms': prop_w_i_n,
'prop_omitted_edges': prop_w_i_e,
'delta': err,
'mask_normals': w_i_n,
'mask_edges': w_i_e,
'mask_all': w_i,
'nearest_points': restore.apply(U),
'deformation_per_step': delta_v_i
}
current_instance = source.copy()
current_instance.points = v_i.copy()
if landmark_group:
info_dict['beta'] = beta
info_dict['lm_err'] = lm_err
current_instance.landmarks[landmark_group] = PointCloud(src_lms)
yield restore.apply(current_instance), info_dict
if stop_criterion < eps:
break
def augment_face_image(img, image_size=256, crop_size=248, angle_range=30, flip=True, warp_mode='constant'):
"""basic image augmentation: random crop, rotation and horizontal flip"""
#from menpo
def round_image_shape(shape, round):
if round not in ['ceil', 'round', 'floor']:
raise ValueError('round must be either ceil, round or floor')
# Ensure that the '+' operator means concatenate tuples
return tuple(getattr(np, round)(shape).astype(np.int))
# taken from MDM
def mirror_landmarks_68(lms, im_size):
return PointCloud(abs(np.array([0, im_size[1]]) - lms.as_vector(
).reshape(-1, 2))[mirrored_parts_68])
# taken from MDM
def mirror_image(im):
im = im.copy()
im.pixels = im.pixels[..., ::-1].copy()
for group in im.landmarks:
lms = im.landmarks[group]
if lms.points.shape[0] == 68:
im.landmarks[group] = mirror_landmarks_68(lms, im.shape)
return im
flip_rand = np.random.random() > 0.5
# rot_rand = np.random.random() > 0.5
# crop_rand = np.random.random() > 0.5
rot_rand = True # like ECT
crop_rand = True # like ECT
if crop_rand:
lim = image_size - crop_size
min_crop_inds = np.random.randint(0, lim, 2)
max_crop_inds = min_crop_inds + crop_size
img = img.crop(min_crop_inds, max_crop_inds)
if flip and flip_rand:
img = mirror_image(img)
if rot_rand:
rot_angle = 2 * angle_range * np.random.random_sample() - angle_range
# img = img.rotate_ccw_about_centre(rot_angle)
# Get image's bounding box coordinates
bbox = bounding_box((0, 0), [img.shape[0] - 1, img.shape[1] - 1])
# Translate to origin and rotate counter-clockwise
trans = Translation(-img.centre(),
skip_checks=True).compose_before(
Rotation.init_from_2d_ccw_angle(rot_angle, degrees=True))
rotated_bbox = trans.apply(bbox)
# Create new translation so that min bbox values go to 0
t = Translation(-rotated_bbox.bounds()[0])
trans.compose_before_inplace(t)
rotated_bbox = trans.apply(bbox)
# Output image's shape is the range of the rotated bounding box
# while respecting the users rounding preference.
shape = round_image_shape(rotated_bbox.range() + 1, 'round')
img = img.warp_to_shape(
shape, trans.pseudoinverse(), warp_landmarks=True, mode=warp_mode)
img = img.resize([image_size, image_size])
return img