def test_json_landmarks_bunny_direct():
lms = mio.import_landmark_file(mio.data_path_to('bunny.ljson'))
labels = {'reye', 'mouth', 'nose', 'leye'}
assert(len(labels - set(lms.labels)) == 0)
assert_allclose(lms['leye'].points, bunny_leye, atol=1e-7)
assert_allclose(lms['reye'].points, bunny_reye, atol=1e-7)
assert_allclose(lms['nose'].points, bunny_nose, atol=1e-7)
assert_allclose(lms['mouth'].points, bunny_mouth, atol=1e-7)
def test_json_landmarks_bunny_direct():
lms = mio.import_landmark_file(mio.data_path_to('bunny.ljson'))
labels = {'reye', 'mouth', 'nose', 'leye'}
assert (len(labels - set(lms.labels)) == 0)
assert_allclose(lms.with_labels('leye').points, bunny_leye, atol=1e-7)
assert_allclose(lms.with_labels('reye').points, bunny_reye, atol=1e-7)
assert_allclose(lms.with_labels('nose').points, bunny_nose, atol=1e-7)
assert_allclose(lms.with_labels('mouth').points, bunny_mouth, atol=1e-7)
def load_lsfm_tassos_concatenated_fw():
x = load_tassos_lsfm_combined_model(
'/vol/atlas/homes/aroussos/results/fit3Dto2D/model/ver2016-12-12_LSFMfrmt_maxNpcInf/all_all_all.mat')
shape_model = x['shape_model']
landmarks = m3io.import_landmark_file(TEMPLATE_LMS_PATH).from_vector(shape_model.mean().points[fw_index_for_lms()])
id_indices = np.arange(x['n_id_comps'])
exp_indices = np.arange(x['n_id_comps'], shape_model.n_components)
return shape_model, landmarks, id_indices, exp_indices
def test_json_landmarks_bunny_direct():
lms = mio.import_landmark_file(mio.data_path_to("bunny.ljson"))["LJSON"]
labels = {"reye", "mouth", "nose", "leye"}
assert len(labels - set(lms.labels)) == 0
assert_allclose(lms.with_labels("leye").points, bunny_leye, atol=1e-7)
assert_allclose(lms.with_labels("reye").points, bunny_reye, atol=1e-7)
assert_allclose(lms.with_labels("nose").points, bunny_nose, atol=1e-7)
assert_allclose(lms.with_labels("mouth").points, bunny_mouth, atol=1e-7)
def test_json_landmarks_bunny_direct():
lms = mio.import_landmark_file(mio.data_path_to("bunny.ljson"))
labels = {"reye", "mouth", "nose", "leye"}
assert len(labels - set(lms.labels)) == 0
assert_allclose(lms["leye"].points, bunny_leye, atol=1e-7)
assert_allclose(lms["reye"].points, bunny_reye, atol=1e-7)
assert_allclose(lms["nose"].points, bunny_nose, atol=1e-7)
assert_allclose(lms["mouth"].points, bunny_mouth, atol=1e-7)
def load_basel_shape():
shape_model = mio.import_pickle('/vol/atlas/databases/lsfm/shape_PCAModel.pkl', encoding='latin1')
landmarks = m3io.import_landmark_file(TEMPLATE_LMS_PATH)
return shape_model, landmarks
def load_basel_concatenated_fw():
x = mio.import_pickle(
'/vol/atlas/homes/jab08/tpami_computational_face/concatenated_model.pkl')
shape_model = x['combined_model']
landmarks = m3io.import_landmark_file(TEMPLATE_LMS_PATH).from_vector(shape_model.mean().points[fw_index_for_lms()])
return shape_model, landmarks, x['id_indices'], x['exp_indices']
def load_fw_mean_id_expression_fw():
shape_model = mio.import_pickle('/vol/atlas/databases/lsfm/expression_model_id_mean.pkl')
landmarks = m3io.import_landmark_file(TEMPLATE_LMS_PATH).lms.from_vector(shape_model.mean().points[fw_index_for_lms()])
return shape_model, landmarks
def load_basel_combined_fw():
shape_model = mio.import_pickle('/vol/atlas/databases/lsfm/basel_combined_model_fw.pkl')
landmarks = m3io.import_landmark_file(TEMPLATE_LMS_PATH).from_vector(shape_model.mean().points[fw_index_for_lms()])
return shape_model, landmarks
def load_lsfm_shape_fwc():
tr = mio.import_pickle('/vol/atlas/databases/lsfm/corrective_translation.pkl')
shape_model = mio.import_pickle('/vol/atlas/databases/lsfm/lsfm_shape_model_fw_cropped.pkl')
landmarks = tr.apply(m3io.import_landmark_file(TEMPLATE_LMS_PATH))
return shape_model, landmarks
import contextlib
import os
import tempfile
from unittest.mock import MagicMock, PropertyMock, patch
import menpo3d.io as mio
test_obj = mio.import_builtin_asset("james.obj")
test_lg = mio.import_landmark_file(mio.data_path_to("bunny.ljson"))
@contextlib.contextmanager
def _temporary_path(extension):
# Create a temporary file and remove it
fake_path = tempfile.NamedTemporaryFile(delete=False, suffix=extension)
fake_path.close()
fake_path = fake_path.name
os.unlink(fake_path)
yield fake_path
if os.path.exists(fake_path):
os.unlink(fake_path)
@patch("menpo3d.io.output.base.Path.exists")
@patch("{}.open".format(__name__), create=True)
def test_export_mesh_obj(mock_open, exists):
exists.return_value = False
fake_path = "/fake/fake.obj"
with open(fake_path) as f:
type(f).name = PropertyMock(return_value=fake_path)
mio.export_mesh(test_obj, f, extension="obj")
from mock import patch, PropertyMock, MagicMock
import menpo3d.io as mio
test_obj = mio.import_builtin_asset('james.obj')
test_lg = mio.import_landmark_file(mio.data_path_to('bunny.ljson'))
@patch('menpo3d.io.output.base.Path.exists')
@patch('{}.open'.format(__name__), create=True)
def test_export_mesh_obj(mock_open, exists):
exists.return_value = False
fake_path = '/fake/fake.obj'
with open(fake_path) as f:
type(f).name = PropertyMock(return_value=fake_path)
mio.export_mesh(test_obj, f, extension='obj')
@patch('menpo.image.base.PILImage')
@patch('menpo3d.io.output.base.Path.exists')
@patch('menpo.io.output.base.Path.open')
def test_export_mesh_obj_textured(mock_open, exists, PILImage):
exists.return_value = False
mock_open.return_value = MagicMock()
fake_path = '/fake/fake.obj'
mio.export_textured_mesh(test_obj, fake_path, extension='obj')
assert PILImage.fromarray.called
@patch('menpo.io.output.landmark.json.dump')
@patch('menpo3d.io.output.base.Path.exists')
bounding_boxes = [
PointCloud(b[:4].reshape(-1, 2)[:, ::-1]).bounding_box()
for b in bounding_boxes
]
return bounding_boxes
aam_fitter = load_balanced_frontal_face_fitter()
__model_path = Path(
'/vol/atlas/homes/aroussos/results/fit3Dto2D/model/ver2016-12-12_LSFMfrmt_maxNpcInf/all_all_all.mat'
)
shape_model_dict = load_tassos_lsfm_combined_model(__model_path)
shape_model = shape_model_dict['shape_model']
landmarks = m3io.import_landmark_file('../ibug68.ljson').lms.from_vector(
shape_model.mean().lms.points[mappings.fw_index_for_lms()])
texture_model = mappings.load_itwmm_texture_fast_dsift_fw()
diagonal = 185
mm = ColouredMorphableModel(shape_model,
texture_model, (landmarks),
holistic_features=fast_dsift,
diagonal=diagonal)
fitter = LucasKanadeMMFitter(mm,
n_shape=200,
n_texture=200,
n_samples=8000,
n_scales=1)
def fit(imagepath):
