def prep_source_img(src: Image,
crop: bool = True,
do_plot: bool = True) -> Image:
src = src.as_masked()
if crop:
src = src.crop_to_landmarks()
src = src.constrain_mask_to_landmarks()
if do_plot:
src.view(new_figure=True)
return src
def wrapper(index, shape):
index = index.decode("utf-8")
prefix = index.split('_')[0]
landmark_indices = list(map(int, index.split('_')[1:]))
if len(landmark_indices) > 1:
min_index, max_index = landmark_indices
landmark_indices = range(min_index, max_index + 1)
kpts = np.zeros(shape[:2], dtype=int)
im = Image(kpts)
mask = np.ones(list(shape[:2]) + [1]).astype(np.float32)
for lms_index in landmark_indices:
filename = (
prefix + '_' + str(lms_index) + '.' + self.lms_extension)
path = self.lms_root / filename
if not path.exists():
continue
lms = mio.import_landmark_file(path.as_posix()).lms
if lms.points.shape[0] != 68:
min_indices, max_indices = lms.bounds()
mask[min_indices[0]:max_indices[0], min_indices[1]:
max_indices[1]] = 0
continue
for i in range(68):
lms_mask = im.as_masked().copy()
patches = np.ones((1, 1, 1, 4, 4), dtype=np.bool)
pc = lms.points[i][None, :]
lms_mask.mask.pixels[...] = False
lms_mask = lms_mask.mask.set_patches(
patches, menpo.shape.PointCloud(pc))
kpts[lms_mask.mask] = i + 1
return kpts.astype(np.int32), mask.astype(np.int32)
def test_image_as_masked():
img = Image(np.random.rand(3, 3, 1), copy=False)
m_img = img.as_masked()
assert type(m_img) == MaskedImage
assert_allclose(m_img.pixels, img.pixels)
def test_image_as_masked():
img = Image(np.random.rand(3, 3, 1), copy=False)
m_img = img.as_masked()
assert(type(m_img) == MaskedImage)
assert_allclose(m_img.pixels, img.pixels)
def test_image_as_masked():
img = Image(np.random.rand(3, 3, 1), copy=False)
m_img = img.as_masked()
assert(type(m_img) == MaskedImage)
assert(np.all(m_img.pixels == img.pixels))
