def _parse_marker_size(marker_size, points):
if marker_size is None:
from menpo.shape import PointCloud
pc = PointCloud(points, copy=False)
# This is the way that mayavi automatically computes the scale factor in
# case the user passes scale_factor = 'auto'. We use it for both the
# marker_size as well as the numbers_size.
xyz_min, xyz_max = pc.bounds()
x_min, y_min, z_min = xyz_min
x_max, y_max, z_max = xyz_max
distance = np.sqrt(((x_max - x_min)**2 + (y_max - y_min)**2 +
(z_max - z_min)**2) / (4 * pc.n_points**0.33))
if distance == 0:
marker_size = 1
else:
marker_size = 0.1 * distance
return marker_size
def _parse_marker_size(marker_size, points):
if marker_size is None:
from menpo.shape import PointCloud
pc = PointCloud(points, copy=False)
# This is the way that mayavi automatically computes the scale factor in
# case the user passes scale_factor = 'auto'. We use it for both the
# marker_size as well as the numbers_size.
xyz_min, xyz_max = pc.bounds()
x_min, y_min, z_min = xyz_min
x_max, y_max, z_max = xyz_max
distance = np.sqrt(((x_max - x_min) ** 2 +
(y_max - y_min) ** 2 +
(z_max - z_min) ** 2) /
(4 * pc.n_points ** 0.33))
if distance == 0:
marker_size = 1
else:
marker_size = 0.1 * distance
return marker_size
def crop_face(img, boundary=50, group=None, shape=(256, 256), order=1):
pc = img.landmarks[group].lms
nan_points = np.any(np.isnan(pc.points).reshape(-1, 2), 1)
pc = PointCloud(pc.points[~nan_points, :])
min_indices, max_indices = pc.bounds(boundary=boundary)
h = max_indices[0] - min_indices[0]
w = max_indices[1] - min_indices[1]
pad = abs(w - h)
try:
index = 1 - int(w > h)
min_indices[index] -= int(pad / 2.)
max_indices[index] += int(pad / 2.) + int(pad) % 2
img = img.crop(min_indices, max_indices, constrain_to_boundary=True)
except Exception as e:
print("Exception in crop_face", e)
img = img.resize(shape, order=order)
return img
