def __init__(self, image_shape):
# flip axis 0 and axis 1 so indexing is as expected
flip_xy = Homogeneous(np.array([[0, 1, 0],
[1, 0, 0],
[0, 0, 1]]))
# scale to get the units correct
scale = Scale(image_shape)
self.flip_and_scale = flip_xy.compose_before(scale)
def homog_compose_before_alignment_nonuniformscale_test():
homog = Homogeneous(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]))
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and target
s = AlignmentUniformScale(source, target)
res = homog.compose_before(s)
assert (type(res) == Homogeneous)
def test_homog_compose_before_nonuniformscale():
homog = Homogeneous(np.array([[0, 1, 0],
[1, 0, 0],
[0, 0, 1]]))
s = NonUniformScale([3, 4])
res = homog.compose_before(s)
assert(type(res) == Homogeneous)
assert_allclose(res.h_matrix, np.array([[0, 3, 0],
[4, 0, 0],
[0, 0, 1]]))
def test_homog_compose_before_alignment_nonuniformscale():
homog = Homogeneous(np.array([[0, 1, 0],
[1, 0, 0],
[0, 0, 1]]))
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1],
[1, 1],
[-1, -5],
[3, -5]]))
target = scale.apply(source)
# estimate the transform from source and target
s = AlignmentUniformScale(source, target)
res = homog.compose_before(s)
assert(type(res) == Homogeneous)
def retrieve_camera_matrix(image, mesh, group=None, initialize=True):
import cv2
drop_h = Homogeneous(np.eye(4)[:3])
flip_xy_yx = Homogeneous(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]))
rows = image.shape[0]
cols = image.shape[1]
max_d = max(rows, cols)
camera_matrix = np.array([[max_d, 0, cols / 2.0], [0, max_d, rows / 2.0],
[0, 0, 1.0]])
distortion_coeffs = np.zeros(4)
# Initial guess for rotation/translation.
if initialize:
r_vec = np.array([[-2.7193267], [-0.14545351], [-0.34661788]])
t_vec = np.array([[0.], [0.], [280.]])
converged, r_vec, t_vec = cv2.solvePnP(
mesh.landmarks[group].lms.points,
image.landmarks[group].lms.points[:, ::-1], camera_matrix,
distortion_coeffs, r_vec, t_vec, 1)
else:
converged, r_vec, t_vec = cv2.solvePnP(
mesh.landmarks[group].lms.points,
image.landmarks[group].lms.points[:, ::-1], camera_matrix,
distortion_coeffs)
rotation_matrix = cv2.Rodrigues(r_vec)[0]
h_camera_matrix = np.eye(4)
h_camera_matrix[:3, :3] = camera_matrix
t_vec = t_vec.ravel()
if t_vec[2] < 0:
print('Position has a negative value in z-axis')
c = Homogeneous(h_camera_matrix)
t = Translation(t_vec)
r = Rotation(rotation_matrix)
view_t = r.compose_before(t)
proj_t = c.compose_before(drop_h).compose_before(flip_xy_yx)
return view_t, c, proj_t
def tcoords_to_image_coords(image_shape):
r"""
Returns a :map:`Homogeneous` transform that converts [0,1]
texture coordinates (tcoords) used on :map:`TexturedTriMesh`
instances to image coordinates, which behave just like image landmarks
do.
The operations that are performed are:
- Flipping the origin from bottom-left to top-left
- Permuting the axis so that st (or uv) -> yx
- Scaling the tcoords by the image shape (denormalising them). Note that
(1, 1) has to map to the highest pixel value, which is actually
(h - 1, w - 1) due to Menpo being 0-based with image operations.
Parameters
----------
image_shape : `tuple`
The shape of the texture that the tcoords index in to.
Returns
-------
:map:`Homogeneous`
A transform that, when applied to texture coordinates, converts them
to image coordinates.
"""
# flip the 'y' st 1 -> 0 and 0 -> 1, moving the axis to upper left
invert_unit_y = Homogeneous(
np.array([[1.0, 0.0, 0.0], [0.0, -1.0, 1.0], [0.0, 0.0, 1.0]])
)
# flip axis 0 and axis 1 so indexing is as expected
flip_xy_yx = Homogeneous(
np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
)
return invert_unit_y.compose_before(flip_xy_yx).compose_before(
Scale(np.array(image_shape) - 1)
)
def __init__(self, image_shape):
# flip axis 0 and axis 1 so indexing is as expected
flip_xy = Homogeneous(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]))
# scale to get the units correct
scale = Scale(image_shape)
self.flip_and_scale = flip_xy.compose_before(scale)
