def get_transform_mat(image_landmarks, output_size, face_type, scale=1.0):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array(image_landmarks)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
mat = umeyama(
np.concatenate([image_landmarks[17:49], image_landmarks[54:55]]),
landmarks_2D_new, True)[0:2]
# get corner points in global space
l_p = transform_points(
np.float32([(0, 0), (1, 0), (1, 1), (0, 1), (0.5, 0.5)]), mat, True)
l_c = l_p[4]
# calc diagonal vectors between corners in global space
tb_diag_vec = (l_p[2] - l_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (l_p[1] - l_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
# calc modifier of diagonal vectors for scale and padding value
padding, remove_align = FaceType_to_padding_remove_align.get(
face_type, 0.0)
mod = (1.0 / scale) * (npla.norm(l_p[0] - l_p[2]) *
(padding * np.sqrt(2.0) + 0.5))
# calc 3 points in global space to estimate 2d affine transform
if not remove_align:
l_t = np.array([
np.round(l_c - tb_diag_vec * mod),
np.round(l_c + bt_diag_vec * mod),
np.round(l_c + tb_diag_vec * mod)
])
else:
# remove_align - face will be centered in the frame but not aligned
l_t = np.array([
np.round(l_c - tb_diag_vec * mod),
np.round(l_c + bt_diag_vec * mod),
np.round(l_c + tb_diag_vec * mod),
np.round(l_c - bt_diag_vec * mod),
])
# get area of face square in global space
area = mathlib.polygon_area(l_t[:, 0], l_t[:, 1])
# calc side of square
side = np.float32(math.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array([
np.round(l_c + [-side, -side]),
np.round(l_c + [side, -side]),
np.round(l_c + [side, side])
])
# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
pts2 = np.float32(((0, 0), (output_size, 0), (output_size, output_size)))
mat = cv2.getAffineTransform(l_t, pts2)
return mat
def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0, full_face_align_top=True):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array (image_landmarks)
padding, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
l_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5)]) , mat, True)
l_c = l_p[4]
tb_diag_vec = (l_p[2]-l_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
if not remove_align:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ) ] )
else:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ),
np.round( l_c - bt_diag_vec*mod ),
] )
area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
side = np.float32(math.sqrt(area) / 2)
l_t = np.array( [ np.round( l_c + [-side,-side] ),
np.round( l_c + [ side,-side] ),
np.round( l_c + [ side, side] ) ] )
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
#if remove_align:
# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
# #import code
# #code.interact(local=dict(globals(), **locals()))
# area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
# side = math.sqrt(area) / 2
# center = transform_points ( [(output_size/2,output_size/2)], mat, True)
# pts1 = np.float32(( center+[-side,-side], center+[side,-side], center+[side,-side] ))
# pts2 = np.float32([[0,0],[output_size,0],[0,output_size]])
# mat = cv2.getAffineTransform(pts1,pts2)
return mat
def get_transform_mat_data (image_landmarks, face_type, scale=1.0):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array (image_landmarks)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# get corner points in global space
l_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5)]) , mat, True)
l_c = l_p[4]
# calc diagonal vectors between corners in global space
tb_diag_vec = (l_p[2]-l_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
# calc modifier of diagonal vectors for scale and padding value
padding, _ = FaceType_to_padding_remove_align.get(face_type, 0.0)
mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
return l_c, tb_diag_vec, bt_diag_vec, mod
def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array (image_landmarks)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# get corner points in global space
g_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5) ]) , mat, True)
g_c = g_p[4]
# calc diagonal vectors between corners in global space
tb_diag_vec = (g_p[2]-g_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (g_p[1]-g_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
# calc modifier of diagonal vectors for scale and padding value
padding, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
mod = (1.0 / scale)* ( npla.norm(g_p[0]-g_p[2])*(padding*np.sqrt(2.0) + 0.5) )
if face_type == FaceType.WHOLE_FACE:
# adjust vertical offset for WHOLE_FACE, 7% below in order to cover more forehead
vec = (g_p[0]-g_p[3]).astype(np.float32)
vec_len = npla.norm(vec)
vec /= vec_len
g_c += vec*vec_len*0.07
elif face_type == FaceType.HEAD:
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# assuming image_landmarks are 3D_Landmarks extracted for HEAD,
# adjust horizontal offset according to estimated yaw
yaw = estimate_averaged_yaw(transform_points (image_landmarks, mat, False))
hvec = (g_p[0]-g_p[1]).astype(np.float32)
hvec_len = npla.norm(hvec)
hvec /= hvec_len
yaw *= np.abs(math.tanh(yaw*2)) # Damp near zero
g_c -= hvec * (yaw * hvec_len / 2.0)
# adjust vertical offset for HEAD, 50% below
vvec = (g_p[0]-g_p[3]).astype(np.float32)
vvec_len = npla.norm(vvec)
vvec /= vvec_len
g_c += vvec*vvec_len*0.50
# calc 3 points in global space to estimate 2d affine transform
if not remove_align:
l_t = np.array( [ g_c - tb_diag_vec*mod,
g_c + bt_diag_vec*mod,
g_c + tb_diag_vec*mod ] )
else:
# remove_align - face will be centered in the frame but not aligned
l_t = np.array( [ g_c - tb_diag_vec*mod,
g_c + bt_diag_vec*mod,
g_c + tb_diag_vec*mod,
g_c - bt_diag_vec*mod,
] )
# get area of face square in global space
area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
# calc side of square
side = np.float32(math.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array( [ g_c + [-side,-side],
g_c + [ side,-side],
g_c + [ side, side] ] )
# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
return mat
