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)
if face_type == FaceType.AVATAR:
centroid = np.mean(image_landmarks, axis=0)
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
a, c = mat[0, 0], mat[1, 0]
scale = math.sqrt((a * a) + (c * c))
padding = (output_size / 64) * 32
mat = np.eye(2, 3)
mat[0, 2] = -centroid[0]
mat[1, 2] = -centroid[1]
mat = mat * scale * (output_size / 3)
mat[:, 2] += output_size / 2
else:
if face_type == FaceType.HALF:
padding = 0
elif face_type == FaceType.FULL:
padding = (output_size / 64) * 12
elif face_type == FaceType.HEAD:
padding = (output_size / 64) * 24
else:
raise ValueError('wrong face_type')
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
mat = mat * (output_size - 2 * padding)
mat[:, 2] += padding
mat *= (1 / scale)
mat[:, 2] += -output_size * (((1 / scale) - 1.0) / 2)
return mat
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)
"""
if face_type == FaceType.AVATAR:
centroid = np.mean (image_landmarks, axis=0)
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
a, c = mat[0,0], mat[1,0]
scale = math.sqrt((a * a) + (c * c))
padding = (output_size / 64) * 32
mat = np.eye ( 2,3 )
mat[0,2] = -centroid[0]
mat[1,2] = -centroid[1]
mat = mat * scale * (output_size / 3)
mat[:,2] += output_size / 2
else:
"""
remove_align = False
if face_type == FaceType.FULL_NO_ALIGN:
face_type = FaceType.FULL
remove_align = True
elif face_type == FaceType.HEAD_NO_ALIGN:
face_type = FaceType.HEAD
remove_align = True
if face_type == FaceType.HALF:
padding = 0
elif face_type == FaceType.FULL:
padding = (output_size / 64) * 12
elif face_type == FaceType.HEAD:
padding = (output_size / 64) * 21
else:
raise ValueError('wrong face_type: ', face_type)
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
mat = mat * (output_size - 2 * padding)
mat[:, 2] += padding
mat *= (1 / scale)
mat[:, 2] += -output_size * (((1 / scale) - 1.0) / 2)
if remove_align:
bbox = transform_points([(0, 0), (0, output_size - 1),
(output_size - 1, output_size - 1),
(output_size - 1, 0)], mat, True)
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 - 1, 0], [0, output_size - 1]])
mat = cv2.getAffineTransform(pts1, 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) )
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 ) ] )
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
#if full_face_align_top and (face_type == FaceType.FULL or face_type == FaceType.FULL_NO_ALIGN):
# #lmrks2 = expand_eyebrows(image_landmarks)
# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
# #y_diff = transform_points( [ np.float32( (0,0) ), y_diff], mat, True)
# #y_diff = y_diff[1]-y_diff[0]
#
# x_diff = np.float32((0,0))
#
# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
# if lmrks2_[0,0] < 0:
# x_diff = lmrks2_[0,0]
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
# elif lmrks2_[1,0] >= output_size:
# x_diff = lmrks2_[1,0]-(output_size-1)
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
#
# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
if remove_align:
bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
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] ))
mat = cv2.getAffineTransform(pts1,pts2)
return mat
def get_transform_mat (image_landmarks, output_size, face_type):
if output_size != 64 and output_size != 128 and output_size != 256 and output_size != 512:
raise ValueError ('get_transform_mat() output_size must be power of 2')
if face_type == 'half_face':
padding = 0
elif face_type == 'full_face':
padding = (output_size // 64) * 12
elif face_type == 'head':
padding = (output_size // 64) * 24
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array (image_landmarks)
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
mat = mat * (output_size - 2 * padding)
mat[:,2] += padding
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
