def align(src_img,
src_pts,
ref_pts,
image_size,
scale=1.0,
transpose_input=False):
w, h = image_size = tuple(image_size)
# Actual offset = new center - old center (scaled)
scale_ = max(w, h) * scale
cx_ref = cy_ref = 0.
offset_x = 0.5 * w - cx_ref * scale_
offset_y = 0.5 * h - cy_ref * scale_
s = np.array(src_pts).astype(np.float32).reshape([-1, 2])
r = np.array(ref_pts).astype(np.float32) * scale_ + np.array(
[[offset_x, offset_y]])
if transpose_input:
s = s.reshape([2, -1]).T
tfm = get_similarity_transform_for_cv2(s, r)
dst_img = cv2.warpAffine(src_img, tfm, image_size)
s_new = np.concatenate([s.reshape([2, -1]), np.ones((1, s.shape[0]))])
s_new = np.matmul(tfm, s_new)
s_new = s_new.reshape([-1]) if transpose_input else s_new.T.reshape([-1])
tfm = tfm.reshape([-1])
return dst_img, s_new, tfm
def warp_and_crop_face(src_img,
facial_pts,
reference_pts=None,
crop_size=(96, 112),
align_type='smilarity'):
"""
Function:
----------
apply affine transform 'trans' to uv
Parameters:
----------
@src_img: 3x3 np.array
input image
@facial_pts: could be
1)a list of K coordinates (x,y)
or
2) Kx2 or 2xK np.array
each row or col is a pair of coordinates (x, y)
@reference_pts: could be
1) a list of K coordinates (x,y)
or
2) Kx2 or 2xK np.array
each row or col is a pair of coordinates (x, y)
or
3) None
if None, use default reference facial points
@crop_size: (w, h)
output face image size
@align_type: transform type, could be one of
1) 'similarity': use similarity transform
2) 'cv2_affine': use the first 3 points to do affine transform,
by calling cv2.getAffineTransform()
3) 'affine': use all points to do affine transform
Returns:
----------
@face_img: output face image with size (w, h) = @crop_size
"""
if reference_pts is None:
if crop_size[0] == 96 and crop_size[1] == 112:
reference_pts = REFERENCE_FACIAL_POINTS
else:
default_square = False
inner_padding_factor = 0
outer_padding = (0, 0)
output_size = crop_size
reference_pts = get_reference_facial_points(
output_size, inner_padding_factor, outer_padding,
default_square)
ref_pts = np.float32(reference_pts)
ref_pts_shp = ref_pts.shape
if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
raise FaceWarpException(
'reference_pts.shape must be (K,2) or (2,K) and K>2')
if ref_pts_shp[0] == 2:
ref_pts = ref_pts.T
src_pts = np.float32(facial_pts)
src_pts_shp = src_pts.shape
if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
raise FaceWarpException(
'facial_pts.shape must be (K,2) or (2,K) and K>2')
if src_pts_shp[0] == 2:
src_pts = src_pts.T
# #print('--->src_pts:\n', src_pts
# #print('--->ref_pts\n', ref_pts
if src_pts.shape != ref_pts.shape:
raise FaceWarpException(
'facial_pts and reference_pts must have the same shape')
if align_type is 'cv2_affine':
tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
# #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
elif align_type is 'affine':
tfm = get_affine_transform_matrix(src_pts, ref_pts)
# #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm))
else:
tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
# #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm))
# #print('--->Transform matrix: '
# #print(('type(tfm):' + str(type(tfm)))
# #print(('tfm.dtype:' + str(tfm.dtype))
# #print( tfm
face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
return face_img