[30.29459953, 51.69630051],

[65.53179932, 51.50139999],

[48.02519989, 71.73660278],

[33.54930115, 92.3655014],

[62.72990036, 92.20410156]

"""Estimate N-D similarity transformation with or without scaling.

num = src.shape[0]

dim = src.shape[1]

# Compute mean of src and dst.

src_mean = src.mean(axis=0)

dst_mean = dst.mean(axis=0)

# Subtract mean from src and dst.

src_demean = src - src_mean

dst_demean = dst - dst_mean

# Eq. (38).

A = dst_demean.T @ src_demean / num

# Eq. (39).

d = np.ones((dim,), dtype=np.double)

if np.linalg.det(A) < 0:

d[dim - 1] = -1

T = np.eye(dim + 1, dtype=np.double)

U, S, V = np.linalg.svd(A)

# Eq. (40) and (43).

rank = np.linalg.matrix_rank(A)

if rank == 0:

return np.nan * T

elif rank == dim - 1:

if np.linalg.det(U) * np.linalg.det(V) > 0:

T[:dim, :dim] = U @ V

else:

s = d[dim - 1]

d[dim - 1] = -1

T[:dim, :dim] = U @ np.diag(d) @ V

d[dim - 1] = s

else:

T[:dim, :dim] = U @ np.diag(d) @ V

if estimate_scale:

# Eq. (41) and (42).

scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d)

else:

scale = scale

T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T)

T[:dim, :dim] *= scale

inner_padding_factor=0.0,

outer_padding=(0, 0),

default_square=False):

tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)

tmp_crop_size = np.array(DEFAULT_CROP_SIZE)

# 0) make the inner region a square

if default_square:

size_diff = max(tmp_crop_size) - tmp_crop_size

tmp_5pts += size_diff / 2

tmp_crop_size += size_diff

if (output_size and

output_size[0] == tmp_crop_size[0] and

output_size[1] == tmp_crop_size[1]):

print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))

return tmp_5pts

if (inner_padding_factor == 0 and

outer_padding == (0, 0)):

if output_size is None:

print('No paddings to do: return default reference points')

return tmp_5pts

else:

raise FaceWarpException(

'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))

# check output size

if not (0 <= inner_padding_factor <= 1.0):

raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')

if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0)

and output_size is None):

output_size = tmp_crop_size * \

(1 + inner_padding_factor * 2).astype(np.int32)

output_size += np.array(outer_padding)

print(' deduced from paddings, output_size = ', output_size)

if not (outer_padding[0] < output_size[0]

and outer_padding[1] < output_size[1]):

raise FaceWarpException('Not (outer_padding[0] < output_size[0]'

'and outer_padding[1] < output_size[1])')

# 1) pad the inner region according inner_padding_factor

# print('---> STEP1: pad the inner region according inner_padding_factor')

if inner_padding_factor > 0:

size_diff = tmp_crop_size * inner_padding_factor * 2

tmp_5pts += size_diff / 2

tmp_crop_size += np.round(size_diff).astype(np.int32)

# print(' crop_size = ', tmp_crop_size)

# print(' reference_5pts = ', tmp_5pts)

# 2) resize the padded inner region

# print('---> STEP2: resize the padded inner region')

size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2

# print(' crop_size = ', tmp_crop_size)

# print(' size_bf_outer_pad = ', size_bf_outer_pad)

if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:

raise FaceWarpException('Must have (output_size - outer_padding)'

'= some_scale * (crop_size * (1.0 + inner_padding_factor)')

scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]

# print(' resize scale_factor = ', scale_factor)

tmp_5pts = tmp_5pts * scale_factor

# size_diff = tmp_crop_size * (scale_factor - min(scale_factor))

# tmp_5pts = tmp_5pts + size_diff / 2

tmp_crop_size = size_bf_outer_pad

# print(' crop_size = ', tmp_crop_size)

# print(' reference_5pts = ', tmp_5pts)

# 3) add outer_padding to make output_size

reference_5point = tmp_5pts + np.array(outer_padding)

tmp_crop_size = output_size

# print('---> STEP3: add outer_padding to make output_size')

# print(' crop_size = ', tmp_crop_size)

# print(' reference_5pts = ', tmp_5pts)

#

# print('===> end get_reference_facial_points\n')

tfm = np.float32([[1, 0, 0], [0, 1, 0]])

n_pts = src_pts.shape[0]

ones = np.ones((n_pts, 1), src_pts.dtype)

src_pts_ = np.hstack([src_pts, ones])

dst_pts_ = np.hstack([dst_pts, ones])

A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)

if rank == 3:

tfm = np.float32([

[A[0, 0], A[1, 0], A[2, 0]],

[A[0, 1], A[1, 1], A[2, 1]]

])

elif rank == 2:

tfm = np.float32([

[A[0, 0], A[1, 0], 0],

[A[0, 1], A[1, 1], 0]

])

facial_pts,

reference_pts=None,

crop_size=(96, 112),

align_type='smilarity'): #smilarity cv2_affine affine

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

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])

tfm_inv = cv2.getAffineTransform(ref_pts[0:3], src_pts[0:3])

elif align_type is 'affine':

tfm = get_affine_transform_matrix(src_pts, ref_pts)

tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)

else:

params, scale = _umeyama(src_pts, ref_pts)

tfm = params[:2, :]

params, _ = _umeyama(ref_pts, src_pts, False, scale=1.0/scale)

tfm_inv = params[:2, :]

face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]), flags=3)