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import cv2
import numpy as np
from .matlab_cp2tform import get_similarity_transform_for_cv2
# reference facial points, a list of coordinates (x,y)
REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
[33.54930115, 92.3655014], [62.72990036, 92.20410156]]
DEFAULT_CROP_SIZE = (96, 112)
class FaceWarpException(Exception):
def __str__(self):
return 'In File {}:{}'.format(__file__, super.__str__(self))
def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
"""
Function:
----------
get reference 5 key points according to crop settings:
0. Set default crop_size:
if default_square:
crop_size = (112, 112)
else:
crop_size = (96, 112)
1. Pad the crop_size by inner_padding_factor in each side;
2. Resize crop_size into (output_size - outer_padding*2),
pad into output_size with outer_padding;
3. Output reference_5point;
Parameters:
----------
@output_size: (w, h) or None
size of aligned face image
@inner_padding_factor: (w_factor, h_factor)
padding factor for inner (w, h)
@outer_padding: (w_pad, h_pad)
each row is a pair of coordinates (x, y)
@default_square: True or False
if True:
default crop_size = (112, 112)
else:
default crop_size = (96, 112);
!!! make sure, if output_size is not None:
(output_size - outer_padding)
= some_scale * (default crop_size * (1.0 +
inner_padding_factor))
Returns:
----------
@reference_5point: 5x2 np.array
each row is a pair of transformed coordinates (x, y)
"""
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]):
return tmp_5pts
if (inner_padding_factor == 0 and outer_padding == (0, 0)):
if output_size is None:
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)
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
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)
# 2) resize the padded inner region
size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
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]
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
# 3) add outer_padding to make output_size
reference_5point = tmp_5pts + np.array(outer_padding)
tmp_crop_size = output_size
return reference_5point
def get_affine_transform_matrix(src_pts, dst_pts):
"""
Function:
----------
get affine transform matrix 'tfm' from src_pts to dst_pts
Parameters:
----------
@src_pts: Kx2 np.array
source points matrix, each row is a pair of coordinates (x, y)
@dst_pts: Kx2 np.array
destination points matrix, each row is a pair of coordinates (x, y)
Returns:
----------
@tfm: 2x3 np.array
transform matrix from src_pts to dst_pts
"""
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]])
return 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
if src_pts.shape != ref_pts.shape:
raise FaceWarpException('facial_pts and reference_pts must have the same shape')
if align_type == 'cv2_affine':
tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
elif align_type == 'affine':
tfm = get_affine_transform_matrix(src_pts, ref_pts)
else:
tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
return face_img
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