You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
220 lines
7.8 KiB
Python
220 lines
7.8 KiB
Python
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
|