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"""
Created on Mon Apr 24 15:43:29 2017
@author: zhaoy
"""
import cv2
import numpy as np
from .matlab_cp2tform import get_similarity_transform_for_cv2
# reference facial points, a list of coordinates (x,y)
dx = 1
dy = 1
REFERENCE_FACIAL_POINTS = [
[30.29459953 + dx, 51.69630051 + dy], # left eye
[65.53179932 + dx, 51.50139999 + dy], # right eye
[48.02519989 + dx, 71.73660278 + dy], # nose
[33.54930115 + dx, 92.3655014 + dy], # left mouth
[62.72990036 + dx, 92.20410156 + dy] # right mouth
]
DEFAULT_CROP_SIZE = (96, 112)
global FACIAL_POINTS
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):
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
h_crop = tmp_crop_size[0]
w_crop = tmp_crop_size[1]
if (output_size):
if (output_size[0] == h_crop and output_size[1] == w_crop):
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)')
factor = inner_padding_factor > 0 or outer_padding[0] > 0
factor = factor or outer_padding[1] > 0
if (factor and output_size is None):
output_size = tmp_crop_size * \
(1 + inner_padding_factor * 2).astype(np.int32)
output_size += np.array(outer_padding)
cond1 = outer_padding[0] < output_size[0]
cond2 = outer_padding[1] < output_size[1]
if not (cond1 and cond2):
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
# 3) add outer_padding to make output_size
reference_5point = tmp_5pts + np.array(outer_padding)
return reference_5point
def get_affine_transform_matrix(src_pts, 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,
ratio=0.84,
reference_pts=None,
crop_size=(96, 112),
align_type='similarity'
'',
return_trans_inv=False):
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)
factor = ratio
ref_pts = (ref_pts - 112 / 2) * factor + 112 / 2
ref_pts *= crop_size[0] / 112.
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, ref_pts)
tfm_inv = cv2.getAffineTransform(ref_pts, src_pts)
elif align_type == 'affine':
tfm = get_affine_transform_matrix(src_pts, ref_pts)
tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)
else:
tfm, tfm_inv = get_similarity_transform_for_cv2(src_pts, ref_pts)
face_img = cv2.warpAffine(
src_img,
tfm, (crop_size[0], crop_size[1]),
borderValue=(255, 255, 255))
if return_trans_inv:
return face_img, tfm_inv
else:
return face_img
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