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Added new face type : head

Browse source 
Now you can replace the head.
Example: https://www.youtube.com/watch?v=xr5FHd0AdlQ
Requirements:
	Post processing skill in Adobe After Effects or Davinci Resolve.
Usage:
1)	Find suitable dst footage with the monotonous background behind head
2)	Use “extract head” script
3)	Gather rich src headset from only one scene (same color and haircut)
4)	Mask whole head for src and dst using XSeg editor
5)	Train XSeg
6)	Apply trained XSeg mask for src and dst headsets
7)	Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. You can use pretrained model for head. Minimum recommended resolution for head is 224.
8)	Extract multiple tracks, using Merger:
a.	Raw-rgb
b.	XSeg-prd mask
c.	XSeg-dst mask
9)	Using AAE or DavinciResolve, do:
a.	Hide source head using XSeg-prd mask: content-aware-fill, clone-stamp, background retraction, or other technique
b.	Overlay new head using XSeg-dst mask

Warning: Head faceset can be used for whole_face or less types of training only with XSeg masking.

XSegEditor: added button ‘view trained XSeg mask’, so you can see which frames should be masked to improve mask quality.
This commit is contained in:
Colombo 2020-04-04 09:28:06 +04:00
commit 2b7364005d
21 changed files with 506 additions and 413 deletions
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0
facelib/FAN.npy → facelib/2DFAN.npy
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facelib/3DFAN.npy Normal file
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5
facelib/FANExtractor.py
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@ -13,8 +13,9 @@ from core.leras import nn
ported from https://github.com/1adrianb/face-alignment
"""
class FANExtractor(object):
def __init__ (self, place_model_on_cpu=False):
model_path = Path(__file__).parent / "FAN.npy"
def __init__ (self, landmarks_3D=False, place_model_on_cpu=False):
model_path = Path(__file__).parent / ( "2DFAN.npy" if not landmarks_3D else "3DFAN.npy")
if not model_path.exists():
raise Exception("Unable to load FANExtractor model")

29
facelib/FaceType.py
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@ -2,16 +2,15 @@ from enum import IntEnum
class FaceType(IntEnum):
#enumerating in order "next contains prev"
MOUTH = -1
HALF = 0
MID_FULL = 1
FULL = 2
FULL_NO_ALIGN = 3
WHOLE_FACE = 4
HEAD = 5
HEAD_NO_ALIGN = 6
HEAD = 10
HEAD_NO_ALIGN = 20
MARK_ONLY = 10, #no align at all, just embedded faceinfo
MARK_ONLY = 100, #no align at all, just embedded faceinfo
@staticmethod
def fromString (s):
@ -24,23 +23,15 @@ class FaceType(IntEnum):
def toString (face_type):
return to_string_dict[face_type]
from_string_dict = {'mouth': FaceType.MOUTH,
'half_face': FaceType.HALF,
'midfull_face': FaceType.MID_FULL,
'full_face': FaceType.FULL,
'whole_face': FaceType.WHOLE_FACE,
'head' : FaceType.HEAD,
'mark_only' : FaceType.MARK_ONLY,
'full_face_no_align' : FaceType.FULL_NO_ALIGN,
'head_no_align' : FaceType.HEAD_NO_ALIGN,
}
to_string_dict = { FaceType.MOUTH : 'mouth',
FaceType.HALF : 'half_face',
to_string_dict = { FaceType.HALF : 'half_face',
FaceType.MID_FULL : 'midfull_face',
FaceType.FULL : 'full_face',
FaceType.FULL_NO_ALIGN : 'full_face_no_align',
FaceType.WHOLE_FACE : 'whole_face',
FaceType.HEAD : 'head',
FaceType.MARK_ONLY :'mark_only',
FaceType.FULL_NO_ALIGN : 'full_face_no_align',
FaceType.HEAD_NO_ALIGN : 'head_no_align'
FaceType.HEAD_NO_ALIGN : 'head_no_align',
FaceType.MARK_ONLY :'mark_only',
}
from_string_dict = { to_string_dict[x] : x for x in to_string_dict.keys() }

274
facelib/LandmarksProcessor.py
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@ -134,86 +134,85 @@ landmarks_68_pt = { "mouth": (48,68),
"nose": (27, 36), # missed one point
"jaw": (0, 17) }
landmarks_68_3D = np.array( [
[-73.393523 , -29.801432 , 47.667532 ],
[-72.775014 , -10.949766 , 45.909403 ],
[-70.533638 , 7.929818 , 44.842580 ],
[-66.850058 , 26.074280 , 43.141114 ],
[-59.790187 , 42.564390 , 38.635298 ],
[-48.368973 , 56.481080 , 30.750622 ],
[-34.121101 , 67.246992 , 18.456453 ],
[-17.875411 , 75.056892 , 3.609035 ],
[0.098749 , 77.061286 , -0.881698 ],
[17.477031 , 74.758448 , 5.181201 ],
[32.648966 , 66.929021 , 19.176563 ],
[46.372358 , 56.311389 , 30.770570 ],
[57.343480 , 42.419126 , 37.628629 ],
[64.388482 , 25.455880 , 40.886309 ],
[68.212038 , 6.990805 , 42.281449 ],
[70.486405 , -11.666193 , 44.142567 ],
[71.375822 , -30.365191 , 47.140426 ],
[-61.119406 , -49.361602 , 14.254422 ],
[-51.287588 , -58.769795 , 7.268147 ],
[-37.804800 , -61.996155 , 0.442051 ],
[-24.022754 , -61.033399 , -6.606501 ],
[-11.635713 , -56.686759 , -11.967398 ],
[12.056636 , -57.391033 , -12.051204 ],
[25.106256 , -61.902186 , -7.315098 ],
[38.338588 , -62.777713 , -1.022953 ],
[51.191007 , -59.302347 , 5.349435 ],
[60.053851 , -50.190255 , 11.615746 ],
[0.653940 , -42.193790 , -13.380835 ],
[0.804809 , -30.993721 , -21.150853 ],
[0.992204 , -19.944596 , -29.284036 ],
[1.226783 , -8.414541 , -36.948060 ],
[-14.772472 , 2.598255 , -20.132003 ],
[-7.180239 , 4.751589 , -23.536684 ],
[0.555920 , 6.562900 , -25.944448 ],
[8.272499 , 4.661005 , -23.695741 ],
[15.214351 , 2.643046 , -20.858157 ],
[-46.047290 , -37.471411 , 7.037989 ],
[-37.674688 , -42.730510 , 3.021217 ],
[-27.883856 , -42.711517 , 1.353629 ],
[-19.648268 , -36.754742 , -0.111088 ],
[-28.272965 , -35.134493 , -0.147273 ],
[-38.082418 , -34.919043 , 1.476612 ],
[19.265868 , -37.032306 , -0.665746 ],
[27.894191 , -43.342445 , 0.247660 ],
[37.437529 , -43.110822 , 1.696435 ],
[45.170805 , -38.086515 , 4.894163 ],
[38.196454 , -35.532024 , 0.282961 ],
[28.764989 , -35.484289 , -1.172675 ],
[-28.916267 , 28.612716 , -2.240310 ],
[-17.533194 , 22.172187 , -15.934335 ],
[-6.684590 , 19.029051 , -22.611355 ],
[0.381001 , 20.721118 , -23.748437 ],
[8.375443 , 19.035460 , -22.721995 ],
[18.876618 , 22.394109 , -15.610679 ],
[28.794412 , 28.079924 , -3.217393 ],
[19.057574 , 36.298248 , -14.987997 ],
[8.956375 , 39.634575 , -22.554245 ],
[0.381549 , 40.395647 , -23.591626 ],
[-7.428895 , 39.836405 , -22.406106 ],
[-18.160634 , 36.677899 , -15.121907 ],
[-24.377490 , 28.677771 , -4.785684 ],
[-6.897633 , 25.475976 , -20.893742 ],
[0.340663 , 26.014269 , -22.220479 ],
[8.444722 , 25.326198 , -21.025520 ],
[24.474473 , 28.323008 , -5.712776 ],
[8.449166 , 30.596216 , -20.671489 ],
[0.205322 , 31.408738 , -21.903670 ],
[-7.198266 , 30.844876 , -20.328022 ] ], dtype=np.float32)
[-73.393523 , -29.801432 , 47.667532 ], #00
[-72.775014 , -10.949766 , 45.909403 ], #01
[-70.533638 , 7.929818 , 44.842580 ], #02
[-66.850058 , 26.074280 , 43.141114 ], #03
[-59.790187 , 42.564390 , 38.635298 ], #04
[-48.368973 , 56.481080 , 30.750622 ], #05
[-34.121101 , 67.246992 , 18.456453 ], #06
[-17.875411 , 75.056892 , 3.609035 ], #07
[0.098749 , 77.061286 , -0.881698 ], #08
[17.477031 , 74.758448 , 5.181201 ], #09
[32.648966 , 66.929021 , 19.176563 ], #10
[46.372358 , 56.311389 , 30.770570 ], #11
[57.343480 , 42.419126 , 37.628629 ], #12
[64.388482 , 25.455880 , 40.886309 ], #13
[68.212038 , 6.990805 , 42.281449 ], #14
[70.486405 , -11.666193 , 44.142567 ], #15
[71.375822 , -30.365191 , 47.140426 ], #16
[-61.119406 , -49.361602 , 14.254422 ], #17
[-51.287588 , -58.769795 , 7.268147 ], #18
[-37.804800 , -61.996155 , 0.442051 ], #19
[-24.022754 , -61.033399 , -6.606501 ], #20
[-11.635713 , -56.686759 , -11.967398 ], #21
[12.056636 , -57.391033 , -12.051204 ], #22
[25.106256 , -61.902186 , -7.315098 ], #23
[38.338588 , -62.777713 , -1.022953 ], #24
[51.191007 , -59.302347 , 5.349435 ], #25
[60.053851 , -50.190255 , 11.615746 ], #26
[0.653940 , -42.193790 , -13.380835 ], #27
[0.804809 , -30.993721 , -21.150853 ], #28
[0.992204 , -19.944596 , -29.284036 ], #29
[1.226783 , -8.414541 , -36.948060 ], #00
[-14.772472 , 2.598255 , -20.132003 ], #01
[-7.180239 , 4.751589 , -23.536684 ], #02
[0.555920 , 6.562900 , -25.944448 ], #03
[8.272499 , 4.661005 , -23.695741 ], #04
[15.214351 , 2.643046 , -20.858157 ], #05
[-46.047290 , -37.471411 , 7.037989 ], #06
[-37.674688 , -42.730510 , 3.021217 ], #07
[-27.883856 , -42.711517 , 1.353629 ], #08
[-19.648268 , -36.754742 , -0.111088 ], #09
[-28.272965 , -35.134493 , -0.147273 ], #10
[-38.082418 , -34.919043 , 1.476612 ], #11
[19.265868 , -37.032306 , -0.665746 ], #12
[27.894191 , -43.342445 , 0.247660 ], #13
[37.437529 , -43.110822 , 1.696435 ], #14
[45.170805 , -38.086515 , 4.894163 ], #15
[38.196454 , -35.532024 , 0.282961 ], #16
[28.764989 , -35.484289 , -1.172675 ], #17
[-28.916267 , 28.612716 , -2.240310 ], #18
[-17.533194 , 22.172187 , -15.934335 ], #19
[-6.684590 , 19.029051 , -22.611355 ], #20
[0.381001 , 20.721118 , -23.748437 ], #21
[8.375443 , 19.035460 , -22.721995 ], #22
[18.876618 , 22.394109 , -15.610679 ], #23
[28.794412 , 28.079924 , -3.217393 ], #24
[19.057574 , 36.298248 , -14.987997 ], #25
[8.956375 , 39.634575 , -22.554245 ], #26
[0.381549 , 40.395647 , -23.591626 ], #27
[-7.428895 , 39.836405 , -22.406106 ], #28
[-18.160634 , 36.677899 , -15.121907 ], #29
[-24.377490 , 28.677771 , -4.785684 ], #30
[-6.897633 , 25.475976 , -20.893742 ], #31
[0.340663 , 26.014269 , -22.220479 ], #32
[8.444722 , 25.326198 , -21.025520 ], #33
[24.474473 , 28.323008 , -5.712776 ], #34
[8.449166 , 30.596216 , -20.671489 ], #35
[0.205322 , 31.408738 , -21.903670 ], #36
[-7.198266 , 30.844876 , -20.328022 ] #37
], dtype=np.float32)
FaceType_to_padding_remove_align = {
FaceType.MOUTH: (0.25, False),
FaceType.HALF: (0.0, False),
FaceType.MID_FULL: (0.0675, False),
FaceType.FULL: (0.2109375, False),
FaceType.FULL_NO_ALIGN: (0.2109375, True),
FaceType.WHOLE_FACE: (0.40, False),
FaceType.HEAD: (1.0, False),
FaceType.HEAD_NO_ALIGN: (1.0, True),
FaceType.HEAD: (0.70, False),
FaceType.HEAD_NO_ALIGN: (0.70, True),
}
def convert_98_to_68(lmrks):
@ -279,11 +278,8 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
if face_type == FaceType.MOUTH:
mat = umeyama(image_landmarks[48:68], mouth_center_landmarks_2D, True)[0:2]
else:
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# get corner points in global space
g_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5) ]) , mat, True)
g_c = g_p[4]
@ -297,16 +293,36 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# calc modifier of diagonal vectors for scale and padding value
padding, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
mod = (1.0 / scale)* ( npla.norm(g_p[0]-g_p[2])*(padding*np.sqrt(2.0) + 0.5) )
if face_type == FaceType.WHOLE_FACE:
# adjust center for WHOLE_FACE, 7% below in order to cover more forehead
# adjust vertical offset for WHOLE_FACE, 7% below in order to cover more forehead
vec = (g_p[0]-g_p[3]).astype(np.float32)
vec_len = npla.norm(vec)
vec /= vec_len
g_c += vec*vec_len*0.07
# calc 3 points in global space to estimate 2d affine transform
elif face_type == FaceType.HEAD:
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# assuming image_landmarks are 3D_Landmarks extracted for HEAD,
# adjust horizontal offset according to estimated yaw
yaw = estimate_averaged_yaw(transform_points (image_landmarks, mat, False))
hvec = (g_p[0]-g_p[1]).astype(np.float32)
hvec_len = npla.norm(hvec)
hvec /= hvec_len
yaw *= np.abs(math.tanh(yaw*2)) # Damp near zero
g_c -= hvec * (yaw * hvec_len / 2.0)
# adjust vertical offset for HEAD, 50% below
vvec = (g_p[0]-g_p[3]).astype(np.float32)
vvec_len = npla.norm(vvec)
vvec /= vvec_len
g_c += vvec*vvec_len*0.50
# calc 3 points in global space to estimate 2d affine transform
if not remove_align:
l_t = np.array( [ g_c - tb_diag_vec*mod,
g_c + bt_diag_vec*mod,
@ -321,10 +337,10 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# get area of face square in global space
area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
# calc side of square
side = np.float32(math.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array( [ g_c + [-side,-side],
g_c + [ side,-side],
@ -334,14 +350,14 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
return mat
def get_rect_from_landmarks(image_landmarks):
mat = get_transform_mat(image_landmarks, 256, FaceType.FULL_NO_ALIGN)
g_p = transform_points ( np.float32([(0,0),(255,255) ]) , mat, True)
(l,t,r,b) = g_p[0][0], g_p[0][1], g_p[1][0], g_p[1][1]
return (l,t,r,b)
def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
@ -393,15 +409,15 @@ def get_image_hull_mask (image_shape, image_landmarks, eyebrows_expand_mod=1.0 )
cv2.fillConvexPoly(hull_mask, cv2.convexHull(merged), (1,) )
return hull_mask
def get_image_eye_mask (image_shape, image_landmarks):
if len(image_landmarks) != 68:
raise Exception('get_image_eye_mask works only with 68 landmarks')
h,w,c = image_shape
hull_mask = np.zeros( (h,w,1),dtype=np.float32)
image_landmarks = image_landmarks.astype(np.int)
cv2.fillConvexPoly( hull_mask, cv2.convexHull( image_landmarks[36:42]), (1,) )
@ -409,7 +425,7 @@ def get_image_eye_mask (image_shape, image_landmarks):
dilate = h // 32
hull_mask = cv2.dilate(hull_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(dilate,dilate)), iterations = 1 )
blur = h // 16
blur = blur + (1-blur % 2)
hull_mask = cv2.GaussianBlur(hull_mask, (blur, blur) , 0)
@ -646,9 +662,9 @@ def mirror_landmarks (landmarks, val):
def get_face_struct_mask (image_shape, image_landmarks, eyebrows_expand_mod=1.0, color=(1,) ):
mask = np.zeros(image_shape[0:2]+( len(color),),dtype=np.float32)
lmrks = expand_eyebrows(image_landmarks, eyebrows_expand_mod)
draw_landmarks (mask, image_landmarks, color=color, draw_circles=False, thickness=2)
draw_landmarks (mask, image_landmarks, color=color, draw_circles=False, thickness=2)
return mask
def draw_landmarks (image, image_landmarks, color=(0,255,0), draw_circles=True, thickness=1, transparent_mask=False):
if len(image_landmarks) != 68:
raise Exception('get_image_eye_mask works only with 68 landmarks')
@ -669,7 +685,7 @@ def draw_landmarks (image, image_landmarks, color=(0,255,0), draw_circles=True,
# closed shapes
cv2.polylines(image, tuple(np.array([v]) for v in (right_eye, left_eye, mouth)),
True, color, thickness=thickness, lineType=cv2.LINE_AA)
if draw_circles:
# the rest of the cicles
for x, y in np.concatenate((right_eyebrow, left_eyebrow, mouth, right_eye, left_eye, nose), axis=0):
@ -692,17 +708,25 @@ def draw_rect_landmarks (image, rect, image_landmarks, face_type, face_size=256,
points = transform_points ( [ ( int(face_size*0.05), 0), ( int(face_size*0.1), int(face_size*0.1) ), ( 0, int(face_size*0.1) ) ], image_to_face_mat, True)
imagelib.draw_polygon (image, points, (0,0,255), 2)
def calc_face_pitch(landmarks):
if not isinstance(landmarks, np.ndarray):
landmarks = np.array (landmarks)
t = ( (landmarks[6][1]-landmarks[8][1]) + (landmarks[10][1]-landmarks[8][1]) ) / 2.0
b = landmarks[8][1]
return float(b-t)
def estimate_averaged_yaw(landmarks):
# Works much better than solvePnP if landmarks from "3DFAN"
if not isinstance(landmarks, np.ndarray):
landmarks = np.array (landmarks)
l = ( (landmarks[27][0]-landmarks[0][0]) + (landmarks[28][0]-landmarks[1][0]) + (landmarks[29][0]-landmarks[2][0]) ) / 3.0
r = ( (landmarks[16][0]-landmarks[27][0]) + (landmarks[15][0]-landmarks[28][0]) + (landmarks[14][0]-landmarks[29][0]) ) / 3.0
return float(r-l)
def estimate_pitch_yaw_roll(aligned_landmarks, size=256):
"""
returns pitch,yaw,roll [-pi...+pi]
returns pitch,yaw,roll [-pi/2...+pi/2]
"""
shape = (size,size)
focal_length = shape[1]
@ -712,19 +736,21 @@ def estimate_pitch_yaw_roll(aligned_landmarks, size=256):
[0, focal_length, camera_center[1]],
[0, 0, 1]], dtype=np.float32)
(_, rotation_vector, translation_vector) = cv2.solvePnP(
landmarks_68_3D,
aligned_landmarks.astype(np.float32),
(_, rotation_vector, _) = cv2.solvePnP(
np.concatenate( (landmarks_68_3D[:27], landmarks_68_3D[30:36]) , axis=0) ,
np.concatenate( (aligned_landmarks[:27], aligned_landmarks[30:36]) , axis=0).astype(np.float32),
camera_matrix,
np.zeros((4, 1)) )
pitch, yaw, roll = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
pitch = np.clip ( pitch, -math.pi, math.pi )
yaw = np.clip ( yaw , -math.pi, math.pi )
roll = np.clip ( roll, -math.pi, math.pi )
half_pi = math.pi / 2.0
pitch = np.clip ( pitch, -half_pi, half_pi )
yaw = np.clip ( yaw , -half_pi, half_pi )
roll = np.clip ( roll, -half_pi, half_pi )
return -pitch, yaw, roll
#if remove_align:
# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
# #import code
@ -758,48 +784,48 @@ def estimate_pitch_yaw_roll(aligned_landmarks, size=256):
"""
def get_averaged_transform_mat (img_landmarks,
img_landmarks_prev,
img_landmarks_next,
average_frame_count,
def get_averaged_transform_mat (img_landmarks,
img_landmarks_prev,
img_landmarks_next,
average_frame_count,
average_center_frame_count,
output_size, face_type, scale=1.0):
l_c_list = []
tb_diag_vec_list = []
bt_diag_vec_list = []
mod_list = []
count = max(average_frame_count,average_center_frame_count)
for i in range ( -count, count+1, 1 ):
for i in range ( -count, count+1, 1 ):
if i < 0:
lmrks = img_landmarks_prev[i] if -i < len(img_landmarks_prev) else None
elif i > 0:
lmrks = img_landmarks_next[i] if i < len(img_landmarks_next) else None
else:
lmrks = img_landmarks
if lmrks is None:
continue
l_c, tb_diag_vec, bt_diag_vec, mod = get_transform_mat_data (lmrks, face_type, scale=scale)
if i >= -average_frame_count and i <= average_frame_count:
tb_diag_vec_list.append(tb_diag_vec)
bt_diag_vec_list.append(bt_diag_vec)
mod_list.append(mod)
if i >= -average_center_frame_count and i <= average_center_frame_count:
l_c_list.append(l_c)
tb_diag_vec = np.mean( np.array(tb_diag_vec_list), axis=0 )
bt_diag_vec = np.mean( np.array(bt_diag_vec_list), axis=0 )
mod = np.mean( np.array(mod_list), axis=0 )
mod = np.mean( np.array(mod_list), axis=0 )
l_c = np.mean( np.array(l_c_list), axis=0 )
return get_transform_mat_by_data (l_c, tb_diag_vec, bt_diag_vec, mod, output_size, face_type)
def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array (image_landmarks)
@ -809,7 +835,7 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
# get corner points in global space
l_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5)]) , mat, True)
l_c = l_p[4]
@ -823,7 +849,7 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# calc modifier of diagonal vectors for scale and padding value
mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
# calc 3 points in global space to estimate 2d affine transform
# calc 3 points in global space to estimate 2d affine transform
if not remove_align:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
@ -838,10 +864,10 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# get area of face square in global space
area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
# calc side of square
side = np.float32(math.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array( [ np.round( l_c + [-side,-side] ),
np.round( l_c + [ side,-side] ),
@ -850,6 +876,6 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
return mat
"""