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added experimental face type 'whole_face'

Browse source 
Basic usage instruction: https://i.imgur.com/w7LkId2.jpg

	'whole_face' requires skill in Adobe After Effects.

	For using whole_face you have to extract whole_face's by using
	4) data_src extract whole_face
	and
	5) data_dst extract whole_face
	Images will be extracted in 512 resolution, so they can be used for regular full_face's and half_face's.

	'whole_face' covers whole area of face include forehead in training square,
	but training mask is still 'full_face'
	therefore it requires manual final masking and composing in Adobe After Effects.

added option 'masked_training'
	This option is available only for 'whole_face' type.
	Default is ON.
	Masked training clips training area to full_face mask,
	thus network will train the faces properly.
	When the face is trained enough, disable this option to train all area of the frame.
	Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask, tune color, and compose whole face include forehead.
This commit is contained in:
Colombo 2020-02-21 16:21:04 +04:00
parent 778fb94246
commit f1d115b63b
10 changed files with 74 additions and 58 deletions
Download patch file Download diff file Expand all files Collapse all files

62
facelib/LandmarksProcessor.py
View file

@ -188,8 +188,9 @@ FaceType_to_padding_remove_align = {
FaceType.MID_FULL: (0.0675, False),
FaceType.FULL: (0.2109375, False),
FaceType.FULL_NO_ALIGN: (0.2109375, True),
FaceType.HEAD: (0.369140625, False),
FaceType.HEAD_NO_ALIGN: (0.369140625, True),
FaceType.WHOLE_FACE: (0.40, False),
FaceType.HEAD: (1.0, False),
FaceType.HEAD_NO_ALIGN: (1.0, True),
}
def convert_98_to_68(lmrks):
@ -257,30 +258,37 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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]
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]
# calc diagonal vectors between corners in global space
tb_diag_vec = (l_p[2]-l_p[0]).astype(np.float32)
tb_diag_vec = (g_p[2]-g_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
bt_diag_vec = (g_p[1]-g_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
# 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(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
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:
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
if not remove_align:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ) ] )
l_t = np.array( [ np.round( g_c - tb_diag_vec*mod ),
np.round( g_c + bt_diag_vec*mod ),
np.round( g_c + tb_diag_vec*mod ) ] )
else:
# remove_align - face will be centered in the frame but not aligned
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ),
np.round( l_c - bt_diag_vec*mod ),
l_t = np.array( [ np.round( g_c - tb_diag_vec*mod ),
np.round( g_c + bt_diag_vec*mod ),
np.round( g_c + tb_diag_vec*mod ),
np.round( g_c - bt_diag_vec*mod ),
] )
# get area of face square in global space
@ -290,9 +298,9 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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] ),
np.round( l_c + [ side, side] ) ] )
l_t = np.array( [ np.round( g_c + [-side,-side] ),
np.round( g_c + [ side,-side] ),
np.round( g_c + [ side, side] ) ] )
# 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) ))
@ -650,26 +658,19 @@ def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, tra
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 calc_face_yaw(landmarks):
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_256px_landmarks):
def estimate_pitch_yaw_roll(aligned_landmarks, size=256):
"""
returns pitch,yaw,roll [-pi...+pi]
"""
shape = (256,256)
shape = (size,size)
focal_length = shape[1]
camera_center = (shape[1] / 2, shape[0] / 2)
camera_matrix = np.array(
@ -679,7 +680,7 @@ def estimate_pitch_yaw_roll(aligned_256px_landmarks):
(_, rotation_vector, translation_vector) = cv2.solvePnP(
landmarks_68_3D,
aligned_256px_landmarks.astype(np.float32),
aligned_landmarks.astype(np.float32),
camera_matrix,
np.zeros((4, 1)) )
@ -689,8 +690,7 @@ def estimate_pitch_yaw_roll(aligned_256px_landmarks):
roll = np.clip ( roll, -math.pi, math.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