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added face marker model InsightFace2D106 that provides better face alignment

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iperov 2022-01-14 15:40:19 +04:00
commit 0c240b53aa
6 changed files with 121 additions and 20 deletions
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37
xlib/face/FLandmarks2D.py
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@ -48,6 +48,9 @@ class FLandmarks2D(IState):
elif type == ELandmarks2D.L68:
if ulmrks_count != 68:
raise ValueError('ulmrks_count must be == 68')
elif type == ELandmarks2D.L106:
if ulmrks_count != 106:
raise ValueError('ulmrks_count must be == 106')
elif type == ELandmarks2D.L468:
if ulmrks_count != 468:
raise ValueError('ulmrks_count must be == 468')
@ -122,8 +125,14 @@ class FLandmarks2D(IState):
lmrks = (self._ulmrks * (w,h)).astype(np.float32)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
if type == ELandmarks2D.L106:
type = ELandmarks2D.L68
lmrks = lmrks[ lmrks_106_to_68_mean_pairs ]
lmrks = lmrks.reshape( (68,2,2)).mean(1)
if type == ELandmarks2D.L68:
mat = Affine2DMat.umeyama( np.concatenate ([ lmrks[17:49] , lmrks[54:55] ]), uni_landmarks_68)
mat = Affine2DMat.umeyama( np.concatenate ([ lmrks[17:36], lmrks[36:37], lmrks[39:40], lmrks[42:43], lmrks[45:46], lmrks[48:49], lmrks[54:55] ]), uni_landmarks_68)
elif type == ELandmarks2D.L468:
src_lmrks = lmrks
dst_lmrks = uni_landmarks_468
@ -227,8 +236,14 @@ class FLandmarks2D(IState):
cv2.fillConvexPoly( mask, cv2.convexHull(lmrks), color)
return mask
lmrks_106_to_68_mean_pairs = [1,9, 10,11, 12,13, 14,15, 16,2, 3,4, 5,6, 7,8, 0,0, 24,23, 22,21, 20,19, 18,32, 31,30, 29,28, 27,26,25,17,
43,43, 48,44, 49,45, 51,47, 50,46,
102,97, 103,98, 104,99, 105,100, 101,101,
72,72, 73,73, 74,74, 86,86, 77,78, 78,79, 80,80, 85,84, 84,83,
35,35, 41,40, 40,42, 39,39, 37,33, 33,36,
89,89, 95,94, 94,96, 93,93, 91,87, 87,90,
52,52, 64,64, 63,63, 71,71, 67,67, 68,68, 61,61, 58,58, 59,59, 53,53, 56,56, 55,55, 65,65, 66,66, 62,62, 70,70, 69,69, 57,57, 60,60, 54,54]
uni_landmarks_68 = np.float32([
[ 0.000213256, 0.106454 ], #17
[ 0.0752622, 0.038915 ], #18
@ -251,18 +266,18 @@ uni_landmarks_68 = np.float32([
[ 0.613373, 0.587326 ], #35
[ 0.121737, 0.216423 ], #36
[ 0.187122, 0.178758 ], #37
[ 0.265825, 0.179852 ], #38
#[ 0.187122, 0.178758 ], #37
#[ 0.265825, 0.179852 ], #38
[ 0.334606, 0.231733 ], #39
[ 0.260918, 0.245099 ], #40
[ 0.182743, 0.244077 ], #41
#[ 0.260918, 0.245099 ], #40
#[ 0.182743, 0.244077 ], #41
[ 0.645647, 0.231733 ], #42
[ 0.714428, 0.179852 ], #43
[ 0.793132, 0.178758 ], #44
#[ 0.714428, 0.179852 ], #43
#[ 0.793132, 0.178758 ], #44
[ 0.858516, 0.216423 ], #45
[ 0.79751, 0.244077 ], #46
[ 0.719335, 0.245099 ], #47
#[ 0.79751, 0.244077 ], #46
#[ 0.719335, 0.245099 ], #47
[ 0.254149, 0.780233 ], #48
[ 0.726104, 0.780233 ], #54