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removing landmarks of lips which are used in face aligning, and leave only corners of mouth landmarks,
result: less scale jittering aligning that will be feeded into AE and produce more stable face before: https://i.imgur.com/gJaW5Y4.gifv after: https://i.imgur.com/Vq7gvhY.gifv
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1 changed files with 93 additions and 20 deletions
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@ -9,25 +9,96 @@ from mathlib.umeyama import umeyama
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from facelib import FaceType
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import math
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mean_face_x = np.array([
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0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
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0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
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0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
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0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
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0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
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0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
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0.553364, 0.490127, 0.42689 ])
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mean_face_y = np.array([
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0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
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0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
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0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
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0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
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0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
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0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
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0.784792, 0.824182, 0.831803, 0.824182 ])
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landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
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landmarks_2D = np.array([
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[ 0.000213256, 0.106454 ], #17
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[ 0.0752622, 0.038915 ], #18
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[ 0.18113, 0.0187482 ], #19
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[ 0.29077, 0.0344891 ], #20
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[ 0.393397, 0.0773906 ], #21
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[ 0.586856, 0.0773906 ], #22
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[ 0.689483, 0.0344891 ], #23
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[ 0.799124, 0.0187482 ], #24
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[ 0.904991, 0.038915 ], #25
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[ 0.98004, 0.106454 ], #26
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[ 0.490127, 0.203352 ], #27
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[ 0.490127, 0.307009 ], #28
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[ 0.490127, 0.409805 ], #29
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[ 0.490127, 0.515625 ], #30
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[ 0.36688, 0.587326 ], #31
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[ 0.426036, 0.609345 ], #32
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[ 0.490127, 0.628106 ], #33
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[ 0.554217, 0.609345 ], #34
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[ 0.613373, 0.587326 ], #35
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[ 0.121737, 0.216423 ], #36
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[ 0.187122, 0.178758 ], #37
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[ 0.265825, 0.179852 ], #38
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[ 0.334606, 0.231733 ], #39
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[ 0.260918, 0.245099 ], #40
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[ 0.182743, 0.244077 ], #41
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[ 0.645647, 0.231733 ], #42
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[ 0.714428, 0.179852 ], #43
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[ 0.793132, 0.178758 ], #44
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[ 0.858516, 0.216423 ], #45
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[ 0.79751, 0.244077 ], #46
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[ 0.719335, 0.245099 ], #47
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[ 0.254149, 0.780233 ], #48
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[ 0.340985, 0.745405 ], #49
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[ 0.428858, 0.727388 ], #50
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[ 0.490127, 0.742578 ], #51
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[ 0.551395, 0.727388 ], #52
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[ 0.639268, 0.745405 ], #53
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[ 0.726104, 0.780233 ], #54
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[ 0.642159, 0.864805 ], #55
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[ 0.556721, 0.902192 ], #56
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[ 0.490127, 0.909281 ], #57
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[ 0.423532, 0.902192 ], #58
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[ 0.338094, 0.864805 ], #59
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[ 0.290379, 0.784792 ], #60
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[ 0.428096, 0.778746 ], #61
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[ 0.490127, 0.785343 ], #62
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[ 0.552157, 0.778746 ], #63
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[ 0.689874, 0.784792 ], #64
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[ 0.553364, 0.824182 ], #65
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[ 0.490127, 0.831803 ], #66
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[ 0.42689 , 0.824182 ] #67
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], dtype=np.float32)
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landmarks_2D_new = np.array([
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[ 0.000213256, 0.106454 ], #17
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[ 0.0752622, 0.038915 ], #18
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[ 0.18113, 0.0187482 ], #19
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[ 0.29077, 0.0344891 ], #20
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[ 0.393397, 0.0773906 ], #21
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[ 0.586856, 0.0773906 ], #22
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[ 0.689483, 0.0344891 ], #23
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[ 0.799124, 0.0187482 ], #24
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[ 0.904991, 0.038915 ], #25
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[ 0.98004, 0.106454 ], #26
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[ 0.490127, 0.203352 ], #27
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[ 0.490127, 0.307009 ], #28
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[ 0.490127, 0.409805 ], #29
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[ 0.490127, 0.515625 ], #30
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[ 0.36688, 0.587326 ], #31
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[ 0.426036, 0.609345 ], #32
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[ 0.490127, 0.628106 ], #33
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[ 0.554217, 0.609345 ], #34
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[ 0.613373, 0.587326 ], #35
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[ 0.121737, 0.216423 ], #36
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[ 0.187122, 0.178758 ], #37
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[ 0.265825, 0.179852 ], #38
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[ 0.334606, 0.231733 ], #39
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[ 0.260918, 0.245099 ], #40
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[ 0.182743, 0.244077 ], #41
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[ 0.645647, 0.231733 ], #42
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[ 0.714428, 0.179852 ], #43
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[ 0.793132, 0.178758 ], #44
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[ 0.858516, 0.216423 ], #45
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[ 0.79751, 0.244077 ], #46
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[ 0.719335, 0.245099 ], #47
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[ 0.254149, 0.780233 ], #48
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[ 0.726104, 0.780233 ], #54
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], dtype=np.float32)
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# 68 point landmark definitions
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landmarks_68_pt = { "mouth": (48,68),
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@ -155,7 +226,9 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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else:
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raise ValueError ('wrong face_type: ', face_type)
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mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
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#mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
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mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
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mat = mat * (output_size - 2 * padding)
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mat[:,2] += padding
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mat *= (1 / scale)
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