mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
dc11ec32be
fixed model sizes from previous update. avoided bug in ML framework(keras) that forces to train the model on random noise. Converter: added blur on the same keys as sharpness Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT Model produces near zero morphing and high detail face. Model has higher failure rate than other models. Keep src and dst faceset in same lighting conditions.
666 lines
22 KiB
Python
666 lines
22 KiB
Python
import colorsys
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import math
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from enum import IntEnum
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import cv2
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import numpy as np
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import numpy.linalg as npla
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import imagelib
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import mathlib
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from facelib import FaceType
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from imagelib import IEPolys
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from mathlib.umeyama import umeyama
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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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"right_eyebrow": (17, 22),
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"left_eyebrow": (22, 27),
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"right_eye": (36, 42),
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"left_eye": (42, 48),
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"nose": (27, 36), # missed one point
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"jaw": (0, 17) }
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landmarks_68_3D = np.array( [
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[-73.393523 , -29.801432 , 47.667532 ],
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[-72.775014 , -10.949766 , 45.909403 ],
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[-70.533638 , 7.929818 , 44.842580 ],
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[-66.850058 , 26.074280 , 43.141114 ],
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[-59.790187 , 42.564390 , 38.635298 ],
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[-48.368973 , 56.481080 , 30.750622 ],
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[-34.121101 , 67.246992 , 18.456453 ],
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[-17.875411 , 75.056892 , 3.609035 ],
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[0.098749 , 77.061286 , -0.881698 ],
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[17.477031 , 74.758448 , 5.181201 ],
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[32.648966 , 66.929021 , 19.176563 ],
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[46.372358 , 56.311389 , 30.770570 ],
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[57.343480 , 42.419126 , 37.628629 ],
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[64.388482 , 25.455880 , 40.886309 ],
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[68.212038 , 6.990805 , 42.281449 ],
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[70.486405 , -11.666193 , 44.142567 ],
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[71.375822 , -30.365191 , 47.140426 ],
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[-61.119406 , -49.361602 , 14.254422 ],
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[-51.287588 , -58.769795 , 7.268147 ],
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[-37.804800 , -61.996155 , 0.442051 ],
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[-24.022754 , -61.033399 , -6.606501 ],
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[-11.635713 , -56.686759 , -11.967398 ],
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[12.056636 , -57.391033 , -12.051204 ],
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[25.106256 , -61.902186 , -7.315098 ],
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[38.338588 , -62.777713 , -1.022953 ],
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[51.191007 , -59.302347 , 5.349435 ],
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[60.053851 , -50.190255 , 11.615746 ],
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[0.653940 , -42.193790 , -13.380835 ],
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[0.804809 , -30.993721 , -21.150853 ],
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[0.992204 , -19.944596 , -29.284036 ],
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[1.226783 , -8.414541 , -36.948060 ],
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[-14.772472 , 2.598255 , -20.132003 ],
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[-7.180239 , 4.751589 , -23.536684 ],
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[0.555920 , 6.562900 , -25.944448 ],
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[8.272499 , 4.661005 , -23.695741 ],
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[15.214351 , 2.643046 , -20.858157 ],
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[-46.047290 , -37.471411 , 7.037989 ],
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[-37.674688 , -42.730510 , 3.021217 ],
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[-27.883856 , -42.711517 , 1.353629 ],
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[-19.648268 , -36.754742 , -0.111088 ],
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[-28.272965 , -35.134493 , -0.147273 ],
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[-38.082418 , -34.919043 , 1.476612 ],
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[19.265868 , -37.032306 , -0.665746 ],
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[27.894191 , -43.342445 , 0.247660 ],
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[37.437529 , -43.110822 , 1.696435 ],
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[45.170805 , -38.086515 , 4.894163 ],
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[38.196454 , -35.532024 , 0.282961 ],
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[28.764989 , -35.484289 , -1.172675 ],
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[-28.916267 , 28.612716 , -2.240310 ],
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[-17.533194 , 22.172187 , -15.934335 ],
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[-6.684590 , 19.029051 , -22.611355 ],
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[0.381001 , 20.721118 , -23.748437 ],
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[8.375443 , 19.035460 , -22.721995 ],
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[18.876618 , 22.394109 , -15.610679 ],
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[28.794412 , 28.079924 , -3.217393 ],
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[19.057574 , 36.298248 , -14.987997 ],
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[8.956375 , 39.634575 , -22.554245 ],
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[0.381549 , 40.395647 , -23.591626 ],
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[-7.428895 , 39.836405 , -22.406106 ],
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[-18.160634 , 36.677899 , -15.121907 ],
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[-24.377490 , 28.677771 , -4.785684 ],
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[-6.897633 , 25.475976 , -20.893742 ],
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[0.340663 , 26.014269 , -22.220479 ],
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[8.444722 , 25.326198 , -21.025520 ],
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[24.474473 , 28.323008 , -5.712776 ],
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[8.449166 , 30.596216 , -20.671489 ],
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[0.205322 , 31.408738 , -21.903670 ],
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[-7.198266 , 30.844876 , -20.328022 ] ], dtype=np.float32)
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def convert_98_to_68(lmrks):
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#jaw
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result = [ lmrks[0] ]
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for i in range(2,16,2):
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result += [ ( lmrks[i] + (lmrks[i-1]+lmrks[i+1])/2 ) / 2 ]
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result += [ lmrks[16] ]
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for i in range(18,32,2):
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result += [ ( lmrks[i] + (lmrks[i-1]+lmrks[i+1])/2 ) / 2 ]
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result += [ lmrks[32] ]
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#eyebrows averaging
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result += [ lmrks[33],
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(lmrks[34]+lmrks[41])/2,
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(lmrks[35]+lmrks[40])/2,
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(lmrks[36]+lmrks[39])/2,
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(lmrks[37]+lmrks[38])/2,
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]
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result += [ (lmrks[42]+lmrks[50])/2,
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(lmrks[43]+lmrks[49])/2,
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(lmrks[44]+lmrks[48])/2,
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(lmrks[45]+lmrks[47])/2,
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lmrks[46]
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]
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#nose
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result += list ( lmrks[51:60] )
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#left eye (from our view)
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result += [ lmrks[60],
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lmrks[61],
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lmrks[63],
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lmrks[64],
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lmrks[65],
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lmrks[67] ]
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#right eye
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result += [ lmrks[68],
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lmrks[69],
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lmrks[71],
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lmrks[72],
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lmrks[73],
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lmrks[75] ]
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#mouth
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result += list ( lmrks[76:96] )
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return np.concatenate (result).reshape ( (68,2) )
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def transform_points(points, mat, invert=False):
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if invert:
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mat = cv2.invertAffineTransform (mat)
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points = np.expand_dims(points, axis=1)
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points = cv2.transform(points, mat, points.shape)
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points = np.squeeze(points)
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return points
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def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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if not isinstance(image_landmarks, np.ndarray):
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image_landmarks = np.array (image_landmarks)
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"""
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if face_type == FaceType.AVATAR:
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centroid = np.mean (image_landmarks, axis=0)
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mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
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a, c = mat[0,0], mat[1,0]
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scale = math.sqrt((a * a) + (c * c))
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padding = (output_size / 64) * 32
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mat = np.eye ( 2,3 )
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mat[0,2] = -centroid[0]
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mat[1,2] = -centroid[1]
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mat = mat * scale * (output_size / 3)
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mat[:,2] += output_size / 2
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else:
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"""
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remove_align = False
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if face_type == FaceType.FULL_NO_ALIGN:
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face_type = FaceType.FULL
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remove_align = True
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elif face_type == FaceType.HEAD_NO_ALIGN:
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face_type = FaceType.HEAD
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remove_align = True
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if face_type == FaceType.HALF:
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padding = 0
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elif face_type == FaceType.FULL:
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padding = (output_size / 64) * 12
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elif face_type == FaceType.HEAD:
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padding = (output_size / 64) * 21
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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( 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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mat[:,2] += -output_size*( ( (1 / scale) - 1.0 ) / 2 )
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if remove_align:
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bbox = transform_points ( [ (0,0), (0,output_size-1), (output_size-1, output_size-1), (output_size-1,0) ], mat, True)
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area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
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side = math.sqrt(area) / 2
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center = transform_points ( [(output_size/2,output_size/2)], mat, True)
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pts1 = np.float32([ center+[-side,-side], center+[side,-side], center+[-side,side] ])
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pts2 = np.float32([[0,0],[output_size-1,0],[0,output_size-1]])
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mat = cv2.getAffineTransform(pts1,pts2)
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return mat
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def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
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if len(lmrks) != 68:
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raise Exception('works only with 68 landmarks')
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lmrks = np.array( lmrks.copy(), dtype=np.int )
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# #nose
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ml_pnt = (lmrks[36] + lmrks[0]) // 2
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mr_pnt = (lmrks[16] + lmrks[45]) // 2
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# mid points between the mid points and eye
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ql_pnt = (lmrks[36] + ml_pnt) // 2
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qr_pnt = (lmrks[45] + mr_pnt) // 2
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# Top of the eye arrays
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bot_l = np.array((ql_pnt, lmrks[36], lmrks[37], lmrks[38], lmrks[39]))
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bot_r = np.array((lmrks[42], lmrks[43], lmrks[44], lmrks[45], qr_pnt))
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# Eyebrow arrays
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top_l = lmrks[17:22]
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top_r = lmrks[22:27]
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# Adjust eyebrow arrays
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lmrks[17:22] = top_l + eyebrows_expand_mod * 0.5 * (top_l - bot_l)
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lmrks[22:27] = top_r + eyebrows_expand_mod * 0.5 * (top_r - bot_r)
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return lmrks
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def get_image_hull_mask (image_shape, image_landmarks, eyebrows_expand_mod=1.0, ie_polys=None, color=(1,) ):
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hull_mask = np.zeros(image_shape[0:2]+( len(color),),dtype=np.float32)
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lmrks = expand_eyebrows(image_landmarks, eyebrows_expand_mod)
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r_jaw = (lmrks[0:9], lmrks[17:18])
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l_jaw = (lmrks[8:17], lmrks[26:27])
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r_cheek = (lmrks[17:20], lmrks[8:9])
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l_cheek = (lmrks[24:27], lmrks[8:9])
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nose_ridge = (lmrks[19:25], lmrks[8:9],)
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r_eye = (lmrks[17:22], lmrks[27:28], lmrks[31:36], lmrks[8:9])
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l_eye = (lmrks[22:27], lmrks[27:28], lmrks[31:36], lmrks[8:9])
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nose = (lmrks[27:31], lmrks[31:36])
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parts = [r_jaw, l_jaw, r_cheek, l_cheek, nose_ridge, r_eye, l_eye, nose]
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for item in parts:
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merged = np.concatenate(item)
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cv2.fillConvexPoly(hull_mask, cv2.convexHull(merged), color )
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if ie_polys is not None:
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ie_polys.overlay_mask(hull_mask)
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return hull_mask
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def alpha_to_color (img_alpha, color):
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if len(img_alpha.shape) == 2:
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img_alpha = img_alpha[...,None]
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h,w,c = img_alpha.shape
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result = np.zeros( (h,w, len(color) ), dtype=np.float32 )
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result[:,:] = color
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return result * img_alpha
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def get_cmask (image_shape, lmrks, eyebrows_expand_mod=1.0):
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h,w,c = image_shape
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hull = get_image_hull_mask (image_shape, lmrks, eyebrows_expand_mod, color=(1,) )
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result = np.zeros( (h,w,3), dtype=np.float32 )
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def process(w,h, data ):
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d = {}
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cur_lc = 0
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all_lines = []
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for s, pts_loop_ar in data:
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lines = []
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for pts, loop in pts_loop_ar:
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pts_len = len(pts)
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lines.append ( [ [ pts[i], pts[(i+1) % pts_len ] ] for i in range(pts_len - (0 if loop else 1) ) ] )
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lines = np.concatenate (lines)
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lc = lines.shape[0]
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all_lines.append(lines)
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d[s] = cur_lc, cur_lc+lc
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cur_lc += lc
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all_lines = np.concatenate (all_lines, 0)
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#calculate signed distance for all points and lines
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line_count = all_lines.shape[0]
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pts_count = w*h
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all_lines = np.repeat ( all_lines[None,...], pts_count, axis=0 ).reshape ( (pts_count*line_count,2,2) )
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|
|
pts = np.empty( (h,w,line_count,2), dtype=np.float32 )
|
|
pts[...,1] = np.arange(h)[:,None,None]
|
|
pts[...,0] = np.arange(w)[:,None]
|
|
pts = pts.reshape ( (h*w*line_count, -1) )
|
|
|
|
a = all_lines[:,0,:]
|
|
b = all_lines[:,1,:]
|
|
pa = pts-a
|
|
ba = b-a
|
|
ph = np.clip ( np.einsum('ij,ij->i', pa, ba) / np.einsum('ij,ij->i', ba, ba), 0, 1 )
|
|
dists = npla.norm ( pa - ba*ph[...,None], axis=1).reshape ( (h,w,line_count) )
|
|
|
|
def get_dists(name, thickness=0):
|
|
s,e = d[name]
|
|
result = dists[...,s:e]
|
|
if thickness != 0:
|
|
result = np.abs(result)-thickness
|
|
return np.min (result, axis=-1)
|
|
|
|
return get_dists
|
|
|
|
l_eye = lmrks[42:48]
|
|
r_eye = lmrks[36:42]
|
|
l_brow = lmrks[22:27]
|
|
r_brow = lmrks[17:22]
|
|
mouth = lmrks[48:60]
|
|
|
|
up_nose = np.concatenate( (lmrks[27:31], lmrks[33:34]) )
|
|
down_nose = lmrks[31:36]
|
|
nose = np.concatenate ( (up_nose, down_nose) )
|
|
|
|
gdf = process ( w,h,
|
|
(
|
|
('eyes', ((l_eye, True), (r_eye, True)) ),
|
|
('brows', ((l_brow, False), (r_brow,False)) ),
|
|
('up_nose', ((up_nose, False),) ),
|
|
('down_nose', ((down_nose, False),) ),
|
|
('mouth', ((mouth, True),) ),
|
|
)
|
|
)
|
|
|
|
|
|
#import code
|
|
#code.interact(local=dict(globals(), **locals()))
|
|
eyes_fall_dist = w // 32
|
|
eyes_thickness = max( w // 64, 1 )
|
|
|
|
brows_fall_dist = w // 32
|
|
brows_thickness = max( w // 256, 1 )
|
|
|
|
nose_fall_dist = w / 12
|
|
nose_thickness = max( w // 96, 1 )
|
|
|
|
mouth_fall_dist = w // 32
|
|
mouth_thickness = max( w // 64, 1 )
|
|
|
|
eyes_mask = gdf('eyes',eyes_thickness)
|
|
eyes_mask = 1-np.clip( eyes_mask/ eyes_fall_dist, 0, 1)
|
|
#eyes_mask = np.clip ( 1- ( np.sqrt( np.maximum(eyes_mask,0) ) / eyes_fall_dist ), 0, 1)
|
|
#eyes_mask = np.clip ( 1- ( np.cbrt( np.maximum(eyes_mask,0) ) / eyes_fall_dist ), 0, 1)
|
|
|
|
brows_mask = gdf('brows', brows_thickness)
|
|
brows_mask = 1-np.clip( brows_mask / brows_fall_dist, 0, 1)
|
|
#brows_mask = np.clip ( 1- ( np.sqrt( np.maximum(brows_mask,0) ) / brows_fall_dist ), 0, 1)
|
|
|
|
mouth_mask = gdf('mouth', mouth_thickness)
|
|
mouth_mask = 1-np.clip( mouth_mask / mouth_fall_dist, 0, 1)
|
|
#mouth_mask = np.clip ( 1- ( np.sqrt( np.maximum(mouth_mask,0) ) / mouth_fall_dist ), 0, 1)
|
|
|
|
def blend(a,b,k):
|
|
x = np.clip ( 0.5+0.5*(b-a)/k, 0.0, 1.0 )
|
|
return (a-b)*x+b - k*x*(1.0-x)
|
|
|
|
|
|
#nose_mask = (a-b)*x+b - k*x*(1.0-x)
|
|
|
|
#nose_mask = np.minimum (up_nose_mask , down_nose_mask )
|
|
#nose_mask = 1-np.clip( nose_mask / nose_fall_dist, 0, 1)
|
|
|
|
nose_mask = blend ( gdf('up_nose', nose_thickness), gdf('down_nose', nose_thickness), nose_thickness*3 )
|
|
nose_mask = 1-np.clip( nose_mask / nose_fall_dist, 0, 1)
|
|
|
|
up_nose_mask = gdf('up_nose', nose_thickness)
|
|
up_nose_mask = 1-np.clip( up_nose_mask / nose_fall_dist, 0, 1)
|
|
#up_nose_mask = np.clip ( 1- ( np.cbrt( np.maximum(up_nose_mask,0) ) / nose_fall_dist ), 0, 1)
|
|
|
|
down_nose_mask = gdf('down_nose', nose_thickness)
|
|
down_nose_mask = 1-np.clip( down_nose_mask / nose_fall_dist, 0, 1)
|
|
#down_nose_mask = np.clip ( 1- ( np.cbrt( np.maximum(down_nose_mask,0) ) / nose_fall_dist ), 0, 1)
|
|
|
|
#nose_mask = np.clip( up_nose_mask + down_nose_mask, 0, 1 )
|
|
#nose_mask /= np.max(nose_mask)
|
|
#nose_mask = np.maximum (up_nose_mask , down_nose_mask )
|
|
#nose_mask = down_nose_mask
|
|
|
|
#nose_mask = np.zeros_like(nose_mask)
|
|
|
|
eyes_mask = eyes_mask * (1-mouth_mask)
|
|
nose_mask = nose_mask * (1-eyes_mask)
|
|
|
|
hull_mask = hull[...,0].copy()
|
|
hull_mask = hull_mask * (1-eyes_mask) * (1-brows_mask) * (1-nose_mask) * (1-mouth_mask)
|
|
|
|
#eyes_mask = eyes_mask * (1-nose_mask)
|
|
|
|
mouth_mask= mouth_mask * (1-nose_mask)
|
|
|
|
brows_mask = brows_mask * (1-nose_mask)* (1-eyes_mask )
|
|
|
|
hull_mask = alpha_to_color(hull_mask, (0,1,0) )
|
|
eyes_mask = alpha_to_color(eyes_mask, (1,0,0) )
|
|
brows_mask = alpha_to_color(brows_mask, (0,0,1) )
|
|
nose_mask = alpha_to_color(nose_mask, (0,1,1) )
|
|
mouth_mask = alpha_to_color(mouth_mask, (0,0,1) )
|
|
|
|
#nose_mask = np.maximum( up_nose_mask, down_nose_mask )
|
|
|
|
result = hull_mask + mouth_mask+ nose_mask + brows_mask + eyes_mask
|
|
result *= hull
|
|
#result = np.clip (result, 0, 1)
|
|
return result
|
|
|
|
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')
|
|
|
|
hull_mask = np.zeros(image_shape[0:2]+(1,),dtype=np.float32)
|
|
|
|
cv2.fillConvexPoly( hull_mask, cv2.convexHull( image_landmarks[36:42]), (1,) )
|
|
cv2.fillConvexPoly( hull_mask, cv2.convexHull( image_landmarks[42:48]), (1,) )
|
|
|
|
return hull_mask
|
|
|
|
def blur_image_hull_mask (hull_mask):
|
|
|
|
maxregion = np.argwhere(hull_mask==1.0)
|
|
miny,minx = maxregion.min(axis=0)[:2]
|
|
maxy,maxx = maxregion.max(axis=0)[:2]
|
|
lenx = maxx - minx;
|
|
leny = maxy - miny;
|
|
masky = int(minx+(lenx//2))
|
|
maskx = int(miny+(leny//2))
|
|
lowest_len = min (lenx, leny)
|
|
ero = int( lowest_len * 0.085 )
|
|
blur = int( lowest_len * 0.10 )
|
|
|
|
hull_mask = cv2.erode(hull_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
|
|
hull_mask = cv2.blur(hull_mask, (blur, blur) )
|
|
hull_mask = np.expand_dims (hull_mask,-1)
|
|
|
|
return hull_mask
|
|
|
|
mirror_idxs = [
|
|
[0,16],
|
|
[1,15],
|
|
[2,14],
|
|
[3,13],
|
|
[4,12],
|
|
[5,11],
|
|
[6,10],
|
|
[7,9],
|
|
|
|
[17,26],
|
|
[18,25],
|
|
[19,24],
|
|
[20,23],
|
|
[21,22],
|
|
|
|
[36,45],
|
|
[37,44],
|
|
[38,43],
|
|
[39,42],
|
|
[40,47],
|
|
[41,46],
|
|
|
|
[31,35],
|
|
[32,34],
|
|
|
|
[50,52],
|
|
[49,53],
|
|
[48,54],
|
|
[59,55],
|
|
[58,56],
|
|
[67,65],
|
|
[60,64],
|
|
[61,63] ]
|
|
|
|
def mirror_landmarks (landmarks, val):
|
|
result = landmarks.copy()
|
|
|
|
for idx in mirror_idxs:
|
|
result [ idx ] = result [ idx[::-1] ]
|
|
|
|
result[:,0] = val - result[:,0] - 1
|
|
return result
|
|
|
|
def draw_landmarks (image, image_landmarks, color=(0,255,0), transparent_mask=False, ie_polys=None):
|
|
if len(image_landmarks) != 68:
|
|
raise Exception('get_image_eye_mask works only with 68 landmarks')
|
|
|
|
int_lmrks = np.array(image_landmarks, dtype=np.int)
|
|
|
|
jaw = int_lmrks[slice(*landmarks_68_pt["jaw"])]
|
|
right_eyebrow = int_lmrks[slice(*landmarks_68_pt["right_eyebrow"])]
|
|
left_eyebrow = int_lmrks[slice(*landmarks_68_pt["left_eyebrow"])]
|
|
mouth = int_lmrks[slice(*landmarks_68_pt["mouth"])]
|
|
right_eye = int_lmrks[slice(*landmarks_68_pt["right_eye"])]
|
|
left_eye = int_lmrks[slice(*landmarks_68_pt["left_eye"])]
|
|
nose = int_lmrks[slice(*landmarks_68_pt["nose"])]
|
|
|
|
# open shapes
|
|
cv2.polylines(image, tuple(np.array([v]) for v in ( right_eyebrow, jaw, left_eyebrow, np.concatenate((nose, [nose[-6]])) )),
|
|
False, color, lineType=cv2.LINE_AA)
|
|
# closed shapes
|
|
cv2.polylines(image, tuple(np.array([v]) for v in (right_eye, left_eye, mouth)),
|
|
True, color, lineType=cv2.LINE_AA)
|
|
# the rest of the cicles
|
|
for x, y in np.concatenate((right_eyebrow, left_eyebrow, mouth, right_eye, left_eye, nose), axis=0):
|
|
cv2.circle(image, (x, y), 1, color, 1, lineType=cv2.LINE_AA)
|
|
# jaw big circles
|
|
for x, y in jaw:
|
|
cv2.circle(image, (x, y), 2, color, lineType=cv2.LINE_AA)
|
|
|
|
if transparent_mask:
|
|
mask = get_image_hull_mask (image.shape, image_landmarks, ie_polys=ie_polys)
|
|
image[...] = ( image * (1-mask) + image * mask / 2 )[...]
|
|
|
|
def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
|
|
draw_landmarks(image, image_landmarks, color=landmarks_color, transparent_mask=transparent_mask, ie_polys=ie_polys)
|
|
imagelib.draw_rect (image, rect, (255,0,0), 2 )
|
|
|
|
image_to_face_mat = get_transform_mat (image_landmarks, face_size, face_type)
|
|
points = transform_points ( [ (0,0), (0,face_size-1), (face_size-1, face_size-1), (face_size-1,0) ], 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)
|
|
|
|
#returns pitch,yaw,roll [-1...+1]
|
|
def estimate_pitch_yaw_roll(aligned_256px_landmarks):
|
|
shape = (256,256)
|
|
focal_length = shape[1]
|
|
camera_center = (shape[1] / 2, shape[0] / 2)
|
|
camera_matrix = np.array(
|
|
[[focal_length, 0, camera_center[0]],
|
|
[0, focal_length, camera_center[1]],
|
|
[0, 0, 1]], dtype=np.float32)
|
|
|
|
(_, rotation_vector, translation_vector) = cv2.solvePnP(
|
|
landmarks_68_3D,
|
|
aligned_256px_landmarks.astype(np.float32),
|
|
camera_matrix,
|
|
np.zeros((4, 1)) )
|
|
|
|
pitch, yaw, roll = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
|
|
pitch = np.clip ( pitch/1.30, -1.0, 1.0 )
|
|
yaw = np.clip ( yaw / 1.11, -1.0, 1.0 )
|
|
roll = np.clip ( roll/3.15, -1.0, 1.0 ) #todo radians
|
|
return -pitch, yaw, roll
|