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removing default yaw_value from DFLIMG files,

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added better pitch/yaw estimator from 68 landmarks,
improving face yaw accuracy for sorting and trainers,
added sort by face-pitch
This commit is contained in:
iperov 2019-02-12 21:31:37 +04:00
parent 535041f7bb
commit 06fe1314d8
13 changed files with 182 additions and 37 deletions
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93
facelib/LandmarksProcessor.py
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@ -2,6 +2,7 @@ import colorsys
import cv2
import numpy as np
from enum import IntEnum
import mathlib
from mathlib.umeyama import umeyama
from utils import image_utils
from facelib import FaceType
@ -36,6 +37,77 @@ 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)
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)
@ -214,6 +286,7 @@ def calc_face_pitch(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)
@ -221,3 +294,23 @@ def calc_face_yaw(landmarks):
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 [-1...+1]
def estimate_pitch_yaw(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, _ = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
pitch = np.clip ( pitch*1.25, -1.0, 1.0 )
yaw = np.clip ( yaw*1.25, -1.0, 1.0 )
return pitch, yaw