import numpy as np import math import featureExtractor import emilianaFeatureExtractor class FaceInfo(): def __init__(self, featureType): self.featureType = featureType self.face_3d = np.array([ [ 0.4551769692672 , 0.300895790030204, -0.764429433974752], [ 0.448998827123556, 0.166995837790733, -0.765143004071253], [ 0.437431554952677, 0.022655479179981, -0.739267175112735], [ 0.415033422928434, -0.088941454648772, -0.747947437846473], [ 0.389123587370091, -0.232380029794684, -0.704788385327458], [ 0.334630113904382, -0.361265387599081, -0.615587579236862], [ 0.263725112132858, -0.460009725616771, -0.491479221041573], [ 0.16241621322721 , -0.558037146073869, -0.339445180872282], [ 0. , -0.621079019321682, -0.287294770748887], [-0.16241621322721 , -0.558037146073869, -0.339445180872282], [-0.263725112132858, -0.460009725616771, -0.491479221041573], [-0.334630113904382, -0.361265387599081, -0.615587579236862], [-0.389123587370091, -0.232380029794684, -0.704788385327458], [-0.415033422928434, -0.088941454648772, -0.747947437846473], [-0.437431554952677, 0.022655479179981, -0.739267175112735], [-0.448998827123556, 0.166995837790733, -0.765143004071253], [-0.4551769692672 , 0.300895790030204, -0.764429433974752], [ 0.385529968662985, 0.402800553948697, -0.310031082540741], [ 0.322196658344302, 0.464439136821772, -0.250558059367669], [ 0.25409760441282 , 0.46420381416882 , -0.208177722146526], [ 0.186875436782135, 0.44706071961879 , -0.145299823706503], [ 0.120880983543622, 0.423566314072968, -0.110757158774771], [-0.120880983543622, 0.423566314072968, -0.110757158774771], [-0.186875436782135, 0.44706071961879 , -0.145299823706503], [-0.25409760441282 , 0.46420381416882 , -0.208177722146526], [-0.322196658344302, 0.464439136821772, -0.250558059367669], [-0.385529968662985, 0.402800553948697, -0.310031082540741], [ 0. , 0.293332603215811, -0.137582088779393], [ 0. , 0.194828701837823, -0.069158109325951], [ 0. , 0.103844017393155, -0.009151819844964], [ 0. , 0. , 0. ], [ 0.080626352317973, -0.041276068128093, -0.134161035564826], [ 0.046439347377934, -0.057675223874769, -0.102990627164664], [ 0. , -0.068753126205604, -0.090545348482397], [-0.046439347377934, -0.057675223874769, -0.102990627164664], [-0.080626352317973, -0.041276068128093, -0.134161035564826], [ 0.315905195966084, 0.298337502555443, -0.285107407636464], [ 0.275252345439353, 0.312721904921771, -0.244558251170671], [ 0.176394511553111, 0.311907184376107, -0.219205360345231], [ 0.131229723798772, 0.284447361805627, -0.234239149487417], [ 0.184124948330084, 0.260179585304867, -0.226590776513707], [ 0.279433549294448, 0.267363071770222, -0.248441437111633], [-0.131229723798772, 0.284447361805627, -0.234239149487417], [-0.176394511553111, 0.311907184376107, -0.219205360345231], [-0.275252345439353, 0.312721904921771, -0.244558251170671], [-0.315905195966084, 0.298337502555443, -0.285107407636464], [-0.279433549294448, 0.267363071770222, -0.248441437111633], [-0.184124948330084, 0.260179585304867, -0.226590776513707], [ 0.121155252430729, -0.208988660580347, -0.160606287940521], [ 0.041356305910044, -0.194484199722098, -0.096159882202821], [ 0. , -0.205180167345702, -0.083299217789729], [-0.041356305910044, -0.194484199722098, -0.096159882202821], [-0.121155252430729, -0.208988660580347, -0.160606287940521], [-0.132325402795928, -0.290857984604968, -0.187067868218105], [-0.064137791831655, -0.325377847425684, -0.158924039726607], [ 0. , -0.343742581679188, -0.113925986025684], [ 0.064137791831655, -0.325377847425684, -0.158924039726607], [ 0.132325402795928, -0.290857984604968, -0.187067868218105], [ 0.181481567104525, -0.243239316141725, -0.231284988892766], [ 0.083999507750469, -0.239717753728704, -0.155256465640701], [ 0. , -0.256058040176369, -0.0950619498899 ], [-0.083999507750469, -0.239717753728704, -0.155256465640701], [-0.181481567104525, -0.243239316141725, -0.231284988892766], [-0.074036069749345, -0.250689938345682, -0.177346470406188], [ 0. , -0.264945854681568, -0.112349967428413], [ 0.074036069749345, -0.250689938345682, -0.177346470406188], # Pupils and eyeball centers [ 0.257990002632141, 0.276080012321472, -0.219998998939991], [-0.257990002632141, 0.276080012321472, -0.219998998939991], [ 0.257990002632141, 0.276080012321472, -0.324570998549461], [-0.257990002632141, 0.276080012321472, -0.324570998549461] ], np.float32) self.reset() self.alive = False self.coord = None self.base_scale_v = self.face_3d[27:30, 1] - self.face_3d[28:31, 1] self.base_scale_h = np.abs(self.face_3d[[0, 36, 42], 0] - self.face_3d[[16, 39, 45], 0]) def reset(self): self.alive = False self.conf = None self.lms = None self.eye_state = None self.rotation = np.array([0.0, 0.0, 0.0], np.float32) self.translation = np.array([0.0, 0.0, 0.0], np.float32) self.success = None self.quaternion = None self.euler = None self.pts_3d = None self.eye_blink = None self.pnp_error = 0 if self.featureType == 0: self.features = featureExtractor.FeatureExtractor() else: self.features = emilianaFeatureExtractor.FeatureExtractor() self.current_features = {} self.contour = np.zeros((21,3)) self.update_contour() def update(self, result, coord): if result is None: self.reset() else: self.conf, (self.lms, self.eye_state) = result self.coord = coord self.alive = True def update_contour(self): self.contour = self.face_3d[[0,1,8,15,16,27,28,29,30,31,32,33,34,35]] def normalize_pts3d(self, pts_3d): # Calculate angle using nose pts_3d[:, 0:2] -= pts_3d[30, 0:2] alpha = (math.atan2(*((pts_3d[27, 0:2]) - pts_3d[30, 0:2])[::-1]) % (2*math.pi)) cosalpha = math.cos(alpha-(math.pi/2)) sinalpha = math.sin(alpha-(math.pi/2)) R = np.matrix([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) pts_3d[:, 0:2] = (pts_3d - pts_3d[30])[:, 0:2].dot(R) + pts_3d[30, 0:2] # Vertical scale pts_3d[:, 1] /= np.mean((pts_3d[27:30, 1] - pts_3d[28:31, 1]) / self.base_scale_v) # Horizontal scale pts_3d[:, 0] /= np.mean(np.abs(pts_3d[[0, 36, 42], 0] - pts_3d[[16, 39, 45], 0]) / self.base_scale_h) return pts_3d def adjust_3d(self): if self.conf < 0.4 or self.pnp_error > 300: return self.pts_3d = self.normalize_pts3d(self.pts_3d) self.current_features = self.features.update(self.pts_3d[:, 0:2]) self.eye_blink = [] self.eye_blink.append(1 - min(max(0, -self.current_features["eye_r"]), 1)) self.eye_blink.append(1 - min(max(0, -self.current_features["eye_l"]), 1))