made some parts of landmarks static
removed a bit where I was copying the same array to face info over and over
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Cowwan
parent
e0ac7feefc
commit
68f350f30e
2 changed files with 35 additions and 109 deletions
138
landmarks.py
138
landmarks.py
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@ -21,6 +21,35 @@ def matrix_to_quaternion(m):
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q = [m[1,2]-m[2,1], m[2,0]-m[0,2], m[0,1]-m[1,0], t]
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return np.array(q) / (math.sqrt(t)* 0.5)
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def landmarks(tensor, crop_info):
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crop_x1, crop_y1, scale_x, scale_y = crop_info
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t_main = tensor[0:66].reshape((66,784))
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t_m = t_main.argmax(1)
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indices = np.expand_dims(t_m, 1)
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t_off_x = np.take_along_axis(tensor[66:132].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_x, 0.0000001, 0.9999999)
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t_off_x = 13.9375 * np.log(p / (1 - p))
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t_x = crop_y1 + scale_y * (223. * np.floor(t_m / 28) / 27.+ t_off_x)
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t_off_y = np.take_along_axis(tensor[132:198].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_y, 0.0000001, 0.9999999)
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t_off_y = 13.9375 * np.log(p / (1 - p))
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t_y = crop_x1 + scale_x * (223. * np.floor(np.mod(t_m, 28)) / 27. + t_off_y)
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t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
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lms = np.stack([t_x, t_y, t_conf], 1)
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lms[np.isnan(lms).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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return (np.average(t_conf), lms)
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def run( models, crop, crop_info):
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output = models.landmarks.run([], {"input": crop})[0][0]
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confidence, lms = landmarks(output, crop_info)
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return (confidence, lms)
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#I haven't touched much here
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#not because I didn't try to improve things
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#but because I caused problems when I did
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@ -31,120 +60,18 @@ class Landmarks():
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self.inverse_camera = np.linalg.inv(self.camera)
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self.width = width
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self.height = height
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self.confidence = 0
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self.face_3d = np.array([
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[ 0.4551769692672 , 0.300895790030204, -0.764429433974752],
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[ 0.448998827123556, 0.166995837790733, -0.765143004071253],
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[ 0.437431554952677, 0.022655479179981, -0.739267175112735],
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[ 0.415033422928434, -0.088941454648772, -0.747947437846473],
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[ 0.389123587370091, -0.232380029794684, -0.704788385327458],
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[ 0.334630113904382, -0.361265387599081, -0.615587579236862],
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[ 0.263725112132858, -0.460009725616771, -0.491479221041573],
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[ 0.16241621322721 , -0.558037146073869, -0.339445180872282],
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[ 0. , -0.621079019321682, -0.287294770748887],
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[-0.16241621322721 , -0.558037146073869, -0.339445180872282],
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[-0.263725112132858, -0.460009725616771, -0.491479221041573],
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[-0.334630113904382, -0.361265387599081, -0.615587579236862],
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[-0.389123587370091, -0.232380029794684, -0.704788385327458],
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[-0.415033422928434, -0.088941454648772, -0.747947437846473],
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[-0.437431554952677, 0.022655479179981, -0.739267175112735],
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[-0.448998827123556, 0.166995837790733, -0.765143004071253],
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[-0.4551769692672 , 0.300895790030204, -0.764429433974752],
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[ 0.385529968662985, 0.402800553948697, -0.310031082540741],
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[ 0.322196658344302, 0.464439136821772, -0.250558059367669],
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[ 0.25409760441282 , 0.46420381416882 , -0.208177722146526],
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[ 0.186875436782135, 0.44706071961879 , -0.145299823706503],
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[ 0.120880983543622, 0.423566314072968, -0.110757158774771],
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[-0.120880983543622, 0.423566314072968, -0.110757158774771],
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[-0.186875436782135, 0.44706071961879 , -0.145299823706503],
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[-0.25409760441282 , 0.46420381416882 , -0.208177722146526],
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[-0.322196658344302, 0.464439136821772, -0.250558059367669],
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[-0.385529968662985, 0.402800553948697, -0.310031082540741],
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[ 0. , 0.293332603215811, -0.137582088779393],
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[ 0. , 0.194828701837823, -0.069158109325951],
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[ 0. , 0.103844017393155, -0.009151819844964],
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[ 0. , 0. , 0. ],
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[ 0.080626352317973, -0.041276068128093, -0.134161035564826],
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[ 0.046439347377934, -0.057675223874769, -0.102990627164664],
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[ 0. , -0.068753126205604, -0.090545348482397],
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[-0.046439347377934, -0.057675223874769, -0.102990627164664],
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[-0.080626352317973, -0.041276068128093, -0.134161035564826],
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[ 0.315905195966084, 0.298337502555443, -0.285107407636464],
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[ 0.275252345439353, 0.312721904921771, -0.244558251170671],
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[ 0.176394511553111, 0.311907184376107, -0.219205360345231],
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[ 0.131229723798772, 0.284447361805627, -0.234239149487417],
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[ 0.184124948330084, 0.260179585304867, -0.226590776513707],
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[ 0.279433549294448, 0.267363071770222, -0.248441437111633],
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[-0.131229723798772, 0.284447361805627, -0.234239149487417],
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[-0.176394511553111, 0.311907184376107, -0.219205360345231],
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[-0.275252345439353, 0.312721904921771, -0.244558251170671],
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[-0.315905195966084, 0.298337502555443, -0.285107407636464],
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[-0.279433549294448, 0.267363071770222, -0.248441437111633],
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[-0.184124948330084, 0.260179585304867, -0.226590776513707],
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[ 0.121155252430729, -0.208988660580347, -0.160606287940521],
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[ 0.041356305910044, -0.194484199722098, -0.096159882202821],
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[ 0. , -0.205180167345702, -0.083299217789729],
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[-0.041356305910044, -0.194484199722098, -0.096159882202821],
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[-0.121155252430729, -0.208988660580347, -0.160606287940521],
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[-0.132325402795928, -0.290857984604968, -0.187067868218105],
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[-0.064137791831655, -0.325377847425684, -0.158924039726607],
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[ 0. , -0.343742581679188, -0.113925986025684],
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[ 0.064137791831655, -0.325377847425684, -0.158924039726607],
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[ 0.132325402795928, -0.290857984604968, -0.187067868218105],
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[ 0.181481567104525, -0.243239316141725, -0.231284988892766],
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[ 0.083999507750469, -0.239717753728704, -0.155256465640701],
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[ 0. , -0.256058040176369, -0.0950619498899 ],
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[-0.083999507750469, -0.239717753728704, -0.155256465640701],
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[-0.181481567104525, -0.243239316141725, -0.231284988892766],
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[-0.074036069749345, -0.250689938345682, -0.177346470406188],
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[ 0. , -0.264945854681568, -0.112349967428413],
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[ 0.074036069749345, -0.250689938345682, -0.177346470406188],
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# Pupils and eyeball centers
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[ 0.257990002632141, 0.276080012321472, -0.219998998939991],
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[-0.257990002632141, 0.276080012321472, -0.219998998939991],
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[ 0.257990002632141, 0.276080012321472, -0.324570998549461],
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[-0.257990002632141, 0.276080012321472, -0.324570998549461]
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], np.float32)
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self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
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self.fail_count = 0
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def run(self, models, crop, crop_info):
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output = models.landmarks.run([], {"input": crop})[0][0]
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self.confidence, lms = self.landmarks(output, crop_info)
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return ( lms)
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def landmarks(self, tensor, crop_info):
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crop_x1, crop_y1, scale_x, scale_y = crop_info
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t_main = tensor[0:66].reshape((66,784))
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t_m = t_main.argmax(1)
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indices = np.expand_dims(t_m, 1)
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t_off_x = np.take_along_axis(tensor[66:132].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_x, 0.0000001, 0.9999999)
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t_off_x = 13.9375 * np.log(p / (1 - p))
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t_x = crop_y1 + scale_y * (223. * np.floor(t_m / 28) / 27.+ t_off_x)
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t_off_y = np.take_along_axis(tensor[132:198].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_y, 0.0000001, 0.9999999)
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t_off_y = 13.9375 * np.log(p / (1 - p))
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t_y = crop_x1 + scale_x * (223. * np.floor(np.mod(t_m, 28)) / 27. + t_off_y)
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t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
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lms = np.stack([t_x, t_y, t_conf], 1)
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lms[np.isnan(lms).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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return (np.average(t_conf), lms)
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#I really need to break this guy up into smaller parts
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def estimate_depth(self, face_info):
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contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
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lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float32)), 0)
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image_pts = np.array(lms)[self.contour_pts, 0:2]
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image_pts = np.array(lms)[contour_pts, 0:2]
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success = False
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if face_info.rotation is not None:
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@ -227,8 +154,7 @@ class Landmarks():
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self.fail_count += 1
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if self.fail_count > 5:
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# Something went wrong with adjusting the 3D model
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print(f"Detected anomaly when 3D fitting face {face_info.id}. Resetting.")
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face_info.face_3d = self.face_3d
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print(f"Detected anomaly when 3D fitting face {0}. Resetting.")
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face_info.rotation = None
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face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
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face_info.update_counts = np.zeros((66,2))
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@ -130,14 +130,14 @@ class Tracker():
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if crop is None:
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return self.early_exit("No valid crops", start)
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start_model = time.perf_counter()
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lms = self.Landmarks.run(self.model, crop, crop_info)
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confidence, lms = landmarks.run(self.model, crop, crop_info)
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#Early exit if below confidence threshold
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if self.Landmarks.confidence < self.threshold:
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if confidence < self.threshold:
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return self.early_exit("Confidence below threshold", start)
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eye_state = self.EyeTracker.get_eye_state(self.model, frame, lms)
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self.face_info.update((self.Landmarks.confidence, (lms, eye_state)), np.array(lms)[:, 0:2].mean(0))
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self.face_info.update((confidence, (lms, eye_state)), np.array(lms)[:, 0:2].mean(0))
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duration_model = 1000 * (time.perf_counter() - start_model)
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start_pnp = time.perf_counter()
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