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Optimized estimate_depth function for a 0.5ms speedup.

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Emiliana 2020-08-09 19:34:14 +02:00 • committed by GitHub
parent 6b876be623
commit 76bd3dcf3a
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1 changed files with 21 additions and 15 deletions
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36
tracker.py
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@ -708,7 +708,17 @@ class Tracker():
rmat, _ = cv2.Rodrigues(rotation)
inverse_rotation = np.linalg.inv(rmat)
pnp_error = 0.0
t_reference = face_info.face_3d.dot(rmat.transpose())
t_reference = t_reference + face_info.translation
t_reference = t_reference.dot(self.camera.transpose())
t_depth = t_reference[:, 2]
t_depth[t_depth == 0] = 0.000001
t_depth_e = np.expand_dims(t_depth[:],1)
t_reference = t_reference[:] / t_depth_e
pts_3d[0:66] = np.stack([lms[0:66,0], lms[0:66,1], np.ones((66,))], 1) * t_depth_e[0:66]
pts_3d[0:66] = (pts_3d[0:66].dot(self.inverse_camera.transpose()) - face_info.translation).dot(inverse_rotation.transpose())
pnp_error = np.power(lms[0:17,0:2] - t_reference[0:17,0:2], 2).sum()
pnp_error += np.power(lms[30,0:2] - t_reference[30,0:2], 2).sum()
for i, pt in enumerate(face_info.face_3d):
if i == 68:
# Right eyeball
@ -737,20 +747,16 @@ class Tracker():
d2 = np.linalg.norm(lms[i,0:2] - lms[45,0:2])
d = d1 + d2
pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
reference = rmat.dot(pt)
reference = reference + face_info.translation
reference = self.camera.dot(reference)
depth = reference[2]
if i < 17 or i == 30:
reference = reference / depth
e1 = lms[i][0] - reference[0]
e2 = lms[i][1] - reference[1]
pnp_error += e1*e1 + e2*e2
pt_3d = np.array([lms[i][0] * depth, lms[i][1] * depth, depth], np.float32)
pt_3d = self.inverse_camera.dot(pt_3d)
pt_3d = pt_3d - face_info.translation
pt_3d = inverse_rotation.dot(pt_3d)
pts_3d[i,:] = pt_3d[:]
if i == 66 or i == 67:
reference = rmat.dot(pt)
reference = reference + face_info.translation
reference = self.camera.dot(reference)
depth = reference[2]
pt_3d = np.array([lms[i][0] * depth, lms[i][1] * depth, depth], np.float32)
pt_3d = self.inverse_camera.dot(pt_3d)
pt_3d = pt_3d - face_info.translation
pt_3d = inverse_rotation.dot(pt_3d)
pts_3d[i,:] = pt_3d[:]
pnp_error = np.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
if pnp_error > 300: