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Made landmarks static

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Cowwan 2024-02-06 21:18:49 -05:00
parent 68f350f30e
commit 31d16b20c0
3 changed files with 91 additions and 107 deletions
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194
landmarks.py
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@ -50,117 +50,101 @@ def run( models, crop, crop_info):
return (confidence, lms)
#I haven't touched much here
#not because I didn't try to improve things
#but because I caused problems when I did
class Landmarks():
def __init__(self, width, height, threshold):
def estimate_depth( face_info, width, height):
camera = np.array([[width, 0, width/2], [0, width, height/2], [0, 0, 1]], np.float32)
inverse_camera = np.linalg.inv(camera)
contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
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)
image_pts = np.array(lms)[contour_pts, 0:2]
self.camera = np.array([[width, 0, width/2], [0, width, height/2], [0, 0, 1]], np.float32)
self.inverse_camera = np.linalg.inv(self.camera)
self.width = width
self.height = height
self.fail_count = 0
success = False
if face_info.rotation is not None:
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
else:
rvec = np.array([0, 0, 0], np.float32)
tvec = np.array([0, 0, 0], np.float32)
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE)
if not success:
face_info.rotation = np.array([0.0, 0.0, 0.0], np.float32)
face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms
rotation = face_info.rotation
translation = face_info.translation
pts_3d = np.zeros((70,3), np.float32)
face_info.rotation = np.transpose(face_info.rotation)
face_info.translation = np.transpose(face_info.translation)
rmat, _ = cv2.Rodrigues(rotation)
inverse_rotation = np.linalg.inv(rmat)
t_reference = face_info.face_3d.dot(rmat.transpose())
t_reference = t_reference + face_info.translation
t_reference = t_reference.dot(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(inverse_camera.transpose()) - face_info.translation).dot(inverse_rotation.transpose())
for i, pt in enumerate(face_info.face_3d[66:70]):
if i == 2:
# Right eyeball
# Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied
eye_center = (pts_3d[36] + pts_3d[39]) / 2.0
d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39])
depth = 0.385 * d_corner
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
pts_3d[68] = pt_3d
continue
if i == 3:
# Left eyeball
eye_center = (pts_3d[42] + pts_3d[45]) / 2.0
d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45])
depth = 0.385 * d_corner
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
pts_3d[69] = pt_3d
continue
if i == 0:
d1 = np.linalg.norm(lms[66,0:2] - lms[36,0:2])
d2 = np.linalg.norm(lms[66,0:2] - lms[39,0:2])
d = d1 + d2
pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d
if i == 1:
d1 = np.linalg.norm(lms[67,0:2] - lms[42,0:2])
d2 = np.linalg.norm(lms[67,0:2] - lms[45,0:2])
d = d1 + d2
pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
if i < 2:
reference = rmat.dot(pt)
reference = reference + face_info.translation
reference = camera.dot(reference)
depth = reference[2]
pt_3d = np.array([lms[66+i][0] * depth, lms[66+i][1] * depth, depth], np.float32)
pt_3d = inverse_camera.dot(pt_3d)
pt_3d = pt_3d - face_info.translation
pt_3d = inverse_rotation.dot(pt_3d)
pts_3d[66+i,:] = pt_3d[:]
#I really need to break this guy up into smaller parts
def estimate_depth(self, face_info):
contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
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)
image_pts = np.array(lms)[contour_pts, 0:2]
success = False
if face_info.rotation is not None:
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
else:
rvec = np.array([0, 0, 0], np.float32)
tvec = np.array([0, 0, 0], np.float32)
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE)
if not success:
face_info.rotation = np.array([0.0, 0.0, 0.0], np.float32)
pts_3d[np.isnan(pts_3d).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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()
if np.isnan(pnp_error):
pnp_error = 9999999.
pnp_error = math.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
if pnp_error > 300:
face_info.fail_count += 1
if face_info.fail_count > 5:
# Something went wrong with adjusting the 3D model
print(f"Detected anomaly when 3D fitting face {0}. Resetting.")
face_info.rotation = None
face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms
face_info.update_counts = np.zeros((66,2))
face_info.update_contour()
else:
face_info.fail_count = 0
rotation = face_info.rotation
translation = face_info.translation
pts_3d = np.zeros((70,3), np.float32)
face_info.rotation = np.transpose(face_info.rotation)
face_info.translation = np.transpose(face_info.translation)
rmat, _ = cv2.Rodrigues(rotation)
inverse_rotation = np.linalg.inv(rmat)
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())
for i, pt in enumerate(face_info.face_3d[66:70]):
if i == 2:
# Right eyeball
# Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied
eye_center = (pts_3d[36] + pts_3d[39]) / 2.0
d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39])
depth = 0.385 * d_corner
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
pts_3d[68] = pt_3d
continue
if i == 3:
# Left eyeball
eye_center = (pts_3d[42] + pts_3d[45]) / 2.0
d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45])
depth = 0.385 * d_corner
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
pts_3d[69] = pt_3d
continue
if i == 0:
d1 = np.linalg.norm(lms[66,0:2] - lms[36,0:2])
d2 = np.linalg.norm(lms[66,0:2] - lms[39,0:2])
d = d1 + d2
pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d
if i == 1:
d1 = np.linalg.norm(lms[67,0:2] - lms[42,0:2])
d2 = np.linalg.norm(lms[67,0:2] - lms[45,0:2])
d = d1 + d2
pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
if i < 2:
reference = rmat.dot(pt)
reference = reference + face_info.translation
reference = self.camera.dot(reference)
depth = reference[2]
pt_3d = np.array([lms[66+i][0] * depth, lms[66+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[66+i,:] = pt_3d[:]
pts_3d[np.isnan(pts_3d).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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()
if np.isnan(pnp_error):
pnp_error = 9999999.
pnp_error = math.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
if pnp_error > 300:
self.fail_count += 1
if self.fail_count > 5:
# Something went wrong with adjusting the 3D model
print(f"Detected anomaly when 3D fitting face {0}. Resetting.")
face_info.rotation = None
face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
face_info.update_counts = np.zeros((66,2))
face_info.update_contour()
else:
self.fail_count = 0
euler = cv2.RQDecomp3x3(rmat)[0]
return True, matrix_to_quaternion(rmat), euler, pnp_error, pts_3d, lms
euler = cv2.RQDecomp3x3(rmat)[0]
return True, matrix_to_quaternion(rmat), euler, pnp_error, pts_3d, lms