def estimate_pitch_yaw_roll(aligned_landmarks, size=256):
"""
returns pitch,yaw,roll [-pi/2...+pi/2]
"""
shape = (size,size)
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, _) = cv2.solvePnP(
np.concatenate( (landmarks_68_3D[:27], landmarks_68_3D[30:36]) , axis=0) ,
np.concatenate( (aligned_landmarks[:27], aligned_landmarks[30:36]) , axis=0).astype(np.float32),
camera_matrix,
np.zeros((4, 1)) )
pitch, yaw, roll = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
half_pi = math.pi / 2.0
pitch = np.clip ( pitch, -half_pi, half_pi )
yaw = np.clip ( yaw , -half_pi, half_pi )
roll = np.clip ( roll, -half_pi, half_pi )
return -pitch, yaw, roll
def estimate_pitch_yaw_roll(aligned_256px_landmarks):
"""
returns pitch,yaw,roll [-pi...+pi]
"""
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, roll = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
pitch = np.clip ( pitch, -math.pi, math.pi )
yaw = np.clip ( yaw , -math.pi, math.pi )
roll = np.clip ( roll, -math.pi, math.pi )
return -pitch, yaw, roll
#if remove_align:
# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
# #import code
# #code.interact(local=dict(globals(), **locals()))
# area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
# side = math.sqrt(area) / 2
# center = transform_points ( [(output_size/2,output_size/2)], mat, True)
# pts1 = np.float32(( center+[-side,-side], center+[side,-side], center+[side,-side] ))
# pts2 = np.float32([[0,0],[output_size,0],[0,output_size]])
# mat = cv2.getAffineTransform(pts1,pts2)
#if full_face_align_top and (face_type == FaceType.FULL or face_type == FaceType.FULL_NO_ALIGN):
# #lmrks2 = expand_eyebrows(image_landmarks)
# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
# #y_diff = transform_points( [ np.float32( (0,0) ), y_diff], mat, True)
# #y_diff = y_diff[1]-y_diff[0]
#
# x_diff = np.float32((0,0))
#
# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
# if lmrks2_[0,0] < 0:
# x_diff = lmrks2_[0,0]
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
# elif lmrks2_[1,0] >= output_size:
# x_diff = lmrks2_[1,0]-(output_size-1)
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
#
# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
def estimate_pitch_yaw_roll(aligned_256px_landmarks):
"""
returns pitch,yaw,roll [-pi...+pi]
"""
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, roll = mathlib.rotationMatrixToEulerAngles(
cv2.Rodrigues(rotation_vector)[0])
pitch = np.clip(pitch, -math.pi, math.pi)
yaw = np.clip(yaw, -math.pi, math.pi)
roll = np.clip(roll, -math.pi, math.pi)
return -pitch, yaw, roll
