def preprocess_3d_data(matched_data, camera_intrinsics ):
camera_matrix = camera_intrinsics["camera_matrix"]
dist_coefs = camera_intrinsics["dist_coefs"]
ref_processed = []
pupil0_processed = []
pupil1_processed = []
is_binocular = len(matched_data[0] ) == 3
for data_point in matched_data:
try:
# taking the pupil normal as line of sight vector
pupil0 = data_point['pupil']
gaze_vector0 = np.array(pupil0['circle_3d']['normal'])
pupil0_processed.append( gaze_vector0 )
if is_binocular: # we have binocular data
pupil1 = data_point['pupil1']
gaze_vector1 = np.array(pupil1['circle_3d']['normal'])
pupil1_processed.append( gaze_vector1 )
# projected point uv to normal ray vector of camera
ref = data_point['ref']
ref_vector = undistort_unproject_pts(ref['screen_pos'] , camera_matrix, dist_coefs).tolist()[0]
ref_vector = ref_vector / np.linalg.norm(ref_vector)
# assuming a fixed (assumed) distance we get a 3d point in world camera 3d coords.
ref_processed.append( np.array(ref_vector) )
except KeyError as e:
# this pupil data point did not have 3d detected data.
pass
return ref_processed,pupil0_processed,pupil1_processed
def preprocess_3d_data_binocular(matched_data, camera_intrinsics , calibration_distance):
camera_matrix = camera_intrinsics["camera_matrix"]
dist_coefs = camera_intrinsics["dist_coefs"]
cal_data = []
for triplet in matched_data:
ref,p0,p1 = triplet['ref'],triplet['pupil0'],triplet['pupil1']
try:
# taking the pupil normal as line of sight vector
# we multiply by a fixed (assumed) distance and
# add the sphere pos to get the 3d gaze point in eye camera 3d coords
sphere_pos0 = np.array(p0['sphere']['center'])
gaze_pt0 = np.array(p0['circle3D']['normal']) * calibration_distance + sphere_pos0
sphere_pos1 = np.array(p1['sphere']['center'])
gaze_pt1 = np.array(p1['circle3D']['normal']) * calibration_distance + sphere_pos1
# projected point uv to normal ray vector of camera
ref_vector = undistort_unproject_pts(ref['screen_pos'] , camera_matrix, dist_coefs).tolist()[0]
ref_vector = ref_vector / np.linalg.norm(ref_vector)
# assuming a fixed (assumed) distance we get a 3d point in world camera 3d coords.
ref_pt_3d = ref_vector*calibration_distance
point_triple_3d = tuple(gaze_pt0), tuple(gaze_pt1) , ref_pt_3d
cal_data.append(point_triple_3d)
except KeyError as e:
# this pupil data point did not have 3d detected data.
pass
return cal_data
def preprocess_3d_data_monocular(matched_data, camera_intrinsics,
calibration_distance):
camera_matrix = camera_intrinsics["camera_matrix"]
dist_coefs = camera_intrinsics["dist_coefs"]
cal_data = []
for pair in matched_data:
ref, pupil = pair['ref'], pair['pupil']
try:
# taking the pupil normal as line of sight vector
# we multiply by a fixed (assumed) distace and
# add the sphere pos to get the 3d gaze point in eye camera 3d coords
sphere_pos = np.array(pupil['sphere']['center'])
gaze_pt_3d = np.array(pupil['circle3D']['normal']
) * calibration_distance + sphere_pos
# projected point uv to normal ray vector of camera
ref_vector = undistort_unproject_pts(ref['screen_pos'],
camera_matrix,
dist_coefs).tolist()[0]
ref_vector = ref_vector / np.linalg.norm(ref_vector)
# assuming a fixed (assumed) distance we get a 3d point in world camera 3d coords.
ref_pt_3d = ref_vector * calibration_distance
point_pair_3d = tuple(gaze_pt_3d), ref_pt_3d
cal_data.append(point_pair_3d)
except KeyError as e:
# this pupil data point did not have 3d detected data.
pass
return cal_data
