def parse_cameras(json_data, image_dp, image_fp_type,
suppress_distortion_warnings, op):
views = {item['key']: item for item in json_data['views']}
intrinsics = {item['key']: item for item in json_data['intrinsics']}
extrinsics = {item['key']: item for item in json_data['extrinsics']}
# IMPORTANT:
# Views contain the description about the dataset and attribute to Pose and Intrinsic data.
# View -> id_pose, id_intrinsic
# Since sometimes some views cannot be localized, there is some missing pose and intrinsic data.
# Extrinsics may contain only a subset of views! (Potentially not all views are contained in the reconstruction)
cams = []
# Iterate over views, and create camera if Intrinsic and Pose data exist
for id, view in views.items(): # Iterate over views
id_view = view[
'key'] # Should be equal to view['value']['ptr_wrapper']['data']['id_view']
view_data = view['value']['ptr_wrapper']['data']
id_pose = view_data['id_pose']
id_intrinsic = view_data['id_intrinsic']
# Check if the view is having corresponding Pose and Intrinsic data
if id_pose in extrinsics.keys() and \
id_intrinsic in intrinsics.keys():
camera = Camera()
camera.image_fp_type = image_fp_type
camera.image_dp = image_dp
camera._relative_fp = os.path.join(view_data['local_path'],
view_data['filename'])
camera._absolute_fp = os.path.join(json_data['root_path'],
view_data['local_path'],
view_data['filename'])
camera.width = view_data['width']
camera.height = view_data['height']
id_intrinsic = view_data['id_intrinsic']
# handle intrinsic params
intrinsic_data = intrinsics[int(
id_intrinsic)]['value']['ptr_wrapper']['data']
polymorphic_name = intrinsics[int(
id_intrinsic)]['value']['polymorphic_name']
if polymorphic_name == 'spherical':
camera.set_panoramic_type(
Camera.panoramic_type_equirectangular)
# create some dummy values
focal_length = 0
cx = camera.width / 2
cy = camera.height / 2
else:
focal_length = intrinsic_data['focal_length']
principal_point = intrinsic_data['principal_point']
cx = principal_point[0]
cy = principal_point[1]
# For Radial there are several options: "None", disto_k1, disto_k3
if 'disto_k3' in intrinsic_data:
radial_distortion = [
float(intrinsic_data['disto_k3'][0]),
float(intrinsic_data['disto_k3'][1]),
float(intrinsic_data['disto_k3'][2])
]
elif 'disto_k1' in intrinsic_data:
radial_distortion = float(intrinsic_data['disto_k1'][0])
else: # No radial distortion, i.e. pinhole camera model
radial_distortion = 0
if not suppress_distortion_warnings:
check_radial_distortion(radial_distortion,
camera._relative_fp, op)
camera_calibration_matrix = np.array([[focal_length, 0, cx],
[0, focal_length, cy],
[0, 0, 1]])
camera.set_calibration(camera_calibration_matrix,
radial_distortion)
extrinsic_params = extrinsics[id_pose]
cam_rotation_list = extrinsic_params['value']['rotation']
camera.set_rotation_mat(
np.array(cam_rotation_list, dtype=float))
camera.set_camera_center_after_rotation(
np.array(extrinsic_params['value']['center'], dtype=float))
camera.view_index = id_view
cams.append(camera)
return cams
def _parse_cameras_from_json_data(json_data, image_dp, image_fp_type,
suppress_distortion_warnings, op):
cams = []
image_index_to_camera_index = {}
is_valid_file = 'views' in json_data and 'intrinsics' in json_data and 'poses' in json_data
if not is_valid_file:
op.report({
'ERROR'
}, 'FILE FORMAT ERROR: Incorrect SfM/JSON file. Must contain the SfM reconstruction results: '
+ 'view, intrinsics and poses.')
return cams, image_index_to_camera_index
views = json_data['views'] # is a list of dicts (view)
intrinsics = json_data['intrinsics'] # is a list of dicts (intrinsic)
extrinsics = json_data['poses'] # is a list of dicts (extrinsic)
# IMPORTANT:
# Views contain the number of input images
# Extrinsics may contain only a subset of views!
# (Not all views are necessarily contained in the reconstruction)
for rec_index, extrinsic in enumerate(extrinsics):
camera = Camera()
view_index = int(extrinsic['poseId'])
image_index_to_camera_index[view_index] = rec_index
corresponding_view = get_element(views, "poseId", view_index, op)
camera.image_fp_type = image_fp_type
camera.image_dp = image_dp
camera._absolute_fp = str(corresponding_view['path'])
camera._relative_fp = os.path.basename(
str(corresponding_view['path']))
camera._undistorted_relative_fp = str(extrinsic['poseId']) + '.exr'
if image_dp is None:
camera._undistorted_absolute_fp = None
else:
camera._undistorted_absolute_fp = os.path.join(
image_dp, camera._undistorted_relative_fp)
camera.width = int(corresponding_view['width'])
camera.height = int(corresponding_view['height'])
id_intrinsic = int(corresponding_view['intrinsicId'])
intrinsic_params = get_element(intrinsics, "intrinsicId",
id_intrinsic, op)
focal_length = float(intrinsic_params['pxFocalLength'])
cx = float(intrinsic_params['principalPoint'][0])
cy = float(intrinsic_params['principalPoint'][1])
if 'distortionParams' in intrinsic_params and len(
intrinsic_params['distortionParams']) > 0:
# TODO proper handling of distortion parameters
radial_distortion = float(
intrinsic_params['distortionParams'][0])
else:
radial_distortion = 0.0
if not suppress_distortion_warnings:
check_radial_distortion(radial_distortion, camera._relative_fp,
op)
camera_calibration_matrix = np.array([[focal_length, 0, cx],
[0, focal_length, cy],
[0, 0, 1]])
camera.set_calibration(camera_calibration_matrix,
radial_distortion)
extrinsic_params = extrinsic['pose']['transform']
cam_rotation_list = extrinsic_params['rotation']
camera.set_rotation_mat(
np.array(cam_rotation_list, dtype=float).reshape(3, 3).T)
camera.set_camera_center_after_rotation(
np.array(extrinsic_params['center'], dtype=float))
camera.view_index = view_index
cams.append(camera)
return cams, image_index_to_camera_index
def parse_cameras(json_data, op):
views = json_data['views']
intrinsics = json_data['intrinsics']
extrinsics = json_data['extrinsics']
# IMPORTANT:
# Views contain the number of input images
# Extrinsics may contain only a subset of views! (Potentially not all views are contained in the reconstruction)
# Matching entries are determined by view['key'] == extrinsics['key']
cams = []
image_index_to_camera_index = {}
for rec_index, extrinsic in enumerate(
extrinsics): # Iterate over extrinsics, not views!
camera = Camera()
# The key is defined w.r.t. view indices (NOT reconstructed camera indices)
view_index = int(extrinsic['key'])
image_index_to_camera_index[view_index] = rec_index
corresponding_view = views[view_index]
camera.file_name = corresponding_view['value']['ptr_wrapper'][
'data']['filename']
camera.width = corresponding_view['value']['ptr_wrapper']['data'][
'width']
camera.height = corresponding_view['value']['ptr_wrapper']['data'][
'height']
id_intrinsic = corresponding_view['value']['ptr_wrapper']['data'][
'id_intrinsic']
# handle intrinsic params
intrinsic_params = intrinsics[int(
id_intrinsic)]['value']['ptr_wrapper']['data']
focal_length = intrinsic_params['focal_length']
principal_point = intrinsic_params['principal_point']
cx = principal_point[0]
cy = principal_point[1]
if 'disto_k3' in intrinsic_params:
op.report({'INFO'},
'3 Radial Distortion Parameters are not supported')
assert False
# For Radial there are several options: "None", disto_k1, disto_k3
if 'disto_k1' in intrinsic_params:
radial_distortion = float(intrinsic_params['disto_k1'][0])
else: # No radial distortion, i.e. pinhole camera model
radial_distortion = 0
camera_calibration_matrix = np.array([[focal_length, 0, cx],
[0, focal_length, cy],
[0, 0, 1]])
camera.set_calibration(camera_calibration_matrix,
radial_distortion)
extrinsic_params = extrinsic['value']
cam_rotation_list = extrinsic_params['rotation']
camera.set_rotation_mat(np.array(cam_rotation_list, dtype=float))
camera.set_camera_center_after_rotation(
np.array(extrinsic_params['center'], dtype=float))
camera.view_index = view_index
cams.append(camera)
return cams, image_index_to_camera_index