def _parse_open3d_json_file(open3d_ifp, image_dp, image_relative_fp_list, image_fp_type, op):
cams = []
with open(open3d_ifp, 'r') as open3d_file:
json_data = json.load(open3d_file)
parameters = json_data['parameters']
if len(parameters) != len(image_relative_fp_list):
# Create some dummy names for missing images
image_relative_fp_list = Open3DFileHandler._create_dummy_fp_list(
len(parameters))
for pinhole_camera_parameter, image_relative_fp in zip(parameters, image_relative_fp_list):
cam = Camera()
cam.image_fp_type = image_fp_type
cam.image_dp = image_dp
cam._relative_fp = image_relative_fp
cam._absolute_fp = os.path.join(image_dp, image_relative_fp)
extrinsic = pinhole_camera_parameter['extrinsic']
# Note: the transformation matrix in the .json file is the inverse of
# the transformation matrix in the .log file
extrinsic_mat = np.linalg.inv(
np.array(extrinsic, dtype=float).reshape((4,4)).T)
intrinsic = pinhole_camera_parameter['intrinsic']
cam.width = intrinsic['width']
cam.height = intrinsic['height']
# Accuracy of rotation matrices is too low => disable test
cam.set_4x4_cam_to_world_mat(extrinsic_mat, check_rotation=False)
intrinsic = intrinsic['intrinsic_matrix']
intrinsic_mat = np.array(intrinsic, dtype=float).reshape((3,3)).T
cam.set_calibration_mat(intrinsic_mat)
cams.append(cam)
return cams
def _parse_cameras(input_file, num_cameras, camera_calibration_matrix, op):
"""
VisualSFM CAMERA coordinate system is the standard CAMERA coordinate system in computer vision (not the same
as in computer graphics like in bundler, blender, etc.)
That means
the y axis in the image is pointing downwards (not upwards)
the camera is looking along the positive z axis (points in front of the camera show a positive z value)
The camera coordinate system in computer vision VISUALSFM uses camera matrices,
which are rotated around the x axis by 180 degree
i.e. the y and z axis of the CAMERA MATRICES are inverted
therefore, the y and z axis of the TRANSLATION VECTOR are also inverted
"""
# op.report({'INFO'}, '_parse_cameras: ...')
cameras = []
for i in range(num_cameras):
line = input_file.readline()
# Read the camera section
# From the docs:
# <Camera> = <File name> <focal length> <quaternion WXYZ> <camera center> <radial distortion> 0
line_values = line.split()
file_name = os.path.basename(line_values[0])
focal_length = float(line_values[1])
quaternion_w = float(line_values[2])
quaternion_x = float(line_values[3])
quaternion_y = float(line_values[4])
quaternion_z = float(line_values[5])
quaternion = np.array(
[quaternion_w, quaternion_x, quaternion_y, quaternion_z],
dtype=float)
camera_center_x = float(line_values[6])
camera_center_y = float(line_values[7])
camera_center_z = float(line_values[8])
center_vec = np.array(
[camera_center_x, camera_center_y, camera_center_z])
radial_distortion = float(line_values[9])
# TODO radial_distortion in camera_calibration_matrix
if camera_calibration_matrix is None:
camera_calibration_matrix = np.array([[focal_length, 0, 0],
[0, focal_length, 0],
[0, 0, 1]])
zero_value = float(line_values[10])
assert (zero_value == 0)
current_camera = Camera()
# Setting the quaternion also sets the rotation matrix
current_camera.set_quaternion(quaternion)
# Set the camera center after rotation
# COMMENT FROM PBA CODE:
# older format for compability
# camera_data[i].SetQuaternionRotation(q); // quaternion from the file
# camera_data[i].SetCameraCenterAfterRotation(c); // camera center from the file
current_camera._center = center_vec
# set the camera view direction as normal w.r.t world coordinates
cam_view_vec_cam_coord = np.array([0, 0, 1]).T
cam_rotation_matrix_inv = np.linalg.inv(
current_camera.get_rotation_mat())
cam_view_vec_world_coord = cam_rotation_matrix_inv.dot(
cam_view_vec_cam_coord)
current_camera.normal = cam_view_vec_world_coord
translation_vec = NVMFileHandler.compute_camera_coordinate_system_translation_vector(
center_vec, current_camera.get_rotation_mat())
current_camera._translation_vec = translation_vec
current_camera.set_calibration_mat(camera_calibration_matrix)
# op.report({'INFO'}, 'Calibration mat:')
# op.report({'INFO'}, str(camera_calibration_matrix))
current_camera.file_name = file_name
current_camera.id = i
cameras.append(current_camera)
# op.report({'INFO'}, '_parse_cameras: Done')
return cameras