def convert_cameras(id_to_col_cameras, id_to_col_images, image_dp,
image_fp_type, suppress_distortion_warnings, op):
# From photogrammetry_importer\ext\read_write_model.py
# CameraModel = collections.namedtuple(
# "CameraModel", ["model_id", "model_name", "num_params"])
# Camera = collections.namedtuple(
# "Camera", ["id", "model", "width", "height", "params"])
# BaseImage = collections.namedtuple(
# "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
cameras = []
for col_image in id_to_col_images.values():
current_camera = Camera()
current_camera.id = col_image.id
current_camera.set_quaternion(col_image.qvec)
current_camera.set_camera_translation_vector_after_rotation(
col_image.tvec)
current_camera.image_fp_type = image_fp_type
current_camera.image_dp = image_dp
current_camera._relative_fp = col_image.name
camera_model = id_to_col_cameras[col_image.camera_id]
# op.report({'INFO'}, 'image_id: ' + str(col_image.id))
# op.report({'INFO'}, 'camera_id: ' + str(col_image.camera_id))
# op.report({'INFO'}, 'camera_model: ' + str(camera_model))
current_camera.width = camera_model.width
current_camera.height = camera_model.height
focal_length, cx, cy, r = parse_camera_param_list(camera_model)
if not suppress_distortion_warnings:
check_radial_distortion(r, current_camera._relative_fp, op)
camera_calibration_matrix = np.array([[focal_length, 0, cx],
[0, focal_length, cy],
[0, 0, 1]])
current_camera.set_calibration(camera_calibration_matrix,
radial_distortion=0)
cameras.append(current_camera)
return cameras
def convert_cameras(id_to_col_cameras, id_to_col_images, op):
# CameraModel = collections.namedtuple(
# "CameraModel", ["model_id", "model_name", "num_params"])
# Camera = collections.namedtuple(
# "Camera", ["id", "model", "width", "height", "params"])
# BaseImage = collections.namedtuple(
# "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
cameras = []
for col_image in id_to_col_images.values():
current_camera = Camera()
current_camera.id = col_image.id
current_camera.set_quaternion(col_image.qvec)
current_camera.set_camera_translation_vector_after_rotation(
col_image.tvec)
current_camera.file_name = col_image.name
camera_models = list(id_to_col_cameras.values())
# Blender supports only one camera model for all images
assert len(camera_models) == 1
camera_model = camera_models[0]
op.report({'INFO'}, 'camera_model: ' + str(camera_model))
current_camera.width = camera_model.width
current_camera.height = camera_model.height
focal_length, cx, cy = parse_camera_param_list(camera_model)
camera_calibration_matrix = np.array([[focal_length, 0, 0],
[0, focal_length, 0],
[0, 0, 1]])
current_camera.set_calibration(camera_calibration_matrix,
radial_distortion=0)
current_camera.set_principal_point([cx, cy])
cameras.append(current_camera)
return cameras
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