def set_intrinsics_of_cameras(self, cameras):
"""Enhances the imported cameras with intrinsic information.
Overwrites the method in :code:`CameraImporter`.
"""
intrinsic_missing = False
for cam in cameras:
if not cam.has_intrinsics():
intrinsic_missing = True
break
if not intrinsic_missing:
log_report("INFO", "Using intrinsics from file (.json).", self)
return cameras, True
else:
log_report(
"INFO",
"Using intrinsics from user options, since not present in the"
+ " reconstruction file (.log).",
self,
)
if math.isnan(self.default_focal_length):
log_report(
"ERROR",
"User must provide the focal length using the import" +
" options.",
self,
)
return [], False
if math.isnan(self.default_pp_x) or math.isnan(self.default_pp_y):
log_report(
"WARNING",
"Setting the principal point to the image center.",
self,
)
for cam in cameras:
if math.isnan(self.default_pp_x) or math.isnan(
self.default_pp_y):
# If no images are provided, the user must provide a
# default principal point
assert cam.width is not None
# If no images are provided, the user must provide a
# default principal point
assert cam.height is not None
default_cx = cam.width / 2.0
default_cy = cam.height / 2.0
else:
default_cx = self.default_pp_x
default_cy = self.default_pp_y
intrinsics = Camera.compute_calibration_mat(
focal_length=self.default_focal_length,
cx=default_cx,
cy=default_cy,
)
cam.set_calibration_mat(intrinsics)
return cameras, True
def _parse_open3d_log_file(open3d_ifp, image_dp, image_relative_fp_list,
image_fp_type, op):
cams = []
with open(open3d_ifp, "r") as open3d_file:
lines = open3d_file.readlines()
# Chunk size: 1 line meta data, 4 lines for the matrix
chunk_size = 5
assert len(lines) % chunk_size == 0
chunks = Open3DFileHandler._chunker(lines, chunk_size)
if len(chunks) != len(image_relative_fp_list):
# Create some dummy names for missing images
image_relative_fp_list = (
Open3DFileHandler._create_dummy_fp_list(len(chunks)))
for chunk, image_relative_fp in zip(chunks,
image_relative_fp_list):
meta_data = chunk[0]
matrix_list = [
Open3DFileHandler._read_matrix_row(chunk[1]),
Open3DFileHandler._read_matrix_row(chunk[2]),
Open3DFileHandler._read_matrix_row(chunk[3]),
]
# Note: the transformation matrix in the .json file is the inverse of
# the transformation matrix in the .log file
extrinsic_matrix = np.asarray(matrix_list, dtype=float)
cam = Camera()
cam.image_fp_type = image_fp_type
cam.image_dp = image_dp
cam._relative_fp = image_relative_fp
image_absolute_fp = os.path.join(image_dp, image_relative_fp)
cam._absolute_fp = image_absolute_fp
# Accuracy of rotation matrices is too low => disable rotation test
cam.set_4x4_cam_to_world_mat(extrinsic_matrix,
check_rotation=False)
cams.append(cam)
return cams
def get_calibration_mat(self, blender_camera):
log_report("INFO", "get_calibration_mat: ...", self)
scene = bpy.context.scene
render_resolution_width = scene.render.resolution_x
render_resolution_height = scene.render.resolution_y
focal_length_in_mm = float(blender_camera.data.lens)
sensor_width_in_mm = float(blender_camera.data.sensor_width)
focal_length_in_pixel = (
float(max(scene.render.resolution_x, scene.render.resolution_y)) *
focal_length_in_mm / sensor_width_in_mm)
max_extent = max(render_resolution_width, render_resolution_height)
p_x = (render_resolution_width / 2.0 -
blender_camera.data.shift_x * max_extent)
p_y = (render_resolution_height / 2.0 -
blender_camera.data.shift_y * max_extent)
calibration_mat = Camera.compute_calibration_mat(focal_length_in_pixel,
cx=p_x,
cy=p_y)
log_report(
"INFO",
"render_resolution_width: " + str(render_resolution_width),
self,
)
log_report(
"INFO",
"render_resolution_height: " + str(render_resolution_height),
self,
)
log_report(
"INFO",
"focal_length_in_pixel: " + str(focal_length_in_pixel),
self,
)
log_report("INFO", "get_calibration_mat: Done", self)
return calibration_mat
def _parse_cameras(json_data, image_dp, image_fp_type,
suppress_distortion_warnings, op):
json_cameras_intrinsics = json_data["cameras"]
views = json_data["shots"]
cams = []
for view_name in views:
view = views[view_name]
camera = Camera()
camera.image_fp_type = image_fp_type
camera.image_dp = image_dp
camera._relative_fp = view_name
camera._absolute_fp = os.path.join(image_dp, view_name)
intrinsic_key = view["camera"]
(
focal_length,
cx,
cy,
width,
height,
radial_distortion,
) = OpenSfMJSONFileHandler._convert_intrinsics(
json_cameras_intrinsics[intrinsic_key],
camera._relative_fp,
suppress_distortion_warnings,
op,
)
camera.height = height
camera.width = width
camera_calibration_matrix = np.array([[focal_length, 0, cx],
[0, focal_length, cy],
[0, 0, 1]])
camera.set_calibration(camera_calibration_matrix,
radial_distortion)
rodrigues_vec = np.array(view["rotation"], dtype=float)
rot_mat = OpenSfMJSONFileHandler._rodrigues_to_matrix(
rodrigues_vec)
camera.set_rotation_mat(rot_mat)
camera.set_camera_translation_vector_after_rotation(
np.array(view["translation"], dtype=float))
cams.append(camera)
return cams
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_meta(meta_ifp, width, height, camera_name, op):
view_specific_dir = os.path.dirname(meta_ifp)
relative_image_fp = os.path.join(view_specific_dir, "undistorted.png")
image_dp = os.path.dirname(view_specific_dir)
camera = Camera()
camera.image_fp_type = Camera.IMAGE_FP_TYPE_RELATIVE
camera.image_dp = image_dp
camera._relative_fp = relative_image_fp
camera._absolute_fp = os.path.join(image_dp, relative_image_fp)
camera.width = width
camera.height = height
ini_config = configparser.RawConfigParser()
ini_config.read(meta_ifp)
focal_length_normalized = float(
ini_config.get(section="camera", option="focal_length"))
pixel_aspect = float(
ini_config.get(section="camera", option="pixel_aspect"))
if pixel_aspect != 1.0:
log_report(
"WARNING",
"Focal length differs in x and y direction," +
" setting it to the average value.",
op,
)
focal_length_normalized = (
focal_length_normalized +
focal_length_normalized * pixel_aspect) / 2
max_extend = max(width, height)
focal_length = focal_length_normalized * max_extend
principal_point_str = ini_config.get(section="camera",
option="principal_point")
principal_point_list = MVEFileHandler.str_to_arr(principal_point_str,
target_type=float)
cx_normalized = principal_point_list[0]
cy_normalized = principal_point_list[1]
cx = cx_normalized * width
cy = cy_normalized * height
calib_mat = Camera.compute_calibration_mat(focal_length, cx, cy)
camera.set_calibration_mat(calib_mat)
radial_distortion_str = ini_config.get(section="camera",
option="radial_distortion")
radial_distortion_vec = np.asarray(
MVEFileHandler.str_to_arr(radial_distortion_str,
target_type=float))
check_radial_distortion(radial_distortion_vec, relative_image_fp, op)
rotation_str = ini_config.get(section="camera", option="rotation")
rotation_mat = np.asarray(
MVEFileHandler.str_to_arr(rotation_str,
target_type=float)).reshape((3, 3))
translation_str = ini_config.get(section="camera",
option="translation")
translation_vec = np.asarray(
MVEFileHandler.str_to_arr(translation_str, target_type=float))
camera.set_rotation_mat(rotation_mat)
camera.set_camera_translation_vector_after_rotation(translation_vec)
return camera
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:
log_report(
"ERROR",
"FILE FORMAT ERROR: Incorrect SfM/JSON file. Must contain the"
+ " SfM reconstruction results: view, intrinsics and poses.",
op,
)
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 = MeshroomFileHandler._get_element(
views, "poseId", view_index)
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 = MeshroomFileHandler._get_element(
intrinsics, "intrinsicId", id_intrinsic)
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 _convert_cameras(
id_to_col_cameras,
id_to_col_images,
image_dp,
image_fp_type,
depth_map_idp=None,
suppress_distortion_warnings=False,
op=None,
):
# 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_rotation_with_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
# log_report('INFO', 'image_dp: ' + str(image_dp))
# log_report('INFO', 'col_image.name: ' + str(col_image.name))
camera_model = id_to_col_cameras[col_image.camera_id]
# log_report('INFO', 'image_id: ' + str(col_image.id))
# log_report('INFO', 'camera_id: ' + str(col_image.camera_id))
# log_report('INFO', 'camera_model: ' + str(camera_model))
current_camera.width = camera_model.width
current_camera.height = camera_model.height
(
fx,
fy,
cx,
cy,
skew,
r,
) = ColmapFileHandler._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([[fx, skew, cx], [0, fy, cy],
[0, 0, 1]])
current_camera.set_calibration(camera_calibration_matrix,
radial_distortion=0)
if depth_map_idp is not None:
geometric_ifp = os.path.join(depth_map_idp,
col_image.name + ".geometric.bin")
photometric_ifp = os.path.join(
depth_map_idp, col_image.name + ".photometric.bin")
if os.path.isfile(geometric_ifp):
depth_map_ifp = geometric_ifp
elif os.path.isfile(photometric_ifp):
depth_map_ifp = photometric_ifp
else:
depth_map_ifp = None
current_camera.set_depth_map_callback(
read_array,
depth_map_ifp,
Camera.DEPTH_MAP_WRT_CANONICAL_VECTORS,
shift_depth_map_to_pixel_center=False,
)
cameras.append(current_camera)
return cameras
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"]}
# Regard 3D stores the polymorhic attribute in the first intrinsic
default_polymorphic_name = (
OpenMVGJSONFileHandler.get_default_polymorphic_name(intrinsics))
# 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_values = intrinsics[int(id_intrinsic)]["value"]
intrinsic_data = intrinsic_values["ptr_wrapper"]["data"]
if "polymorphic_name" in intrinsic_values:
polymorphic_name = intrinsic_values["polymorphic_name"]
else:
polymorphic_name = default_polymorphic_name
log_report(
"WARNING",
"Key polymorphic_name in intrinsic with id " +
str(id_intrinsic) +
" is missing, substituting with polymorphic_name of first intrinsic.",
op,
)
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(
json_data,
image_dp,
image_fp_type,
suppress_distortion_warnings,
op=None,
):
# For each input image there exists an entry in "views". In contrast,
# "extrinsics" contains only information of registered images (i.e.
# reconstructed camera poses) and may contain only information for a
# subset of images.
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"]}
# Regard 3D stores the polymorhic attribute in the first intrinsic
default_polymorphic_name = (
OpenMVGJSONFileHandler._get_default_polymorphic_name(intrinsics))
cams = []
# Iterate over views and create a camera if intrinsic and extrinsic
# parameters exist
for id, view in views.items(): # Iterate over views
id_view = view["key"]
# view["value"]["ptr_wrapper"]["data"] 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_values = intrinsics[int(id_intrinsic)]["value"]
intrinsic_data = intrinsic_values["ptr_wrapper"]["data"]
if "polymorphic_name" in intrinsic_values:
polymorphic_name = intrinsic_values["polymorphic_name"]
else:
polymorphic_name = default_polymorphic_name
log_report(
"WARNING",
"Key polymorphic_name in intrinsic with id " +
str(id_intrinsic) +
" is missing, substituting with polymorphic_name of" +
" first intrinsic.",
op,
)
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_with_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(
cls,
input_file,
num_cameras,
camera_calibration_matrix,
image_dp,
image_fp_type,
suppress_distortion_warnings,
op=None,
):
"""
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)
and 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
"""
# log_report('INFO', '_parse_cameras: ...', op)
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()
relative_path = line_values[0].replace("/", os.sep)
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])
if not suppress_distortion_warnings:
check_radial_distortion(radial_distortion, relative_path, op)
if camera_calibration_matrix is None:
# In this case, we have no information about the principal point
# We assume that the principal point lies in the center
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_rotation_with_quaternion(quaternion)
# Set the camera center after rotation
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_as_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 = cls._compute_translation_vector(
center_vec, current_camera.get_rotation_as_rotation_mat())
current_camera._translation_vec = translation_vec
current_camera.set_calibration(camera_calibration_matrix,
radial_distortion=radial_distortion)
# log_report('INFO', 'Calibration mat:', op)
# log_report('INFO', str(camera_calibration_matrix), op)
current_camera.image_fp_type = image_fp_type
current_camera.image_dp = image_dp
current_camera._relative_fp = relative_path
current_camera.id = i
cameras.append(current_camera)
# log_report('INFO', '_parse_cameras: Done', op)
return cameras
def export_selected_cameras_and_vertices_of_meshes(self, odp):
log_report("INFO",
"export_selected_cameras_and_vertices_of_meshes: ...", self)
cameras = []
points = []
point_index = 0
camera_index = 0
for obj in bpy.context.selected_objects:
if obj.type == "CAMERA":
log_report("INFO", "obj.name: " + str(obj.name), self)
calibration_mat = self.get_calibration_mat(obj)
# log_report('INFO', 'calibration_mat:', self)
# log_report('INFO', str(calibration_mat), self)
camera_matrix_computer_vision = (
self.get_computer_vision_camera_matrix(obj))
cam = Camera()
cam.id = camera_index
cam.set_relative_fp(str(obj.name), Camera.IMAGE_FP_TYPE_NAME)
cam.image_dp = odp
cam.width = bpy.context.scene.render.resolution_x
cam.height = bpy.context.scene.render.resolution_y
cam.set_calibration(calibration_mat, radial_distortion=0)
cam.set_4x4_cam_to_world_mat(camera_matrix_computer_vision)
cameras.append(cam)
camera_index += 1
else:
if obj.data is not None:
obj_points = []
for vert in obj.data.vertices:
coord_world = obj.matrix_world @ vert.co
obj_points.append(
Point(
coord=coord_world,
color=[0, 255, 0],
id=point_index,
scalars=[],
))
point_index += 1
points += obj_points
log_report(
"INFO",
"export_selected_cameras_and_vertices_of_meshes: Done",
self,
)
return cameras, points