def parse_synth_out(synth_out_ifp):
points3D = []
with open(synth_out_ifp, "r") as input_file:
meta_data_line = input_file.readline()
num_cameras, num_points = MVEFileHandler.readline_as_numbers(
input_file, target_type=int)
# The camera information provided in the synth_0.out file is incomplete
# Thus, we use the camera information provided in the view folders
# Consume the lines corresponding to the (incomplete) camera information
for cam_idx in range(num_cameras):
intrinsic_line = MVEFileHandler.readline_as_numbers(
input_file, target_type=float)
rotation_mat = MVEFileHandler.parse_rotation_matrix(input_file)
camera_translation = np.asarray(
MVEFileHandler.readline_as_numbers(input_file,
target_type=float))
for point_idx in range(num_points):
coord = MVEFileHandler.readline_as_numbers(input_file,
target_type=float)
color = MVEFileHandler.readline_as_numbers(input_file,
target_type=int)
measurement_line = MVEFileHandler.readline_as_numbers(
input_file, target_type=int)
point = Point(coord=coord,
color=color,
id=point_idx,
scalars=[])
points3D.append(point)
return points3D
def _parse_points_from_json_data(json_data, image_index_to_camera_index,
op):
points = []
is_valid_file = "structure" in json_data
if not is_valid_file:
log_report(
"ERROR",
"FILE FORMAT ERROR: Incorrect SfM/JSON file. Must contain " +
" the SfM reconstruction results: structure.",
op,
)
return points
structure = json_data["structure"]
for json_point in structure:
custom_point = Point(
coord=np.array(json_point["X"], dtype=float),
color=np.array(json_point["color"], dtype=int),
id=int(json_point["landmarkId"]),
scalars=[],
)
points.append(custom_point)
return points
def add_points_as_object_with_particle_system(
points,
reconstruction_collection,
mesh_type="CUBE",
point_extent=0.01,
add_particle_color_emission=True,
particle_overwrite_color=None,
op=None,
):
"""Add a point cloud as particle system."""
log_report("INFO", "Adding Points as Particle System: ...", op)
stop_watch = StopWatch()
# The particle systems in Blender do not work for large particle numbers
# (see https://developer.blender.org/T81103). Thus, we represent large
# point clouds with multiple smaller particle systems.
max_number_particles = 10000
particle_system_collection = add_collection("Particle System",
reconstruction_collection)
point_cloud_obj_list = []
for i in range(0, len(points), max_number_particles):
particle_obj_name = f"Particle Shape {i}"
particle_material_name = f"Point Cloud Material {i}"
point_cloud_obj_name = f"Particle Point Cloud {i}"
points_subset = points[i:i + max_number_particles]
coords, colors = Point.split_points(points_subset,
normalize_colors=True)
particle_obj = _add_particle_obj(
colors,
particle_obj_name,
particle_material_name,
particle_overwrite_color,
add_particle_color_emission,
mesh_type,
point_extent,
particle_system_collection,
)
point_cloud_obj = _add_particle_system_obj(
coords,
particle_obj,
point_cloud_obj_name,
particle_system_collection,
)
point_cloud_obj_list.append(point_cloud_obj)
bpy.context.view_layer.update()
log_report("INFO", "Duration: " + str(stop_watch.get_elapsed_time()), op)
log_report("INFO", "Adding Points as Particle System: Done", op)
return point_cloud_obj_list
def get_selected_cameras_and_vertices_of_meshes(self, odp):
"""Get selected cameras and vertices."""
log_report("INFO", "get_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":
obj_name = str(obj.name).replace(" ", "_")
log_report("INFO", "obj_name: " + 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(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",
"get_selected_cameras_and_vertices_of_meshes: Done",
self,
)
return cameras, points
def _parse_points(json_data, op):
points = []
json_points = json_data["points"]
for point_id in json_points:
json_point = json_points[point_id]
custom_point = Point(
coord=np.array(json_point["coordinates"], dtype=float),
color=np.array(json_point["color"], dtype=int),
id=point_id,
scalars=[],
)
points.append(custom_point)
return points
def _convert_points(id_to_col_points3D):
# From photogrammetry_importer\ext\read_write_model.py
# Point3D = collections.namedtuple(
# "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
col_points3D = id_to_col_points3D.values()
points3D = []
for col_point3D in col_points3D:
current_point = Point(
coord=col_point3D.xyz,
color=col_point3D.rgb,
id=col_point3D.id,
scalars=None,
)
points3D.append(current_point)
return points3D
def _parse_nvm_points(input_file, num_3D_points):
points = []
for point_index in range(num_3D_points):
# From the VSFM docs:
# <Point> = <XYZ> <RGB> <number of measurements> <List of Measurements>
point_line = input_file.readline()
point_line_elements = (point_line.rstrip()).split()
xyz_vec = list(map(float, point_line_elements[0:3]))
rgb_vec = list(map(int, point_line_elements[3:6]))
current_point = Point(coord=xyz_vec,
color=rgb_vec,
id=point_index,
scalars=None)
points.append(current_point)
return points
def draw_points(
points,
add_points_to_point_cloud_handle,
reconstruction_collection=None,
object_anchor_handle_name="OpenGL Point Cloud",
op=None,
):
"""Draw points using OpenGL."""
log_report("INFO", "Add particle draw handlers", op)
coords, colors = Point.split_points(points, normalize_colors=True)
object_anchor_handle = _draw_coords_with_color(
coords,
colors,
add_points_to_point_cloud_handle,
reconstruction_collection,
object_anchor_handle_name,
op=op,
)
return object_anchor_handle
def draw_points(
op,
points,
add_points_to_point_cloud_handle,
reconstruction_collection=None,
object_anchor_handle_name="OpenGL Point Cloud",
):
log_report("INFO", "Add particle draw handlers", op)
coords, colors = Point.split_points(points)
object_anchor_handle = draw_coords_with_color(
op,
coords,
colors,
add_points_to_point_cloud_handle,
reconstruction_collection,
object_anchor_handle_name,
)
return object_anchor_handle
def add_points_as_mesh_vertices(
points,
reconstruction_collection,
add_color_as_custom_property=True,
op=None,
):
"""Add a point cloud as mesh."""
log_report("INFO", "Adding Points as Mesh: ...", op)
stop_watch = StopWatch()
point_cloud_obj_name = "Mesh Point Cloud"
point_cloud_mesh = bpy.data.meshes.new(point_cloud_obj_name)
point_cloud_mesh.update()
point_cloud_mesh.validate()
coords, colors = Point.split_points(points, normalize_colors=False)
point_cloud_mesh.from_pydata(coords, [], [])
point_cloud_obj = add_obj(point_cloud_mesh, point_cloud_obj_name,
reconstruction_collection)
if add_color_as_custom_property:
point_cloud_obj["colors"] = colors
log_report("INFO", "Duration: " + str(stop_watch.get_elapsed_time()), op)
log_report("INFO", "Adding Points as Mesh: Done", op)
return point_cloud_obj
def parse_point_data_file(ifp, op):
log_report("INFO", "Parse Point Data File: ...")
# https://pyntcloud.readthedocs.io/en/latest/io.html
# https://www.cloudcompare.org/doc/wiki/index.php?title=FILE_I/O
module_spec = importlib.util.find_spec("pyntcloud")
if module_spec is None:
log_report(
"ERROR",
"Importing this file type requires the pyntcloud library.",
op,
)
assert False
from pyntcloud import PyntCloud
assert os.path.isfile(ifp)
ext = os.path.splitext(ifp)[1].lower()
if ext in [".asc", ".pts"]:
sep = " "
data_semantics = (
PointDataFileHandler.get_data_semantics_from_ascii(
ifp, sep, has_header=True))
names = PointDataFileHandler.convert_data_semantics_to_list(
data_semantics)
point_cloud = PyntCloud.from_file(ifp,
sep=sep,
header=0,
names=names)
pseudo_color = data_semantics.pseudo_color
elif ext == ".csv":
sep = ","
data_semantics = (
PointDataFileHandler.get_data_semantics_from_ascii(
ifp, sep, has_header=False))
names = PointDataFileHandler.convert_data_semantics_to_list(
data_semantics)
point_cloud = PyntCloud.from_file(ifp,
sep=sep,
header=0,
names=names)
pseudo_color = data_semantics.pseudo_color
else:
pseudo_color = False
point_cloud = PyntCloud.from_file(ifp)
xyz_arr = point_cloud.points.loc[:, ["x", "y", "z"]].to_numpy()
if set(["red", "green", "blue"]).issubset(point_cloud.points.columns):
color_arr = point_cloud.points.loc[:, ["red", "green", "blue"
]].to_numpy()
if pseudo_color:
color_arr *= 255
else:
color_arr = np.ones_like(xyz_arr) * 255
num_points = xyz_arr.shape[0]
points = []
for idx in range(num_points):
point = Point(
coord=xyz_arr[idx].astype("float64"),
color=color_arr[idx].astype("int"),
id=idx,
scalars=dict(),
)
points.append(point)
log_report("INFO", f"Number Points {len(points)}")
log_report("INFO", "Parse Point Data File: Done")
return points
def __ply_data_vertices_to_vetex_list(ply_data):
vertex_data_type_names = ply_data["vertex"].data.dtype.names
use_color = False
if (
"red" in vertex_data_type_names
and "green" in vertex_data_type_names
and "blue" in vertex_data_type_names
):
use_color = True
vertices = []
value_keys = [
x
for x, y in sorted(
ply_data["vertex"].data.dtype.fields.items(),
key=lambda k: k[1],
)
]
non_scalar_value_keys = [
"x",
"y",
"z",
"red",
"green",
"blue",
"nx",
"ny",
"nz",
"measurements",
]
scalar_value_keys = [
value_key
for value_key in value_keys
if not value_key in non_scalar_value_keys
]
log_report(
"INFO", "Found the following vertex properties: " + str(value_keys)
)
# scalar_value_keys = [value_key for (value_key, some_value) in ]
# logger.info(scalar_value_keys)
log_report(
"INFO", "Found " + str(len(ply_data["vertex"].data)) + " vertices"
)
for point_index, line in enumerate(ply_data["vertex"].data):
coord = np.array([line["x"], line["y"], line["z"]])
if use_color:
color = np.array([line["red"], line["green"], line["blue"]])
else:
color = np.array([255, 255, 255])
scalars = dict()
for scalar_value_key in scalar_value_keys:
scalars[scalar_value_key] = line[scalar_value_key]
current_point = Point(
coord=coord, color=color, id=point_index, scalars=None
)
vertices.append(current_point)
ply_data_vertex_dtype = ply_data["vertex"].dtype
ply_data_vertex_data_dtype = ply_data["vertex"].data.dtype
return vertices, ply_data_vertex_dtype, ply_data_vertex_data_dtype
def write_ply_file_from_vertex_mat(output_path_to_file, vertex_mat):
vertices = []
for entry in vertex_mat:
vertices.append(Point(coord=entry))
PLYFileHandler.write_ply_file(output_path_to_file, vertices)
def parse_points(json_data, op, view_index_to_absolute_fp=None):
compute_color = True
try:
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
except ImportError:
log_report(
"WARNING",
"Can not compute point cloud color information, since Pillow is not installed.",
op,
)
compute_color = False
if view_index_to_absolute_fp is None:
log_report(
"WARNING",
"Can not compute point cloud color information, since path to images is not correctly set.",
op,
)
compute_color = False
if compute_color:
log_report(
"INFO",
"Try to collect color information from files (this might take a while)",
op,
)
view_index_to_image = {}
for view_index, absolute_fp in view_index_to_absolute_fp.items():
if os.path.isfile(absolute_fp):
pil_image = Image.open(absolute_fp)
view_index_to_image[view_index] = pil_image
else:
log_report(
"WARNING",
"Can not compute point cloud color information, since image file path is incorrect.",
op,
)
compute_color = False
break
if compute_color:
log_report(
"INFO",
"Compute color information from files (this might take a while)",
op,
)
points = []
structure = json_data["structure"]
for json_point in structure:
r = g = b = 0
# color information can only be computed if input files are provided
if compute_color:
for observation in json_point["value"]["observations"]:
view_index = int(observation["key"])
# REMARK: The order of ndarray.shape (first height, then width) is complimentary to
# pils image.size (first width, then height).
# That means
# height, width = segmentation_as_matrix.shape
# width, height = image.size
# Therefore: x_in_openmvg_file == x_image == y_ndarray
# and y_in_openmvg_file == y_image == x_ndarray
x_in_json_file = float(
observation["value"]["x"][0]) # x has index 0
y_in_json_file = float(
observation["value"]["x"][1]) # y has index 1
current_image = view_index_to_image[view_index]
current_r, current_g, current_b = current_image.getpixel(
(x_in_json_file, y_in_json_file))
r += current_r
g += current_g
b += current_b
# normalize the rgb values
amount_observations = len(json_point["value"]["observations"])
r /= amount_observations
g /= amount_observations
b /= amount_observations
custom_point = Point(
coord=np.array(json_point["value"]["X"], dtype=float),
color=np.array([r, g, b], dtype=int),
id=int(json_point["key"]),
scalars=[],
)
points.append(custom_point)
return points
