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sem3d_gen_images.py
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import numpy as np
import os
import scipy.misc
# load the configuration file and define variables
print("Loading configuration file")
import argparse
import json
parser = argparse.ArgumentParser(description='Semantic3D')
parser.add_argument('--config', type=str, default="config.json", metavar='N',
help='config file')
args = parser.parse_args()
json_data=open(args.config).read()
config = json.loads(json_data)
if config["training"]:
input_dir = config["train_input_dir"]
directory = config["train_results_root_dir"]
cam_number = config["train_cam_number"]
create_mesh = config["train_create_mesh"]
create_views = config["train_create_views"]
create_images = config["train_create_images"]
else:
input_dir = config["test_input_dir"]
directory = config["test_results_root_dir"]
cam_number = config["test_cam_number"]
create_mesh = config["test_create_mesh"]
create_views = config["test_create_views"]
create_images = config["test_create_images"]
voxels_directory = os.path.join(directory,"voxels")
image_directory = os.path.join(directory,config["images_dir"])
voxel_size = config["voxel_size"]
imsize = config["imsize"]
# create directories if not already existing
if not os.path.exists(directory):
os.makedirs(directory)
if not os.path.exists(voxels_directory):
os.makedirs(voxels_directory)
if not os.path.exists(image_directory):
os.makedirs(image_directory)
if(config["training"]):
# training filenames
filenames = [
"bildstein_station1_xyz_intensity_rgb",
# "bildstein_station3_xyz_intensity_rgb",
# "bildstein_station5_xyz_intensity_rgb",
# "domfountain_station1_xyz_intensity_rgb",
# "domfountain_station2_xyz_intensity_rgb",
# "domfountain_station3_xyz_intensity_rgb",
# "neugasse_station1_xyz_intensity_rgb",
# "sg27_station1_intensity_rgb",
# "sg27_station2_intensity_rgb",
#"sg27_station4_intensity_rgb",
#"sg27_station5_intensity_rgb",
#"sg27_station9_intensity_rgb",
#"sg28_station4_intensity_rgb",
#"untermaederbrunnen_station1_xyz_intensity_rgb",
#"untermaederbrunnen_station3_xyz_intensity_rgb"
]
else: # testing filename
filenames = [
"sg27_station4_intensity_rgb",
"sg27_station5_intensity_rgb",
"sg27_station9_intensity_rgb",
"sg28_station4_intensity_rgb",
"untermaederbrunnen_station1_xyz_intensity_rgb",
"untermaederbrunnen_station3_xyz_intensity_rgb"
# "birdfountain_station1_xyz_intensity_rgb",
# "castleblatten_station1_intensity_rgb",
# "castleblatten_station5_xyz_intensity_rgb",
# "marketplacefeldkirch_station1_intensity_rgb",
# "marketplacefeldkirch_station4_intensity_rgb",
# "marketplacefeldkirch_station7_intensity_rgb",
# "sg27_station10_intensity_rgb",
# "sg27_station3_intensity_rgb",
# "sg27_station6_intensity_rgb",
# "sg27_station8_intensity_rgb",
# "sg28_station2_intensity_rgb",
# "sg28_station5_xyz_intensity_rgb",
# "stgallencathedral_station1_intensity_rgb",
# "stgallencathedral_station3_intensity_rgb",
# "stgallencathedral_station6_intensity_rgb"
]
if create_mesh:
import pointcloud_tools.lib.python.PcTools as PcTls
for filename in filenames:
print(filename)
# create the mesher
semantizer = PcTls.Semantic3D()
semantizer.set_voxel_size(voxel_size)
#loading data and voxelization
print(" -- loading data")
if config["training"]:
semantizer.load_Sem3D_labels(os.path.join(input_dir,filename+".txt").encode('utf_8'),
os.path.join(input_dir,filename+".labels").encode('utf-8'))
else:
semantizer.load_Sem3D(os.path.join(input_dir,filename+".txt").encode('utf-8'))
# estimate normals
print(" -- estimating normals")
semantizer.estimate_normals_regression(100)
print(" -- estimating noise")
semantizer.estimate_noise_radius(1.)
print(" -- estimating Z orient")
semantizer.estimate_z_orient()
#save points and labels
print(" -- saving plys")
semantizer.savePLYFile(os.path.join(voxels_directory,filename+"_points.ply").encode('utf-8'))
semantizer.savePLYFile_composite(os.path.join(voxels_directory,filename+"_composite.ply").encode('utf-8'))
if config["training"]:
semantizer.savePLYFile_labels(os.path.join(voxels_directory,filename+"_labels.ply").encode('utf-8'))
print(" -- building mesh")
semantizer.build_mesh(False)
semantizer.save_mesh(os.path.join(voxels_directory,filename+"_mesh.ply").encode('utf-8'))
semantizer.save_mesh_composite(os.path.join(voxels_directory,filename+"_mesh_composite.ply").encode('utf-8'))
if config["training"]:
semantizer.save_mesh_labels(os.path.join(voxels_directory,filename+"_mesh_labels.ply").encode('utf-8'))
print(" -- extracting vertices")
vertices = semantizer.get_vertices_numpy()
np.savez(os.path.join(voxels_directory,filename+"_vertices").encode('utf_8'), vertices)
print(" -- extracting normals")
normals = semantizer.get_normals_numpy()
np.savez(os.path.join(voxels_directory,filename+"_normals").encode('utf_8'), normals)
print(" -- extracting faces")
faces = semantizer.get_faces_numpy()
np.savez(os.path.join(voxels_directory,filename+"_faces").encode('utf_8'), faces)
print(" -- extracting colors")
colors = semantizer.get_colors_numpy()
np.savez(os.path.join(voxels_directory,filename+"_colors").encode('utf_8'), colors)
print(" -- extracting composite")
composite = semantizer.get_composite_numpy()
np.savez(os.path.join(voxels_directory,filename+"_composite").encode('utf_8'), composite)
if config["training"]:
print(" -- extracting labels")
labels = semantizer.get_labels_numpy()
np.savez(os.path.join(voxels_directory,filename+"_labels").encode('utf_8'), labels)
print(" -- extracting labels colors")
labelsColors = semantizer.get_labelsColors_numpy()
np.savez(os.path.join(voxels_directory,filename+"_labelsColors").encode('utf_8'), labelsColors)
# """ commented out ViewGeneratorLauncher """
# if create_views:
#
# # from python.viewGenerator import ViewGeneratorLauncher
# from python.viewGenerator import ViewGeneratorNoDisplay as ViewGenerator
#
# # launcher = ViewGeneratorLauncher()
#
# for filename in filenames:
# print(filename)
# view_gen = ViewGenerator()
# view_gen.initialize_acquisition(
# directory,
# image_directory,
# filename
# )
# view_gen.set_camera_generator(ViewGenerator.cam_generator_random_vertical_cone)
# view_gen.opts["imsize"]= imsize
# view_gen.generate_cameras_scales(cam_number, distances=[5,10,20])
# view_gen.paintGL()
# # launcher.launch(view_gen)
if create_views:
from python.viewGenerator import ViewGeneratorLauncher
from python.viewGenerator import ViewGeneratorNoDisplay as ViewGenerator
launcher = ViewGeneratorLauncher()
for filename in filenames:
print(filename)
view_gen = ViewGenerator()
view_gen.initialize_acquisition(
directory,
image_directory,
filename
)
view_gen.set_camera_generator(ViewGenerator.cam_generator_random_vertical_cone)
view_gen.opts["imsize"]= imsize
view_gen.generate_cameras_scales(cam_number, distances=[5,10,20])
view_gen.init()
launcher.launch(view_gen)
if create_images:
from python.imageGenerator import ImageGenerator
for filename in filenames:
print(filename)
# generate images
print(" -- generating images")
im_gen = ImageGenerator()
if config["training"]:
im_gen.set_isTraining(True)
im_gen.initialize_acquisition(
voxels_directory,
image_directory,
filename
)
im_gen.generate_images()