def create_config():
config_fp = 'configs/cto.cfg'
config_with_default_values_fp = 'configs/cto_default_values.cfg'
if not os.path.isfile(config_fp):
logger.info(
'Config file missing ... create a copy from config with default values.'
)
copyfile(os.path.abspath(config_with_default_values_fp),
os.path.abspath(config_fp))
logger.info(
'Adjust the input path in the created config file (cto.cfg)')
assert False
return Config(config_fp=config_fp)
def get_option_value(self, option, target_type, section=None):
"""
:param section:
:param option:
:param target_type:
:return:
"""
if section is None:
section = Config.default_section
try:
if target_type == list:
option_str = self.config.get(section, option)
option_str = Config.detect_missing_commas(option_str)
option_str = Config.remove_appended_commas(option_str)
result = json.loads(option_str)
else:
option_str = self.config.get(section, option)
option_str = option_str.split('#')[0].rstrip()
if target_type == bool: # Allow True/False bool values in addition to 1/0
result = option_str == 'True' or option_str == 'T' or option_str == '1'
else:
result = target_type(option_str)
except configparser.NoOptionError as NoOptErr:
logger.info('ERROR: ' + str(NoOptErr))
logger.info('CONFIG FILE: ' + self.config_fp)
assert False # Option Missing
except:
logger.info('option_str: ' + str(option_str))
raise
return result
def examine_model_format(model_idp):
txt_ext = '.txt'
cameras_txt_fp, images_txt_fp, points3D_txt_fp = compute_model_fps(
model_idp, txt_ext)
txt_model_present = check_model_completness(cameras_txt_fp, images_txt_fp,
points3D_txt_fp)
bin_ext = '.bin'
cameras_bin_fp, images_bin_fp, points3D_bin_fp = compute_model_fps(
model_idp, bin_ext)
bin_model_present = check_model_completness(cameras_bin_fp, images_bin_fp,
points3D_bin_fp)
logger.vinfo('txt_model_present', txt_model_present)
logger.vinfo('bin_model_present', bin_model_present)
logger.vinfo('model_idp', str(model_idp))
# If both model formats are present, we use the txt format
if txt_model_present:
logger.info('Found TXT model in ' + str(model_idp))
return txt_ext
else:
logger.info('Found BIN model in ' + str(model_idp))
return bin_ext
def compute_depth_maps_from_geometry(mesh_ifp,
camera_trajectory,
ordered_image_names,
depth_map_callback,
config=None):
logger.info('create_depth_maps_from_mesh: ... ')
num_params = len(camera_trajectory.parameters)
logger.vinfo('num_params', num_params)
camera_parameter_list = camera_trajectory.parameters
num_images = None
if num_images is not None:
camera_parameter_list = camera_parameter_list[:num_images]
assert os.path.isfile(mesh_ifp)
# required for certain methods called below
off_screen_rendering = True
for image_name, camera_parameters in zip(ordered_image_names,
camera_parameter_list):
extrinsics = camera_parameters.extrinsic
cam_to_world_mat_computer_vision = invert_transformation_mat(
extrinsics)
# http://www.open3d.org/docs/release/python_api/open3d.camera.PinholeCameraIntrinsic.html
intrinsics = camera_parameters.intrinsic
render_compatible_camera_parameters = build_vtk_render_compatible_camera_parameters(
camera_parameters)
width = intrinsics.width
height = intrinsics.height
render_interface = RenderInterface(
off_screen_rendering=off_screen_rendering,
width=width,
height=height,
background_color=(0, 127, 127))
# Can we avoid this redundant loading
render_interface.load_vtk_mesh_or_point_cloud(mesh_ifp,
texture_ifp=None)
render_interface.set_active_cam_from_computer_vision_cam_to_world_mat(
cam_to_world_mat_computer_vision,
render_compatible_camera_parameters.intrinsic.intrinsic_matrix,
width,
height,
max_clipping_range=sys.float_info.max)
render_interface.render()
#render_interface.show_z_buffer()
if not off_screen_rendering:
render_interface.render_and_start()
# We apply an affine transformation to the depth_map images
# to compensate differences in the intrinsic parameters
affine_mat = build_affine_transformation_matrix(
camera_parameters, render_compatible_camera_parameters)
depth_map = render_interface.get_computer_vision_depth_buffer_as_numpy_arr(
)
color_image = render_interface.get_rgba_buffer_as_numpy_arr()
color_image = cv2.warpAffine(
color_image, affine_mat,
(color_image.shape[1], color_image.shape[0]), cv2.WARP_INVERSE_MAP,
cv2.BORDER_CONSTANT, 0)
depth_map = cv2.warpAffine(depth_map, affine_mat,
(depth_map.shape[1], depth_map.shape[0]),
cv2.WARP_INVERSE_MAP, cv2.BORDER_CONSTANT,
0)
depth_map_callback(image_name, depth_map, color_image)
# if not off_screen_rendering:
# render_interface.render_and_start()
logger.info('create_depth_maps_from_mesh: Done ')
def log_option_value_pairs(self):
for val in self.config.values():
logger.info(val)
target_type,
section=section)
return result
def log_option_value_pairs(self):
for val in self.config.values():
logger.info(val)
def write_state_to_disc(self):
with open(self.path_to_working_copy, 'w') as configfile:
self.config.write(configfile)
if __name__ == '__main__':
logger.info('Main called')
config = Config(path_to_config_file='example.cfg')
section_option_value_pairs = [('option1', '125'), ('option2', 'aaa'),
('option1', '222'), ('option3', '213')]
config.add_option_value_pairs(section_option_value_pairs,
section='Section1')
option_value_pairs = [('option5', '333'), ('option6', '555')]
config.add_option_value_pairs(option_value_pairs)
config.log_option_value_pairs()
config.write_state_to_disc()
some_number = config.get_option_value('option1', int, section='Section1')
logger.info(some_number)
def compute_depth_maps_from_geometry(mesh_ifp, camera_trajectory,
ordered_image_names, depth_map_callback,
config):
logger.info('create_depth_maps_from_mesh: ... ')
# https://github.com/intel-isl/Open3D/blob/master/cpp/open3d/visualization/Visualizer/ViewControl.cpp#L189
# bool ViewControl::ConvertFromPinholeCameraParameters(
# ...
# window_height_ != intrinsic.height_ ||
# window_width_ != intrinsic.width_ ||
# intrinsic.intrinsic_matrix_(0, 2) !=
# (double)window_width_ / 2.0 - 0.5 ||
# intrinsic.intrinsic_matrix_(1, 2) !=
# (double)window_height_ / 2.0 - 0.5) {
# utility::LogWarning(
# "[ViewControl] ConvertFromPinholeCameraParameters() failed "
# "because window height and width do not match.");
# Therefore, only specific intrinsic matrices are allowed
num_params = len(camera_trajectory.parameters)
logger.vinfo('num_params', num_params)
camera_parameter_list = camera_trajectory.parameters
num_images = None
if num_images is not None:
camera_parameter_list = camera_parameter_list[:num_images]
# http://www.open3d.org/docs/release/python_api/open3d.visualization.html
# http://www.open3d.org/docs/release/python_api/open3d.visualization.Visualizer.html
vis = o3d.visualization.Visualizer()
show_rendering = False
for image_name, camera_parameters in zip(ordered_image_names,
camera_parameter_list):
# http://www.open3d.org/docs/release/python_api/open3d.camera.PinholeCameraIntrinsic.html
intrinsics = camera_parameters.intrinsic
if show_rendering:
if intrinsics.width > 1920 or intrinsics.height > 1080:
# https://github.com/intel-isl/Open3D/issues/2036
logger.warning(
'THERE IS A KNOWN ISSUE FOR VISUALIZING WINDOW SIZES GREATER THAN THE DEFAULT VALUES: '
+ '({}, {}) vs ({}, {})'.format(
intrinsics.width, intrinsics.height, 1920, 1080))
logger.warning(
'Setting show_rendering=False should avoid this problem ')
vis.create_window(width=intrinsics.width,
height=intrinsics.height,
left=0,
top=0,
visible=show_rendering)
mesh = o3d.io.read_triangle_mesh(mesh_ifp)
vis.add_geometry(mesh)
view_control = vis.get_view_control()
render_compatible_camera_parameters = build_o3d_render_compatible_camera_parameters(
camera_parameters)
view_control.convert_from_pinhole_camera_parameters(
render_compatible_camera_parameters)
# http://www.open3d.org/docs/release/tutorial/Advanced/non_blocking_visualization.html
# vis.update_geometry(pcd)
vis.poll_events() # CRUCIAL
vis.update_renderer()
# We apply an affine transformation to the depth_map images
# to compensate differences in the intrinsic parameters
affine_mat = build_affine_transformation_matrix(
camera_parameters, render_compatible_camera_parameters)
# http://www.open3d.org/docs/release/python_api/open3d.visualization.Visualizer.html
color_image = np.asarray(
vis.capture_screen_float_buffer(do_render=False), dtype=np.float32)
depth_map = np.asarray(vis.capture_depth_float_buffer(do_render=False),
dtype=np.float32)
color_image = cv2.warpAffine(
color_image, affine_mat,
(color_image.shape[1], color_image.shape[0]), cv2.WARP_INVERSE_MAP,
cv2.BORDER_CONSTANT, 0)
depth_map = cv2.warpAffine(depth_map, affine_mat,
(depth_map.shape[1], depth_map.shape[0]),
cv2.WARP_INVERSE_MAP, cv2.BORDER_CONSTANT,
0)
depth_map_callback(image_name, depth_map, color_image)
logger.info('create_depth_maps_from_mesh: Done ')
def show_color_and_depth_renderings(self, image_name,
color_image_original_ifp,
depth_map_original_ifp,
color_image_from_mesh,
depth_map_from_mesh,
depth_map_from_mesh_masked):
logger.info('show_color_and_depth_renderings: ...')
# if not image_name == '100_7100_resized.JPG':
# return
main_fig, ax_arr_2_by_5 = plt.subplots(
nrows=2,
ncols=5,
gridspec_kw={'height_ratios': (1, 1)}
# gridspec_kw=dict(
# # https://stackoverflow.com/questions/34921930/in-a-matplotlib-plot-consisting-of-histogram-subplots-how-can-the-height-and-ba
# height_ratios=(1, 1),
# wspace=0.1,
# hspace=0.1,
# top=0.9,
# bottom=0,
# left=0.1,
# right=0.9)
)
depth_map_from_mesh_nan = copy.deepcopy(depth_map_from_mesh)
depth_map_from_mesh_nan[depth_map_from_mesh_nan == 0] = np.nan
main_fig.suptitle(image_name)
width_in_inches = 1080 / main_fig.dpi
height_in_inches = 1080 / main_fig.dpi
main_fig.set_size_inches((width_in_inches, height_in_inches))
depth_map_original = self.read_depth_map(depth_map_original_ifp)
depth_map_original_nan = copy.deepcopy(depth_map_original)
depth_map_original_nan[depth_map_original_nan == 0] = np.nan
min_original = np.nanmin(depth_map_original_nan)
max_original = np.nanmax(depth_map_original_nan)
min_mesh = np.nanmin(depth_map_from_mesh_nan)
min_value = min(min_original, min_mesh)
max_mesh = np.nanmax(depth_map_original_nan)
max_value = max(max_original, max_mesh)
color_image_original_ax = ax_arr_2_by_5[0, 0]
color_image_original_ax.set_title('color_image_original')
color_image_original_ax.imshow(mpimg.imread(color_image_original_ifp))
color_image_original_ax = ax_arr_2_by_5[0, 1]
color_image_original_ax.set_title('color_image_from_mesh')
color_image_original_ax.imshow(color_image_from_mesh)
depth_map_original_ax = ax_arr_2_by_5[0, 2]
depth_map_original_ax.set_title('depth_map_original_nan')
depth_map_original_ax.imshow(depth_map_original_nan,
vmin=min_value,
vmax=max_value)
depth_map_original_ax.annotate('min_original: ' + str(min_original),
(0, 0), (0, -20),
xycoords='axes fraction',
textcoords='offset points',
va='top')
depth_map_original_ax.annotate('max_original: ' + str(max_original),
(0, 0), (0, -40),
xycoords='axes fraction',
textcoords='offset points',
va='top')
depth_map_from_mesh_ax = ax_arr_2_by_5[0, 3]
depth_map_from_mesh_ax.set_title('depth_map_from_mesh_nan')
depth_map_from_mesh_ax.imshow(depth_map_from_mesh_nan,
vmin=min_value,
vmax=max_value)
depth_map_from_mesh_ax.annotate('min_mesh: ' + str(min_mesh), (0, 0),
(0, -20),
xycoords='axes fraction',
textcoords='offset points',
va='top')
depth_map_from_mesh_ax.annotate('max_mesh: ' + str(max_mesh), (0, 0),
(0, -40),
xycoords='axes fraction',
textcoords='offset points',
va='top')
if self.use_original_depth_maps_as_mask:
depth_map_from_mesh_masked_nan = copy.deepcopy(
depth_map_from_mesh_masked)
depth_map_from_mesh_masked_nan[depth_map_from_mesh_masked ==
0] = np.nan
depth_map_from_mesh_masked_ax = ax_arr_2_by_5[0, 4]
depth_map_from_mesh_masked_ax.set_title(
'depth_map_from_mesh_masked_nan')
depth_map_from_mesh_masked_ax.imshow(
depth_map_from_mesh_masked_nan, vmin=min_value, vmax=max_value)
show_histogram = False
if show_histogram:
num_bins = 10
# https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html
depth_map_original_hist_ax = ax_arr_2_by_5[1, 0]
depth_map_original_hist_ax.hist(depth_map_original,
range=(min_value, max_value))
depth_map_from_mesh_hist_ax = ax_arr_2_by_5[1, 1]
depth_map_from_mesh_hist_ax.hist(depth_map_from_mesh,
range=(min_value, max_value))
if self.use_original_depth_maps_as_mask:
depth_map_from_mesh_masked_hist_ax = ax_arr_2_by_5[1, 2]
depth_map_from_mesh_masked_hist_ax.hist(
depth_map_from_mesh_masked, range=(min_value, max_value))
# main_fig.subplots_adjust(right=0.8)
# cbar_ax = main_fig.add_axes([0.85, 0.15, 0.05, 0.7])
# main_fig.colorbar(im, cax=cbar_ax)
plt.show()
# ColmapDepthMapHandler.visualize_depth_map(depth_map_original, show=False)
# ColmapDepthMapHandler.visualize_depth_map(depth_map_from_mesh, show=True)
logger.info('show_color_and_depth_renderings: ...')