def run(config):
print("making fragments from RGBD sequence.")
make_clean_folder(join(config["path_dataset"], config["folder_fragment"]))
[color_files, depth_files,
pose_files] = get_rgbd_file_lists(config["path_dataset"],
config["has_tracking"])
n_files = len(color_files)
n_fragments = int(
math.ceil(float(n_files) / config['n_frames_per_fragment']))
if config["python_multi_threading"] is True:
from joblib import Parallel, delayed
import multiprocessing
import subprocess
MAX_THREAD = min(multiprocessing.cpu_count(), n_fragments)
Parallel(n_jobs=MAX_THREAD)(delayed(process_single_fragment)
(fragment_id, color_files, depth_files,
pose_files, n_files, n_fragments, config)
for fragment_id in range(n_fragments))
else:
for fragment_id in range(n_fragments):
process_single_fragment(fragment_id, color_files, depth_files,
pose_files, n_files, n_fragments, config)
def run(config):
print("register fragments.")
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
ply_file_names = get_file_list(
join(config["path_dataset"], config["folder_fragment"]), ".ply")
make_clean_folder(join(config["path_dataset"], config["folder_scene"]))
make_posegraph_for_scene(ply_file_names, config)
optimize_posegraph_for_scene(config["path_dataset"], config)
def startRecordingCallback(req):
global record, enable_tracking, relative_frame, tracking_frame, tacking_frame_count, path_output, path_depth, path_color, path_pose, frame_count, sensor_data
rospy.loginfo(rospy.get_caller_id() + ": Start Recording")
enable_tracking = req.enable_tracking
path_output = req.directory
path_depth = join(path_output, "depth")
path_color = join(path_output, "color")
make_clean_folder(path_output)
make_clean_folder(path_depth)
make_clean_folder(path_color)
if enable_tracking:
sensor_data = deque()
tracking_frame = req.tracking_frame
relative_frame = req.relative_frame
path_pose = join(path_output, "pose")
make_clean_folder(path_pose)
frame_count = 0
tacking_frame_count = 0
record = True
return StartRecordingResponse(True)
def startRecordingCallback(req):
global record, record_rosbag, pipeline, frame_count
global path_output, path_depth, path_color, path_bag
rospy.loginfo(rospy.get_caller_id() + "Start Recording")
path_output = req.directory
path_depth = join(path_output, "depth")
path_color = join(path_output, "color")
path_bag = join(path_output, "realsense.bag")
make_clean_folder(path_output)
make_clean_folder(path_depth)
make_clean_folder(path_color)
frame_count = 0
# Create a pipeline
if pipeline is None:
pipeline = rs.pipeline()
#Create a config and configure the pipeline to stream
# different resolutions of color and depth streams
config = rs.config()
if record_imgs or record_rosbag:
# note: using 640 x 480 depth resolution produces smooth depth boundaries
# using rs.format.bgr8 for color image format for OpenCV based image visualization
config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
if record_rosbag:
config.enable_record_to_file(path_bag)
# Start streaming
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
# Using preset HighAccuracy for recording
if record_rosbag or record_imgs:
depth_sensor.set_option(rs.option.visual_preset, Preset.HighAccuracy)
# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_scale = depth_sensor.get_depth_scale()
# We will not display the background of objects more than
# clipping_distance_in_meters meters away
clipping_distance_in_meters = 3 # 3 meter
clipping_distance = clipping_distance_in_meters / depth_scale
# Create an align object
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames.
align_to = rs.stream.color
align = rs.align(align_to)
record = True
return StartRecordingResponse(True)
def archiveData(path_output):
global depth_images, color_images, rgb_poses, intrinsics
path_depth = join(path_output, "depth")
path_color = join(path_output, "color")
path_pose = join(path_output, "pose")
make_clean_folder(path_output)
make_clean_folder(path_depth)
make_clean_folder(path_color)
make_clean_folder(path_pose)
for s in range(len(color_images)):
# Save your OpenCV2 image as a jpeg
o3d.io.write_image("%s/%06d.png" % (path_depth, s), depth_images[s])
o3d.io.write_image("%s/%06d.jpg" % (path_color, s), color_images[s])
write_pose("%s/%06d.pose" % (path_pose, s), rgb_poses[s])
save_intrinsic_as_json(join(path_output, "camera_intrinsic.json"),
intrinsics)