def parse_args():
parser = argparse.ArgumentParser(
description='Computes a surface mesh of an object in the database')
parser.add_argument('-s',
'--session_id',
metavar='SESSION_ID',
dest='session_id',
type=str,
default='',
help='The session id to reconstruct.')
parser.add_argument('--all',
dest='compute_all',
action='store_const',
const=True,
default=False,
help='Compute meshes for all possible sessions.')
parser.add_argument('--visualize',
dest='visualize',
action='store_const',
const=True,
default=False,
help='Turn on visiualization')
object_recognition.dbtools.add_db_arguments(parser)
sched_group = parser.add_argument_group('Scheduler Options')
from ecto.opts import scheduler_options
scheduler_options(sched_group, default_scheduler='Threadpool')
args = parser.parse_args()
if args.compute_all == False and args.session_id == '':
parser.print_usage()
print "You must either supply a session id, or --all."
sys.exit(1)
return args
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Compute a LINE-MOD model and saves it to the DB.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Find a plane in an RGBD image.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Computes the odometry between frames.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Compute a LINE-MOD model and saves it to the DB.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
parser = argparse.ArgumentParser(description='My awesome program thing.')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
cropper = Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.0, z_max=1.0)
ror = RadiusOutlierRemoval("ror", min_neighbors=1, search_radius=1.0)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='/ecto_pcl/cloud_filtered')
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> voxel_grid[:],
voxel_grid[:] >> ror[:],
ror[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Find a plane in an RGBD image.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
parser = argparse.ArgumentParser(
description='My awesome program thing.')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
cropper = Cropper("cropper",
x_min=-0.25,
x_max=0.25,
y_min=-0.25,
y_max=0.25,
z_min=0.0,
z_max=1.0)
convex_hull = ConvexHull("convex_hull")
ror = RadiusOutlierRemoval("ror", min_neighbors=1, search_radius=1.0)
sor = StatisticalOutlierRemoval("sor", mean_k=1)
extract_indices = ExtractIndices("extract_indices", negative=False)
extract_clusters = EuclideanClusterExtraction("extract_clusters",
min_cluster_size=50,
cluster_tolerance=0.005)
extract_largest_cluster = ExtractLargestCluster(
"extract_largest_cluster")
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='/ecto_pcl/sample_output')
plasm.connect(
cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> extract_clusters[:],
extract_clusters[:] >> extract_largest_cluster["clusters"],
msg2cloud[:] >> extract_largest_cluster["input"],
extract_largest_cluster[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
# msg2cloud[:] >> extract_indices["input"],
# voxel_grid[:] >> cropper[:],
# cropper[:] >> extract_clusters[:],
# extract_indices[:] >> extract_clusters[:],
# extract_clusters[:] >> extract_largest_cluster["clusters"],
# cropper[:] >> extract_largest_cluster["input"],
# extract_largest_cluster[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Test orb on images.')
parser.add_argument('-i,--input', dest='input',
help='The input dir. %(default)s', default='./images')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def parse_args():
import argparse
parser = argparse.ArgumentParser(
description='Computes the odometry between frames.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
parser = argparse.ArgumentParser(description='Ecto Largest Cluster')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.015)
cropper = Cropper("cropper",
x_min=-0.25,
x_max=0.25,
y_min=-0.25,
y_max=0.25,
z_min=0.0,
z_max=1.0)
extract_clusters = EuclideanClusterExtraction("extract_clusters",
min_cluster_size=50,
cluster_tolerance=0.02)
extract_largest_cluster = ExtractLargestCluster(
"extract_largest_cluster")
nan_filter = PassThrough("nan_removal")
colorize = ColorizeClusters("colorize", max_clusters=100)
extract_indices = ExtractIndices("extract_indices", negative=False)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='/ecto_pcl/cloud_filtered')
output = ecto_pcl.XYZRGB
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> nan_filter[:],
# nan_filter[:] >> voxel_grid[:],
# voxel_grid[:] >> extract_clusters[:],
# extract_clusters[:] >> extract_largest_cluster["clusters"],
# voxel_grid[:] >> extract_largest_cluster["input"],
# extract_largest_cluster[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
#
# run_plasm(options, plasm, locals=vars())
plasm.connect(
cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:],
voxel_grid[:] >> extract_clusters[:],
extract_clusters[:] >> extract_largest_cluster["clusters"],
voxel_grid[:] >> extract_largest_cluster["input"],
extract_largest_cluster["output"] >> 'out')
run_plasm(options, plasm, locals=output)
def parse_args():
epilog = textwrap.dedent(
'''Capturing requires a ROS openni device (http://ros.org/wiki/openni),
with registered rgb and depth. You may want to do the following before
capturing data with this program:
$ roslaunch openni_launch openni.launch
In a seperate terminal:
$ rosrun dynamic_reconfigure dynparam set /camera/driver depth_registration True
To switch into high resolution mode:
$ rosrun dynamic_reconfigure dynparam set /camera/driver image_mode 1
To switch to vga mode:
$ rosrun dynamic_reconfigure dynparam set /camera/driver image_mode 2
''')
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent(
'''Captures data appropriate for training object recognition pipelines.
Assumes an opposing dot pattern, (black dots on white background, white
dots on black background), is the scene, and captures views of the
object sparsely, depending on the delta setting.'''),
)
parser.add_argument(
'-o',
'--output',
metavar='BAG_FILE',
dest='bag',
type=str,
default='',
help='A bagfile to write to.')
parser.add_argument(
'-a',
'--angle_thresh',
metavar='RADIANS',
dest='angle_thresh',
type=float,
default=math.pi / 36,
help='''The delta angular threshold in pose. Default is pi/36 radians.
Frames will not be recorded unless they are not closer to any other pose by this amount.
''')
from ecto.opts import scheduler_options
#add ecto scheduler args.
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group, default_scheduler='Threadpool')
args = parser.parse_args()
if len(args.bag) < 1:
print parser.print_help()
sys.exit(1)
return args
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Test orb on images.')
parser.add_argument('train', help='train image.')
parser.add_argument('test', help='test image.')
parser.add_argument('output', help='the output dir.')
scheduler_options(parser.add_argument_group('Scheduler'), default_niter=1)
options = parser.parse_args()
return options
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Test orb on images.')
parser.add_argument('-i,--input',
dest='input',
help='The input dir. %(default)s',
default='./images')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
parser = argparse.ArgumentParser(
description='My awesome program thing.')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
passthru_z = PassThrough("passthru_z",
filter_field_name="z",
filter_limit_min=0.0,
filter_limit_max=1.0)
passthru_x = PassThrough("passthru_x",
filter_field_name="x",
filter_limit_min=-0.25,
filter_limit_max=0.25)
passthru_y = PassThrough("passthru_y",
filter_field_name="y",
filter_limit_min=-0.25,
filter_limit_max=0.25)
cropper = Cropper("cropper",
x_min=-0.25,
x_max=0.25,
y_min=-0.25,
y_max=0.25,
z_min=0.0,
z_max=1.0)
viewer = CloudViewer("viewer", window_name="Clouds!")
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='/ecto_pcl/sample_output')
plasm.connect(
cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> voxel_grid[:],
voxel_grid[:] >> passthru_z[:], passthru_z[:] >> passthru_x[:],
passthru_x[:] >> passthru_y[:], passthru_y[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
# voxel_grid[:] >> cropper[:],
# cropper[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
parser = argparse.ArgumentParser(
description='Ecto Euclidean Clustering')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.015)
cropper = Cropper("cropper",
x_min=-0.25,
x_max=0.25,
y_min=-0.25,
y_max=0.25,
z_min=0.3,
z_max=1.0)
passthru_z = PassThrough("passthru_z",
filter_field_name="z",
filter_limit_min=0.3,
filter_limit_max=1.0)
passthru_x = PassThrough("passthru_x",
filter_field_name="x",
filter_limit_min=-0.25,
filter_limit_max=0.25)
passthru_y = PassThrough("passthru_y",
filter_field_name="y",
filter_limit_min=-0.25,
filter_limit_max=0.25)
extract_clusters = EuclideanClusterExtraction("extract_clusters",
min_cluster_size=50,
cluster_tolerance=0.02)
nan_filter = PassThrough("nan_removal")
colorize = ColorizeClusters("colorize", max_clusters=100)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='/ecto_pcl/cloud_filtered')
plasm.connect(
cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:], voxel_grid[:] >> cropper[:],
cropper["output"] >> extract_clusters["input"],
extract_clusters[:] >> colorize["clusters"],
cropper[:] >> colorize["input"], colorize[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description='Ecto Euclidean Clustering')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# A VoxelGrid cell
voxel_grid = ecto_pcl.VoxelGrid("voxel_grid", leaf_size=0.01)
# A Cropper cell
cropper = ecto_pcl.Cropper("cropper", x_min=-0.35, x_max=0.35, y_min=-0.35, y_max=0.35, z_min=0.3, z_max=1.5)
# A EuclideanClusterExtraction cell
extract_clusters = ecto_pcl.EuclideanClusterExtraction("extract_clusters", min_cluster_size=50, cluster_tolerance=0.02)
# A cell to filter NaN values from the point cloud
nan_filter = ecto_pcl.PassThrough("nan_removal")
# A cell to colorize the clusters
colorize = ecto_pcl.ColorizeClusters("colorize", max_clusters=100)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='camera/depth_registered/points')
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
# A cell to convert a Ecto/PCL point cloud back to a point cloud message
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='ecto_pcl/cloud_filtered')
# Create the plasm by connecting the cells
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:],
voxel_grid[:] >> cropper[:],
cropper[:] >> extract_clusters[:],
extract_clusters[:] >> colorize["clusters"],
cropper[:] >> colorize["input"],
colorize[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# Run the plasm
run_plasm(options, plasm, locals=vars())
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Test orb on images.')
parser.add_argument('-i,--input', dest='input',
help='The input dir. %(default)s', default='./images')
parser.add_argument('-o,--output', dest='output', type=str,
help='The output directory for this template. Default: %(default)s', default='./')
factory = cell_options(parser, ORB, 'ORB')
scheduler_options(parser.add_argument_group('Scheduler'), default_niter=1)
options = parser.parse_args()
options.niter = 1
options.orb_factory = factory
return options
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description="Ecto PCL Demo")
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# Create three PassThrough cells along the x, y and z dimensions
passthru_z = ecto_pcl.PassThrough(
"passthru_z", filter_field_name="z", filter_limit_min=0.0, filter_limit_max=1.0
)
passthru_x = ecto_pcl.PassThrough(
"passthru_x", filter_field_name="x", filter_limit_min=-0.25, filter_limit_max=0.25
)
passthru_y = ecto_pcl.PassThrough(
"passthru_y", filter_field_name="y", filter_limit_min=-0.25, filter_limit_max=0.25
)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name="camera/depth_registered/points")
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A publisher cell with the desired topic name
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub", topic_name="ecto_pcl/cloud_filtered")
# Create the plasm by connecting the cells
plasm.connect(
cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> passthru_z[:],
passthru_z[:] >> passthru_x[:],
passthru_x[:] >> passthru_y[:],
passthru_y[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:],
)
# Run the plasm
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
parser = argparse.ArgumentParser(description='Ecto Largest Cluster')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.015)
cropper = Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.0, z_max=1.0)
extract_clusters = EuclideanClusterExtraction("extract_clusters", min_cluster_size=50, cluster_tolerance=0.02)
extract_largest_cluster = ExtractLargestCluster("extract_largest_cluster")
nan_filter = PassThrough("nan_removal")
colorize = ColorizeClusters("colorize", max_clusters=100)
extract_indices = ExtractIndices("extract_indices", negative=False)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='/ecto_pcl/cloud_filtered')
output = ecto_pcl.XYZRGB
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> nan_filter[:],
# nan_filter[:] >> voxel_grid[:],
# voxel_grid[:] >> extract_clusters[:],
# extract_clusters[:] >> extract_largest_cluster["clusters"],
# voxel_grid[:] >> extract_largest_cluster["input"],
# extract_largest_cluster[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
#
# run_plasm(options, plasm, locals=vars())
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:],
voxel_grid[:] >> extract_clusters[:],
extract_clusters[:] >> extract_largest_cluster["clusters"],
voxel_grid[:] >> extract_largest_cluster["input"],
extract_largest_cluster["output"] >> 'out')
run_plasm(options, plasm, locals=output)
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
parser = argparse.ArgumentParser(description='My awesome program thing.')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
cropper = Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.0, z_max=1.0)
convex_hull = ConvexHull("convex_hull")
ror = RadiusOutlierRemoval("ror", min_neighbors=1, search_radius=1.0)
sor = StatisticalOutlierRemoval("sor", mean_k=1)
extract_indices = ExtractIndices("extract_indices", negative=False)
extract_clusters = EuclideanClusterExtraction("extract_clusters", min_cluster_size=50, cluster_tolerance=0.005)
extract_largest_cluster = ExtractLargestCluster("extract_largest_cluster")
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='/ecto_pcl/sample_output')
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> extract_clusters[:],
extract_clusters[:] >> extract_largest_cluster["clusters"],
msg2cloud[:] >> extract_largest_cluster["input"],
extract_largest_cluster[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
# msg2cloud[:] >> extract_indices["input"],
# voxel_grid[:] >> cropper[:],
# cropper[:] >> extract_clusters[:],
# extract_indices[:] >> extract_clusters[:],
# extract_clusters[:] >> extract_largest_cluster["clusters"],
# cropper[:] >> extract_largest_cluster["input"],
# extract_largest_cluster[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def execute_detection(cmd,path):
parser = create_parser()
# add ecto options
scheduler_options(parser)
pid = os.getpid()
print("pid = ", pid)
args = parser.parse_args(['-c', cmd])
args = parser.parse_args(['--config_file', path])
# create the plasm that will run the detection
# args = parser.parse_args()
ork_params, _args = read_arguments(args)
plasm = create_plasm(ork_params)
# run the detection plasm
run_plasm(args, plasm)
print "Exit Finished"
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description='Ecto PCL Cropper Demo')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# Create the Cropper cell with the desired dimensions (in meters)
cropper = ecto_pcl.Cropper("cropper",
x_min=-0.25,
x_max=0.25,
y_min=-0.25,
y_max=0.25,
z_min=0.0,
z_max=1.0)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='camera/depth_registered/points')
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A publisher cell with the desired topic name
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='ecto_pcl/cloud_filtered')
# Create the plasm by connecting the cells
plasm.connect(cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> cropper[:],
cropper[:] >> cloud2msg[:], cloud2msg[:] >> cloud_pub[:])
# Run the plasm
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
parser = argparse.ArgumentParser(description="My awesome program thing.")
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
passthru_z = PassThrough("passthru_z", filter_field_name="z", filter_limit_min=0.0, filter_limit_max=1.0)
passthru_x = PassThrough("passthru_x", filter_field_name="x", filter_limit_min=-0.25, filter_limit_max=0.25)
passthru_y = PassThrough("passthru_y", filter_field_name="y", filter_limit_min=-0.25, filter_limit_max=0.25)
cropper = Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.0, z_max=1.0)
viewer = CloudViewer("viewer", window_name="Clouds!")
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name="/camera/depth_registered/points")
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub", topic_name="/ecto_pcl/sample_output")
plasm.connect(
cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> voxel_grid[:],
voxel_grid[:] >> passthru_z[:],
passthru_z[:] >> passthru_x[:],
passthru_x[:] >> passthru_y[:],
passthru_y[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:],
)
# plasm.connect(cloud_sub[:] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
# voxel_grid[:] >> cropper[:],
# cropper[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description='Ecto PCL Demo')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# Create three PassThrough cells along the x, y and z dimensions
passthru_z = ecto_pcl.PassThrough("passthru_z", filter_field_name="z", filter_limit_min=0.0, filter_limit_max=1.0)
passthru_x = ecto_pcl.PassThrough("passthru_x", filter_field_name="x", filter_limit_min=-0.25, filter_limit_max=0.25)
passthru_y = ecto_pcl.PassThrough("passthru_y", filter_field_name="y", filter_limit_min=-0.25, filter_limit_max=0.25)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='camera/depth_registered/points')
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A publisher cell with the desired topic name
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='ecto_pcl/cloud_filtered')
# Create the plasm by connecting the cells
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> passthru_z[:],
passthru_z[:] >> passthru_x[:],
passthru_x[:] >> passthru_y[:],
passthru_y[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# Run the plasm
run_plasm(options, plasm, locals=vars())
def parse_args():
parser = argparse.ArgumentParser(description='Computes a surface mesh of an object in the database')
parser.add_argument('-s', '--session_id', metavar='SESSION_ID', dest='session_id', type=str, default='',
help='The session id to reconstruct.')
parser.add_argument('--all', dest='compute_all', action='store_const',
const=True, default=False,
help='Compute meshes for all possible sessions.')
parser.add_argument('--visualize', dest='visualize', action='store_const',
const=True, default=False,
help='Turn on visiualization')
object_recognition.dbtools.add_db_arguments(parser)
sched_group = parser.add_argument_group('Scheduler Options')
from ecto.opts import scheduler_options
scheduler_options(sched_group, default_scheduler='Threadpool')
args = parser.parse_args()
if args.compute_all == False and args.session_id == '':
parser.print_usage()
print "You must either supply a session id, or --all."
sys.exit(1)
return args
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
parser = argparse.ArgumentParser(description='Ecto Euclidean Clustering')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.015)
cropper = Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.3, z_max=1.0)
passthru_z = PassThrough("passthru_z", filter_field_name="z", filter_limit_min=0.3, filter_limit_max=1.0)
passthru_x = PassThrough("passthru_x", filter_field_name="x", filter_limit_min=-0.25, filter_limit_max=0.25)
passthru_y = PassThrough("passthru_y", filter_field_name="y", filter_limit_min=-0.25, filter_limit_max=0.25)
extract_clusters = EuclideanClusterExtraction("extract_clusters", min_cluster_size=50, cluster_tolerance=0.02)
nan_filter = PassThrough("nan_removal")
colorize = ColorizeClusters("colorize", max_clusters=100)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='/ecto_pcl/cloud_filtered')
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:],
voxel_grid[:] >> cropper[:],
cropper["output"] >> extract_clusters["input"],
extract_clusters[:] >> colorize["clusters"],
cropper[:] >> colorize["input"],
colorize[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
run_plasm(options, plasm, locals=vars())
def parse_args():
''' Setup some argparse stuffs.'''
import argparse
import textwrap
parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''
Captures data appropriate for training object recognition pipelines.
Press 's' to save a bundle of messages to the bag.'''),
)
parser.add_argument('-o', '--output', metavar='BAG_FILE', dest='bag', type=str,
default='',
help='A bagfile to write to.')
from ecto.opts import scheduler_options
#add ecto scheduler args.
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group, default_scheduler='Threadpool')
args = parser.parse_args()
if len(args.bag) < 1:
print parser.print_help()
print "Missing output filename."
sys.exit(1)
return args
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description='Ecto PCL Cropper Demo')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# Create the Cropper cell with the desired dimensions (in meters)
cropper = ecto_pcl.Cropper("cropper", x_min=-0.25, x_max=0.25, y_min=-0.25, y_max=0.25, z_min=0.0, z_max=1.0)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='camera/depth_registered/points')
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A publisher cell with the desired topic name
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='ecto_pcl/cloud_filtered')
# Create the plasm by connecting the cells
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> cropper[:],
cropper[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# Run the plasm
run_plasm(options, plasm, locals=vars())
def do_ecto():
# ecto options
parser = argparse.ArgumentParser(
description='Publish images from directory.')
scheduler_options(parser)
args = parser.parse_args()
#this will read all images in the path
path = '/home/sam/Code/vision/VOCdevkit/VOC2012/JPEGImages'
images = ImageReader(path=os.path.expanduser(path))
mat2image = ecto_ros.Mat2Image(encoding='rgb8')
pub_rgb = ImagePub("image_pub", topic_name='/camera/rgb/image_raw')
display = imshow(name='image', waitKey=5000)
plasm = ecto.Plasm()
plasm.connect(
images['image'] >> mat2image['image'],
images['image'] >> display['image'],
mat2image['image'] >> pub_rgb['input'],
)
ecto.view_plasm(plasm)
run_plasm(args, plasm, locals=vars())
import ecto, ecto_pcl, ecto_ros, ecto_pcl_ros
import ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
from ecto.opts import scheduler_options, run_plasm
import sys
import time
import os
import argparse
import cloud_treatment_ecto.cloud_treatment as cloud_treatment
ecto_ros.init(sys.argv, "locate_ladder")
parser = argparse.ArgumentParser(description='Treatment that segments the'
'ladder steps from the rest on the scene')
scheduler_options(parser)
parser.add_argument('-p',
'--pcdfile',
default='ladder_test_5.pcd',
help='The pcdfile to input')
options = parser.parse_args()
pcdfilename = options.pcdfile
plasm = ecto.Plasm()
reader = cloud_treatment.PCDReaderCell("Reader_ecto", filename=pcdfilename)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/worldmodel_main/pointcloud_vis')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
description='Saves images from a connect on keystroke.')
parser.add_argument('-o,--output',
dest='output',
help='The output directory. Default: %(default)s',
default='./images/%s' %
(time.strftime('%Y-%b-%d-%H.%M.%S')))
parser.add_argument('-d,--device',
dest='device',
default=0,
help='The video device number. Default: %(default)s')
parser.add_argument('--width', dest='width', default=640)
parser.add_argument('--height', dest='height', default=480)
parser.add_argument('--preview', action='store_true')
#ecto options
scheduler_options(parser.add_argument_group('Scheduler Options'))
args = parser.parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
#camera = VideoCapture(video_device=1,width=int(args.width),height=int(args.height))
source = create_source(
package_name='image_pipeline',
source_type='OpenNISource') #optionally pass keyword args here...
depth_saver = ImageSaver(
filename_format=os.path.join(args.output, 'frame_%010d_depth.png'))
image_saver = ImageSaver(
filename_format=os.path.join(args.output, 'frame_%010d_image.png'))
#mono_saver = ImageSaver(filename_format=os.path.join(args.output, 'frame_%010d_mono.png'))
import argparse
parser = argparse.ArgumentParser(description='My awesome program thing.')
#our local command line args.
parser.add_argument('--value',
metavar='VALUE',
dest='value',
type=float,
default=3,
help='A value to use a constant.')
parser.add_argument('--factor',
metavar='FACTOR',
dest='factor',
type=float,
default=5,
help='The factor to multiply the constant with.')
#add ecto scheduler args.
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group)
options = parser.parse_args()
c = Constant(value=options.value)
m = Multiply(factor=options.factor)
pr = Printer()
plasm = ecto.Plasm()
plasm.connect(c[:] >> m[:], m[:] >> pr[:])
#run the scheduler given the options, plasm, and our local variables.
run_plasm(options, plasm, locals=vars())
import ecto, ecto_pcl, ecto_ros, ecto_pcl_ros
import ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
from ecto.opts import scheduler_options, run_plasm
import sys
import time
import os
import argparse
import cloud_treatment_ecto.cloud_treatment as cloud_treatment
ecto_ros.init(sys.argv, "locate_ladder")
parser = argparse.ArgumentParser(description='Treatment that segments the'
'ladder steps from the rest on the scene')
scheduler_options(parser)
parser.add_argument('-p', '--pcdfile'
, default='ladder_test_5.pcd', help='The pcdfile to input')
options = parser.parse_args()
pcdfilename = options.pcdfile
plasm = ecto.Plasm()
reader = cloud_treatment.PCDReaderCell( "Reader_ecto",
filename=pcdfilename)
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub",
topic_name='/worldmodel_main/pointcloud_vis')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg_main = ecto_pcl_ros.PointCloud2Message("cloud2msg_main")
def __init__(self):
ecto_ros.init(sys.argv,"ecto_tracker")
parser = argparse.ArgumentParser(description='Ecto Tracker.')
scheduler_options(parser)
options = parser.parse_args()
plasm = ecto.Plasm()
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
passthru_z = PassThrough("passthru_z", filter_field_name="z", filter_limit_min=0.2, filter_limit_max=1.0)
passthru_x = PassThrough("passthru_x", filter_field_name="x", filter_limit_min=-0.5, filter_limit_max=0.5)
passthru_y = PassThrough("passthru_y", filter_field_name="y", filter_limit_min=-0.5, filter_limit_max=0.5)
nan_filter = PassThrough("nan_removal")
cropper = Cropper("cropper", x_min=-0.5, x_max=0.5, y_min=-0.5, y_max=0.5, z_min=0.0, z_max=1.0)
#convex_hull = ConvexHull("convex_hull")
#
extract_stuff = ExtractPolygonalPrismData("extract_stuff", height_min=0.01, height_max=0.2)
extract_indices = ExtractIndices("extract_indices", negative=False)
extract_clusters = EuclideanClusterExtraction("extract_clusters", min_cluster_size=50, cluster_tolerance=0.005)
extract_largest_cluster = ExtractLargestCluster("extract_largest_cluster")
colorize = ColorizeClusters("colorize", max_clusters=3)
merge = MergeClouds("merge")
viewer = CloudViewer("viewer", window_name="Clouds!")
#
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='/ecto_pcl/sample_output')
# plasm.connect(cloud_sub["output"] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
## voxel_grid[:] >> extract_indices[:],
# voxel_grid[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:],)
#
## plasm += [voxel_grid['output'] >> extract_clusters[:],
## extract_clusters[:] >> colorize["clusters"],
## cloud_generator[:] >> merge["input"],
## colorize[:] >> merge["input2"],
## colorize[:] >> viewer[:]
## ]
#
#
plasm.connect(cloud_sub[:] >> msg2cloud[:],
msg2cloud[:] >> voxel_grid[:],
voxel_grid[:] >> cropper[:],
cropper[:] >> extract_clusters[:],
msg2cloud[:] >> extract_indices["input"],
extract_clusters[:] >> colorize["clusters"],
extract_indices[:] >> colorize["input"],
msg2cloud[:] >> merge["input"],
colorize[:] >> merge["input2"],
merge[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:],)
# plasm.connect(cloud_sub["output"] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid[:],
# #voxel_grid[:] >> nan_filter[:],
# #nan_filter[:] >> extract_indices["indices"],
# voxel_grid[:] >> extract_indices["indices"],
# msg2cloud[:] >> extract_indices["input"],)
# #extract_indices[:] >> extract_clusters[:],)
## extract_clusters[:] >> colorize["clusters"],
## extract_indices[:] >> colorize["input"],
## msg2cloud[:] >> merge["input"],
## colorize[:] >> merge["input2"],
## colorize[:] >> viewer[:],)
# plasm.connect(cloud_sub["output"] >> msg2cloud[:],
# msg2cloud[:] >> voxel_grid["input"],
# voxel_grid["output"] >> passthru_z["input"],
# passthru_z["output"] >> passthru_x["input"],
# passthru_x["output"] >> passthru_y["input"],
# passthru_y["output"] >> extract_indices["input"],
# passthru_y["output"] >> extract_clusters["input"],
# extract_clusters[:] >> colorize["clusters"],
# extract_indices[:] >> colorize["input"],
# msg2cloud[:] >> merge["input"],
# colorize[:] >> merge["input2"],
# colorize[:] >> viewer[:],)
#
# #passthru_y["output"] >> cloud2msg[:],
# #cloud2msg[:] >> cloud_pub[:],)
run_plasm(options, plasm, locals=vars())
#sched = ecto.Scheduler(plasm)
#sched.execute_async()
time.sleep(10)
#
import ecto
import ecto_test
import yaml
import argparse
from ecto.opts import scheduler_options, CellYamlFactory, cell_options, run_plasm
parser = argparse.ArgumentParser(description='My awesome program thing.')
parser.add_argument('-i,--input',
metavar='IMAGE_FILE',
dest='imagefile',
type=str,
default='',
help='an image file to load.')
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group, default_niter=2)
multiply_factory = cell_options(parser, ecto_test.Multiply, prefix='mult')
const_factory = cell_options(parser,
ecto.Constant(value=0.50505),
prefix='const')
options = parser.parse_args()
print options.mult_factor
assert options.mult_factor == 3.14
c = const_factory(options)
m = multiply_factory(options)
cyaml = CellYamlFactory(c, 'const')
print cyaml.dump()
c = cyaml.load(yaml.load(cyaml.dump()))
import ecto
from ecto.opts import scheduler_options, run_plasm
from ecto import Constant
from ecto_test import Multiply, Printer
import argparse
parser = argparse.ArgumentParser(description='My awesome program thing.')
#our local command line args.
parser.add_argument('--value', metavar='VALUE', dest='value',
type=float, default=3, help='A value to use a constant.')
parser.add_argument('--factor', metavar='FACTOR', dest='factor',
type=float, default=5, help='The factor to multiply the constant with.')
#add ecto scheduler args.
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group)
options = parser.parse_args()
c = Constant(value=options.value)
m = Multiply(factor=options.factor)
pr = Printer()
plasm = ecto.Plasm()
plasm.connect(c[:] >> m[:],
m[:] >> pr[:]
)
#run the scheduler given the options, plasm, and our local variables.
run_plasm(options, plasm, locals=vars())
def __init__(self):
ecto_ros.init(sys.argv,"ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(description='Ecto Tabletop Segmentation')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=ecto_pcl.XYZRGB)
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2("cloud_pub",topic_name='ecto_pcl/cloud_filtered')
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.01)
graph = [cloud_sub["output"] >> msg2cloud[:],
msg2cloud[:] >> voxel_grid[:]
]
# estimate normals, segment and find convex hull
normals = NormalEstimation("normals", k_search=0, radius_search=0.02)
planar_segmentation = SACSegmentationFromNormals("planar_segmentation",
model_type=SACMODEL_NORMAL_PLANE,
eps_angle=0.09, distance_threshold=0.1)
project_inliers = ProjectInliers("project_inliers", model_type=SACMODEL_NORMAL_PLANE)
nan_filter = PassThrough('nan_removal')
convex_hull = ConvexHull("convex_hull")
graph += [voxel_grid[:] >> normals[:],
voxel_grid[:] >> planar_segmentation["input"],
normals[:] >> planar_segmentation["normals"],
voxel_grid[:] >> project_inliers["input"],
planar_segmentation["model"] >> project_inliers["model"],
project_inliers[:] >> nan_filter[:],
nan_filter[:] >> convex_hull[:]
]
# extract stuff on table from original high-res cloud and show in viewer
extract_stuff = ExtractPolygonalPrismData("extract_stuff", height_min=0.01, height_max=0.2)
extract_indices = ExtractIndices("extract_indices", negative=False)
viewer = CloudViewer("viewer", window_name="Clouds!")
graph += [msg2cloud[:] >> extract_stuff["input"],
convex_hull[:] >> extract_stuff["planar_hull"],
extract_stuff[:] >> extract_indices["indices"],
msg2cloud[:] >> extract_indices["input"],
extract_indices[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:]
]
plasm.connect(graph)
# Run the plasm
run_plasm(options, plasm, locals=vars())
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
import ecto
import ecto.ecto_test as ecto_test
import yaml
import argparse
from ecto.opts import scheduler_options, CellYamlFactory, cell_options, run_plasm
parser = argparse.ArgumentParser(description='My awesome program thing.')
parser.add_argument('-i,--input', metavar='IMAGE_FILE', dest='imagefile',
type=str, default='', help='an image file to load.')
group = parser.add_argument_group('ecto scheduler options')
scheduler_options(group, default_niter=2)
multiply_factory = cell_options(parser, ecto_test.Multiply, prefix='mult')
const_factory = cell_options(parser, ecto.Constant(value=0.50505), prefix='const')
options = parser.parse_args()
print options.mult_factor
assert options.mult_factor == 3.14
c = const_factory(options)
m = multiply_factory(options)
cyaml = CellYamlFactory(c,'const')
print cyaml.dump()
c = cyaml.load(yaml.load(cyaml.dump()))
assert c.params.value == 0.50505
def __init__(self):
ecto_ros.init(sys.argv, "ecto_pcl_demo")
# Create the argument parser
parser = argparse.ArgumentParser(
description='Ecto Euclidean Clustering')
# Use the parser to create scheduler options
scheduler_options(parser)
options = parser.parse_args()
# Create the overall plasm
plasm = ecto.Plasm()
# A VoxelGrid cell
voxel_grid = ecto_pcl.VoxelGrid("voxel_grid", leaf_size=0.01)
# A Cropper cell
cropper = ecto_pcl.Cropper("cropper",
x_min=-0.35,
x_max=0.35,
y_min=-0.35,
y_max=0.35,
z_min=0.3,
z_max=1.5)
# A EuclideanClusterExtraction cell
extract_clusters = ecto_pcl.EuclideanClusterExtraction(
"extract_clusters", min_cluster_size=50, cluster_tolerance=0.02)
# A cell to filter NaN values from the point cloud
nan_filter = ecto_pcl.PassThrough("nan_removal")
# A cell to colorize the clusters
colorize = ecto_pcl.ColorizeClusters("colorize", max_clusters=100)
# Create the subscriber cell with the appropriate point cloud topic
cloud_sub = ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='camera/depth_registered/points')
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud",
format=ecto_pcl.XYZRGB)
# A cell to convert a Ecto/PCL point cloud back to a point cloud message
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
# A cell to convert a point cloud ROS message to an Ecto/PCL point cloud object
cloud_pub = ecto_sensor_msgs.Publisher_PointCloud2(
"cloud_pub", topic_name='ecto_pcl/cloud_filtered')
# Create the plasm by connecting the cells
plasm.connect(
cloud_sub[:] >> msg2cloud[:], msg2cloud[:] >> nan_filter[:],
nan_filter[:] >> voxel_grid[:], voxel_grid[:] >> cropper[:],
cropper[:] >> extract_clusters[:],
extract_clusters[:] >> colorize["clusters"],
cropper[:] >> colorize["input"], colorize[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
# Run the plasm
run_plasm(options, plasm, locals=vars())
def parse_args(self):
self.parser = argparse.ArgumentParser(
description='Generate observations by 3d rendering.')
dbtools.add_db_arguments(self.parser)
scheduler_options(self.parser)
self.options = self.parser.parse_args()
#!/usr/bin/env python
import ecto
from ecto_opencv.highgui import imshow, ImageSaver
from image_pipeline.io.source import create_source
from ecto.opts import run_plasm, scheduler_options
import argparse
import sys
import os
parser = argparse.ArgumentParser(description='Saves images from a connect on keystroke.')
parser.add_argument('-o,--output', dest='output', help='The output directory. Default: %(default)s', default='./images')
#ecto options
scheduler_options(parser.add_argument_group('Scheduler Options'))
args = parser.parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
source = create_source(package_name='image_pipeline', source_type='OpenNISource') #optionally pass keyword args here...
depth_saver = ecto.If('depth saver', cell=ImageSaver(filename_format=os.path.join(args.output, 'depth%04d.png')))
image_saver = ecto.If('rgb saver', cell=ImageSaver(filename_format=os.path.join(args.output, 'image%04d.png')))
display = imshow(name='RGB', triggers=dict(save=ord('s')))
plasm = ecto.Plasm()
plasm.connect(source['image'] >> (display['image'], image_saver['image']),
source['depth'] >> (imshow(name='Depth')['image'], depth_saver['image']),