def setUp(self):
# rerun resize cell and set params
options.rerun = 'resize'
options.resize_to = 1600
# rebuild app
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
def setUp(self):
# rerun resize cell and set params
options.rerun = 'resize'
options.resize_to = 1600
# rebuild app
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
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 __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 setup_module():
# Run tests
print '%s' % options
options.project_path = context.tests_data_path
# options.rerun_all = True
app, plasm = appSetup(options)
print 'Run Setup: Initial Run'
run_plasm(options, plasm)
def setup_module():
# Run tests
print '%s' % options
options.project_path = context.tests_data_path
# options.rerun_all = True
app, plasm = appSetup(options)
print 'Run Setup: Initial Run'
run_plasm(options, plasm)
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 test_bb(options):
mm = MyBlackBox(start=10, step=3, amount=2, niter=2)
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
assert mm.outputs.value == 39
run_plasm(options, plasm)
assert mm.outputs.value == 69
def test_bb(options):
mm = MyBlackBox(start=10, step=3, amount=2, niter=2)
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
assert mm.outputs.value == 39
run_plasm(options, plasm)
assert mm.outputs.value == 69
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 test_bb(options):
mm = MyBlackBox(start=10, step=3, amount=2, niter=2)
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
# final value is start + step*(2*5-1)+amount
assert mm.outputs.value == 39
run_plasm(options, plasm)
# final value is start + step*(2*(5+5)-1)+amount
assert mm.outputs.value == 69
def test_bb(options):
mm = MyBlackBox(start=10, step=3, amount=2, niter=2)
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
# final value is start + step*(2*5-1)+amount
assert mm.outputs.value == 39
run_plasm(options, plasm)
# final value is start + step*(2*(5+5)-1)+amount
assert mm.outputs.value == 69
def generate_pointclouds_in_object_space(dbs, session, args):
object_name = dbs[session.object_id]['object_name']
if not os.path.exists(object_name):
os.mkdir(object_name)
obs_ids = models.find_all_observations_for_session(dbs, session.id)
if len(obs_ids) == 0:
raise RuntimeError("There are no observations available.")
db_reader = capture.ObservationReader(
'Database Source', db_params=dbtools.args_to_db_params(args))
#observation dealer will deal out each observation id.
observation_dealer = ecto.Dealer(
tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
depthTo3d = calib.DepthTo3d('Depth ~> 3D')
rescale_depth = capture.RescaledRegisteredDepth(
'Depth scaling') #this is for SXGA mode scale handling.
point_cloud_transform = reconstruction.PointCloudTransform(
'Object Space Transform', do_transform=False
) #keeps the points in camera coordinates, but populates the global sensor position and orientatino.
point_cloud_converter = conversion.MatToPointCloudXYZRGB('To Point Cloud')
to_ecto_pcl = ecto_pcl.PointCloudT2PointCloud('converter',
format=ecto_pcl.XYZRGB)
plasm = ecto.Plasm()
plasm.connect(observation_dealer[:] >> db_reader['observation'],
db_reader['K'] >> depthTo3d['K'],
db_reader['image'] >> rescale_depth['image'],
db_reader['depth'] >> rescale_depth['depth'],
rescale_depth[:] >> depthTo3d['depth'],
depthTo3d['points3d'] >> point_cloud_converter['points'],
db_reader['image'] >> point_cloud_converter['image'],
db_reader['mask'] >> point_cloud_converter['mask'],
db_reader['R', 'T'] >> point_cloud_transform['R', 'T'],
point_cloud_converter['point_cloud'] >> to_ecto_pcl[:],
to_ecto_pcl[:] >> point_cloud_transform['cloud'])
ply_writer = ecto_pcl.PLYWriter('PLY Saver',
filename_format='%s/cloud_%%05d.ply' %
(object_name))
pcd_writer = ecto_pcl.PCDWriter('PCD Saver',
filename_format='%s/cloud_%%05d.pcd' %
(object_name))
plasm.connect(point_cloud_transform['view'] >> (ply_writer['input'],
pcd_writer['input']))
if args.visualize:
global cloud_view
plasm.connect(
point_cloud_transform['view'] >> cloud_view,
db_reader['image'] >> imshows['image'],
db_reader['depth'] >> imshows['depth'],
db_reader['mask'] >> imshows['mask'],
)
from ecto.opts import run_plasm
run_plasm(args, 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 do_ecto(self):
plasm = ecto.Plasm()
plasm.connect(self.graph)
# sched = ecto.Scheduler(plasm)
# sched.execute(niter=1)
ecto.view_plasm(plasm)
# add ecto scheduler args.
run_plasm(self.options, plasm, locals=vars())
def test_bb_fail(options):
mm = MyBlackBox("MaMaMa", start=10, step=3, fail=True)
print mm.__doc__
assert 'fail' in mm.__doc__
assert mm.name() == 'MaMaMa'
plasm = ecto.Plasm()
plasm.insert(mm)
try:
run_plasm(options, plasm)
fail()
except ecto.CellException, e:
print "Good:"
print str(e)
assert "I hate life" in str(e)
def test_bb_fail(options):
mm = MyBlackBox("MaMaMa", start=10, step=3, fail=True)
print mm.__doc__
assert 'fail' in mm.__doc__
assert mm.name() == 'MaMaMa'
plasm = ecto.Plasm()
plasm.insert(mm)
try:
run_plasm(options, plasm)
fail()
except ecto.CellException, e:
print "Good:"
print str(e)
assert "I hate life" in str(e)
def generate_pointclouds_in_object_space(dbs, session, args):
object_name = dbs[session.object_id]['object_name']
if not os.path.exists(object_name):
os.mkdir(object_name)
obs_ids = models.find_all_observations_for_session(dbs, session.id)
if len(obs_ids) == 0:
raise RuntimeError("There are no observations available.")
db_reader = capture.ObservationReader('Database Source', db_params=dbtools.args_to_db_params(args))
#observation dealer will deal out each observation id.
observation_dealer = ecto.Dealer(tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
depthTo3d = calib.DepthTo3d('Depth ~> 3D')
rescale_depth = capture.RescaledRegisteredDepth('Depth scaling') #this is for SXGA mode scale handling.
point_cloud_transform = reconstruction.PointCloudTransform('Object Space Transform',do_transform=False)#keeps the points in camera coordinates, but populates the global sensor position and orientatino.
point_cloud_converter = conversion.MatToPointCloudXYZRGB('To Point Cloud')
to_ecto_pcl = ecto_pcl.PointCloudT2PointCloud('converter', format=ecto_pcl.XYZRGB)
plasm = ecto.Plasm()
plasm.connect(
observation_dealer[:] >> db_reader['observation'],
db_reader['K'] >> depthTo3d['K'],
db_reader['image'] >> rescale_depth['image'],
db_reader['depth'] >> rescale_depth['depth'],
rescale_depth[:] >> depthTo3d['depth'],
depthTo3d['points3d'] >> point_cloud_converter['points'],
db_reader['image'] >> point_cloud_converter['image'],
db_reader['mask'] >> point_cloud_converter['mask'],
db_reader['R', 'T'] >> point_cloud_transform['R', 'T'],
point_cloud_converter['point_cloud'] >> to_ecto_pcl[:],
to_ecto_pcl[:] >> point_cloud_transform['cloud']
)
ply_writer = ecto_pcl.PLYWriter('PLY Saver',
filename_format='%s/cloud_%%05d.ply' % (object_name))
pcd_writer = ecto_pcl.PCDWriter('PCD Saver',
filename_format='%s/cloud_%%05d.pcd' % (object_name))
plasm.connect(point_cloud_transform['view'] >> (ply_writer['input'], pcd_writer['input'])
)
if args.visualize:
global cloud_view
plasm.connect(
point_cloud_transform['view'] >> cloud_view,
db_reader['image'] >> imshows['image'],
db_reader['depth'] >> imshows['depth'],
db_reader['mask'] >> imshows['mask'],
)
from ecto.opts import run_plasm
run_plasm(args, 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 __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 test_bb2(options):
# start is going to be ignored as it is set to 20 by default
mm = MyBlackBox2(start=0, step=3, amount1=10, amount2=50)
# make sure the declare functions work
p = ecto.Tendrils()
i = ecto.Tendrils()
o = ecto.Tendrils()
mm.declare_params(p)
mm.declare_io(p, i, o)
# run the BlackBox
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
# final value is start + step*(5-1)+amount1+amount2
assert mm.outputs.value == 92
run_plasm(options, plasm)
# final value is start + step*(5+5-1)+amount1+amount2
assert mm.outputs.value == 107
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 test_bb2(options):
# start is going to be ignored as it is set to 20 by default
mm = MyBlackBox2(start=0, step=3, amount1=10, amount2=50)
# make sure the declare functions work
p=ecto.Tendrils()
i=ecto.Tendrils()
o=ecto.Tendrils()
mm.declare_params(p)
mm.declare_io(p,i,o)
# run the BlackBox
plasm = ecto.Plasm()
plasm.insert(mm)
options.niter = 5
run_plasm(options, plasm)
# final value is start + step*(5-1)+amount1+amount2
assert mm.outputs.value == 92
run_plasm(options, plasm)
# final value is start + step*(5+5-1)+amount1+amount2
assert mm.outputs.value == 107
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 __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 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())
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 setUp(self):
options.rerun = 'odm_georeferencing'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
def setUp(self):
options.rerun = 'odm_orthophoto'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
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())
#!/usr/bin/env python
import argparse
from object_recognition.capture import create_openni_bag_capture_plasm
from ecto.opts import scheduler_options, run_plasm
import ecto_ros
import sys
parser = argparse.ArgumentParser(
description='Capture a bag of data from the kinect.')
scheduler_options(parser)
parser.add_argument('bagname', nargs=1, type=str)
options = parser.parse_args()
plasm = create_openni_bag_capture_plasm(options.bagname[0])
ecto_ros.init(sys.argv, 'data_capture')
run_plasm(options, plasm, locals)
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']),
display['save'] >> (image_saver['__test__'], depth_saver['__test__'])
)
run_plasm(args, plasm, locals=vars())
def setUp(self):
options.rerun = 'pmvs'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
#!/usr/bin/env python
import argparse
from object_recognition.capture import create_openni_bag_capture_plasm
from ecto.opts import scheduler_options, run_plasm
import ecto_ros
import sys
parser = argparse.ArgumentParser(description='Capture a bag of data from the kinect.')
scheduler_options(parser)
parser.add_argument('bagname', nargs=1, type=str)
options = parser.parse_args()
plasm = create_openni_bag_capture_plasm(options.bagname[0])
ecto_ros.init(sys.argv, 'data_capture')
run_plasm(options, plasm, locals)
def setUp(self):
options.rerun = 'pmvs'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
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())
'-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
if "__main__" == __name__:
argv = sys.argv[:]
ecto_ros.strip_ros_args(sys.argv)
options = parse_args()
ecto_ros.init(argv, "openni_capture", False)
plasm = openni_capture.create_capture_plasm(options.bag,
options.angle_thresh)
from ecto.opts import run_plasm
run_plasm(options, plasm)
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)
def setUp(self):
options.rerun = 'odm_texturing'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
def match(options,
scale=1.0,
train_scale=1 / 3.0,
scale_factor=1.05,
n_levels=5,
n_features_train=10000,
n_features_test=1000,
match_distance=120,
):
if not os.path.exists(options.output):
os.makedirs(options.output)
train = imread('Train image', image_file=options.train,
mode=ImageMode.GRAYSCALE)
test = imread('Test image', image_file=options.test,
mode=ImageMode.GRAYSCALE)
train_name, _extension = os.path.splitext(os.path.basename(options.train))
test_name, _extension = os.path.splitext(os.path.basename(options.test))
print 'Scale %d is %f' % (0, scale)
for i in range(n_levels - 1):
scale *= 1.0 / scale_factor
print 'Scale %d is %f' % (i + 1, scale)
orb_train = ORB(n_features=n_features_train,
n_levels=n_levels, scale_factor=scale_factor)
orb_test = ORB(n_features=n_features_test, n_levels=1)
plasm = ecto.Plasm()
scale = Scale(factor=train_scale, interpolation=AREA)
plasm.connect(train['image'] >> scale['image'])
train_pts = hookup_orb(plasm, scale, orb_train)
test_pts = hookup_orb(plasm, test, orb_test)
matcher = Matcher()
plasm.connect(orb_train['descriptors'] >> matcher['train'],
orb_test['descriptors'] >> matcher['test'],
)
matchesMat = MatchesToMat()
h_est = MatchRefinement(match_distance=match_distance)
plasm.connect(train_pts['points'] >> h_est['train'],
test_pts['points'] >> h_est['test'],
matcher['matches'] >> h_est['matches'],
h_est['matches'] >> matchesMat['matches']
)
match_draw = DrawMatches()
output_base = os.path.join(options.output,
'%(train)s_%(test)s' %
dict(train=train_name, test=test_name))
match_image = output_base + '.png'
image_saver = ImageSaver(filename=match_image)
plasm.connect(train_pts['points'] >> match_draw['train'],
test_pts['points'] >> match_draw['test'],
h_est['matches'] >> match_draw['matches'],
h_est['matches_mask'] >> match_draw['matches_mask'],
scale['image'] >> match_draw['train_image'],
test['image'] >> match_draw['test_image'],
match_draw[:] >> image_saver[:]
)
match_eval = MatchEval(output=options.output)
plasm.connect(matchesMat['matches'] >> match_eval['matches'],
train_pts['points'] >> match_eval['train'],
test_pts['points'] >> match_eval['test'],
h_est['matches_mask'] >> match_eval['inliers'],
)
run_plasm(options, plasm, locals=vars())
viewer = cloud_treatment.CloudViewerCell("Viewer_ecto",
window_name="PCD Viewer")
graph = [cloud_sub["output"] >> msg2cloud[:],
msg2cloud[:] >> passthrough3d["input"],
passthrough3d["output"] >> stepsegmenter["input"],
#principalcomponent["centroids"] >> viewer["VIPoints"],
#principalcomponent["rectangles"] >> viewer["rectangles"],
stepsegmenter["clusters"] >> colorize["clusters"],
passthrough3d["output"] >> colorize["input"],
stepsegmenter["clusters"] >> principalcomponent["clusters"],
passthrough3d["output"] >> principalcomponent["input"],
#colorize[:] >> viewer["input"]
passthrough3d["output"] >> cloud2msg_main[:],
cloud2msg_main[:] >> cloud_pub_main[:],
colorize[:] >> cloud2msg_seg[:],
cloud2msg_seg[:] >> cloud_pub_seg[:]
]
#graph = [cloud_sub["output"] >> msg2cloud[:],
# msg2cloud[:] >> cloud2msg[:],
# cloud2msg[:] >> cloud_pub[:]
# ]
plasm = ecto.Plasm()
plasm.connect(graph)
run_plasm(options, plasm, locals=vars())
#plasm.execute()
print "plasm_ecto executed"
def setUp(self):
options.rerun = 'odm_orthophoto'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
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'))
plasm = ecto.Plasm()
plasm.connect(
# camera['image'] >> imshow(name='Mono')['image'],
source['depth'] >> imshow(name='Depth')['image'],
source['image'] >> imshow(name='RGB')['image'])
if not args.preview:
plasm.connect(
source['image'] >> image_saver['image'],
source['depth'] >> depth_saver['image'],
# camera['image'] >> mono_saver['image'],
)
run_plasm(args, plasm, locals=vars())
def setUp(self):
options.rerun = 'odm_texturing'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)
return options
options = parse_args()
images = ImageReader(path=options.input)
orb_m = ORB(n_features=5000, n_levels=10)
rgb2gray = cvtColor(flag=Conversion.RGB2GRAY)
_stats = ORBstats()
stats = ecto.If(cell=_stats)
stats.inputs.__test__ = False
accum = DescriptorAccumulator()
plasm = ecto.Plasm()
plasm.connect(
images['image'] >> rgb2gray['image'],
rgb2gray['image'] >> orb_m['image'],
orb_m['descriptors'] >> accum[:],
accum[:] >> stats['descriptors'],
)
run_plasm(options, plasm, locals=vars())
_stats.process()
hist = _stats.outputs.distances.toarray()
np.savetxt('hist.txt', hist)
print np.sum(hist)
plt.plot(hist)
plt.xlim((0, 255))
plt.xticks((0, 64, 128, 192, 256))
plt.savefig('fig.pdf')
def setUp(self):
options.rerun = 'odm_georeferencing'
self.app, self.plasm = appSetup(options)
run_plasm(options, self.plasm)