def __init__(self, plasm):
"""
sinks: a list of the sinks to use
"""
ecto.BlackBox.__init__(self, plasm)
self._object_ids_splitter = ecto.Passthrough()
self._Rs_splitter = ecto.Passthrough()
self._Ts_splitter = ecto.Passthrough()
# try to import ecto_ros
self._do_use_ros = True
try:
__import__('ecto_ros')
except:
self._do_use_ros = False
if self._do_use_ros:
import ecto_ros
ecto_ros.init(sys.argv, "ecto_node")
self._image_message_splitter = ecto.Passthrough()
# add the different possible outputs
self._cell_factory = {}
self._cells = []
def do_ecto(bagname, msg_counts, Scheduler):
ecto_ros.init(sys.argv, "image_sub_node")
subs = dict(
image=ImageSub(topic_name='/camera/rgb/image_color', queue_size=0),
depth=ImageSub(topic_name='/camera/depth/image', queue_size=0),
depth_info=CameraInfoSub(topic_name='/camera/depth/camera_info', queue_size=0),
image_info=CameraInfoSub(topic_name='/camera/rgb/camera_info', queue_size=0),
)
sync = ecto_ros.Synchronizer('Synchronizator', subs=subs
)
counter_rgb = ecto.Counter()
counter_depth = ecto.Counter()
counter_rgb_info = ecto.Counter()
counter_depth_info = ecto.Counter()
graph = [
sync["image"] >> counter_rgb[:],
sync["depth"] >> counter_depth[:],
sync["image_info"] >> counter_rgb_info[:],
sync["depth_info"] >> counter_depth_info[:],
]
plasm = ecto.Plasm()
plasm.connect(graph)
sched = Scheduler(plasm)
sched.execute_async()
rosbag = play_bag(bagname, delay=3, rate=1)
wait_bag(rosbag)
time.sleep(1)
sched.stop()
print "expecting RGB count:", msg_counts['/camera/rgb/image_color']
print "RGB count:", counter_rgb.outputs.count
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 create_capture_plasm(bag_name, angle_thresh, segmentation_cell, n_desired,
orb_template='', res=SXGA_RES, fps=FPS_30,
orb_matches=False,
preview=False, use_turn_table=True):
'''
Creates a plasm that will capture openni data into a bag, using a dot pattern to sparsify views.
@param bag_name: A filename for the bag, will write to this file.
@param angle_thresh: The angle threshhold in radians to sparsify the views with.
'''
graph = []
# Create ros node, subscribing to openni topics
argv = sys.argv[:]
ecto_ros.strip_ros_args(argv)
ecto_ros.init(argv, "object_capture_msd_pcl", False)
cloud_sub = ecto_ros.ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
rgb_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image("rgb_image_sub", topic_name='/camera/rgb/image_raw')
depth_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image("depth_image_sub", topic_name='/camera/depth_registered/image_raw')
rgb_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo("rgb_camera_info_sub", topic_name='/camera/rgb/camera_info')
depth_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo("depth_camera_info_sub", topic_name='/camera/depth_registered/camera_info')
# Converte ros topics to cv types
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=XYZRGB)
image2cv = ecto_ros.Image2Mat()
depth2cv = ecto_ros.Image2Mat()
rgb_info2cv = ecto_ros.CameraInfo2Cv()
depth_info2cv = ecto_ros.CameraInfo2Cv()
graph += [ cloud_sub['output'] >> msg2cloud[:],
rgb_image_sub[:] >> image2cv[:],
depth_image_sub[:] >> depth2cv[:],
rgb_camera_info_sub[:] >> rgb_info2cv[:],
depth_camera_info_sub[:] >> depth_info2cv[:]
]
#display the mask
display = highgui.imshow(name='Poses')
graph += [image2cv[:] >> display[:],
]
# convert the image to grayscale
# rgb2gray = imgproc.cvtColor('rgb -> gray', flag=imgproc.Conversion.RGB2GRAY)
# graph += [image2cv[:] >> rgb2gray[:] ]
# get a pose use the dot pattern: there might be a scale ambiguity as this is 3d only
poser = OpposingDotPoseEstimator(rows=5, cols=3,
pattern_type=calib.ASYMMETRIC_CIRCLES_GRID,
square_size=0.04, debug=True)
graph += [ image2cv[:] >> poser['color_image'],
rgb_info2cv['K'] >> poser['K_image'],
image2cv[:] >> poser['image'] ]
plasm = ecto.Plasm()
plasm.connect(graph)
return (plasm, segmentation_cell) # return segmentation for tuning of parameters.
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 __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)
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_ros():
"""
Function that should be called before starting any cell depending on ROS
"""
# check if roscore is running
proc = subprocess.Popen(['rostopic', 'list'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
_out, err = proc.communicate()
if err:
print('roscore not started: start it or your ROS cell will error out at runtime')
else:
ecto_ros.init(sys.argv, DEFAULT_NODE_NAME, False)
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 init_ros():
"""
Function that should be called before starting any cell depending on ROS
"""
# check if roscore is running
proc = subprocess.Popen(['rostopic', 'list'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
_out, err = proc.communicate()
if err:
print(
'roscore not started: start it or your ROS cell will error out at runtime'
)
else:
ecto_ros.init(sys.argv, DEFAULT_NODE_NAME, False)
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")
# 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='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 __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, plasm):
"""
sinks: a list of the sinks to use
"""
ecto.BlackBox.__init__(self, plasm)
# try to import ecto_ros
self._do_use_ros = True
try:
__import__('ecto_ros')
except:
self._do_use_ros = False
if self._do_use_ros:
import ecto_ros
ecto_ros.init(sys.argv, "ecto_node")
# add the different possible outputs
self._cell_factory = {}
self._cells = []
self._outputs = {}
def do_ecto(bagname, msg_counts, Scheduler):
ecto_ros.init(sys.argv, "image_sub_node")
sub_rgb = ImageSub("image_sub", topic_name='/camera/rgb/image_color', queue_size=0)
im2mat_rgb = ecto_ros.Image2Mat()
counter_rgb = ecto.Counter()
graph = [
sub_rgb["output"] >> im2mat_rgb["image"],
im2mat_rgb[:] >> counter_rgb[:],
]
plasm = ecto.Plasm()
plasm.connect(graph)
sched = Scheduler(plasm)
sched.execute_async()
rosbag = play_bag(bagname, delay=0.5)
wait_bag(rosbag)
sched.stop()
print "expecting RGB count:", msg_counts['/camera/rgb/image_color']
print "RGB count:", counter_rgb.outputs.count
assert msg_counts['/camera/rgb/image_color'] >= counter_rgb.outputs.count
assert counter_rgb.outputs.count != 0
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 do_ecto(bagname, msg_counts, Scheduler):
ecto_ros.init(sys.argv, "image_sub_node")
sub_rgb = ImageSub("image_sub",
topic_name='/camera/rgb/image_color',
queue_size=0)
im2mat_rgb = ecto_ros.Image2Mat()
counter_rgb = ecto.Counter()
graph = [
sub_rgb["output"] >> im2mat_rgb["image"],
im2mat_rgb[:] >> counter_rgb[:],
]
plasm = ecto.Plasm()
plasm.connect(graph)
sched = Scheduler(plasm)
sched.execute_async()
rosbag = play_bag(bagname, delay=0.5)
wait_bag(rosbag)
sched.stop()
print "expecting RGB count:", msg_counts['/camera/rgb/image_color']
print "RGB count:", counter_rgb.outputs.count
assert msg_counts['/camera/rgb/image_color'] >= counter_rgb.outputs.count
assert counter_rgb.outputs.count != 0
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 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())
# abstract the input.
import ecto
from ecto_opencv import highgui, calib, imgproc
from fiducial_pose_est import *
#lil bit of ros
PKG = 'ecto_ros' # this package name
import roslib
roslib.load_manifest(PKG)
import ecto_ros, ecto_sensor_msgs, ecto_geometry_msgs
import sys
ImageSub = ecto_sensor_msgs.Subscriber_Image
CameraInfoSub = ecto_sensor_msgs.Subscriber_CameraInfo
if "__main__" == __name__:
ecto_ros.init(sys.argv, "pose_estimator")
plasm = ecto.Plasm()
sched = ecto.schedulers.Singlethreaded(plasm)
debug = True
poser = OpposingDotPoseEstimator(plasm,
rows=5,
cols=3,
pattern_type="acircles",
square_size=0.04,
debug=debug)
camera_info = calib.CameraIntrinsics('Camera Info',
camera_file="camera.yml")
subs = dict(
image=ImageSub(topic_name='camera/rgb/image_color', queue_size=0),
# add arguments for the source and sink
Sink.add_arguments(parser)
params, args, pipeline_params, do_display, db_params, db = read_arguments(parser, argv[1:])
model_ids = []
object_ids = set()
for object_id in params['object_ids']:
for model_id in models.find_model_for_object(db, object_id, 'MMOD'):
model_ids.append(str(model_id))
object_ids.add(object_id)
model_documents = DbDocuments(db_params, model_ids)
# TODO handle this properly...
ecto_ros.init(argv, "mmod_testing", False)#not anonymous.
source = Source.parse_arguments(params['source'])
sink = Sink.parse_arguments(args, db, db_params, object_ids)
#hook up the tester
mmod_tester = MModTester(thresh_match=0.93,color_filter_thresh=0.8,skip_x=8,skip_y=8, model_documents=model_documents)
# Connect the detector to the source
pyr_img = mmod.Pyramid(n_levels=3)
pyr_depth = mmod.Pyramid(n_levels=3)
for key in source.outputs.iterkeys():
if key in mmod_tester.inputs.keys():
if key == 'image':
#!/usr/bin/env python
"""
This file is meant to be used with test_client.py: it starts a recognition server for 10 seconds
"""
from object_recognition_core.utils.training_detection_args import read_arguments_from_string
from object_recognition_ros.server import RecognitionServer
import rospy
import sys
import ecto_ros
import roslib
import rospy
if __name__ == '__main__':
ecto_ros.init([], "test_server_ecto_ros", False)
rospy.init_node('test_server')
ork_params = read_arguments_from_string("""
sink:
type: 'Publisher'
module: 'object_recognition_ros.io'
pipeline:
type: ConstantDetector
module: object_recognition_core.ecto_cells.pipelines
outputs: [sink]
""")
server = RecognitionServer(ork_params)
# timeout for the test
rospy.sleep(10.0)
#!/usr/bin/env python
"""
This sample shows how to interact with ROS cloud subscribers and publishers.
"""
import sys, ecto, ecto_pcl, ecto_ros, ecto_pcl_ros, ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
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/sample_output')
plasm.connect(cloud_sub['output'] >> msg2cloud[:],
msg2cloud[:] >> cloud2msg[:],
cloud2msg[:] >> cloud_pub[:])
if __name__ == "__main__":
from ecto.opts import doit
ecto_ros.init(sys.argv,"ecto_pcl_rossample")
ecto.view_plasm(plasm)
doit(plasm, description='Read a pcd through ROS.')
normals[:] >> segment["normals"],
# project inliers, find convex hull
voxgrid[:] >> project["input"],
segment["model"] >> project["model"],
project[:] >> conhull[:],
# extract stuff on table from original high-res cloud
grabber[:] >> prism["input"],
conhull[:] >> prism["planar_hull"],
prism[:] >> extract["indices"],
grabber[:] >> extract["input"],
#convert output to ros msg and pub.
extract[:] >> clusters[:],
clusters[:] >> largest["clusters"],
extract[:] >> largest["input"],
largest[:] >> pcl2msg[:],
pcl2msg[:] >> pub[:]
)
sched = ecto.schedulers.Threadpool(plasm)
#sched.execute()
sched.execute_async()
from IPython.Shell import IPShellEmbed
ipshell = IPShellEmbed()
ipshell()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "extract_largest_cluster")
do_ecto()
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)
#!/usr/bin/env python
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)
pcdfile = os.path.join(os.path.dirname(__file__), 'test.pcd')
if len(sys.argv) > 1:
pcdfile = sys.argv[1]
reader = ecto_pcl.PCDReader("Reader", filename=pcdfile)
filters = hirop_perception.RegionFilters("tester", maxZ=1)
fusion = hirop_perception.PclFusion()
viewer = ecto_pcl.CloudViewer("viewer", window_name="PCD Viewer")
saver = ecto_pcl.PCDWriter("Saver")
source = hirop_perception.PointCloudRos('source1',
topic_name="/camera/depth/points",
world_frame='base_link')
plasm.connect(source["output"] >> fusion["input"], source["T"] >> fusion["T"],
source["R"] >> fusion["R"], fusion["output"] >> viewer["input"])
if __name__ == "__main__":
ecto_ros.init(sys.argv, "ros_test")
sched = ecto.Scheduler(plasm)
sched.execute(niter=1)
#sleep 10 seconds and exit.
time.sleep(20)
sched.execute(niter=1)
time.sleep(30)
pcd_msg2depth_msg = ecto_ros.PointCloud22DepthImage()
plasm = ecto.Plasm()
plasm.connect([
bagreader['info'] >> bagwriter['info_rgb'],
bagreader['info'] >> bagwriter['info_depth'],
bagreader['rgb'] >> bagwriter['rgb'],
bagreader['depth'] >> pcd_msg2depth_msg['cloud'],
pcd_msg2depth_msg['image'] >> bagwriter['depth']
])
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute()
return tmp_bag_path
if __name__ == '__main__':
ecto_ros.init(sys.argv, "ecto_node")
parser = ArgumentParser()
parser.add_argument('--folder',
dest='folder',
help='The Folder where all the NIST bags are located',
required=True)
add_db_arguments(parser)
args = parser.parse_args()
# process all the bags in the folder
for file_name in os.listdir(args.folder):
if not file_name.endswith('.bag'):
continue
# persist the bad to the DB
object_index = int(file_name[7:9])
sync = ecto_ros.Synchronizer('Synchronizator', subs=subs
)
im2mat_rgb = ecto_ros.Image2Mat('rgb -> cv::Mat')
camera_info = ecto_ros.CameraInfo2Cv('camera_info -> cv::Mat')
poser = OpposingDotPoseEstimator(plasm,
rows=5, cols=3,
pattern_type=calib.ASYMMETRIC_CIRCLES_GRID,
square_size=0.04, debug=True)
bgr2rgb = imgproc.cvtColor('rgb -> bgr', flag=imgproc.Conversion.RGB2BGR)
rgb2gray = imgproc.cvtColor('rgb -> gray', flag=imgproc.Conversion.RGB2GRAY)
display = highgui.imshow('Poses', name='Poses', waitKey=5, autoSize=True)
graph = [sync['image'] >> im2mat_rgb[:],
im2mat_rgb[:] >> (rgb2gray[:], bgr2rgb[:]),
bgr2rgb[:] >> poser['color_image'],
rgb2gray[:] >> poser['image'],
poser['debug_image'] >> display['input'],
sync['image_ci'] >> camera_info['camera_info'],
camera_info['K'] >> poser['K'],
]
plasm.connect(graph)
if __name__ == '__main__':
import sys
ecto_ros.init(sys.argv, "opposing_dots_pose", False)
from ecto.opts import doit
doit(plasm, description='Estimate the pose of an opposing dot fiducial.')
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())
description='Generate observations by 3d rendering.')
dbtools.add_db_arguments(self.parser)
scheduler_options(self.parser)
self.options = self.parser.parse_args()
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())
if __name__ == '__main__':
ecto_ros.init(sys.argv, "observation_renderer")
observation_renderer = ObservationRenderer()
# observation_renderer.connect_debug()
observation_renderer.connect_ros_pose()
# observation_renderer.connect_ros_image()
observation_renderer.connect_ros_point_cloud()
observation_renderer.connect_db_inserter()
observation_renderer.do_ecto()
#!/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)
4) We then extract the indices of all points that are above the
plane formed by the convex hull.
5) Finally, we extract the point cloud corresponding to these
indices, and display it.
"""
import sys
import ecto, ecto_ros, ecto_pcl_ros, ecto_pcl
import ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
from ecto_pcl import *
from ecto.opts import doit as ecto_doit, scheduler_options, run_plasm
import argparse
import time
ecto_ros.init(sys.argv, "ecto_tracker")
parser = argparse.ArgumentParser(description='Ecto Tracker.')
scheduler_options(parser)
options = parser.parse_args()
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(
subs = dict(image=ImageSub(topic_name='image',queue_size=0),
depth=ImageSub(topic_name='depth',queue_size=0),
)
#The synchronizer expects this dict, and will have an output foreach key:value pair
sync = ecto_ros.Synchronizer('Synchronizator', subs=subs)
#some rosmsg ~> opencv type converters
rgb = ecto_ros.Image2Mat()
depth = ecto_ros.Image2Mat()
#declare a graph that just shows the images using opencv highgui
graph = [
sync["image"] >> rgb["image"],
sync["depth"] >> depth["image"],
rgb["image"] >> imshow(name="rgb")["image"],
depth["image"] >> imshow(name="depth")["image"]
]
#add the graph to our ecto plasm
plasm = ecto.Plasm()
plasm.connect(graph)
#We'll use a threadpool based scheduler to execute this one.
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute() #execute forever
if __name__ == "__main__":
import sys
ecto_ros.init(sys.argv, "ecto_node") #strips out ros remappings and initializes roscpp
do_ecto()
import ecto, ecto_pcl, ecto_pcl_ros
import ecto_ros, ecto_sensor_msgs
import sys
PointCloudSub = ecto_sensor_msgs.Subscriber_PointCloud2
PointCloudPub = ecto_sensor_msgs.Publisher_PointCloud2
def do_ecto():
plasm = ecto.Plasm()
sub = PointCloudSub("Subscriber", topic_name='/camera/rgb/points')
grabber = ecto_pcl_ros.Message2PointCloud("Message2Cloud", format=ecto_pcl.XYZRGB)
pcl2msg = ecto_pcl_ros.PointCloud2Message("Cloud2Message")
pub = PointCloudPub("Cloud Publisher", topic_name='/ecto_pcl/pass_it_on')
plasm.connect(sub['output'] >> grabber[:],
grabber[:] >> pcl2msg[:],
pcl2msg[:] >> pub[:])
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "pass_it_on")
do_ecto()
def create_capture_plasm(bag_name,
angle_thresh,
segmentation_cell,
n_desired,
orb_template='',
res=SXGA_RES,
fps=FPS_30,
orb_matches=False,
preview=False,
use_turn_table=True):
'''
Creates a plasm that will capture openni data into a bag, using a dot pattern to sparsify views.
@param bag_name: A filename for the bag, will write to this file.
@param angle_thresh: The angle threshhold in radians to sparsify the views with.
'''
graph = []
# Create ros node, subscribing to openni topics
argv = sys.argv[:]
ecto_ros.strip_ros_args(argv)
ecto_ros.init(argv, "object_capture_msd_pcl", False)
cloud_sub = ecto_ros.ecto_sensor_msgs.Subscriber_PointCloud2(
"cloud_sub", topic_name='/camera/depth_registered/points')
rgb_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image(
"rgb_image_sub", topic_name='/camera/rgb/image_raw')
depth_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image(
"depth_image_sub", topic_name='/camera/depth_registered/image_raw')
rgb_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo(
"rgb_camera_info_sub", topic_name='/camera/rgb/camera_info')
depth_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo(
"depth_camera_info_sub",
topic_name='/camera/depth_registered/camera_info')
# Converte ros topics to cv types
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=XYZRGB)
image2cv = ecto_ros.Image2Mat()
depth2cv = ecto_ros.Image2Mat()
rgb_info2cv = ecto_ros.CameraInfo2Cv()
depth_info2cv = ecto_ros.CameraInfo2Cv()
graph += [
cloud_sub['output'] >> msg2cloud[:], rgb_image_sub[:] >> image2cv[:],
depth_image_sub[:] >> depth2cv[:],
rgb_camera_info_sub[:] >> rgb_info2cv[:],
depth_camera_info_sub[:] >> depth_info2cv[:]
]
#display the mask
display = highgui.imshow(name='Poses')
graph += [
image2cv[:] >> display[:],
]
# convert the image to grayscale
# rgb2gray = imgproc.cvtColor('rgb -> gray', flag=imgproc.Conversion.RGB2GRAY)
# graph += [image2cv[:] >> rgb2gray[:] ]
# get a pose use the dot pattern: there might be a scale ambiguity as this is 3d only
poser = OpposingDotPoseEstimator(
rows=5,
cols=3,
pattern_type=calib.ASYMMETRIC_CIRCLES_GRID,
square_size=0.04,
debug=True)
graph += [
image2cv[:] >> poser['color_image'],
rgb_info2cv['K'] >> poser['K_image'], image2cv[:] >> poser['image']
]
plasm = ecto.Plasm()
plasm.connect(graph)
return (plasm, segmentation_cell
) # return segmentation for tuning of parameters.
import ecto_ros, ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
import sys
ImageSub = ecto_sensor_msgs.Subscriber_Image
ImagePub = ecto_sensor_msgs.Publisher_Image
def do_ecto():
sub_rgb = ImageSub("image_sub", topic_name='/camera/rgb/image_mono')
sub_depth = ImageSub("depth_sub", topic_name='/camera/depth/image')
pub_rgb = ImagePub("image_pub", topic_name='my_image')
pub_depth = ImagePub("depth_pub", topic_name='my_depth')
graph = [
sub_rgb["output"] >> pub_rgb["input"],
sub_depth["output"] >> pub_depth["input"]
]
plasm = ecto.Plasm()
plasm.connect(graph)
ecto.view_plasm(plasm)
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "image_pub")
do_ecto()
#!/usr/bin/env python
"""
This file is meant to be used with test_server.py: it starts a recognition server for 5 seconds
"""
from object_recognition_core.utils.training_detection_args import read_arguments_from_string
from object_recognition_ros.server import RecognitionServer
import rospy
import sys
import ecto_ros
#if __name__ == '__main__':
# rospy.init_node('test_server')
# ork_params = read_arguments_from_string(open('config_detection_test.json'))
# server = RecognitionServer(ork_params)
# rospy.sleep(5.0)
import roslib
import rospy
from std_msgs.msg import String
if __name__ == '__main__':
original_argv = sys.argv
ecto_ros.init(original_argv, "server_test", False)
rospy.init_node('test_server')
ork_params = read_arguments_from_string(open('config_detection_test.json'))
server = RecognitionServer(ork_params)
while not rospy.is_shutdown():
rospy.sleep(1.0)
def create_capture_plasm(bag_name, angle_thresh, segmentation_cell, n_desired,
sensor, res=SXGA_RES, fps=FPS_30,
orb_matches=False,
preview=False, use_turn_table=True):
'''
Creates a plasm that will capture openni data into a bag, using a dot pattern to sparsify views.
@param bag_name: A filename for the bag, will write to this file.
@param angle_thresh: The angle threshhold in radians to sparsify the views with.
'''
graph = []
# Create ros node, subscribing to openni topics
argv = sys.argv[:]
ecto_ros.strip_ros_args(argv)
ecto_ros.init(argv, "object_capture_msd_pcl", False)
cloud_sub = ecto_ros.ecto_sensor_msgs.Subscriber_PointCloud2("cloud_sub", topic_name='/camera/depth_registered/points')
# openni&freenect requires '/camera/rgb/image_rect_color', openni2 just need '/camera/rgb/image_raw'
if sensor in ['kinect']:
rgb_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image("rgb_image_sub", topic_name='/camera/rgb/image_color')
elif sensor in ['xtion']:
rgb_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image("rgb_image_sub", topic_name='/camera/rgb/image_raw')
depth_image_sub = ecto_ros.ecto_sensor_msgs.Subscriber_Image("depth_image_sub", topic_name='/camera/depth_registered/image_raw')
rgb_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo("rgb_camera_info_sub", topic_name='/camera/rgb/camera_info')
depth_camera_info_sub = ecto_ros.ecto_sensor_msgs.Subscriber_CameraInfo("depth_camera_info_sub", topic_name='/camera/depth_registered/camera_info')
# Converte ros topics to cv types
msg2cloud = ecto_pcl_ros.Message2PointCloud("msg2cloud", format=XYZRGB)
image2cv = ecto_ros.Image2Mat()
depth2cv = ecto_ros.Image2Mat()
rgb_info2cv = ecto_ros.CameraInfo2Cv()
depth_info2cv = ecto_ros.CameraInfo2Cv()
graph += [ cloud_sub['output'] >> msg2cloud[:],
rgb_image_sub[:] >> image2cv[:],
depth_image_sub[:] >> depth2cv[:],
rgb_camera_info_sub[:] >> rgb_info2cv[:],
depth_camera_info_sub[:] >> depth_info2cv[:]
]
"""
rgb_display = highgui.imshow(name='rgb')
depth_display = highgui.imshow(name='depth')
graph += [image2cv[:] >> rgb_display[:],
depth2cv[:] >> depth_display[:],
]
# Create pointcloud viewer
viewer = CloudViewer("viewer", window_name="Clouds!")
graph += [ msg2cloud[:] >> viewer[:] ]
"""
# Process pointcloud
# Cut off some parts
cut_x = PassThrough(filter_field_name="x",
filter_limit_min=-0.2,
filter_limit_max=0.2)
cut_y = PassThrough(filter_field_name="y",
filter_limit_min=-0.5,
filter_limit_max=0.5)
cut_z = PassThrough(filter_field_name="z",
filter_limit_min=-0.0,
filter_limit_max=1.0)
graph += [ msg2cloud[:] >> cut_x[:],
cut_x[:] >> cut_y[:],
cut_y[:] >> cut_z[:] ]
# Voxel filter
voxel_grid = VoxelGrid("voxel_grid", leaf_size=0.002)
graph += [ cut_z[:] >> voxel_grid[:] ]
# Find plane and delete it
normals = NormalEstimation("normals", k_search=0, radius_search=0.02)
graph += [ voxel_grid[:] >> normals[:] ]
plane_finder = SACSegmentationFromNormals("planar_segmentation",
model_type=SACMODEL_NORMAL_PLANE,
eps_angle=0.09, distance_threshold=0.1)
plane_deleter = ExtractIndices(negative=True)
graph += [ voxel_grid[:] >> plane_finder['input'],
normals[:] >> plane_finder['normals'] ]
graph += [ voxel_grid[:] >> plane_deleter['input'],
plane_finder['inliers'] >> plane_deleter['indices'] ]
# Create pointcloud viewer
viewer = CloudViewer("viewer", window_name="Clouds!")
graph += [ plane_deleter[:] >> viewer[:] ]
# Compute vfh
vfh_signature = VFHEstimation(radius_search=0.01)
graph += [ plane_deleter[:] >> vfh_signature['input'],
normals[:] >> vfh_signature['normals'] ]
# convert the image to grayscale
rgb2gray = imgproc.cvtColor('rgb -> gray', flag=imgproc.Conversion.RGB2GRAY)
graph += [image2cv[:] >> rgb2gray[:] ]
# get a pose use the dot pattern: there might be a scale ambiguity as this is 3d only
poser = OpposingDotPoseEstimator(rows=5, cols=3,
pattern_type=calib.ASYMMETRIC_CIRCLES_GRID,
square_size=0.04, debug=True)
graph += [ image2cv[:] >> poser['color_image'],
rgb_info2cv['K'] >> poser['K_image'],
rgb2gray[:] >> poser['image'] ] #poser gives us R&T from camera to dot pattern
points3d = calib.DepthTo3d()
graph += [ depth_info2cv['K'] >> points3d['K'],
depth2cv['image'] >> points3d['depth']
]
plane_est = PlaneFinder(min_size=10000)
compute_normals = ComputeNormals()
# Convert K if the resolution is different (the camera should output that)
graph += [ # find the normals
depth_info2cv['K'] >> compute_normals['K'],
points3d['points3d'] >> compute_normals['points3d'],
# find the planes
compute_normals['normals'] >> plane_est['normals'],
depth_info2cv['K'] >> plane_est['K'],
points3d['points3d'] >> plane_est['points3d'] ]
pose_filter = object_recognition_capture.ecto_cells.capture.PlaneFilter();
# make sure the pose is centered at the origin of the plane
graph += [ depth_info2cv['K'] >> pose_filter['K_depth'],
poser['R', 'T'] >> pose_filter['R', 'T'],
plane_est['planes', 'masks'] >> pose_filter['planes', 'masks'] ]
delta_pose = ecto.If('delta R|T', cell=object_recognition_capture.DeltaRT(angle_thresh=angle_thresh,
n_desired=n_desired))
poseMsg = RT2PoseStamped(frame_id='/camera_rgb_optical_frame')
graph += [ pose_filter['R', 'T', 'found'] >> delta_pose['R', 'T', 'found'],
pose_filter['R', 'T'] >> poseMsg['R', 'T'] ]
trainer_ = Trainer()
graph += [ pose_filter['R', 'T'] >> trainer_['R', 'T'],
vfh_signature['output'] >> trainer_['features'],
delta_pose['last'] >> trainer_['last'],
delta_pose['novel'] >> trainer_['novel'],
plane_deleter[:] >> trainer_['input'] ]
novel = delta_pose['novel']
cloud2msg = ecto_pcl_ros.PointCloud2Message("cloud2msg")
graph += [ plane_deleter[:] >> cloud2msg[:] ]
baggers = dict(points=PointCloudBagger(topic_name='/camera/depth_registered/points'),
pose=PoseBagger(topic_name='/camera/pose')
)
bagwriter = ecto.If('Bag Writer if R|T', cell=ecto_ros.BagWriter(baggers=baggers, bag=bag_name))
pcd_writer = ecto.If('msd pcd writer', cell = PCDWriter())
graph += [ cloud2msg[:] >> bagwriter['points'],
poseMsg['pose'] >> bagwriter['pose']
]
graph += [ plane_deleter['output'] >> pcd_writer['input'] ]
delta_pose.inputs.__test__ = True
pcd_writer.inputs.__test__ = True
graph += [novel >> (bagwriter['__test__'])]
graph += [novel >> (pcd_writer['__test__'])]
rt2pose = RT2PoseStamped(frame_id = "camera_rgb_optical_frame")
rt2pose_filtered = RT2PoseStamped(frame_id = "camera_rgb_optical_frame")
posePub = PosePublisher("pose_pub", topic_name='pattern_pose')
posePub_filtered = PosePublisher("pose_pub_filtered", topic_name='pattern_pose_filtered')
graph += [ poser['R', 'T'] >> rt2pose['R', 'T'],
rt2pose['pose'] >> posePub['input'],
pose_filter['R', 'T'] >> rt2pose_filtered['R', 'T'],
rt2pose_filtered['pose'] >> posePub_filtered['input'] ]
plasm = ecto.Plasm()
plasm.connect(graph)
return (plasm, segmentation_cell) # return segmentation for tuning of parameters.
# add arguments for the source and sink
Sink.add_arguments(parser)
params, args, pipeline_params, do_display, db_params, db = read_arguments(
parser, argv[1:])
model_ids = []
object_ids = set()
for object_id in params['object_ids']:
for model_id in models.find_model_for_object(db, object_id, 'MMOD'):
model_ids.append(str(model_id))
object_ids.add(object_id)
model_documents = DbDocuments(db_params, model_ids)
# TODO handle this properly...
ecto_ros.init(argv, "mmod_testing", False) #not anonymous.
source = Source.parse_arguments(params['source'])
sink = Sink.parse_arguments(args, db, db_params, object_ids)
#hook up the tester
mmod_tester = MModTester(thresh_match=0.93,
color_filter_thresh=0.8,
skip_x=8,
skip_y=8,
model_documents=model_documents)
# Connect the detector to the source
pyr_img = mmod.Pyramid(n_levels=3)
pyr_depth = mmod.Pyramid(n_levels=3)
#!/usr/bin/env python
"""
A VoxelGrid can be used to downsample very dense clouds.
This can be especially useful when estimating normals.
"""
import ecto, ecto_pcl, ecto_pcl_ros
import ecto_ros, ecto_sensor_msgs
import sys
PointCloudSub = ecto_sensor_msgs.Subscriber_PointCloud2
PointCloudPub = ecto_sensor_msgs.Publisher_PointCloud2
plasm = ecto.Plasm()
sub = PointCloudSub("Subscriber", topic_name="/camera/depth_registered/points")
grabber = ecto_pcl_ros.Message2PointCloud("Message2Cloud", format=ecto_pcl.XYZRGB)
voxgrid = ecto_pcl.VoxelGrid("VGrid", leaf_size=0.05)
pcl2msg = ecto_pcl_ros.PointCloud2Message("Cloud2Message")
pub = PointCloudPub("Cloud Publisher", topic_name="/ecto_pcl/sample_output")
plasm.connect(sub["output"] >> grabber[:], grabber[:] >> voxgrid[:], voxgrid[:] >> pcl2msg[:], pcl2msg[:] >> pub[:])
if __name__ == "__main__":
ecto_ros.init(sys.argv, "voxel_grid")
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute()
import ecto
import ecto_ros, ecto_ros.ecto_sensor_msgs as ecto_sensor_msgs
from ecto_opencv import highgui
import sys
ImageSub = ecto_sensor_msgs.Subscriber_Image
def do_ecto():
sub_rgb = ImageSub("image_sub",topic_name='/camera/rgb/image_mono')
sub_depth = ImageSub("depth_sub",topic_name='/camera/depth/image')
im2mat_rgb = ecto_ros.Image2Mat()
im2mat_depth = ecto_ros.Image2Mat()
graph = [
sub_rgb["output"]>>im2mat_rgb["image"],
im2mat_rgb["image"] >> highgui.imshow("rgb show",name="rgb")[:],
sub_depth["output"]>> im2mat_depth["image"],
im2mat_depth["image"] >> highgui.imshow("depth show",name="depth")[:]
]
plasm = ecto.Plasm()
plasm.connect(graph)
ecto.view_plasm(plasm)
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv,"image_node")
do_ecto()
image_info=CameraInfoSub(topic_name='/camera/rgb/camera_info',queue_size=0),
)
sync = ecto_ros.Synchronizer('Synchronizator', subs=subs
)
drift_printer = ecto_ros.DriftPrinter()
im2mat_rgb = ecto_ros.Image2Mat()
im2mat_depth = ecto_ros.Image2Mat()
graph = [
sync["image"] >> im2mat_rgb["image"],
im2mat_rgb["image"] >> highgui.imshow("rgb show", name="rgb", waitKey=5)[:],
sync[:] >> drift_printer[:],
sync["depth"] >> im2mat_depth["image"],
im2mat_depth["image"] >> highgui.imshow("depth show", name="depth", waitKey= -1)[:]
]
plasm = ecto.Plasm()
plasm.connect(graph)
ecto.view_plasm(plasm)
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
#sched = ecto.schedulers.Singlethreaded(plasm)
#sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "ecto_node")
do_ecto()
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())
roslib.load_manifest(PKG)
import ecto
import ecto_ros, ecto_sensor_msgs
import sys
ImageSub = ecto_sensor_msgs.Subscriber_Image
ImagePub = ecto_sensor_msgs.Publisher_Image
def do_ecto():
sub_rgb = ImageSub("image_sub", topic_name="/camera/rgb/image_mono")
sub_depth = ImageSub("depth_sub", topic_name="/camera/depth/image")
pub_rgb = ImagePub("image_pub", topic_name="my_image")
pub_depth = ImagePub("depth_pub", topic_name="my_depth")
graph = [sub_rgb["output"] >> pub_rgb["input"], sub_depth["output"] >> pub_depth["input"]]
plasm = ecto.Plasm()
plasm.connect(graph)
ecto.view_plasm(plasm)
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "image_pub")
do_ecto()
import sys
PointCloudSub = ecto_sensor_msgs.Subscriber_PointCloud2
PointCloudPub = ecto_sensor_msgs.Publisher_PointCloud2
def do_ecto():
plasm = ecto.Plasm()
sub = PointCloudSub("Subscriber", topic_name='/camera/depth_registered/points')
grabber = ecto_pcl_ros.Message2PointCloud("Message2Cloud", format=ecto_pcl.XYZRGB)
passthru = ecto_pcl.PassThrough("Pass", filter_field_name="z", filter_limit_max=2.0)
pcl2msg = ecto_pcl_ros.PointCloud2Message("Cloud2Message", )
pub = PointCloudPub("Cloud Publisher", topic_name='/ecto_pcl/sample_output')
plasm.connect(sub['output'] >> grabber[:],
grabber[:] >> passthru[:],
passthru[:] >> pcl2msg[:],
pcl2msg[:] >> pub[:])
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "passthrough")
do_ecto()
'-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)
)
#The synchronizer expects this dict, and will have an output foreach key:value pair
sync = ecto_ros.Synchronizer('Synchronizator', subs=subs)
#some rosmsg ~> opencv type converters
rgb = ecto_ros.Image2Mat()
depth = ecto_ros.Image2Mat()
#declare a graph that just shows the images using opencv highgui
graph = [
sync["image"] >> rgb["image"], sync["depth"] >> depth["image"],
rgb["image"] >> imshow(name="rgb")["image"],
depth["image"] >> imshow(name="depth")["image"]
]
#add the graph to our ecto plasm
plasm = ecto.Plasm()
plasm.connect(graph)
#We'll use a threadpool based scheduler to execute this one.
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute() #execute forever
if __name__ == "__main__":
import sys
ecto_ros.init(
sys.argv,
"ecto_node") #strips out ros remappings and initializes roscpp
do_ecto()
#!/usr/bin/env python
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')
sub = PointCloudSub("Subscriber", topic_name='/camera/depth_registered/points')
grabber = ecto_pcl_ros.Message2PointCloud("Message2Cloud", format=ecto_pcl.XYZRGB)
p1 = ecto_pcl.PassThrough("Pass", filter_field_name="z", filter_limit_max=2.0)
p2 = ecto_pcl.PassThrough("Pass2", filter_field_name="z", filter_limit_min=3.0, filter_limit_max=8.0)
voxgrid = ecto_pcl.VoxelGrid("VGrid", leaf_size=0.1)
merge = ecto_pcl.MergeClouds("Merge")
pcl2msg = ecto_pcl_ros.PointCloud2Message("Cloud2Message")
pub = PointCloudPub("Cloud Publisher", topic_name='/ecto_pcl/sample_output')
plasm.connect(sub['output'] >> grabber[:],
grabber[:] >> p1[:],
grabber[:] >> p2[:],
p1[:] >> voxgrid[:],
voxgrid[:] >> merge["input"],
p2[:] >> merge["input2"],
merge[:] >> pcl2msg[:],
pcl2msg[:] >> pub[:])
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
if __name__ == "__main__":
ecto_ros.init(sys.argv, "merge_clouds")
do_ecto()
]
plasm = ecto.Plasm()
plasm.connect(graph)
sched = Scheduler(plasm)
sched.execute_async()
rosbag = play_bag(bagname, delay=1,
rate=0.5) #rate hack for fidelity in message count FIXME
wait_bag(rosbag)
time.sleep(0.1)
sched.stop()
print "expecting RGB count:", msg_counts['/camera/rgb/image_color']
print "RGB count:", counter_rgb.outputs.count
print "expecting Depth count:", msg_counts['/camera/depth/image']
print "Depth count:", counter_depth.outputs.count
assert msg_counts['/camera/rgb/image_color'] >= counter_rgb.outputs.count
assert msg_counts['/camera/rgb/image_color'] >= counter_depth.outputs.count
assert counter_rgb.outputs.count != 0
assert counter_depth.outputs.count != 0
if __name__ == "__main__":
bagname = sys.argv[1]
msg_counts = bag_counts(bagname)
try:
roscore = start_roscore(delay=1)
ecto_ros.init(sys.argv, "image_sub_node")
do_ecto(bagname, msg_counts, ecto.schedulers.Singlethreaded)
finally:
roscore.terminate()
return srv.ComputeECGraphResponse(self.outputgraph, self.found_objects)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('yaml_file', type=str)
parser.add_argument('--debug', action='store_true', default=False)
parser.add_argument('--showgraph', action='store_true', default=False)
parser.add_argument('--service', action='store_true', default=False)
myargv = rospy.myargv(argv=sys.argv)[1:]
args = parser.parse_args(myargv)
rospy.init_node('plasm_yaml_ros_service')
ecto_ros.init(myargv, 'plasm_yaml_ros_service', anonymous=False)
rospack = rospkg.RosPack()
if any([args.yaml_file.startswith(x) for x in [".", "/"]]):
yml_file = [args.yaml_file]
else:
yml_file = map(
lambda x: os.path.join(rospack.get_path("ecto_rbo_yaml"), "data/",
x), [args.yaml_file])
ecto_plasm, ecto_cells = plasm_yaml_factory.load(yml_file,
debug=args.debug)
rospy.loginfo("Plasm initialized: %s" % (yml_file))
if args.showgraph:
import sys
import math
visualize = True
debug_graphs = True
dir = "/wg/stor2a/mkrainin/object_data/perception challenge/object_meshes"
#noise
rotation = 10*math.pi/180
translation = 0.03
#optimization params
use_icp = False #uses sensor model instead if false
restarts = 10
if __name__ == "__main__":
node_name = "corrector_test"
ecto_ros.init(sys.argv, node_name,False)
"""
Pose refinement subplasm
"""
sub_plasm = ecto.Plasm()
#subscribers/converters
sub_cloud = ecto_sensor_msgs.Subscriber_PointCloud2("Cloud2 Subscriber",
topic_name="/camera/rgb/points")
sub_info = ecto_sensor_msgs.Subscriber_CameraInfo("Cam Info Subscriber",
topic_name="/camera/rgb/camera_info")
msg2cloud = ecto_pcl_ros.Message2PointCloud("Cloud2 To Type-Erased",
format=ecto_pcl.XYZRGB)
def handle_clean(self, req):
print "cleannig point cloude"
self.pclFunsion.params.clean = True
self.sched = ecto.Scheduler(self.cleanPlasm)
lock.acquire()
self.keepRun = self.sched.execute(niter=1)
lock.release()
self.pclFunsion.params.clean = False
return CleanPCLResponse(0)
def exit(signum, frame):
print('Stopping perception bridge')
exit()
if __name__ == "__main__":
rospy.init_node("perception_bridge")
ecto_ros.init(sys.argv, "ros_perception")
p = PerceptionSever()
p.start()
signal.signal(signal.SIGINT, exit)
while not rospy.is_shutdown():
signal.signal(signal.SIGINT, exit)
lock.acquire()
if not p.keepRun:
print('Stopping perception bridge')
exit()
lock.release()
time.sleep(0.5)