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_GraspableObject.py

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"""autogenerated by genmsg_py from GraspableObject.msg. Do not edit."""
import roslib.message
import struct

import sensor_msgs.msg
import std_msgs.msg
import geometry_msgs.msg
import object_manipulation_msgs.msg
import household_objects_database_msgs.msg

class GraspableObject(roslib.message.Message):
  _md5sum = "fb38ca9beebc9a80929b7a0397d84cf3"
  _type = "object_manipulation_msgs/GraspableObject"
  _has_header = False #flag to mark the presence of a Header object
  _full_text = """# an object that the object_manipulator can work on

# a graspable object can be represented in multiple ways. This message
# can contain all of them. Which one is actually used is up to the receiver
# of this message. When adding new representations, one must be careful that
# they have reasonable lightweight defaults indicating that that particular
# representation is not available.

# the tf frame to be used as a reference frame when combining information from
# the different representations below
string reference_frame_id

# potential recognition results from a database of models
# all poses are relative to the object reference pose
household_objects_database_msgs/DatabaseModelPose[] potential_models

# the point cloud itself
sensor_msgs/PointCloud cluster

# a region of a PointCloud2 of interest
object_manipulation_msgs/SceneRegion region

# the name that this object has in the collision environment
string collision_name
================================================================================
MSG: household_objects_database_msgs/DatabaseModelPose
# Informs that a specific model from the Model Database has been 
# identified at a certain location

# the database id of the model
int32 model_id

# the pose that it can be found in
geometry_msgs/PoseStamped pose

# a measure of the confidence level in this detection result
float32 confidence

# the name of the object detector that generated this detection result
string detector_name

================================================================================
MSG: geometry_msgs/PoseStamped
# A Pose with reference coordinate frame and timestamp
Header header
Pose pose

================================================================================
MSG: std_msgs/Header
# Standard metadata for higher-level stamped data types.
# This is generally used to communicate timestamped data 
# in a particular coordinate frame.
# 
# sequence ID: consecutively increasing ID 
uint32 seq
#Two-integer timestamp that is expressed as:
# * stamp.secs: seconds (stamp_secs) since epoch
# * stamp.nsecs: nanoseconds since stamp_secs
# time-handling sugar is provided by the client library
time stamp
#Frame this data is associated with
# 0: no frame
# 1: global frame
string frame_id

================================================================================
MSG: geometry_msgs/Pose
# A representation of pose in free space, composed of postion and orientation. 
Point position
Quaternion orientation

================================================================================
MSG: geometry_msgs/Point
# This contains the position of a point in free space
float64 x
float64 y
float64 z

================================================================================
MSG: geometry_msgs/Quaternion
# This represents an orientation in free space in quaternion form.

float64 x
float64 y
float64 z
float64 w

================================================================================
MSG: sensor_msgs/PointCloud
# This message holds a collection of 3d points, plus optional additional
# information about each point.

# Time of sensor data acquisition, coordinate frame ID.
Header header

# Array of 3d points. Each Point32 should be interpreted as a 3d point
# in the frame given in the header.
geometry_msgs/Point32[] points

# Each channel should have the same number of elements as points array,
# and the data in each channel should correspond 1:1 with each point.
# Channel names in common practice are listed in ChannelFloat32.msg.
ChannelFloat32[] channels

================================================================================
MSG: geometry_msgs/Point32
# This contains the position of a point in free space(with 32 bits of precision).
# It is recommeded to use Point wherever possible instead of Point32.  
# 
# This recommendation is to promote interoperability.  
#
# This message is designed to take up less space when sending
# lots of points at once, as in the case of a PointCloud.  

float32 x
float32 y
float32 z
================================================================================
MSG: sensor_msgs/ChannelFloat32
# This message is used by the PointCloud message to hold optional data
# associated with each point in the cloud. The length of the values
# array should be the same as the length of the points array in the
# PointCloud, and each value should be associated with the corresponding
# point.

# Channel names in existing practice include:
#   "u", "v" - row and column (respectively) in the left stereo image.
#              This is opposite to usual conventions but remains for
#              historical reasons. The newer PointCloud2 message has no
#              such problem.
#   "rgb" - For point clouds produced by color stereo cameras. uint8
#           (R,G,B) values packed into the least significant 24 bits,
#           in order.
#   "intensity" - laser or pixel intensity.
#   "distance"

# The channel name should give semantics of the channel (e.g.
# "intensity" instead of "value").
string name

# The values array should be 1-1 with the elements of the associated
# PointCloud.
float32[] values

================================================================================
MSG: object_manipulation_msgs/SceneRegion
# Point cloud
sensor_msgs/PointCloud2 cloud

# Indices for the region of interest
int32[] mask

# One of the corresponding 2D images, if applicable
sensor_msgs/Image image

# The disparity image, if applicable
sensor_msgs/Image disparity_image

# Camera info for the camera that took the image
sensor_msgs/CameraInfo cam_info

# a 3D region of interest for grasp planning
geometry_msgs/PoseStamped  roi_box_pose
geometry_msgs/Vector3      roi_box_dims

================================================================================
MSG: sensor_msgs/PointCloud2
# This message holds a collection of N-dimensional points, which may
# contain additional information such as normals, intensity, etc. The
# point data is stored as a binary blob, its layout described by the
# contents of the "fields" array.

# The point cloud data may be organized 2d (image-like) or 1d
# (unordered). Point clouds organized as 2d images may be produced by
# camera depth sensors such as stereo or time-of-flight.

# Time of sensor data acquisition, and the coordinate frame ID (for 3d
# points).
Header header

# 2D structure of the point cloud. If the cloud is unordered, height is
# 1 and width is the length of the point cloud.
uint32 height
uint32 width

# Describes the channels and their layout in the binary data blob.
PointField[] fields

bool    is_bigendian # Is this data bigendian?
uint32  point_step   # Length of a point in bytes
uint32  row_step     # Length of a row in bytes
uint8[] data         # Actual point data, size is (row_step*height)

bool is_dense        # True if there are no invalid points

================================================================================
MSG: sensor_msgs/PointField
# This message holds the description of one point entry in the
# PointCloud2 message format.
uint8 INT8    = 1
uint8 UINT8   = 2
uint8 INT16   = 3
uint8 UINT16  = 4
uint8 INT32   = 5
uint8 UINT32  = 6
uint8 FLOAT32 = 7
uint8 FLOAT64 = 8

string name      # Name of field
uint32 offset    # Offset from start of point struct
uint8  datatype  # Datatype enumeration, see above
uint32 count     # How many elements in the field

================================================================================
MSG: sensor_msgs/Image
# This message contains an uncompressed image
# (0, 0) is at top-left corner of image
#

Header header        # Header timestamp should be acquisition time of image
                     # Header frame_id should be optical frame of camera
                     # origin of frame should be optical center of cameara
                     # +x should point to the right in the image
                     # +y should point down in the image
                     # +z should point into to plane of the image
                     # If the frame_id here and the frame_id of the CameraInfo
                     # message associated with the image conflict
                     # the behavior is undefined

uint32 height         # image height, that is, number of rows
uint32 width          # image width, that is, number of columns

# The legal values for encoding are in file src/image_encodings.cpp
# If you want to standardize a new string format, join
# ros-users@lists.sourceforge.net and send an email proposing a new encoding.

string encoding       # Encoding of pixels -- channel meaning, ordering, size
                      # taken from the list of strings in src/image_encodings.cpp

uint8 is_bigendian    # is this data bigendian?
uint32 step           # Full row length in bytes
uint8[] data          # actual matrix data, size is (step * rows)

================================================================================
MSG: sensor_msgs/CameraInfo
# This message defines meta information for a camera. It should be in a
# camera namespace on topic "camera_info" and accompanied by up to five
# image topics named:
#
#   image_raw - raw data from the camera driver, possibly Bayer encoded
#   image            - monochrome, distorted
#   image_color      - color, distorted
#   image_rect       - monochrome, rectified
#   image_rect_color - color, rectified
#
# The image_pipeline contains packages (image_proc, stereo_image_proc)
# for producing the four processed image topics from image_raw and
# camera_info. The meaning of the camera parameters are described in
# detail at http://www.ros.org/wiki/image_pipeline/CameraInfo.
#
# The image_geometry package provides a user-friendly interface to
# common operations using this meta information. If you want to, e.g.,
# project a 3d point into image coordinates, we strongly recommend
# using image_geometry.
#
# If the camera is uncalibrated, the matrices D, K, R, P should be left
# zeroed out. In particular, clients may assume that K[0] == 0.0
# indicates an uncalibrated camera.

#######################################################################
#                     Image acquisition info                          #
#######################################################################

# Time of image acquisition, camera coordinate frame ID
Header header    # Header timestamp should be acquisition time of image
                 # Header frame_id should be optical frame of camera
                 # origin of frame should be optical center of camera
                 # +x should point to the right in the image
                 # +y should point down in the image
                 # +z should point into the plane of the image


#######################################################################
#                      Calibration Parameters                         #
#######################################################################
# These are fixed during camera calibration. Their values will be the #
# same in all messages until the camera is recalibrated. Note that    #
# self-calibrating systems may "recalibrate" frequently.              #
#                                                                     #
# The internal parameters can be used to warp a raw (distorted) image #
# to:                                                                 #
#   1. An undistorted image (requires D and K)                        #
#   2. A rectified image (requires D, K, R)                           #
# The projection matrix P projects 3D points into the rectified image.#
#######################################################################

# The image dimensions with which the camera was calibrated. Normally
# this will be the full camera resolution in pixels.
uint32 height
uint32 width

# The distortion model used. Supported models are listed in
# sensor_msgs/distortion_models.h. For most cameras, "plumb_bob" - a
# simple model of radial and tangential distortion - is sufficent.
string distortion_model

# The distortion parameters, size depending on the distortion model.
# For "plumb_bob", the 5 parameters are: (k1, k2, t1, t2, k3).
float64[] D

# Intrinsic camera matrix for the raw (distorted) images.
#     [fx  0 cx]
# K = [ 0 fy cy]
#     [ 0  0  1]
# Projects 3D points in the camera coordinate frame to 2D pixel
# coordinates using the focal lengths (fx, fy) and principal point
# (cx, cy).
float64[9]  K # 3x3 row-major matrix

# Rectification matrix (stereo cameras only)
# A rotation matrix aligning the camera coordinate system to the ideal
# stereo image plane so that epipolar lines in both stereo images are
# parallel.
float64[9]  R # 3x3 row-major matrix

# Projection/camera matrix
#     [fx'  0  cx' Tx]
# P = [ 0  fy' cy' Ty]
#     [ 0   0   1   0]
# By convention, this matrix specifies the intrinsic (camera) matrix
#  of the processed (rectified) image. That is, the left 3x3 portion
#  is the normal camera intrinsic matrix for the rectified image.
# It projects 3D points in the camera coordinate frame to 2D pixel
#  coordinates using the focal lengths (fx', fy') and principal point
#  (cx', cy') - these may differ from the values in K.
# For monocular cameras, Tx = Ty = 0. Normally, monocular cameras will
#  also have R = the identity and P[1:3,1:3] = K.
# For a stereo pair, the fourth column [Tx Ty 0]' is related to the
#  position of the optical center of the second camera in the first
#  camera's frame. We assume Tz = 0 so both cameras are in the same
#  stereo image plane. The first camera always has Tx = Ty = 0. For
#  the right (second) camera of a horizontal stereo pair, Ty = 0 and
#  Tx = -fx' * B, where B is the baseline between the cameras.
# Given a 3D point [X Y Z]', the projection (x, y) of the point onto
#  the rectified image is given by:
#  [u v w]' = P * [X Y Z 1]'
#         x = u / w
#         y = v / w
#  This holds for both images of a stereo pair.
float64[12] P # 3x4 row-major matrix


#######################################################################
#                      Operational Parameters                         #
#######################################################################
# These define the image region actually captured by the camera       #
# driver. Although they affect the geometry of the output image, they #
# may be changed freely without recalibrating the camera.             #
#######################################################################

# Binning refers here to any camera setting which combines rectangular
#  neighborhoods of pixels into larger "super-pixels." It reduces the
#  resolution of the output image to
#  (width / binning_x) x (height / binning_y).
# The default values binning_x = binning_y = 0 is considered the same
#  as binning_x = binning_y = 1 (no subsampling).
uint32 binning_x
uint32 binning_y

# Region of interest (subwindow of full camera resolution), given in
#  full resolution (unbinned) image coordinates. A particular ROI
#  always denotes the same window of pixels on the camera sensor,
#  regardless of binning settings.
# The default setting of roi (all values 0) is considered the same as
#  full resolution (roi.width = width, roi.height = height).
RegionOfInterest roi

================================================================================
MSG: sensor_msgs/RegionOfInterest
# This message is used to specify a region of interest within an image.
#
# When used to specify the ROI setting of the camera when the image was
# taken, the height and width fields should either match the height and
# width fields for the associated image; or height = width = 0
# indicates that the full resolution image was captured.

uint32 x_offset  # Leftmost pixel of the ROI
                 # (0 if the ROI includes the left edge of the image)
uint32 y_offset  # Topmost pixel of the ROI
                 # (0 if the ROI includes the top edge of the image)
uint32 height    # Height of ROI
uint32 width     # Width of ROI

# True if a distinct rectified ROI should be calculated from the "raw"
# ROI in this message. Typically this should be False if the full image
# is captured (ROI not used), and True if a subwindow is captured (ROI
# used).
bool do_rectify

================================================================================
MSG: geometry_msgs/Vector3
# This represents a vector in free space. 

float64 x
float64 y
float64 z
"""
  __slots__ = ['reference_frame_id','potential_models','cluster','region','collision_name']
  _slot_types = ['string','household_objects_database_msgs/DatabaseModelPose[]','sensor_msgs/PointCloud','object_manipulation_msgs/SceneRegion','string']

  def __init__(self, *args, **kwds):
    """
    Constructor. Any message fields that are implicitly/explicitly
    set to None will be assigned a default value. The recommend
    use is keyword arguments as this is more robust to future message
    changes.  You cannot mix in-order arguments and keyword arguments.
    
    The available fields are:
       reference_frame_id,potential_models,cluster,region,collision_name
    
    @param args: complete set of field values, in .msg order
    @param kwds: use keyword arguments corresponding to message field names
    to set specific fields. 
    """
    if args or kwds:
      super(GraspableObject, self).__init__(*args, **kwds)
      #message fields cannot be None, assign default values for those that are
      if self.reference_frame_id is None:
        self.reference_frame_id = ''
      if self.potential_models is None:
        self.potential_models = []
      if self.cluster is None:
        self.cluster = sensor_msgs.msg.PointCloud()
      if self.region is None:
        self.region = object_manipulation_msgs.msg.SceneRegion()
      if self.collision_name is None:
        self.collision_name = ''
    else:
      self.reference_frame_id = ''
      self.potential_models = []
      self.cluster = sensor_msgs.msg.PointCloud()
      self.region = object_manipulation_msgs.msg.SceneRegion()
      self.collision_name = ''

  def _get_types(self):
    """
    internal API method
    """
    return self._slot_types

  def serialize(self, buff):
    """
    serialize message into buffer
    @param buff: buffer
    @type  buff: StringIO
    """
    try:
      _x = self.reference_frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.potential_models)
      buff.write(_struct_I.pack(length))
      for val1 in self.potential_models:
        buff.write(_struct_i.pack(val1.model_id))
        _v1 = val1.pose
        _v2 = _v1.header
        buff.write(_struct_I.pack(_v2.seq))
        _v3 = _v2.stamp
        _x = _v3
        buff.write(_struct_2I.pack(_x.secs, _x.nsecs))
        _x = _v2.frame_id
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        _v4 = _v1.pose
        _v5 = _v4.position
        _x = _v5
        buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
        _v6 = _v4.orientation
        _x = _v6
        buff.write(_struct_4d.pack(_x.x, _x.y, _x.z, _x.w))
        buff.write(_struct_f.pack(val1.confidence))
        _x = val1.detector_name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.cluster.header.seq, _x.cluster.header.stamp.secs, _x.cluster.header.stamp.nsecs))
      _x = self.cluster.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.cluster.points)
      buff.write(_struct_I.pack(length))
      for val1 in self.cluster.points:
        _x = val1
        buff.write(_struct_3f.pack(_x.x, _x.y, _x.z))
      length = len(self.cluster.channels)
      buff.write(_struct_I.pack(length))
      for val1 in self.cluster.channels:
        _x = val1.name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        length = len(val1.values)
        buff.write(_struct_I.pack(length))
        pattern = '<%sf'%length
        buff.write(struct.pack(pattern, *val1.values))
      _x = self
      buff.write(_struct_3I.pack(_x.region.cloud.header.seq, _x.region.cloud.header.stamp.secs, _x.region.cloud.header.stamp.nsecs))
      _x = self.region.cloud.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.cloud.height, _x.region.cloud.width))
      length = len(self.region.cloud.fields)
      buff.write(_struct_I.pack(length))
      for val1 in self.region.cloud.fields:
        _x = val1.name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        _x = val1
        buff.write(_struct_IBI.pack(_x.offset, _x.datatype, _x.count))
      _x = self
      buff.write(_struct_B2I.pack(_x.region.cloud.is_bigendian, _x.region.cloud.point_step, _x.region.cloud.row_step))
      _x = self.region.cloud.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      buff.write(_struct_B.pack(self.region.cloud.is_dense))
      length = len(self.region.mask)
      buff.write(_struct_I.pack(length))
      pattern = '<%si'%length
      buff.write(struct.pack(pattern, *self.region.mask))
      _x = self
      buff.write(_struct_3I.pack(_x.region.image.header.seq, _x.region.image.header.stamp.secs, _x.region.image.header.stamp.nsecs))
      _x = self.region.image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.image.height, _x.region.image.width))
      _x = self.region.image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.region.image.is_bigendian, _x.region.image.step))
      _x = self.region.image.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.region.disparity_image.header.seq, _x.region.disparity_image.header.stamp.secs, _x.region.disparity_image.header.stamp.nsecs))
      _x = self.region.disparity_image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.disparity_image.height, _x.region.disparity_image.width))
      _x = self.region.disparity_image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.region.disparity_image.is_bigendian, _x.region.disparity_image.step))
      _x = self.region.disparity_image.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.region.cam_info.header.seq, _x.region.cam_info.header.stamp.secs, _x.region.cam_info.header.stamp.nsecs))
      _x = self.region.cam_info.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.cam_info.height, _x.region.cam_info.width))
      _x = self.region.cam_info.distortion_model
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.region.cam_info.D)
      buff.write(_struct_I.pack(length))
      pattern = '<%sd'%length
      buff.write(struct.pack(pattern, *self.region.cam_info.D))
      buff.write(_struct_9d.pack(*self.region.cam_info.K))
      buff.write(_struct_9d.pack(*self.region.cam_info.R))
      buff.write(_struct_12d.pack(*self.region.cam_info.P))
      _x = self
      buff.write(_struct_6IB3I.pack(_x.region.cam_info.binning_x, _x.region.cam_info.binning_y, _x.region.cam_info.roi.x_offset, _x.region.cam_info.roi.y_offset, _x.region.cam_info.roi.height, _x.region.cam_info.roi.width, _x.region.cam_info.roi.do_rectify, _x.region.roi_box_pose.header.seq, _x.region.roi_box_pose.header.stamp.secs, _x.region.roi_box_pose.header.stamp.nsecs))
      _x = self.region.roi_box_pose.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_10d.pack(_x.region.roi_box_pose.pose.position.x, _x.region.roi_box_pose.pose.position.y, _x.region.roi_box_pose.pose.position.z, _x.region.roi_box_pose.pose.orientation.x, _x.region.roi_box_pose.pose.orientation.y, _x.region.roi_box_pose.pose.orientation.z, _x.region.roi_box_pose.pose.orientation.w, _x.region.roi_box_dims.x, _x.region.roi_box_dims.y, _x.region.roi_box_dims.z))
      _x = self.collision_name
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
    except struct.error as se: self._check_types(se)
    except TypeError as te: self._check_types(te)

  def deserialize(self, str):
    """
    unpack serialized message in str into this message instance
    @param str: byte array of serialized message
    @type  str: str
    """
    try:
      if self.cluster is None:
        self.cluster = sensor_msgs.msg.PointCloud()
      if self.region is None:
        self.region = object_manipulation_msgs.msg.SceneRegion()
      end = 0
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.reference_frame_id = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.potential_models = []
      for i in range(0, length):
        val1 = household_objects_database_msgs.msg.DatabaseModelPose()
        start = end
        end += 4
        (val1.model_id,) = _struct_i.unpack(str[start:end])
        _v7 = val1.pose
        _v8 = _v7.header
        start = end
        end += 4
        (_v8.seq,) = _struct_I.unpack(str[start:end])
        _v9 = _v8.stamp
        _x = _v9
        start = end
        end += 8
        (_x.secs, _x.nsecs,) = _struct_2I.unpack(str[start:end])
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        _v8.frame_id = str[start:end]
        _v10 = _v7.pose
        _v11 = _v10.position
        _x = _v11
        start = end
        end += 24
        (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
        _v12 = _v10.orientation
        _x = _v12
        start = end
        end += 32
        (_x.x, _x.y, _x.z, _x.w,) = _struct_4d.unpack(str[start:end])
        start = end
        end += 4
        (val1.confidence,) = _struct_f.unpack(str[start:end])
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.detector_name = str[start:end]
        self.potential_models.append(val1)
      _x = self
      start = end
      end += 12
      (_x.cluster.header.seq, _x.cluster.header.stamp.secs, _x.cluster.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.cluster.header.frame_id = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.cluster.points = []
      for i in range(0, length):
        val1 = geometry_msgs.msg.Point32()
        _x = val1
        start = end
        end += 12
        (_x.x, _x.y, _x.z,) = _struct_3f.unpack(str[start:end])
        self.cluster.points.append(val1)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.cluster.channels = []
      for i in range(0, length):
        val1 = sensor_msgs.msg.ChannelFloat32()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.name = str[start:end]
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        pattern = '<%sf'%length
        start = end
        end += struct.calcsize(pattern)
        val1.values = struct.unpack(pattern, str[start:end])
        self.cluster.channels.append(val1)
      _x = self
      start = end
      end += 12
      (_x.region.cloud.header.seq, _x.region.cloud.header.stamp.secs, _x.region.cloud.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cloud.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.cloud.height, _x.region.cloud.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.region.cloud.fields = []
      for i in range(0, length):
        val1 = sensor_msgs.msg.PointField()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.name = str[start:end]
        _x = val1
        start = end
        end += 9
        (_x.offset, _x.datatype, _x.count,) = _struct_IBI.unpack(str[start:end])
        self.region.cloud.fields.append(val1)
      _x = self
      start = end
      end += 9
      (_x.region.cloud.is_bigendian, _x.region.cloud.point_step, _x.region.cloud.row_step,) = _struct_B2I.unpack(str[start:end])
      self.region.cloud.is_bigendian = bool(self.region.cloud.is_bigendian)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cloud.data = str[start:end]
      start = end
      end += 1
      (self.region.cloud.is_dense,) = _struct_B.unpack(str[start:end])
      self.region.cloud.is_dense = bool(self.region.cloud.is_dense)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      pattern = '<%si'%length
      start = end
      end += struct.calcsize(pattern)
      self.region.mask = struct.unpack(pattern, str[start:end])
      _x = self
      start = end
      end += 12
      (_x.region.image.header.seq, _x.region.image.header.stamp.secs, _x.region.image.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.image.height, _x.region.image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.region.image.is_bigendian, _x.region.image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.region.disparity_image.header.seq, _x.region.disparity_image.header.stamp.secs, _x.region.disparity_image.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.disparity_image.height, _x.region.disparity_image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.region.disparity_image.is_bigendian, _x.region.disparity_image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.region.cam_info.header.seq, _x.region.cam_info.header.stamp.secs, _x.region.cam_info.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cam_info.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.cam_info.height, _x.region.cam_info.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cam_info.distortion_model = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      pattern = '<%sd'%length
      start = end
      end += struct.calcsize(pattern)
      self.region.cam_info.D = struct.unpack(pattern, str[start:end])
      start = end
      end += 72
      self.region.cam_info.K = _struct_9d.unpack(str[start:end])
      start = end
      end += 72
      self.region.cam_info.R = _struct_9d.unpack(str[start:end])
      start = end
      end += 96
      self.region.cam_info.P = _struct_12d.unpack(str[start:end])
      _x = self
      start = end
      end += 37
      (_x.region.cam_info.binning_x, _x.region.cam_info.binning_y, _x.region.cam_info.roi.x_offset, _x.region.cam_info.roi.y_offset, _x.region.cam_info.roi.height, _x.region.cam_info.roi.width, _x.region.cam_info.roi.do_rectify, _x.region.roi_box_pose.header.seq, _x.region.roi_box_pose.header.stamp.secs, _x.region.roi_box_pose.header.stamp.nsecs,) = _struct_6IB3I.unpack(str[start:end])
      self.region.cam_info.roi.do_rectify = bool(self.region.cam_info.roi.do_rectify)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.roi_box_pose.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 80
      (_x.region.roi_box_pose.pose.position.x, _x.region.roi_box_pose.pose.position.y, _x.region.roi_box_pose.pose.position.z, _x.region.roi_box_pose.pose.orientation.x, _x.region.roi_box_pose.pose.orientation.y, _x.region.roi_box_pose.pose.orientation.z, _x.region.roi_box_pose.pose.orientation.w, _x.region.roi_box_dims.x, _x.region.roi_box_dims.y, _x.region.roi_box_dims.z,) = _struct_10d.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.collision_name = str[start:end]
      return self
    except struct.error as e:
      raise roslib.message.DeserializationError(e) #most likely buffer underfill


  def serialize_numpy(self, buff, numpy):
    """
    serialize message with numpy array types into buffer
    @param buff: buffer
    @type  buff: StringIO
    @param numpy: numpy python module
    @type  numpy module
    """
    try:
      _x = self.reference_frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.potential_models)
      buff.write(_struct_I.pack(length))
      for val1 in self.potential_models:
        buff.write(_struct_i.pack(val1.model_id))
        _v13 = val1.pose
        _v14 = _v13.header
        buff.write(_struct_I.pack(_v14.seq))
        _v15 = _v14.stamp
        _x = _v15
        buff.write(_struct_2I.pack(_x.secs, _x.nsecs))
        _x = _v14.frame_id
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        _v16 = _v13.pose
        _v17 = _v16.position
        _x = _v17
        buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
        _v18 = _v16.orientation
        _x = _v18
        buff.write(_struct_4d.pack(_x.x, _x.y, _x.z, _x.w))
        buff.write(_struct_f.pack(val1.confidence))
        _x = val1.detector_name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.cluster.header.seq, _x.cluster.header.stamp.secs, _x.cluster.header.stamp.nsecs))
      _x = self.cluster.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.cluster.points)
      buff.write(_struct_I.pack(length))
      for val1 in self.cluster.points:
        _x = val1
        buff.write(_struct_3f.pack(_x.x, _x.y, _x.z))
      length = len(self.cluster.channels)
      buff.write(_struct_I.pack(length))
      for val1 in self.cluster.channels:
        _x = val1.name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        length = len(val1.values)
        buff.write(_struct_I.pack(length))
        pattern = '<%sf'%length
        buff.write(val1.values.tostring())
      _x = self
      buff.write(_struct_3I.pack(_x.region.cloud.header.seq, _x.region.cloud.header.stamp.secs, _x.region.cloud.header.stamp.nsecs))
      _x = self.region.cloud.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.cloud.height, _x.region.cloud.width))
      length = len(self.region.cloud.fields)
      buff.write(_struct_I.pack(length))
      for val1 in self.region.cloud.fields:
        _x = val1.name
        length = len(_x)
        buff.write(struct.pack('<I%ss'%length, length, _x))
        _x = val1
        buff.write(_struct_IBI.pack(_x.offset, _x.datatype, _x.count))
      _x = self
      buff.write(_struct_B2I.pack(_x.region.cloud.is_bigendian, _x.region.cloud.point_step, _x.region.cloud.row_step))
      _x = self.region.cloud.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      buff.write(_struct_B.pack(self.region.cloud.is_dense))
      length = len(self.region.mask)
      buff.write(_struct_I.pack(length))
      pattern = '<%si'%length
      buff.write(self.region.mask.tostring())
      _x = self
      buff.write(_struct_3I.pack(_x.region.image.header.seq, _x.region.image.header.stamp.secs, _x.region.image.header.stamp.nsecs))
      _x = self.region.image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.image.height, _x.region.image.width))
      _x = self.region.image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.region.image.is_bigendian, _x.region.image.step))
      _x = self.region.image.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.region.disparity_image.header.seq, _x.region.disparity_image.header.stamp.secs, _x.region.disparity_image.header.stamp.nsecs))
      _x = self.region.disparity_image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.disparity_image.height, _x.region.disparity_image.width))
      _x = self.region.disparity_image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.region.disparity_image.is_bigendian, _x.region.disparity_image.step))
      _x = self.region.disparity_image.data
      length = len(_x)
      # - if encoded as a list instead, serialize as bytes instead of string
      if type(_x) in [list, tuple]:
        buff.write(struct.pack('<I%sB'%length, length, *_x))
      else:
        buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_3I.pack(_x.region.cam_info.header.seq, _x.region.cam_info.header.stamp.secs, _x.region.cam_info.header.stamp.nsecs))
      _x = self.region.cam_info.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.region.cam_info.height, _x.region.cam_info.width))
      _x = self.region.cam_info.distortion_model
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.region.cam_info.D)
      buff.write(_struct_I.pack(length))
      pattern = '<%sd'%length
      buff.write(self.region.cam_info.D.tostring())
      buff.write(self.region.cam_info.K.tostring())
      buff.write(self.region.cam_info.R.tostring())
      buff.write(self.region.cam_info.P.tostring())
      _x = self
      buff.write(_struct_6IB3I.pack(_x.region.cam_info.binning_x, _x.region.cam_info.binning_y, _x.region.cam_info.roi.x_offset, _x.region.cam_info.roi.y_offset, _x.region.cam_info.roi.height, _x.region.cam_info.roi.width, _x.region.cam_info.roi.do_rectify, _x.region.roi_box_pose.header.seq, _x.region.roi_box_pose.header.stamp.secs, _x.region.roi_box_pose.header.stamp.nsecs))
      _x = self.region.roi_box_pose.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_10d.pack(_x.region.roi_box_pose.pose.position.x, _x.region.roi_box_pose.pose.position.y, _x.region.roi_box_pose.pose.position.z, _x.region.roi_box_pose.pose.orientation.x, _x.region.roi_box_pose.pose.orientation.y, _x.region.roi_box_pose.pose.orientation.z, _x.region.roi_box_pose.pose.orientation.w, _x.region.roi_box_dims.x, _x.region.roi_box_dims.y, _x.region.roi_box_dims.z))
      _x = self.collision_name
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
    except struct.error as se: self._check_types(se)
    except TypeError as te: self._check_types(te)

  def deserialize_numpy(self, str, numpy):
    """
    unpack serialized message in str into this message instance using numpy for array types
    @param str: byte array of serialized message
    @type  str: str
    @param numpy: numpy python module
    @type  numpy: module
    """
    try:
      if self.cluster is None:
        self.cluster = sensor_msgs.msg.PointCloud()
      if self.region is None:
        self.region = object_manipulation_msgs.msg.SceneRegion()
      end = 0
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.reference_frame_id = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.potential_models = []
      for i in range(0, length):
        val1 = household_objects_database_msgs.msg.DatabaseModelPose()
        start = end
        end += 4
        (val1.model_id,) = _struct_i.unpack(str[start:end])
        _v19 = val1.pose
        _v20 = _v19.header
        start = end
        end += 4
        (_v20.seq,) = _struct_I.unpack(str[start:end])
        _v21 = _v20.stamp
        _x = _v21
        start = end
        end += 8
        (_x.secs, _x.nsecs,) = _struct_2I.unpack(str[start:end])
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        _v20.frame_id = str[start:end]
        _v22 = _v19.pose
        _v23 = _v22.position
        _x = _v23
        start = end
        end += 24
        (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
        _v24 = _v22.orientation
        _x = _v24
        start = end
        end += 32
        (_x.x, _x.y, _x.z, _x.w,) = _struct_4d.unpack(str[start:end])
        start = end
        end += 4
        (val1.confidence,) = _struct_f.unpack(str[start:end])
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.detector_name = str[start:end]
        self.potential_models.append(val1)
      _x = self
      start = end
      end += 12
      (_x.cluster.header.seq, _x.cluster.header.stamp.secs, _x.cluster.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.cluster.header.frame_id = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.cluster.points = []
      for i in range(0, length):
        val1 = geometry_msgs.msg.Point32()
        _x = val1
        start = end
        end += 12
        (_x.x, _x.y, _x.z,) = _struct_3f.unpack(str[start:end])
        self.cluster.points.append(val1)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.cluster.channels = []
      for i in range(0, length):
        val1 = sensor_msgs.msg.ChannelFloat32()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.name = str[start:end]
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        pattern = '<%sf'%length
        start = end
        end += struct.calcsize(pattern)
        val1.values = numpy.frombuffer(str[start:end], dtype=numpy.float32, count=length)
        self.cluster.channels.append(val1)
      _x = self
      start = end
      end += 12
      (_x.region.cloud.header.seq, _x.region.cloud.header.stamp.secs, _x.region.cloud.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cloud.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.cloud.height, _x.region.cloud.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.region.cloud.fields = []
      for i in range(0, length):
        val1 = sensor_msgs.msg.PointField()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        start = end
        end += length
        val1.name = str[start:end]
        _x = val1
        start = end
        end += 9
        (_x.offset, _x.datatype, _x.count,) = _struct_IBI.unpack(str[start:end])
        self.region.cloud.fields.append(val1)
      _x = self
      start = end
      end += 9
      (_x.region.cloud.is_bigendian, _x.region.cloud.point_step, _x.region.cloud.row_step,) = _struct_B2I.unpack(str[start:end])
      self.region.cloud.is_bigendian = bool(self.region.cloud.is_bigendian)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cloud.data = str[start:end]
      start = end
      end += 1
      (self.region.cloud.is_dense,) = _struct_B.unpack(str[start:end])
      self.region.cloud.is_dense = bool(self.region.cloud.is_dense)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      pattern = '<%si'%length
      start = end
      end += struct.calcsize(pattern)
      self.region.mask = numpy.frombuffer(str[start:end], dtype=numpy.int32, count=length)
      _x = self
      start = end
      end += 12
      (_x.region.image.header.seq, _x.region.image.header.stamp.secs, _x.region.image.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.image.height, _x.region.image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.region.image.is_bigendian, _x.region.image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.region.disparity_image.header.seq, _x.region.disparity_image.header.stamp.secs, _x.region.disparity_image.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.disparity_image.height, _x.region.disparity_image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.region.disparity_image.is_bigendian, _x.region.disparity_image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.disparity_image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.region.cam_info.header.seq, _x.region.cam_info.header.stamp.secs, _x.region.cam_info.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cam_info.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.region.cam_info.height, _x.region.cam_info.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.cam_info.distortion_model = str[start:end]
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      pattern = '<%sd'%length
      start = end
      end += struct.calcsize(pattern)
      self.region.cam_info.D = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=length)
      start = end
      end += 72
      self.region.cam_info.K = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=9)
      start = end
      end += 72
      self.region.cam_info.R = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=9)
      start = end
      end += 96
      self.region.cam_info.P = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=12)
      _x = self
      start = end
      end += 37
      (_x.region.cam_info.binning_x, _x.region.cam_info.binning_y, _x.region.cam_info.roi.x_offset, _x.region.cam_info.roi.y_offset, _x.region.cam_info.roi.height, _x.region.cam_info.roi.width, _x.region.cam_info.roi.do_rectify, _x.region.roi_box_pose.header.seq, _x.region.roi_box_pose.header.stamp.secs, _x.region.roi_box_pose.header.stamp.nsecs,) = _struct_6IB3I.unpack(str[start:end])
      self.region.cam_info.roi.do_rectify = bool(self.region.cam_info.roi.do_rectify)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.region.roi_box_pose.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 80
      (_x.region.roi_box_pose.pose.position.x, _x.region.roi_box_pose.pose.position.y, _x.region.roi_box_pose.pose.position.z, _x.region.roi_box_pose.pose.orientation.x, _x.region.roi_box_pose.pose.orientation.y, _x.region.roi_box_pose.pose.orientation.z, _x.region.roi_box_pose.pose.orientation.w, _x.region.roi_box_dims.x, _x.region.roi_box_dims.y, _x.region.roi_box_dims.z,) = _struct_10d.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.collision_name = str[start:end]
      return self
    except struct.error as e:
      raise roslib.message.DeserializationError(e) #most likely buffer underfill

_struct_I = roslib.message.struct_I
_struct_IBI = struct.Struct("<IBI")
_struct_6IB3I = struct.Struct("<6IB3I")
_struct_B = struct.Struct("<B")
_struct_12d = struct.Struct("<12d")
_struct_f = struct.Struct("<f")
_struct_i = struct.Struct("<i")
_struct_BI = struct.Struct("<BI")
_struct_10d = struct.Struct("<10d")
_struct_3f = struct.Struct("<3f")
_struct_3I = struct.Struct("<3I")
_struct_9d = struct.Struct("<9d")
_struct_B2I = struct.Struct("<B2I")
_struct_4d = struct.Struct("<4d")
_struct_2I = struct.Struct("<2I")
_struct_3d = struct.Struct("<3d")

---

object_manipulation_msgs
Author(s): Matei Ciocarlie
autogenerated on Fri Mar 1 18:03:28 2013