_ObjectRecognitionGuiGoal.py

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

import object_recognition_gui.msg
import geometry_msgs.msg
import std_msgs.msg
import geometric_shapes_msgs.msg
import sensor_msgs.msg

class ObjectRecognitionGuiGoal(roslib.message.Message):
  _md5sum = "a0e81c2c24c7bbc0a1bc7c21f4c1b1c6"
  _type = "object_recognition_gui/ObjectRecognitionGuiGoal"
  _has_header = False #flag to mark the presence of a Header object
  _full_text = """# ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ======

#the original sensor data (depth/disparity image)
sensor_msgs/Image image
sensor_msgs/CameraInfo camera_info

#list of mesh/pose hypotheses for each recognized point cluster
ModelHypothesisList[] model_hypotheses

================================================================================
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: 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: 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: object_recognition_gui/ModelHypothesisList
ModelHypothesis[] hypotheses

#initial guess if this can be a correct recognition result at all
bool accept
================================================================================
MSG: object_recognition_gui/ModelHypothesis
#describes a hypothesis about a recognized object (mesh+pose)

geometric_shapes_msgs/Shape mesh
geometry_msgs/PoseStamped pose

================================================================================
MSG: geometric_shapes_msgs/Shape
byte SPHERE=0
byte BOX=1
byte CYLINDER=2
byte MESH=3

byte type


#### define sphere, box, cylinder ####
# the origin of each shape is considered at the shape's center

# for sphere
# radius := dimensions[0]

# for cylinder
# radius := dimensions[0]
# length := dimensions[1]
# the length is along the Z axis

# for box
# size_x := dimensions[0]
# size_y := dimensions[1]
# size_z := dimensions[2]
float64[] dimensions


#### define mesh ####

# list of triangles; triangle k is defined by tre vertices located
# at indices triangles[3k], triangles[3k+1], triangles[3k+2]
int32[] triangles
geometry_msgs/Point[] vertices

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

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

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

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

float64 x
float64 y
float64 z
float64 w

"""
  __slots__ = ['image','camera_info','model_hypotheses']
  _slot_types = ['sensor_msgs/Image','sensor_msgs/CameraInfo','object_recognition_gui/ModelHypothesisList[]']

  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:
       image,camera_info,model_hypotheses
    
    @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(ObjectRecognitionGuiGoal, self).__init__(*args, **kwds)
      #message fields cannot be None, assign default values for those that are
      if self.image is None:
        self.image = sensor_msgs.msg.Image()
      if self.camera_info is None:
        self.camera_info = sensor_msgs.msg.CameraInfo()
      if self.model_hypotheses is None:
        self.model_hypotheses = []
    else:
      self.image = sensor_msgs.msg.Image()
      self.camera_info = sensor_msgs.msg.CameraInfo()
      self.model_hypotheses = []

  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
      buff.write(_struct_3I.pack(_x.image.header.seq, _x.image.header.stamp.secs, _x.image.header.stamp.nsecs))
      _x = self.image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.image.height, _x.image.width))
      _x = self.image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.image.is_bigendian, _x.image.step))
      _x = self.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.camera_info.header.seq, _x.camera_info.header.stamp.secs, _x.camera_info.header.stamp.nsecs))
      _x = self.camera_info.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.camera_info.height, _x.camera_info.width))
      _x = self.camera_info.distortion_model
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.camera_info.D)
      buff.write(_struct_I.pack(length))
      pattern = '<%sd'%length
      buff.write(struct.pack(pattern, *self.camera_info.D))
      buff.write(_struct_9d.pack(*self.camera_info.K))
      buff.write(_struct_9d.pack(*self.camera_info.R))
      buff.write(_struct_12d.pack(*self.camera_info.P))
      _x = self
      buff.write(_struct_6IB.pack(_x.camera_info.binning_x, _x.camera_info.binning_y, _x.camera_info.roi.x_offset, _x.camera_info.roi.y_offset, _x.camera_info.roi.height, _x.camera_info.roi.width, _x.camera_info.roi.do_rectify))
      length = len(self.model_hypotheses)
      buff.write(_struct_I.pack(length))
      for val1 in self.model_hypotheses:
        length = len(val1.hypotheses)
        buff.write(_struct_I.pack(length))
        for val2 in val1.hypotheses:
          _v1 = val2.mesh
          buff.write(_struct_b.pack(_v1.type))
          length = len(_v1.dimensions)
          buff.write(_struct_I.pack(length))
          pattern = '<%sd'%length
          buff.write(struct.pack(pattern, *_v1.dimensions))
          length = len(_v1.triangles)
          buff.write(_struct_I.pack(length))
          pattern = '<%si'%length
          buff.write(struct.pack(pattern, *_v1.triangles))
          length = len(_v1.vertices)
          buff.write(_struct_I.pack(length))
          for val4 in _v1.vertices:
            _x = val4
            buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
          _v2 = val2.pose
          _v3 = _v2.header
          buff.write(_struct_I.pack(_v3.seq))
          _v4 = _v3.stamp
          _x = _v4
          buff.write(_struct_2I.pack(_x.secs, _x.nsecs))
          _x = _v3.frame_id
          length = len(_x)
          buff.write(struct.pack('<I%ss'%length, length, _x))
          _v5 = _v2.pose
          _v6 = _v5.position
          _x = _v6
          buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
          _v7 = _v5.orientation
          _x = _v7
          buff.write(_struct_4d.pack(_x.x, _x.y, _x.z, _x.w))
        buff.write(_struct_B.pack(val1.accept))
    except struct.error, se: self._check_types(se)
    except TypeError, 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.image is None:
        self.image = sensor_msgs.msg.Image()
      if self.camera_info is None:
        self.camera_info = sensor_msgs.msg.CameraInfo()
      end = 0
      _x = self
      start = end
      end += 12
      (_x.image.header.seq, _x.image.header.stamp.secs, _x.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.image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.image.height, _x.image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.image.is_bigendian, _x.image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.camera_info.header.seq, _x.camera_info.header.stamp.secs, _x.camera_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.camera_info.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.camera_info.height, _x.camera_info.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.camera_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.camera_info.D = struct.unpack(pattern, str[start:end])
      start = end
      end += 72
      self.camera_info.K = _struct_9d.unpack(str[start:end])
      start = end
      end += 72
      self.camera_info.R = _struct_9d.unpack(str[start:end])
      start = end
      end += 96
      self.camera_info.P = _struct_12d.unpack(str[start:end])
      _x = self
      start = end
      end += 25
      (_x.camera_info.binning_x, _x.camera_info.binning_y, _x.camera_info.roi.x_offset, _x.camera_info.roi.y_offset, _x.camera_info.roi.height, _x.camera_info.roi.width, _x.camera_info.roi.do_rectify,) = _struct_6IB.unpack(str[start:end])
      self.camera_info.roi.do_rectify = bool(self.camera_info.roi.do_rectify)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.model_hypotheses = []
      for i in xrange(0, length):
        val1 = object_recognition_gui.msg.ModelHypothesisList()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        val1.hypotheses = []
        for i in xrange(0, length):
          val2 = object_recognition_gui.msg.ModelHypothesis()
          _v8 = val2.mesh
          start = end
          end += 1
          (_v8.type,) = _struct_b.unpack(str[start:end])
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          pattern = '<%sd'%length
          start = end
          end += struct.calcsize(pattern)
          _v8.dimensions = struct.unpack(pattern, str[start:end])
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          pattern = '<%si'%length
          start = end
          end += struct.calcsize(pattern)
          _v8.triangles = struct.unpack(pattern, str[start:end])
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          _v8.vertices = []
          for i in xrange(0, length):
            val4 = geometry_msgs.msg.Point()
            _x = val4
            start = end
            end += 24
            (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
            _v8.vertices.append(val4)
          _v9 = val2.pose
          _v10 = _v9.header
          start = end
          end += 4
          (_v10.seq,) = _struct_I.unpack(str[start:end])
          _v11 = _v10.stamp
          _x = _v11
          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
          _v10.frame_id = str[start:end]
          _v12 = _v9.pose
          _v13 = _v12.position
          _x = _v13
          start = end
          end += 24
          (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
          _v14 = _v12.orientation
          _x = _v14
          start = end
          end += 32
          (_x.x, _x.y, _x.z, _x.w,) = _struct_4d.unpack(str[start:end])
          val1.hypotheses.append(val2)
        start = end
        end += 1
        (val1.accept,) = _struct_B.unpack(str[start:end])
        val1.accept = bool(val1.accept)
        self.model_hypotheses.append(val1)
      return self
    except struct.error, 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
      buff.write(_struct_3I.pack(_x.image.header.seq, _x.image.header.stamp.secs, _x.image.header.stamp.nsecs))
      _x = self.image.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.image.height, _x.image.width))
      _x = self.image.encoding
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_BI.pack(_x.image.is_bigendian, _x.image.step))
      _x = self.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.camera_info.header.seq, _x.camera_info.header.stamp.secs, _x.camera_info.header.stamp.nsecs))
      _x = self.camera_info.header.frame_id
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      _x = self
      buff.write(_struct_2I.pack(_x.camera_info.height, _x.camera_info.width))
      _x = self.camera_info.distortion_model
      length = len(_x)
      buff.write(struct.pack('<I%ss'%length, length, _x))
      length = len(self.camera_info.D)
      buff.write(_struct_I.pack(length))
      pattern = '<%sd'%length
      buff.write(self.camera_info.D.tostring())
      buff.write(self.camera_info.K.tostring())
      buff.write(self.camera_info.R.tostring())
      buff.write(self.camera_info.P.tostring())
      _x = self
      buff.write(_struct_6IB.pack(_x.camera_info.binning_x, _x.camera_info.binning_y, _x.camera_info.roi.x_offset, _x.camera_info.roi.y_offset, _x.camera_info.roi.height, _x.camera_info.roi.width, _x.camera_info.roi.do_rectify))
      length = len(self.model_hypotheses)
      buff.write(_struct_I.pack(length))
      for val1 in self.model_hypotheses:
        length = len(val1.hypotheses)
        buff.write(_struct_I.pack(length))
        for val2 in val1.hypotheses:
          _v15 = val2.mesh
          buff.write(_struct_b.pack(_v15.type))
          length = len(_v15.dimensions)
          buff.write(_struct_I.pack(length))
          pattern = '<%sd'%length
          buff.write(_v15.dimensions.tostring())
          length = len(_v15.triangles)
          buff.write(_struct_I.pack(length))
          pattern = '<%si'%length
          buff.write(_v15.triangles.tostring())
          length = len(_v15.vertices)
          buff.write(_struct_I.pack(length))
          for val4 in _v15.vertices:
            _x = val4
            buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
          _v16 = val2.pose
          _v17 = _v16.header
          buff.write(_struct_I.pack(_v17.seq))
          _v18 = _v17.stamp
          _x = _v18
          buff.write(_struct_2I.pack(_x.secs, _x.nsecs))
          _x = _v17.frame_id
          length = len(_x)
          buff.write(struct.pack('<I%ss'%length, length, _x))
          _v19 = _v16.pose
          _v20 = _v19.position
          _x = _v20
          buff.write(_struct_3d.pack(_x.x, _x.y, _x.z))
          _v21 = _v19.orientation
          _x = _v21
          buff.write(_struct_4d.pack(_x.x, _x.y, _x.z, _x.w))
        buff.write(_struct_B.pack(val1.accept))
    except struct.error, se: self._check_types(se)
    except TypeError, 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.image is None:
        self.image = sensor_msgs.msg.Image()
      if self.camera_info is None:
        self.camera_info = sensor_msgs.msg.CameraInfo()
      end = 0
      _x = self
      start = end
      end += 12
      (_x.image.header.seq, _x.image.header.stamp.secs, _x.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.image.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.image.height, _x.image.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.image.encoding = str[start:end]
      _x = self
      start = end
      end += 5
      (_x.image.is_bigendian, _x.image.step,) = _struct_BI.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.image.data = str[start:end]
      _x = self
      start = end
      end += 12
      (_x.camera_info.header.seq, _x.camera_info.header.stamp.secs, _x.camera_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.camera_info.header.frame_id = str[start:end]
      _x = self
      start = end
      end += 8
      (_x.camera_info.height, _x.camera_info.width,) = _struct_2I.unpack(str[start:end])
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      start = end
      end += length
      self.camera_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.camera_info.D = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=length)
      start = end
      end += 72
      self.camera_info.K = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=9)
      start = end
      end += 72
      self.camera_info.R = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=9)
      start = end
      end += 96
      self.camera_info.P = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=12)
      _x = self
      start = end
      end += 25
      (_x.camera_info.binning_x, _x.camera_info.binning_y, _x.camera_info.roi.x_offset, _x.camera_info.roi.y_offset, _x.camera_info.roi.height, _x.camera_info.roi.width, _x.camera_info.roi.do_rectify,) = _struct_6IB.unpack(str[start:end])
      self.camera_info.roi.do_rectify = bool(self.camera_info.roi.do_rectify)
      start = end
      end += 4
      (length,) = _struct_I.unpack(str[start:end])
      self.model_hypotheses = []
      for i in xrange(0, length):
        val1 = object_recognition_gui.msg.ModelHypothesisList()
        start = end
        end += 4
        (length,) = _struct_I.unpack(str[start:end])
        val1.hypotheses = []
        for i in xrange(0, length):
          val2 = object_recognition_gui.msg.ModelHypothesis()
          _v22 = val2.mesh
          start = end
          end += 1
          (_v22.type,) = _struct_b.unpack(str[start:end])
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          pattern = '<%sd'%length
          start = end
          end += struct.calcsize(pattern)
          _v22.dimensions = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=length)
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          pattern = '<%si'%length
          start = end
          end += struct.calcsize(pattern)
          _v22.triangles = numpy.frombuffer(str[start:end], dtype=numpy.int32, count=length)
          start = end
          end += 4
          (length,) = _struct_I.unpack(str[start:end])
          _v22.vertices = []
          for i in xrange(0, length):
            val4 = geometry_msgs.msg.Point()
            _x = val4
            start = end
            end += 24
            (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
            _v22.vertices.append(val4)
          _v23 = val2.pose
          _v24 = _v23.header
          start = end
          end += 4
          (_v24.seq,) = _struct_I.unpack(str[start:end])
          _v25 = _v24.stamp
          _x = _v25
          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
          _v24.frame_id = str[start:end]
          _v26 = _v23.pose
          _v27 = _v26.position
          _x = _v27
          start = end
          end += 24
          (_x.x, _x.y, _x.z,) = _struct_3d.unpack(str[start:end])
          _v28 = _v26.orientation
          _x = _v28
          start = end
          end += 32
          (_x.x, _x.y, _x.z, _x.w,) = _struct_4d.unpack(str[start:end])
          val1.hypotheses.append(val2)
        start = end
        end += 1
        (val1.accept,) = _struct_B.unpack(str[start:end])
        val1.accept = bool(val1.accept)
        self.model_hypotheses.append(val1)
      return self
    except struct.error, e:
      raise roslib.message.DeserializationError(e) #most likely buffer underfill

_struct_I = roslib.message.struct_I
_struct_6IB = struct.Struct("<6IB")
_struct_b = struct.Struct("<b")
_struct_12d = struct.Struct("<12d")
_struct_9d = struct.Struct("<9d")
_struct_BI = struct.Struct("<BI")
_struct_3I = struct.Struct("<3I")
_struct_B = struct.Struct("<B")
_struct_4d = struct.Struct("<4d")
_struct_2I = struct.Struct("<2I")
_struct_3d = struct.Struct("<3d")

---

object_recognition_gui
Author(s): David Gossow
autogenerated on Fri Jan 11 09:12:01 2013