cb_detector.py

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#! /usr/bin/python
#***********************************************************
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#* Author: Eitan Marder-Eppstein
#***********************************************************
PKG = 'kinect_depth_calibration'
NAME = 'image_cb_detector_node'
import roslib; roslib.load_manifest(PKG)

import rospy
import cv
import math
import threading
import numpy
import tf
import PyKDL

from sensor_msgs.msg import Image, CameraInfo
from geometry_msgs.msg import PoseStamped
from tf_conversions import posemath

from cv_bridge import CvBridge, CvBridgeError
from kinect_depth_calibration.srv import GetCheckerboardPose, GetCheckerboardPoseResponse

class ImageCbDetector:
  def get_board_corners(self, corners, corners_x, corners_y):
    return (corners[0], corners[corners_x  - 1], 
        corners[(corners_y - 1) * corners_x], corners[len(corners) - 1])

  def detect(self, image, corners_x, corners_y, spacing_x, spacing_y, width_scaling, height_scaling):
    #resize the image base on the scaling parameters we've been configured with
    scaled_width = int(.5 + image.width * width_scaling)
    scaled_height = int(.5 + image.height * height_scaling)
    
    #in cvMat its row, col so height comes before width
    image_scaled = cv.CreateMat(scaled_height, scaled_width, cv.GetElemType(image))
    cv.Resize(image, image_scaled, cv.CV_INTER_LINEAR)

    #Here, we'll actually call the openCV detector    
    found, corners = cv.FindChessboardCorners(image_scaled, (corners_x, corners_y), cv.CV_CALIB_CB_ADAPTIVE_THRESH)

    if found:
      board_corners = self.get_board_corners(corners, corners_x, corners_y)
      
      #find the perimeter of the checkerboard
      perimeter = 0.0
      for i in range(len(board_corners)):
        next = (i + 1) % 4
        xdiff = board_corners[i][0] - board_corners[next][0]
        ydiff = board_corners[i][1] - board_corners[next][1]
        perimeter += math.sqrt(xdiff * xdiff + ydiff * ydiff)

      #estimate the square size in pixels
      square_size = perimeter / ((corners_x - 1 + corners_y - 1) * 2)
      radius = int(square_size * 0.5 + 0.5)

      corners = cv.FindCornerSubPix(image_scaled, corners, (radius, radius), (-1, -1), (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 30, 0.1))

      #uncomment to debug chessboard detection
      print 'Chessboard found'
      #cv.DrawChessboardCorners(image_scaled, (corners_x, corners_y), corners, 1)
      #cv.NamedWindow("image_scaled")
      #cv.ShowImage("image_scaled", image_scaled)
      #cv.WaitKey(600)

      object_points = None

      #we'll also generate the object points if the user has specified spacing
      if spacing_x != None and spacing_y != None:
        object_points = cv.CreateMat(3, corners_x * corners_y, cv.CV_32FC1)

        for y in range(corners_y):
          for x in range(corners_x):
            cv.SetReal2D(object_points, 0, y*corners_x + x, x * spacing_x)
            cv.SetReal2D(object_points, 1, y*corners_x + x, y * spacing_y)
            cv.SetReal2D(object_points, 2, y*corners_x + x, 0.0)

      #not sure why opencv functions return non opencv compatible datatypes... but they do so we'll convert
      corners_cv = cv.CreateMat(2, corners_x * corners_y, cv.CV_32FC1)
      for i in range(corners_x * corners_y):
        cv.SetReal2D(corners_cv, 0, i, corners[i][0])
        cv.SetReal2D(corners_cv, 1, i, corners[i][1])

      return (corners_cv, object_points)

    else:
      #cv.NamedWindow("image_scaled")
      #cv.ShowImage("image_scaled", image_scaled)
      #cv.WaitKey(600)
      rospy.logwarn("Didn't find checkerboard")
      return (None, None)

class ImageCbDetectorNode:
  def __init__(self):
    self.corners_x = rospy.get_param('~corners_x', 6)
    self.corners_y = rospy.get_param('~corners_y', 6)
    self.spacing_x = rospy.get_param('~spacing_x', None)
    self.spacing_y = rospy.get_param('~spacing_y', None)
    self.width_scaling = rospy.get_param('~width_scaling', 1)
    self.height_scaling = rospy.get_param('~height_scaling', 1)

    self.im_cb_detector = ImageCbDetector()

    self.image_sub = rospy.Subscriber("image_stream", Image, self.callback)
    self.caminfo_sub = rospy.Subscriber("camera_info", CameraInfo, self.cam_info_cb) 
    self.pose_pub = rospy.Publisher("board_pose", PoseStamped, latch=True)
    self.pose_srv = None
    self.bridge = CvBridge()
    self.mutex = threading.RLock()
    self.cam_info = None
    self.ros_image = None

  def intrinsic_matrix_from_info(self, cam_info):
    intrinsic_matrix = cv.CreateMat(3, 3, cv.CV_32FC1)

    #Because we only want the upper 3x3 (normal) portion of the rectified intrinsic matrix
    for i in range(0, 3):
      for j in range(0, 3):
        intrinsic_matrix[i, j] = cam_info.P[4*i+j]

    return intrinsic_matrix

  def attempt_to_advertise(self):
    #we'll only advertise the service after we've received a cam_info/image pair
    if self.ros_image != None and self.cam_info != None:
      self.pose_srv = rospy.Service("get_checkerboard_pose", GetCheckerboardPose, self.find_checkerboard_service)

  def cam_info_cb(self, cam_info):
    with self.mutex:
      self.cam_info = cam_info
      if self.pose_srv == None:
        self.attempt_to_advertise()

  def callback(self, ros_image):
    #copy over the latest image to be used when a service call is made
    with self.mutex:
      self.ros_image = ros_image
      if self.pose_srv == None:
        self.attempt_to_advertise()

  def find_checkerboard_service(self, req):
    poses = []
    min_x = self.ros_image.width
    min_y = self.ros_image.height
    max_x = 0
    max_y = 0
    for i in range(10):
        rospy.sleep(0.5)

        #copy the image over
        with self.mutex:
          image = self.ros_image

        result = self.find_checkerboard_pose(image, req.corners_x, 
                                                 req.corners_y, req.spacing_x, 
                                                 req.spacing_y, self.width_scaling, 
                                                 self.height_scaling)
        if result is None:
            rospy.logerr("Couldn't find a checkerboard")
            continue

        p, corners = result
        poses.append(p)
        for j in range(corners.cols):
            x = cv.GetReal2D(corners, 0, j)
            y = cv.GetReal2D(corners, 1, j)
            min_x = min(min_x, x)
            max_x = max(max_x, x)
            min_y = min(min_y, y)
            max_y = max(max_y, y)

    # convert all poses to twists
    twists = []
    for p in poses:
      twists.append(PyKDL.diff(PyKDL.Frame.Identity(), posemath.fromMsg(p.pose)))

    # get average twist 
    twist_res = PyKDL.Twist()
    PyKDL.SetToZero(twist_res)
    for t in twists:
      for i in range(3):
        twist_res.vel[i] += t.vel[i]/len(poses)
        twist_res.rot[i] += t.rot[i]/len(poses)

    # get noise
    noise_rot = 0
    noise_vel = 0
    for t in twists:
      n_vel = (t.vel - twist_res.vel).Norm()
      n_rot = (t.rot - twist_res.rot).Norm()
      if n_vel > noise_vel:
        noise_vel = n_vel
      if n_rot > noise_rot:
        noise_rot = n_rot

    # set service resul
    pose_res = PyKDL.addDelta(PyKDL.Frame.Identity(), twist_res, 1.0)
    pose_msg = PoseStamped()
    pose_msg.header = poses[0].header
    pose_msg.pose = posemath.toMsg(pose_res)
    return GetCheckerboardPoseResponse(pose_msg, min_x, max_x, min_y, max_y, noise_vel, noise_rot)


  def find_checkerboard_pose(self, ros_image, corners_x, corners_y, spacing_x, spacing_y, width_scaling, height_scaling):
    #we need to convert the ros image to an opencv image
    try:
      image = self.bridge.imgmsg_to_cv(ros_image, "mono8")
    except CvBridgeError, e:
      rospy.logerror("Error importing image %s" % e)
      return

    corners, model = self.im_cb_detector.detect(image, corners_x, corners_y, spacing_x, spacing_y, width_scaling, height_scaling)

    if corners != None and model != None and self.cam_info != None:
      #find the pose of the checkerboard
      rot = cv.CreateMat(3, 1, cv.CV_32FC1)
      trans = cv.CreateMat(3, 1, cv.CV_32FC1)
      with self.mutex:
        intrinsic_matrix = self.intrinsic_matrix_from_info(self.cam_info)
        kc = cv.CreateMat(1, 4, cv.CV_32FC1)
        cv.Set(kc, 0.0)
        cv.FindExtrinsicCameraParams2(model, corners, intrinsic_matrix, kc, rot, trans)

      #We want to build a transform now, but first we have to convert the 
      #rotation vector we get back from OpenCV into a rotation matrix
      rot_mat = cv.CreateMat(3, 3, cv.CV_32FC1)
      cv.Rodrigues2(rot, rot_mat)

      #Now we need to convert this rotation matrix into a quaternion
      #This can be done better in never versions of opencv, but we need to be boxturtle compatible
      numpy_mat = numpy.fromstring(rot_mat.tostring(), dtype = numpy.float32).reshape((3,3))

      #of course, tf expects all matricies passed to it to be 4x4... so we'll build a full matrix here
      full_pose = numpy.zeros((4, 4))

      #first, copy in the rotation matrix
      full_pose[0:3, 0:3] = numpy_mat[0:3, 0:3]

      #next, we'll copy in the translation
      full_pose[0:3, 3] = [trans[i, 0] for i in range(3)]

      #and make sure to add a 1 in the lower right corner
      full_pose[3][3] = 1.0

      rospy.logdebug("%s" % numpy_mat)
      rospy.logdebug("%s" % full_pose)

      tf_trans = tf.transformations.translation_from_matrix(full_pose)
      tf_rot = tf.transformations.quaternion_from_matrix(full_pose)

      #and now we'll actually build our pose stamped
      board_pose = PoseStamped()
      board_pose.header = ros_image.header
      board_pose.pose.position.x = tf_trans[0]
      board_pose.pose.position.y = tf_trans[1]
      board_pose.pose.position.z = tf_trans[2]
      board_pose.pose.orientation.x = tf_rot[0]
      board_pose.pose.orientation.y = tf_rot[1]
      board_pose.pose.orientation.z = tf_rot[2]
      board_pose.pose.orientation.w = tf_rot[3]
      rospy.logdebug("%s" % board_pose)

      #we'll publish the pose so we can display it in rviz
      self.pose_pub.publish(board_pose)
      return (board_pose, corners)

def cb_detector_main(argv=None):
  rospy.init_node(NAME, anonymous=False)
  cb_detector = ImageCbDetectorNode()
  rospy.spin()

if __name__ == '__main__':
  cb_detector_main()

---

kinect_depth_calibration
Author(s): Eitan Marder-Eppstein
autogenerated on Fri Jan 11 09:54:11 2013