Python CameraInfo.R方法代码示例

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
 def publish(self, event):
     if self.imgmsg is None:
         return
     now = rospy.Time.now()
     # setup ros message and publish
     with self.lock:
         self.imgmsg.header.stamp = now
         self.imgmsg.header.frame_id = self.frame_id
     self.pub.publish(self.imgmsg)
     if self.publish_info:
         info = CameraInfo()
         info.header.stamp = now
         info.header.frame_id = self.frame_id
         info.width = self.imgmsg.width
         info.height = self.imgmsg.height
         if self.fovx is not None and self.fovy is not None:
             fx = self.imgmsg.width / 2.0 / \
                 np.tan(np.deg2rad(self.fovx / 2.0))
             fy = self.imgmsg.height / 2.0 / \
                 np.tan(np.deg2rad(self.fovy / 2.0))
             cx = self.imgmsg.width / 2.0
             cy = self.imgmsg.height / 2.0
             info.K = np.array([fx, 0, cx,
                                0, fy, cy,
                                0, 0, 1.0])
             info.P = np.array([fx, 0, cx, 0,
                                0, fy, cy, 0,
                                0, 0, 1, 0])
             info.R = [1, 0, 0, 0, 1, 0, 0, 0, 1]
         self.pub_info.publish(info)

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
 def publish( self ):
     # Get the image.
     image = self.__videoDeviceProxy.getImageRemote( self.__videoDeviceProxyName );
         
     # Create Image message.
     ImageMessage = Image();
     ImageMessage.header.stamp.secs = image[ 5 ];
     ImageMessage.width = image[ 0 ];
     ImageMessage.height = image[ 1 ];
     ImageMessage.step = image[ 2 ] * image[ 0 ];
     ImageMessage.is_bigendian = False;
     ImageMessage.encoding = 'bgr8';
     ImageMessage.data = image[ 6 ];
     
     self.__imagePublisher.publish( ImageMessage );
     
     # Create CameraInfo message.
     # Data from the calibration phase is hard coded for now.
     CameraInfoMessage = CameraInfo();
     CameraInfoMessage.header.stamp.secs = image[ 5 ];
     CameraInfoMessage.width = image[ 0 ];
     CameraInfoMessage.height = image[ 1 ];
     CameraInfoMessage.D = [ -0.0769218451517258, 0.16183180613612602, 0.0011626049774280595, 0.0018733894100460534, 0.0 ];
     CameraInfoMessage.K = [ 581.090096189648, 0.0, 341.0926325830606, 0.0, 583.0323248080421, 241.02441593704128, 0.0, 0.0, 1.0 ];
     CameraInfoMessage.R = [ 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0 ];
     CameraInfoMessage.P = [ 580.5918359588198, 0.0, 340.76398441334106, 0.0, 0.0, 582.4042541534321, 241.04182225157172, 0.0, 0.0, 0.0, 1.0, 0.0] ;
     CameraInfoMessage.distortion_model = 'plumb_bob';
     
     #CameraInfoMessage.roi.x_offset = self.__ROIXOffset;
     #CameraInfoMessage.roi.y_offset = self.__ROIYOffset;
     #CameraInfoMessage.roi.width = self.__ROIWidth;
     #CameraInfoMessage.roi.height = self.__ROIHeight;
     #CameraInfoMessage.roi.do_rectify = self.__ROIDoRectify;
     
     self.__cameraInfoPublisher.publish( CameraInfoMessage );

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
def yaml_to_CameraInfo(calib_yaml):
    """
    Parse a yaml file containing camera calibration data (as produced by
    rosrun camera_calibration cameracalibrator.py) into a
    sensor_msgs/CameraInfo msg.

    Parameters
    ----------
    yaml_fname : str
        Path to yaml file containing camera calibration data

    Returns
    -------
    camera_info_msg : sensor_msgs.msg.CameraInfo
        A sensor_msgs.msg.CameraInfo message containing the camera calibration
        data
    """
    # Load data from file
    calib_data = yaml.load(calib_yaml)
    # Parse
    camera_info_msg = CameraInfo()
    camera_info_msg.width = calib_data["image_width"]
    camera_info_msg.height = calib_data["image_height"]
    camera_info_msg.K = calib_data["camera_matrix"]["data"]
    camera_info_msg.D = calib_data["distortion_coefficients"]["data"]
    camera_info_msg.R = calib_data["rectification_matrix"]["data"]
    camera_info_msg.P = calib_data["projection_matrix"]["data"]
    camera_info_msg.distortion_model = calib_data["distortion_model"]
    return camera_info_msg

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
  def __init__(self):
    rospy.init_node('image_converter', anonymous=True)
    self.filtered_face_locations = rospy.get_param('camera_topic')
    self.image_pub = rospy.Publisher(self.filtered_face_locations,Image)
    self.image_info = rospy.Publisher('camera_info',CameraInfo)
    self.bridge = CvBridge()
    cap = cv2.VideoCapture('/home/icog-labs/Videos/video.mp4')
    print cap.get(5)
    path = rospkg.RosPack().get_path("robots_config")
    path = path + "/robot/camera.yaml"
    stream = file(path, 'r')
    calib_data = yaml.load(stream)
    cam_info = CameraInfo()
    cam_info.width = calib_data['image_width']
    cam_info.height = calib_data['image_height']
    cam_info.K = calib_data['camera_matrix']['data']
    cam_info.D = calib_data['distortion_coefficients']['data']
    cam_info.R = calib_data['rectification_matrix']['data']
    cam_info.P = calib_data['projection_matrix']['data']
    cam_info.distortion_model = calib_data['distortion_model']
    while (not rospy.is_shutdown()) and (cap.isOpened()):
        self.keystroke = cv2.waitKey(1000 / 30)
        ret, frame = cap.read()

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # cv2.imshow('frame', gray)
        self.image_pub.publish(self.bridge.cv2_to_imgmsg(gray, "mono8"))
        self.image_info.publish(cam_info)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
    def __init__(self):
        rospy.init_node('image_publish')
        name = sys.argv[1]
        image = cv2.imread(name)
        #cv2.imshow("im", image)
        #cv2.waitKey(5)

        hz = rospy.get_param("~rate", 1)
        frame_id = rospy.get_param("~frame_id", "map")
        use_image = rospy.get_param("~use_image", True)
        rate = rospy.Rate(hz)

        self.ci_in = None
        ci_sub = rospy.Subscriber('camera_info_in', CameraInfo,
                                  self.camera_info_callback, queue_size=1)

        if use_image:
            pub = rospy.Publisher('image', Image, queue_size=1)
        ci_pub = rospy.Publisher('camera_info', CameraInfo, queue_size=1)

        msg = Image()
        msg.header.stamp = rospy.Time.now()
        msg.header.frame_id = frame_id
        msg.encoding = 'bgr8'
        msg.height = image.shape[0]
        msg.width = image.shape[1]
        msg.step = image.shape[1] * 3
        msg.data = image.tostring()
        if use_image:
            pub.publish(msg)

        ci = CameraInfo()
        ci.header = msg.header
        ci.height = msg.height
        ci.width = msg.width
        ci.distortion_model ="plumb_bob"
        # TODO(lucasw) need a way to set these values- have this node
        # subscribe to an input CameraInfo?
        ci.D = [0.0, 0.0, 0.0, 0, 0]
        ci.K = [500.0, 0.0, msg.width/2, 0.0, 500.0, msg.height/2, 0.0, 0.0, 1.0]
        ci.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
        ci.P = [500.0, 0.0, msg.width/2, 0.0, 0.0, 500.0, msg.height/2, 0.0,  0.0, 0.0, 1.0, 0.0]
        # ci_pub.publish(ci)

        # TODO(lwalter) only run this is hz is positive,
        # otherwise wait for input trigger message to publish an image
        while not rospy.is_shutdown():
            if self.ci_in is not None:
                ci = self.ci_in

            msg.header.stamp = rospy.Time.now()
            ci.header = msg.header
            if use_image:
                pub.publish(msg)
            ci_pub.publish(ci)

            if hz <= 0:
                rospy.sleep()
            else:
                rate.sleep()

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
    def send_test_messages(self, filename):
        self.msg_received = False
        # Publish the camera info TODO make this a field in the annotations file to dictate the source calibration file
        msg_info = CameraInfo()
        msg_info.height = 480
        msg_info.width = 640
        msg_info.distortion_model = "plumb_bob"
        msg_info.D = [-0.28048157543793056, 0.05674481026365553, -0.000988764087143394, -0.00026869128565781613, 0.0]
        msg_info.K = [315.128501, 0.0, 323.069638, 0.0, 320.096636, 218.012581, 0.0, 0.0, 1.0]
        msg_info.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
        msg_info.P = [217.48876953125, 0.0, 321.3154072384932, 0.0, 0.0, 250.71084594726562, 202.30416165274983, 0.0,
                      0.0, 0.0, 1.0, 0.0]
        msg_info.roi.do_rectify = False

        # Publish the test image
        img = cv2.imread(filename)
        cvb = CvBridge()
        msg_raw = cvb.cv2_to_imgmsg(img, encoding="bgr8")
        self.pub_info.publish(msg_info)
        self.pub_raw.publish(msg_raw)

        # Wait for the message to be received
        timeout = rospy.Time.now() + rospy.Duration(10)  # Wait at most 5 seconds for the node to reply
        while not self.msg_received and not rospy.is_shutdown() and rospy.Time.now() < timeout:
            rospy.sleep(0.1)
        self.assertLess(rospy.Time.now(), timeout, "Waiting for apriltag detection timed out.")

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
    def default(self, ci="unused"):
        if not self.component_instance.capturing:
            return  # press [Space] key to enable capturing

        image_local = self.data["image"]

        image = Image()
        image.header = self.get_ros_header()
        image.header.frame_id += "/base_image"
        image.height = self.component_instance.image_height
        image.width = self.component_instance.image_width
        image.encoding = "rgba8"
        image.step = image.width * 4

        # VideoTexture.ImageRender implements the buffer interface
        image.data = bytes(image_local)

        # fill this 3 parameters to get correcty image with stereo camera
        Tx = 0
        Ty = 0
        R = [1, 0, 0, 0, 1, 0, 0, 0, 1]

        intrinsic = self.data["intrinsic_matrix"]

        camera_info = CameraInfo()
        camera_info.header = image.header
        camera_info.height = image.height
        camera_info.width = image.width
        camera_info.distortion_model = "plumb_bob"
        camera_info.K = [
            intrinsic[0][0],
            intrinsic[0][1],
            intrinsic[0][2],
            intrinsic[1][0],
            intrinsic[1][1],
            intrinsic[1][2],
            intrinsic[2][0],
            intrinsic[2][1],
            intrinsic[2][2],
        ]
        camera_info.R = R
        camera_info.P = [
            intrinsic[0][0],
            intrinsic[0][1],
            intrinsic[0][2],
            Tx,
            intrinsic[1][0],
            intrinsic[1][1],
            intrinsic[1][2],
            Ty,
            intrinsic[2][0],
            intrinsic[2][1],
            intrinsic[2][2],
            0,
        ]

        self.publish(image)
        self.topic_camera_info.publish(camera_info)

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
	def createMsg(self):
		msg = CameraInfo()
		msg.height = 480
		msg.width = 640
		msg.distortion_model = "plumb_bob"
		msg.D = [0.08199114285264993, -0.04549390835713936, -0.00040960290587863145, 0.0009833748346549968, 0.0]
		msg.K = [723.5609128875188, 0.0, 315.9845354772031, 0.0, 732.2615109685506, 242.26660681756633,\
				 0.0, 0.0, 1.0]
		msg.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
		msg.P = [737.1736450195312, 0.0, 316.0324931112791, 0.0, 0.0, 746.1573486328125, 241.59042622688867,\
				 0.0, 0.0, 0.0, 1.0, 0.0]
		self.msg = msg

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
def parse_yaml(filename):
    stream = file(filename, 'r')
    calib_data = yaml.load(stream)
    cam_info = CameraInfo()
    cam_info.width = calib_data['image_width']
    cam_info.height = calib_data['image_height']
    cam_info.K = calib_data['camera_matrix']['data']
    cam_info.D = calib_data['distortion_coefficients']['data']
    cam_info.R = calib_data['rectification_matrix']['data']
    cam_info.P = calib_data['projection_matrix']['data']
    cam_info.distortion_model = calib_data['distortion_model']
    return cam_info

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
 def parse_yaml(self, filename):
     stream = file(filename, "r")
     calib_data = yaml.load(stream)
     cam_info = CameraInfo()
     cam_info.width = calib_data["image_width"]
     cam_info.height = calib_data["image_height"]
     cam_info.K = calib_data["camera_matrix"]["data"]
     cam_info.D = calib_data["distortion_coefficients"]["data"]
     cam_info.R = calib_data["rectification_matrix"]["data"]
     cam_info.P = calib_data["projection_matrix"]["data"]
     cam_info.distortion_model = calib_data["distortion_model"]
     return cam_info

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
def load_cam_info(calib_file):
    cam_info = CameraInfo()
    with open(calib_file,'r') as cam_calib_file :
        cam_calib = yaml.load(cam_calib_file)
        cam_info.height = cam_calib['image_height']
        cam_info.width = cam_calib['image_width']
        cam_info.K = cam_calib['camera_matrix']['data']
        cam_info.D = cam_calib['distortion_coefficients']['data']
        cam_info.R = cam_calib['rectification_matrix']['data']
        cam_info.P = cam_calib['projection_matrix']['data']
        cam_info.distortion_model = cam_calib['distortion_model']
    return cam_info

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
def yaml_to_CameraInfo(yaml_fname):
    with open(yaml_fname, "r") as file_handle:
        calib_data = yaml.load(file_handle)
 
    camera_info_msg = CameraInfo()
    camera_info_msg.width = calib_data["image_width"]
    camera_info_msg.height = calib_data["image_height"]
    camera_info_msg.K = calib_data["camera_matrix"]["data"]
    camera_info_msg.D = calib_data["distortion_coefficients"]["data"]
    camera_info_msg.R = calib_data["rectification_matrix"]["data"]
    camera_info_msg.P = calib_data["projection_matrix"]["data"]
    camera_info_msg.distortion_model = calib_data["distortion_model"]
    return camera_info_msg

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
    def get_cam_info(self, cam_name):
        cam_name = "calibrations/" + cam_name + ".yaml"
        stream = file(os.path.join(os.path.dirname(__file__), cam_name), 'r')

        calib_data = yaml.load(stream)

        cam_info = CameraInfo()
        cam_info.width = calib_data['image_width']
        cam_info.height = calib_data['image_height']
        cam_info.K = calib_data['camera_matrix']['data']
        cam_info.D = calib_data['distortion_coefficients']['data']
        cam_info.R = calib_data['rectification_matrix']['data']
        cam_info.P = calib_data['projection_matrix']['data']
        cam_info.distortion_model = calib_data['distortion_model']
        return cam_info

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
def GetCameraInfo(width, height):
	cam_info = CameraInfo()
	cam_info.width = width
	cam_info.height = height
	cam_info.distortion_model = model
	#cam_info.D = [0.0]*5
	#cam_info.K = [0.0]*9
	#cam_info.R = [0.0]*9
	#cam_info.P = [0.0]*12
	cam_info.D = D
        cam_info.K = K
        cam_info.R = R
        cam_info.P = P
	cam_info.binning_x = 0
	cam_info.binning_y = 0
	return cam_info

# 需要导入模块: from sensor_msgs.msg import CameraInfo [as 别名]
# 或者: from sensor_msgs.msg.CameraInfo import R [as 别名]
    def toCameraInfo(self):
        msg = CameraInfo()

        (msg.width, msg.height) = self.size

        if self.D.size > 5:
            msg.distortion_model = "rational_polynomial"
        else:
            msg.distortion_model = "plumb_bob"

        msg.D = numpy.ravel(self.D).copy().tolist()
        msg.K = numpy.ravel(self.K).copy().tolist()
        msg.R = numpy.ravel(self.R).copy().tolist()
        msg.P = numpy.ravel(self.P).copy().tolist()

        return msg