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()
def sim():
global t, pose, camInfoMsg
global centerPub, targetVelPub, camVelPub, camInfoPub, br, tfl
global switchTimer, imageTimer, estimatorOn
rospy.init_node("sim")
estimatorOn = True
centerPub = rospy.Publisher("markerCenters",Center,queue_size=10)
targetVelPub = rospy.Publisher("ugv0/body_vel",TwistStamped,queue_size=10)
camVelPub = rospy.Publisher("image/body_vel",TwistStamped,queue_size=10)
cameraName = rospy.get_param(rospy.get_name()+"/camera","camera")
camInfoPub = rospy.Publisher(cameraName+"/camera_info",CameraInfo,queue_size=1)
br = tf.TransformBroadcaster()
tfl = tf.TransformListener()
# Camera parameters
camInfoMsg = CameraInfo()
camInfoMsg.D = distCoeffs.tolist()
camInfoMsg.K = camMat.reshape((-1)).tolist()
# Wait for node to get cam info
while (camVelPub.get_num_connections() == 0) and (not rospy.is_shutdown()):
# publish camera parameters
camInfoPub.publish(camInfoMsg)
rospy.sleep(0.5)
# Publishers
rospy.Timer(rospy.Duration(1.0/velRate),velPubCB)
imageTimer = rospy.Timer(rospy.Duration(1.0/frameRate),imagePubCB)
rospy.Timer(rospy.Duration(0.5),camInfoPubCB)
switchTimer = rospy.Timer(rospy.Duration(11.0),switchCB,oneshot=True)
# Initial conditions
startTime = rospy.get_time()
camPos = np.array([0,-1,1.5])
camOrient = np.array([-1*np.sqrt(2)/2,0,0,np.sqrt(2)/2])
targetPos = np.array([0,1,0])
targetOrient = np.array([0,0,0,1])
pose = np.concatenate((camPos,camOrient,targetPos,targetOrient))
r = rospy.Rate(intRate)
h = 1.0/intRate
while not rospy.is_shutdown():
t = np.array(rospy.get_time() - startTime)
poseDot = poseDyn(t,pose)
pose = pose + poseDot*h
# Send Transform
camPos = pose[0:3]
camOrient = pose[3:7]
targetPos = pose[7:10]
targetOrient = pose[10:]
br.sendTransform(targetPos,targetOrient,rospy.Time.now(),"ugv0","world")
br.sendTransform(camPos,camOrient,rospy.Time.now(),"image","world")
r.sleep()
def talker():
pub = rospy.Publisher('camera_info', CameraInfo, queue_size=10)
rospy.init_node('talker', anonymous=True)
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
data = CameraInfo()
#[[ 820.29938083 0. 408.73210359]
#[ 0. 827.01247867 237.04151422]
#[ 0. 0. 1. ]]
data.K = (820,0.,408.73210359,0.,827.01247867,827.01247867,0.,0.,1.)
pub.publish(data)
rate.sleep()
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 );
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
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.")
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
def makeROSInfo(image): ci = CameraInfo() head = Header() head.stamp = rospy.Time.now() ci.header = head w,h = image.shape[:2] ci.width = w ci.height = h ci.distortion_model = 'plumb_bob' return ci
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)
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
self.pub_info.publish(info)
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
def publish(self):
now = rospy.Time.now()
bridge = cv_bridge.CvBridge()
img_bgr = cv2.imread(self.file_name)
if img_bgr is None:
jsk_logwarn('cannot read the image at {}'
.format(self.file_name))
return
# resolve encoding
encoding = rospy.get_param('~encoding', 'bgr8')
if getCvType(encoding) == 0:
# mono8
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
elif getCvType(encoding) == 2:
# 16UC1
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
img = img.astype(np.float32)
img = img / 255 * (2 ** 16)
img = img.astype(np.uint16)
elif getCvType(encoding) == 5:
# 32FC1
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
img = img.astype(np.float32)
img /= 255
elif getCvType(encoding) == 16:
# 8UC3
if encoding in ('rgb8', 'rgb16'):
# RGB
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
else:
img = img_bgr
else:
jsk_logerr('unsupported encoding: {0}'.format(encoding))
return
# setup ros message and publish
imgmsg = bridge.cv2_to_imgmsg(img, encoding=encoding)
imgmsg.header.stamp = now
imgmsg.header.frame_id = self.frame_id
self.pub.publish(imgmsg)
if self.publish_info:
info = CameraInfo()
info.header.stamp = now
info.header.frame_id = self.frame_id
info.width = imgmsg.width
info.height = imgmsg.height
self.pub_info.publish(info)
def sim():
global t, pose, camInfoMsg
global centerPub, velPub, camInfoPub, br, tfl
rospy.init_node("sim")
centerPub = rospy.Publisher("markerCenters",Center,queue_size=10)
velPub = rospy.Publisher("image/local_vel",TwistStamped,queue_size=10)
cameraName = rospy.get_param(rospy.get_name()+"/camera","camera")
camInfoPub = rospy.Publisher(cameraName+"/camera_info",CameraInfo,queue_size=1)
br = tf.TransformBroadcaster()
tfl = tf.TransformListener()
# Camera parameters
camInfoMsg = CameraInfo()
camInfoMsg.D = distCoeffs.tolist()
camInfoMsg.K = camMat.reshape((-1)).tolist()
# Wait for node to get cam info
while (velPub.get_num_connections() == 0) and (not rospy.is_shutdown()):
# publish camera parameters
camInfoPub.publish(camInfoMsg)
rospy.sleep(0.5)
# Publishers
rospy.Timer(rospy.Duration(1.0/velRate),velPubCB)
rospy.Timer(rospy.Duration(1.0/frameRate),imagePubCB)
rospy.Timer(rospy.Duration(0.5),camInfoPubCB)
# Initial conditions
startTime = rospy.get_time()
camPos = np.array([0,-1,1.5])
camOrient = np.array([-1*np.sqrt(2)/2,0,0,np.sqrt(2)/2])
targetPos = np.array([-1.5,1.5,0])
targetOrient = np.array([0,0,0,1])
pose = np.concatenate((camPos,camOrient,targetPos,targetOrient))
r = rospy.Rate(intRate)
h = 1.0/intRate
while not rospy.is_shutdown():
t = np.array(rospy.get_time() - startTime)
poseDot = poseDyn(t,pose)
pose = pose + poseDot*h
r.sleep()
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
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
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
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
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"]
cam_info.binning_x = calib_data["binning_x"]
cam_info.binning_y = calib_data["binning_y"]
cam_info.roi.x_offset = calib_data["roi"]["x_offset"]
cam_info.roi.y_offset = calib_data["roi"]["y_offset"]
cam_info.roi.height = calib_data["roi"]["height"]
cam_info.roi.width = calib_data["roi"]["width"]
cam_info.roi.do_rectify = calib_data["roi"]["do_rectify"]
return cam_info
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']
cam_info.binning_x = calib_data['binning_x']
cam_info.binning_y = calib_data['binning_y']
cam_info.roi.x_offset = calib_data['roi']['x_offset']
cam_info.roi.y_offset = calib_data['roi']['y_offset']
cam_info.roi.height = calib_data['roi']['height']
cam_info.roi.width = calib_data['roi']['width']
cam_info.roi.do_rectify = calib_data['roi']['do_rectify']
return cam_info
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
def test():
rospy.wait_for_service(service_name)
# build theoretical calibration for Unibrain Fire-i camera in
# 640x480 mode
cinfo = CameraInfo()
cinfo.width = 1360
cinfo.height = 1024
#cx = (cinfo.width - 1.0)/2.0
#cy = (cinfo.height - 1.0)/2.0
#fx = fy = 0.0043 # Unibrain Fire-i focal length
#fx = 1046.16197
#fy = 1043.87094
#cx = 695.62128
#cy = 544.32183
#k1 = -0.23268
#k2 = 0.08580
#p1 = 0.00098
#p2 = -0.00022
#cinfo.K = [fx, 0., cx, 0., fy, cy, 0., 0., 1.]
#cinfo.R = [1., 0., 0., 0., 1., 0., 0., 0., 1.]
#cinfo.P = [fx, 0., cx, 0., 0., fy, cy, 0., 0., 0., 1., 0.]
cinfo.K = [430.21554970319971, 0.0, 306.6913434743704, 0.0, 430.53169252696676, 227.22480030078816, 0.0, 0.0, 1.0]
cinfo.D = [-0.33758562758914146, 0.11161239414304096, -0.00021819272592442094, -3.029195446330518e-05, 0]
#cinfo.K = [4827.94, 0, 1223.5, 0, 4835.62, 1024.5, 0, 0, 1]
#cinfo.D = [-0.41527, 0.31874, -0.00197, 0.00071, 0]
#cinfo.R = [1, 0, 0, 0, 1, 0, 0, 0, 1]
#cinfo.P = [4827.94, 0, 1223.5, 0, 0, 4835.62, 1024.5, 0, 0, 0, 1, 0]
try:
set_camera_info = rospy.ServiceProxy(service_name, SetCameraInfo)
response = set_camera_info(cinfo)
rospy.loginfo("set_camera_info: success=" + str(response.success)
+ ", status=" + str(response.status_message))
return response.success
except rospy.ServiceException, e:
print "Service call failed: %s"%e
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)
def get_camera_info(hard_coded=True):
if hard_coded:
cx = 319.5
cy = 239.5
fx = 525.5
fy = 525.5
return (cx, cy, fx, fy)
#if we are using a different camera, then
#we can listen to the ros camera info topic for that device
#and get our values here.
else:
import image_geometry
from sensor_msgs.msg import CameraInfo
cam_info = CameraInfo()
cam_info.height = 480
cam_info.width = 640
cam_info.distortion_model = "plumb_bob"
cam_info.D = [0.0, 0.0, 0.0, 0.0, 0.0]
cam_info.K = [525.0, 0.0, 319.5, 0.0, 525.0, 239.5, 0.0, 0.0, 1.0]
cam_info.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
cam_info.P = [525.0, 0.0, 319.5, 0.0, 0.0, 525.0, 239.5, 0.0, 0.0, 0.0, 1.0, 0.0]
cam_info.binning_x = 0
cam_info.binning_y = 0
cam_info.roi.x_offset = 0
cam_info.roi.y_offset = 0
cam_info.roi.height = 0
cam_info.roi.width = 0
cam_info.roi.do_rectify = False
camera_model = image_geometry.PinholeCameraModel()
camera_model.fromCameraInfo(cam_info)
return camera_model.cx(), camera_model.cy(), camera_model.fx(), camera_model.fy()
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
def test_with_known_image(self):
filename = rospy.get_param("~filename")
self.setup() # Setup the node
# Publish the camera info
msg_info = CameraInfo()
msg_info.height = 480
msg_info.width = 640
msg_info.K = [331.026328, 0.0, 319.035097, 0.0, 335.330339, 216.450133, 0.0, 0.0, 1.0]
self.pub_info.publish(msg_info)
# Publish the test image
img = cv2.imread(filename)
cvb = CvBridge()
msg_raw = cvb.cv2_to_imgmsg(img, encoding="bgr8")
self.pub_raw.publish(msg_raw)
# Wait for the message to be received
timeout = rospy.Time.now() + rospy.Duration(5) # 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.")
# Check that an apriltag with id=108 was detected
found = 0
for tag in self.msg_tags.detections:
if tag.id == 108:
found += 1
self.assertAlmostEqual(tag.pose.pose.position.x, 0.0, delta=0.1) # Allow 10 cm of error margin
self.assertAlmostEqual(tag.pose.pose.position.y, 0.0, delta=0.1) # Allow 10 cm of error margin
self.assertAlmostEqual(tag.pose.pose.position.z, 0.305, delta=0.1) # Allow 10 cm of error margin
# Convert the quat to an angle about z
rot = tag.pose.pose.orientation
ang = tr.euler_from_quaternion((rot.x, rot.y, rot.z, rot.w))[2] # z axis angle only
self.assertAlmostEqual(ang, 0, delta=15 * np.pi / 180) # Allow up to 15 degrees of error margin
self.assertGreaterEqual(found, 1, "Expected apriltag with id=108 not found.")
def stream(self):
url = 'http://%s/nphMotionJpeg' % self.axis.hostname
url = url + "?resolultion=%dx%d" % (self.width, self.height)
fp = urllib.urlopen(url)
print 'Streaming \n'
while self.notdone:
boundary = fp.readline()
header = {}
while 1:
line = fp.readline()
if line == "\r\n": break
line = line.strip()
parts = line.split(": ", 1)
header[parts[0]] = parts[1]
content_length = int(header['Content-length'])
img = fp.read(content_length)
line = fp.readline()
msg = CompressedImage()
msg.header.stamp = rospy.Time.now()
msg.format = "jpeg"
msg.data = img
self.axis.pub.publish(msg)
cimsg = CameraInfo()
cimsg.header.stamp = msg.header.stamp
cimsg.width = self.width
cimsg.height = self.height
self.axis.caminfo_pub.publish(cimsg)
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.height = self.component_instance.image_height
image.width = self.component_instance.image_width
image.encoding = self.encoding
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.D = [0]
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]
if self.pub_tf:
self.publish_with_robot_transform(image)
else:
self.publish(image)
self.topic_camera_info.publish(camera_info)
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
def fetch_image(self, cam):
cam.simulate()
if not cam.pixels:
return None, None
cv_img = cv.CreateImageHeader((cam.width , cam.height), cv.IPL_DEPTH_8U, 3)
cv.SetData(cv_img, cam.pixels, cam.width*3)
cv.ConvertImage(cv_img, cv_img, cv.CV_CVTIMG_FLIP)
im = self.bridge.cv_to_imgmsg(cv_img, "rgb8")
caminfo = CameraInfo()
caminfo.header = im.header
caminfo.height = cam.height
caminfo.width = cam.width
caminfo.D = 5*[0.]
caminfo.K = sum([list(r) for r in cam.K],[])
caminfo.P = sum([list(r) for r in cam.P],[])
caminfo.R = sum([list(r) for r in cam.R],[])
return im, caminfo
def pickle_to_info(info_pickle):
info = CameraInfo()
info.header.stamp = rospy.Time()
info.header.seq = info_pickle.header.seq
info.header.frame_id = info_pickle.header.frame_id
info.roi.x_offset = info_pickle.roi.x_offset
info.roi.y_offset = info_pickle.roi.y_offset
info.roi.height = info_pickle.roi.height
info.roi.width = info_pickle.roi.width
info.height = info_pickle.height
info.width = info_pickle.width
info.D = info_pickle.D
info.K = info_pickle.K
info.R = info_pickle.R
info.P = info_pickle.P
return info
def __init__(self,
apriltag_ns="apriltag",
init_node=False,
full_img_get_path="/tmp/get_image.png",
full_img_save_path="/tmp/save_image.png"):
# start apriltag interface node
if (init_node):
rospy.init_node(apriltag_ns.strip("/") + "_interface")
self.image_frame_id = None
# set up subs, pubs, and services
camera_info_topic = rospy.get_param(
"/" + apriltag_ns + "/camera_info_topic",
default="camera/color/image_raw").strip("/")
rospy.wait_for_service("/" + apriltag_ns + "/snap_picture")
rospy.wait_for_service("/" + apriltag_ns +
"/single_image_tag_detection")
self.sub_camera_info = rospy.Subscriber("/" + camera_info_topic,
CameraInfo,
self.camera_info_cb)
self.srv_snap_picture = rospy.ServiceProxy(
"/" + apriltag_ns + "/snap_picture", SnapPicture)
self.request = AnalyzeSingleImageRequest()
self.request.full_path_where_to_get_image = full_img_get_path
self.request.full_path_where_to_save_image = full_img_save_path
self.srv_analyze_image = rospy.ServiceProxy(
"/" + apriltag_ns + "/single_image_tag_detection",
AnalyzeSingleImage)
self.pub_transforms = rospy.Publisher("/static_transforms",
TransformStamped,
queue_size=50)
rospy.sleep(0.5)
# wait to receive camera info (means that we are properly subbed)
while (self.request.camera_info == CameraInfo()
and not rospy.is_shutdown()):
pass
print("Initialized InterbotixAprilTagInterface!\n")
def __init__(self):
self.pub = rospy.Publisher("camera/cameraInfo",
CameraInfo,
queue_size=1)
self.pub_Image = rospy.Publisher("camera/image_raw",
Image,
queue_size=1)
self.image = Image()
self.bridge = CvBridge()
self.Cap = cv2.VideoCapture(int(camera_id))
self.Cap.set(cv2.CAP_PROP_FPS, fps)
self.Cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
self.kill = False
#self.Cap.set(cv2.CV_CAP_PROP_BUFFERSIZE, 1);
#os.system(os.path.join(self.script_dir,'camConfig.sh')) #Set the camera Gain and Exposure Parameters
if path_to_calibration_file is not None:
camera_name, self.camera_info = readCalibration(os.path.join(self.script_dir,\
path_to_calibration_file))
else:
self.camera_info = CameraInfo()
def make_camera_info(self):
self.camera_info = CameraInfo()
ros_pack = rospkg.RosPack()
cam_file = open(
ros_pack.get_path('ep_bringup') + '/config/head_camera.yaml')
cam_data = yaml.safe_load(cam_file)
self.camera_info.header = rospy.Header()
self.camera_info.header.frame_id = cam_data["camera_name"]
self.camera_info.distortion_model = cam_data["distortion_model"]
self.camera_info.width = cam_data['image_width']
self.camera_info.height = cam_data['image_height']
self.camera_info.binning_x = 0
self.camera_info.binning_y = 0
self.camera_info.K = cam_data['camera_matrix']['data']
self.camera_info.D = cam_data['distortion_coefficients']['data']
self.camera_info.R = cam_data['rectification_matrix']['data']
self.camera_info.P = cam_data['projection_matrix']['data']
def __init__(
self,
settingsFile,
cameraInfoFile,
):
""" Init the cameraModel as PinholeCameraModel with camera intrinsics and params
=============
settingsFile : json file storing 3D-2D point pairs and params for optimization
cameraInfoFile : yaml file storing camera intrinsics and params
"""
self.settings = json.load(settingsFile)
self.cameraParams = yaml.load(cameraInfoFile)
self.cameraModel = PinholeCameraModel()
self.cameraInfo = CameraInfo()
self._fillCameraInfo()
self.cameraModel.fromCameraInfo(self.cameraInfo)
print('Camera Model is initialized, ready to calibrate ...\n')
def __init__(self):
rospy.init_node('pub_camera_info')
yaml_file = rospy.get_param('~yaml_file')
# Load data from file
with open(yaml_file, "r") as file_handle:
calib_data = yaml.load(file_handle)
self.camera_info_msg = CameraInfo()
try:
self.camera_info_msg.width = calib_data["image_width"]
self.camera_info_msg.height = calib_data["image_height"]
self.camera_info_msg.K = calib_data["camera_matrix"]["data"]
self.camera_info_msg.D = calib_data["distortion_coefficients"]["data"]
self.camera_info_msg.R = calib_data["rectification_matrix"]["data"]
self.camera_info_msg.P = calib_data["projection_matrix"]["data"]
self.camera_info_msg.distortion_model = calib_data["distortion_model"]
except TypeError, e:
rospy.logwarn('Camera info not found... shutting down')
sys.exit(0)
def on_timer(self, _):
now = rospy.Time.now()
future = now + rospy.Duration(1.0 / self.updates_per_second)
# publish camera intrinsics
intrinsics = self.cam.get_intrinsics_as_bunch()
intrinsic_msg = CameraInfo(**intrinsics.__dict__)
intrinsic_msg.header.stamp = now
intrinsic_msg.header.frame_id = self.frame_id
self.intr_pub.publish(intrinsic_msg)
# publish camera transform
translation, rotation = self.cam.get_ROS_tf()
self.tf_b.sendTransform(
translation,
rotation,
future,
self.frame_id,
'/map',
)
def createCameraInfoMsg(timestamp, camera_definition):
cam_info_msg = CameraInfo()
cam_info_msg.header.stamp = timestamp
cam_info_msg.header.frame_id = camera_definition.frame_id
f = camera_definition.focal_length
center_x = math.floor(camera_definition.width / 2.0) + 0.5
center_y = math.floor(camera_definition.height / 2.0) + 0.5
cam_info_msg.width = camera_definition.width
cam_info_msg.height = camera_definition.height
k = [f, 0, center_x, 0, f, center_y, 0, 0, 1.0]
cam_info_msg.K = k
cam_info_msg.P = [
k[0], k[1], k[2], 0, k[3], k[4], k[5], 0, k[6], k[7], k[8], 0
]
cam_info_msg.distortion_model = "plumb_bob"
cam_info_msg.D = [0.0] * 5
return cam_info_msg
def main():
rospy.init_node('pub_gap_camera', anonymous=True)
image_pub = rospy.Publisher("himax_camera", Image)
bridge = CvBridge()
cv_file = cv2.FileStorage(config.folder_path + "/data/calibration.yaml",
cv2.FILE_STORAGE_READ)
k = cv_file.getNode("k").mat()
D = cv_file.getNode("D").mat()
size = cv_file.getNode("size").mat()
cv_file.release()
k = k.flatten().tolist()
D = D.flatten().tolist()
camera_info = CameraInfo()
camera_info.K = k
camera_info.D = D
camera_info.height = size[0][0]
camera_info.width = size[1][0]
print(camera_info)
it = ImageTransformer()
new_size, shift_x, shift_y = it.get_crop_parameters(
config.folder_path + "/data/calibration.yaml",
config.folder_path + "/data/bebop_calibration.yaml")
shift_x = int(shift_x)
shift_y = int(shift_y)
pipe_name = config.folder_path + "/pulp/image_pipe"
if not os.path.exists(pipe_name):
os.mkfifo(pipe_name)
pipein = os.open(pipe_name, os.O_RDONLY)
fcntl.fcntl(pipein, F_SETPIPE_SZ, 1000000)
frame_id = 1
while not rospy.is_shutdown():
data = read_from_pipe(pipein)
if data is not None:
cv_image = np.reshape(data,
(config.himax_height, config.himax_width))
cv_image = cv_image[shift_y:shift_y + new_size[1],
shift_x:shift_x + new_size[0]]
msg = bridge.cv2_to_imgmsg(cv_image)
msg.header.stamp = rospy.Time.now()
image_pub.publish(msg)
rospy.sleep(0)
os.close(pipein)
def get_camera_params():
# read parameters
width = rospy.get_param('~width', 640)
height = rospy.get_param('~height', 480)
Fx = rospy.get_param('~Fx', 360)
Fy = rospy.get_param('~Fy', 360)
Cx = rospy.get_param('~Cx', 360)
Cy = rospy.get_param('~Cy', 240)
# create sensor message
camera_info_msg = CameraInfo()
camera_info_msg.distortion_model = 'plumb_bob'
camera_info_msg.width = width
camera_info_msg.height = height
camera_info_msg.roi.width = width
camera_info_msg.roi.height = height
camera_info_msg.K = [Fx, 0, Cx, 0, Fy, Cy, 0, 0, 1]
camera_info_msg.D = [0, 0, 0, 0]
camera_info_msg.P = [Fx, 0, Cx, 0, 0, Fy, Cy, 0, 0, 0, 1, 0]
camera_info_msg.header.frame_id = "front_center_optical"
return camera_info_msg
def __init__(self):
rospy.init_node("little_stereo_camera", anonymous = True)
self.bridge = CvBridge()
self.left_image_pub = rospy.Publisher("stereo/left/image_raw", Image, queue_size=1)
self.left_image_info_pub = rospy.Publisher("stereo/left/camera_info", CameraInfo, queue_size=1)
self.right_image_pub = rospy.Publisher("stereo/right/image_raw", Image, queue_size=1)
self.right_image_info_pub = rospy.Publisher("stereo/right/camera_info", CameraInfo, queue_size=1)
self.camera_info = CameraInfo()
self.msg_header = Header()
self.ros_image = Image()
self.ros_image.height = 480
self.ros_image.width = 640
cam_id= rospy.get_param("cam_id", 0)
dir_path = os.path.dirname(os.path.realpath(__file__))
self.left_yaml_file = dir_path+"/left.yaml"
self.right_yaml_file = dir_path+"/right.yaml"
self.cam=cv2.VideoCapture(cam_id)
cam_mode_dict={
'LEFT_EYE_MODE':1,
'RIGHT_EYE_MODE':2,
'RED_BLUE_MODE':3,
'BINOCULAR_MODE':4,
}
cam_mode=cam_mode_dict['BINOCULAR_MODE']
command_list=[
"uvcdynctrl -d /dev/video{cam_id} -S 6:8 '(LE)0x50ff'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:15 '(LE)0x00f6'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:8 '(LE)0x2500'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:8 '(LE)0x5ffe'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:15 '(LE)0x0003'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:15 '(LE)0x0002'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:15 '(LE)0x0012'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:15 '(LE)0x0004'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:8 '(LE)0x76c3'",
"uvcdynctrl -d /dev/video{cam_id} -S 6:10 '(LE)0x0{cam_mode}00'",
]
for command in command_list:
subprocess.Popen(shlex.split(command.format(cam_id=cam_id,cam_mode=cam_mode)))
def test_update(self):
config, robot_params, P = self.load()
camera_points = [
ImagePoint(0, 0),
ImagePoint(1, 0),
ImagePoint(0, 1),
ImagePoint(1, 1)
]
block = CameraChainSensor(
config,
CameraMeasurement(camera_id="camA",
cam_info=CameraInfo(P=P),
image_points=camera_points),
ChainMeasurement(chain_id="chainA",
chain_state=JointState(position=[0])))
block.update_config(robot_params)
target = matrix([[1, 2, 1, 2], [0, 0, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]])
h = block.compute_expected(target)
z = block.get_measurement()
r = block.compute_residual(target)
self.assertAlmostEqual(
linalg.norm(h - matrix([[0, 1, 0, 1], [0, 0, 1, 1]]).T), 0.0, 6)
self.assertAlmostEqual(
linalg.norm(z - matrix([[0, 1, 0, 1], [0, 0, 1, 1]]).T), 0.0, 6)
self.assertAlmostEqual(linalg.norm(r), 0.0, 6)
# Test Sparsity
sparsity = block.build_sparsity_dict()
self.assertEqual(sparsity['transforms']['transformA'],
[1, 1, 1, 1, 1, 1])
self.assertEqual(sparsity['transforms']['transformB'],
[1, 1, 1, 1, 1, 1])
self.assertEqual(sparsity['dh_chains']['chainA']['dh'], [[1, 1, 1, 1]])
self.assertEqual(sparsity['rectified_cams']['camA']['baseline_shift'],
1)
def __init__(self, net, meta):
self.image_pub = rospy.Publisher("/object_detect",
Image,
queue_size=10)
self.point_pub = rospy.Publisher("/point_find",
PointStamped,
queue_size=10)
self.net = net
self.meta = meta
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("/camera/color/image_raw", Image,
self.callback)
self.image_depth_sub = rospy.Subscriber(
"/camera/aligned_depth_to_color/image_raw", Image,
self.depth_callback)
self.camera_info_sub = rospy.Subscriber(
"/camera/aligned_depth_to_color/camera_info", CameraInfo,
self.info_callback)
self.depth_image = Image()
self.camera_info = CameraInfo()
self.point = PointStamped()
def write_to_yaml(self, out_filename):
# create CameraInfo message
ci = CameraInfo()
# ci.header = self.header
# ci.height = self.im_size[1]
# ci.width = self.im_size[0]
# ci.distortion_model = self.dist_model
# ci.D[:] = self.D.flatten()
# ci.K[:] = self.K.flatten()
# ci.R[:] = self.R.flatten()
# ci.P[:9] = self.P.flatten()
# write to YAML
done = saveCalibration(new_info=ci,
url=out_filename,
cname=self.camera_name)
if done:
print '{:s} written'.format(out_filename)
else:
print 'WARNING: {:s} camera info not saved'.format(
self.camera_name)
def talker():
cam = CameraInfo()
cam.width = 1
cam.height = 1
cam.header.frame_id = "/camera"
cam.distortion_model = "plumb_bob"
rospy.init_node('publish_caminfo', anonymous=True)
pub = rospy.Publisher('camera_info', CameraInfo)
rate = rospy.Rate(20) # 10hz
while not rospy.is_shutdown():
f = math.sin(rospy.get_time() / (2* math.pi) * 600)
f *=f
cam.K[0] = f
cam.K[4] = f
cam.header.stamp=rospy.Time.now()
pub.publish(cam)
rate.sleep()
def __init__(self):
rospy.init_node('siftpublisher', anonymous=True)
self.bridge = CvBridge()
self.cv_image = None
self.CamInfo = CameraInfo()
self.pose_pub = rospy.Publisher('pose', PoseStamped, queue_size=1, latch=True)
self.pose_status_pub = rospy.Publisher('poseStatus', Bool, queue_size=1, latch=True)
self.gripper_size_pub = rospy.Publisher('GripperPosition', Int64, queue_size=1, latch=True)
self.object_height_pub = rospy.Publisher('ObjectHeight', Float32, queue_size=1, latch=True)
self.image_pub = rospy.Publisher('ImagePosition', Image)
self.rate = rospy.Rate(1)
self.curr_stamp = rospy.Time.now()
self.counter=0
self.x_pos = np.zeros(11,dtype=float)
self.y_pos = np.zeros(11,dtype=float)
self.z_pos = np.zeros(11,dtype=float)
self.x_rot = np.zeros(11,dtype=float)
self.y_rot = np.zeros(11,dtype=float)
self.z_rot = np.zeros(11,dtype=float)
self.id = '2'
self.offset = 0
def __init__(self, addr='localhost', req_port='50020'):
'''
Initialize Pupil_ZMQ_ROS object, init ZMQ and ROS
:param addr: 'localhost' if pupil_capture or pupil_service is
running localy
:param req_port: port number of Pupil_Remote plugin
'''
self.zmq_req = None
self.zmq_sub = None
self.ros_gaze_publisher = None
self.ros_pupil_publisher = None
self.ros_world_img_publisher = None
self.ros_world_calibration_publisher = None
self.cameraInfo = CameraInfo()
self.ros_eye0_img_publisher = None
self.ros_eye1_img_publisher = None
self.cv_bridge = CvBridge()
self.ros_started = True
self.seq = 0
self.init_zmq(addr, req_port)
self.init_ros()
def __init__(self, filename="", camera_info={}):
self.pub = rospy.Publisher('camera_info', CameraInfo, queue_size=1)
self.sub = rospy.Subscriber('image', Image, self.image_cb)
if len(filename) == 0 and len(camera_info) == 0:
rospy.logwarn('Default camera info will be published')
if len(filename) and len(camera_info):
rospy.logwarn('Supplied camera_info takes prioriy over filename')
info = {
'image_height': 0,
'image_width': 0,
'distortion_model': '',
'distortion_coefficients': {
'data': [0, 0, 0, 0, 0]
},
'camera_matrix': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0]
},
'rectification_matrix': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0]
},
'projection_matrix': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
}
if len(filename):
with open(filename, 'r') as stream:
info = yaml.safe_load(stream)
if len(camera_info):
info = camera_info
self.info = CameraInfo(height=info['image_height'],
width=info['image_width'],
distortion_model=info['distortion_model'],
D=info['distortion_coefficients']['data'],
K=info['camera_matrix']['data'],
R=info['rectification_matrix']['data'],
P=info['projection_matrix']['data'])
rospy.loginfo('[{}] Loaded camera_info:\n{}'.format(
rospy.get_name(), self.info))
def __init__(self, camera_name, im_size, frame_id, pos, ori):
self.camera_name = camera_name
self.im_size = im_size
# create Header
self.header = Header()
self.header.frame_id = frame_id
# me being naive
fovy = 45
znear = 0.01
zfar = 50
cx = im_size[0] / 2.
cy = im_size[1] / 2.
fy = (im_size[1] / 2.) / np.tan(fovy*np.pi/180.0)
fx = fy
self.K = np.array([[fx, 0, cx],
[ 0, fy, cy],
[ 0, 0, 1]])
self.R = np.eye(3, dtype=np.float64)
self.P = self.K
self.ci = CameraInfo()
self.ci.header = self.header
self.ci.height = self.im_size[1]
self.ci.width = self.im_size[0]
self.ci.K[:] = self.K.flatten()
self.ci.R[:] = self.R.flatten()
self.ci.P[:9] = self.P.flatten()
self.caminfo_pub = rospy.Publisher(camera_name + "_info",
CameraInfo,
queue_size=5)
self.pos = pos
self.ori = ori
self.tf_br = tf.TransformBroadcaster()
def __init__(self):
self.image_pub = rospy.Publisher(
"/carina/sensor/camera/left/image_detection", Image, queue_size=1)
self.pub_cluster_tfsign = rospy.Publisher("/tfsign_cluster",
PointCloud2,
queue_size=1)
self.image_sub = rospy.Subscriber(
"/carina/sensor/camera/left/image_raw", Image, self.callback)
self.info_sub_l = rospy.Subscriber(
"/carina/sensor/camera/left/camera_info",
CameraInfo,
self.callback_info_left,
queue_size=1)
self.pcl_sub = rospy.Subscriber(
"/carina/sensor/lidar/velodyne_obstacles_full",
PointCloud2,
self.point_cloud_callback,
queue_size=1)
self.bridge = CvBridge()
self.camInfo = CameraInfo()
self.tf_l = tf.TransformListener()
# self.tf_ros = tf.Transformer()
self.sign_l_x = 0.0
self.sign_l_y = 0.0
self.sign_r_x = 0.0
self.sign_r_y = 0.0
self.points = np.zeros((1, 3))
self.model = PinholeCameraModel()
if os.path.isfile("clf/clf_svc_hog.pkl"):
print("[INFO] loading classifier: SVC trained on HoG features...")
self.svc = joblib.load("clf/clf_svc_hog.pkl")
print("[INFO] Classifer is loaded as instance ::svc::")
else:
print("[INFO] pre-trained classifier not found.")
def debagify_msg(self, msg):
"""
The messages read from the rosbags do not have the correct classes so we need to reinitialize themself.
"""
if 'Header' in str(type(msg)):
newMsg = Header()
newMsg.seq = msg.seq
newMsg.stamp.secs = msg.stamp.secs
newMsg.stamp.nsecs = msg.stamp.nsecs
newMsg.frame_id = msg.frame_id
return newMsg
if 'RegionOfInterest' in str(type(msg)):
newMsg = RegionOfInterest()
newMsg.x_offset = msg.x_offset
newMsg.y_offset = msg.y_offset
newMsg.height = msg.height
newMsg.width = msg.width
newMsg.do_rectify = msg.do_rectify
return newMsg
if '_sensor_msgs__CompressedImage' in str(type(msg)):
im = self.bridge.compressed_imgmsg_to_cv2(msg).copy()
newMsg = self.bridge.cv2_to_compressed_imgmsg(im)
newMsg.header = self.debagify_msg(msg.header)
return newMsg
if '_sensor_msgs__CameraInfo' in str(type(msg)):
newMsg = CameraInfo()
newMsg.header = self.debagify_msg(msg.header)
newMsg.height = msg.height
newMsg.width = msg.width
newMsg.distortion_model = msg.distortion_model
newMsg.D = msg.D
newMsg.P = msg.P
newMsg.R = msg.R
newMsg.K = msg.K
newMsg.binning_x = msg.binning_x
newMsg.binning_y = msg.binning_y
newMsg.roi = self.debagify_msg(msg.roi)
return newMsg
raise Exception("Message type %s could not be debagified" %
str(type(msg)))
def cb_camera_info(self, msg):
# unsubscribe from camera_info
self.loginfo('Camera info message received. Unsubscribing from camera_info topic.')
# noinspection PyBroadException
try:
self.sub_camera_info.shutdown()
except BaseException:
pass
# ---
H, W = msg.height, msg.width
# create new camera info
self.camera_model = PinholeCameraModel()
self.camera_model.fromCameraInfo(msg)
# find optimal rectified pinhole camera
with self.profiler('/cb/camera_info/get_optimal_new_camera_matrix'):
rect_camera_K, _ = cv2.getOptimalNewCameraMatrix(
self.camera_model.K,
self.camera_model.D,
(W, H),
self.alpha.value
)
# create rectification map
with self.profiler('/cb/camera_info/init_undistort_rectify_map'):
self.mapx, self.mapy = cv2.initUndistortRectifyMap(
self.camera_model.K,
self.camera_model.D,
None,
rect_camera_K,
(W, H),
cv2.CV_32FC1
)
# pack rectified camera info into a CameraInfo message
self.rect_camera_info = CameraInfo(
width=W,
height=H,
K=rect_camera_K.flatten().tolist(),
R=np.eye(3).flatten().tolist(),
P=np.zeros((3, 4)).flatten().tolist(),
)
def __init__(self):
header = Header(frame_id='upward_looking_camera_link')
self._cam_info = \
CameraInfo(
header=header,
height=480,
width=640,
distortion_model='plumb_bob',
D=[0.0, 0.0, 0.0, 0.0, 0.0],
K=[504.78216005824515, 0.0, 320.5, 0.0, 504.78216005824515, 240.5, 0.0, 0.0, 1.0],
R=[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0],
P=[504.78216005824515, 0.0, 320.5, -0.0, 0.0, 504.78216005824515, 240.5, 0.0, 0.0, 0.0, 1.0, 0.0],
binning_x=0,
binning_y=0,
roi=RegionOfInterest(x_offset=0,
y_offset=0,
height=0,
width=0,
do_rectify=False
)
)
self._pub = rospy.Publisher('/upward_looking_camera/camera_info/fake',CameraInfo,queue_size=10)
def run(self):
# publish image 16 times per second
rate = rospy.Rate(16)
while not rospy.is_shutdown():
observation, reward, done, _ = env.step(self.action)
env.render()
if done:
observation = env.reset()
# publish image to image topic
image_msg = CompressedImage()
image_msg.header.stamp = rospy.Time.now()
image_msg.format = "jpeg"
image = cv2.cvtColor(np.ascontiguousarray(observation), cv2.COLOR_BGR2RGB)
image_msg.data = np.array(cv2.imencode('.jpg', image)[1]).tostring()
self.image_pub.publish(image_msg)
# publish camera info to camera info topic
self.info_pub.publish(CameraInfo())
rate.sleep()
def __init__(self):
self.bbox_tfsign_pub = rospy.Publisher("/carina/sensor/tfsigns_box",
BoundingBoxArray,
queue_size=1)
self.image_sub = rospy.Subscriber(
"/carina/sensor/camera/left/image_raw", Image,
self.callback_image_left)
# self.info_sub_l = rospy.Subscriber("/carina/sensor/camera/left/camera_info", CameraInfo, self.callback_info_left, queue_size=1)
self.bridge = CvBridge()
self.camInfo = CameraInfo()
self.model = PinholeCameraModel()
if os.path.isfile("clf_google/clf_svc_hog.pkl"):
print("[INFO] loading classifier: SVC trained on HoG features...")
self.svc = joblib.load("clf_google/clf_svc_hog.pkl")
print("[INFO] Classifer is loaded as instance ::svc::")
else:
print("[INFO] pre-trained classifier not found.")
def __init__(self):
# OpenCV Bridge
self.bridge = CvBridge()
# Initialize global messages
self.depth = Image()
self.odometry = Odometry()
self.camera_info = CameraInfo()
# ZED Image subscribers for RGB image
sub1 = rospy.Subscriber('/camera/zed_node/rgb/image_rect_color', Image, self.zed_rgb_cb)
#sub2 = rospy.Subscriber('/camera/zed_node/right/image_rect_color', Image, self.zed_right_cb)
sub3 = rospy.Subscriber('/camera/zed_node/odom', Odometry, self.odom_cb)
sub4 = rospy.Subscriber('/camera/zed_node/rgb/camera_info', CameraInfo, self.camera_info_cb)
sub5 = rospy.Subscriber('/camera/zed_node/depth/depth_registered', Image, self.depth_cb)
# Mono Camera Image Subscriber
sub = rospy.Subscriber('/mono_image_color', Image, self.mono_cb)
print('Object Detector listening for images..')
self.segmented_pub = rospy.Publisher('/segmented_img', Image, queue_size=1)
self.segmented_pub_2 = rospy.Publisher('/segmented_img_2', Image, queue_size=1)
self.odom_pub = rospy.Publisher('/odom', Odometry, queue_size=1)
self.camera_info_pub = rospy.Publisher('/camera_info', CameraInfo, queue_size=1)
self.depth_pub = rospy.Publisher('/depth_registered', Image, queue_size=1)
self.count = 0
self.gate_mission = True
self.path_marker_mission = False
self.dummy_segment = False # For testing
# Load configuration file here
# config_string = rospy.get_param("/object_detector_config")
# self.config = yaml.load(config_string)
rospy.init_node('object_detector')
rospy.spin()
def __init__(self):
rospy.init_node('pub_camera_info')
yaml_file = rospy.get_param('~yaml_file')
# Load data from file
with open(yaml_file, "r") as file_handle:
calib_data = yaml.load(file_handle)
self.camera_info_msg = CameraInfo()
self.camera_info_msg.width = calib_data["image_width"]
self.camera_info_msg.height = calib_data["image_height"]
self.camera_info_msg.K = calib_data["camera_matrix"]["data"]
self.camera_info_msg.D = calib_data["distortion_coefficients"]["data"]
self.camera_info_msg.R = calib_data["rectification_matrix"]["data"]
self.camera_info_msg.P = calib_data["projection_matrix"]["data"]
self.camera_info_msg.distortion_model = calib_data["distortion_model"]
self.sub_image = rospy.Subscriber('image_raw', Image, self.recv_image)
self.pub_info = rospy.Publisher('camera_info',
CameraInfo,
latch=True,
queue_size=1)
def __init__(self):
self.detector_id = "gate"
self.numBits = 8
self.imageWidth = 640
self.imageHeight = 480
self.maxVal = 2**self.numBits - 1
self.gate_dimensions = np.array(rospy.get_param("object_tags/gate/dimensions")).astype(float)
self.gate_width = self.gate_dimensions[0]
self.gate_height = self.gate_dimensions[2]
self.left_img_flag = False
self.stereo_left = Image()
self.left_camera_info = CameraInfo()
self.left_stream = rospy.Subscriber("/albatross/stereo_camera_left_front/camera_image", Image, self.left_callback)
self.left_camera_info = rospy.Subscriber("/albatross/stereo_camera_left_front/camera_info", CameraInfo, self.camera_info_callback)
self.prev = [None, None]
self.cv_bridge = CvBridge()
self.gate_detection_pub = rospy.Publisher("gate_detections", Image, queue_size=10)
rospy.wait_for_service("detector_bucket/register_object_detection")
self.registration_service = rospy.ServiceProxy("detector_bucket/register_object_detection", RegisterObjectDetections)
self.spin_callback = rospy.Timer(rospy.Duration(.010), self.spin)
def publish_camera_info(self, height, width):
camera_info_msg = CameraInfo()
camera_info_msg.header.stamp = self.stamp
camera_info_msg.header.frame_id = self.base_frame + "camera"
camera_info_msg.height = height
camera_info_msg.width = width
f_y = self.mat_from_fov_and_resolution(
self.h_fov_to_v_fov(1.39626, height, width),
height)
f_x = self.mat_from_fov_and_resolution(1.39626, width)
camera_info_msg.K = [f_x, 0, width / 2,
0, f_y, height / 2,
0, 0, 1]
camera_info_msg.P = [f_x, 0, width / 2, 0,
0, f_y, height / 2, 0,
0, 0, 1, 0]
self.pub_camera_info.publish(camera_info_msg)
def __init__(self):
"""
Constructor
"""
rospy.init_node("ros_yolo", anonymous=True, disable_signals=False)
self.bridge = CvBridge()
self.rgb_lock = threading.Lock()
self.depth_lock = threading.Lock()
self.cameraModel = PinholeCameraModel()
self.cameraInfo = CameraInfo()
self.sub_color = rospy.Subscriber("/camera/color/image_raw",
Image,
self.color_callback,
queue_size=1)
self.sub_depth = rospy.Subscriber(
"/camera/aligned_depth_to_color/image_raw",
Image,
self.depth_callback,
queue_size=1)
self.sub_camera_info = rospy.Subscriber("/camera/depth/camera_info",
CameraInfo,
self.camera_info_callback,
queue_size=1)
self.target_pub = rospy.Publisher("/hand_detection/target_camera",
PoseStamped)
self.color_img = np.zeros((480, 640, 3), np.uint8)
self.depth_img = np.zeros((480, 640), np.uint8)
self.img_width = 640
self.img_height = 480
self.hand_counter = 0
self.not_hand_counter = 0
self.hand_3D_pos = np.empty((0, 3), int)
def __init__(self):
#Get our publisher ready
# self.image_pub = rospy.Publisher("image_topic",Image,queue_size = 10)
#Get CvBridge object ready. This will do the conversion cv2->ROS
self.imu = self.ImuState()
self.image_sub = rospy.Subscriber("/camera1/image_rect_color", Image,
self.recv)
# self.landing_sub = rospy.Subscriber("/landing_pad/image_rect",Image,self.find_tag)
self.camera_info_sub = rospy.Subscriber("/camera1/camera_info",
CameraInfo, self.get_cam_info)
self.imu_sub = rospy.Subscriber("/mavros/imu/data", Imu,
self.update_orient)
self.track_pub = rospy.Publisher("/tracker/tracked",
Bool,
queue_size=20)
self.tag_track_sub = rospy.Subscriber("/detections/tracked", Bool,
self.tag_cb)
self.heading_pub = rospy.Publisher("tracker/heading",
TwistStamped,
queue_size=10)
self.state_pub = rospy.Publisher("/lezl/state",
TwistStamped,
queue_size=10)
self.landing_pub = rospy.Publisher("/camera1/target_rect",
Image,
queue_size=20)
self.camera_info_pub = rospy.Publisher("/landing_pad/camera_info",
CameraInfo,
queue_size=20)
self.listener = tf.TransformListener()
# self.image_sub = rospy.Subscriber("/tracker/image",Image,self.recv)
self.bridge = CvBridge()
self.tracked = Bool()
self.tag_tracked = Bool()
self.tracked.data = False
self.tag_tracked.data = False
self.heading = TwistStamped()
self.cam_info = CameraInfo()