def chessboard_size(self, lcorners, rcorners, board):
"""
Compute the square edge length from two sets of matching undistorted points
given the current calibration.
"""
# Project the points to 3d
cam = image_geometry.StereoCameraModel()
cam.fromCameraInfo(*self.as_message())
disparities = [(x0 - x1)
for ((x0, y0), (x1, y1)) in zip(lcorners, rcorners)]
pt3d = [
cam.projectPixelTo3d((x, y), d)
for ((x, y), d) in zip(lcorners, disparities)
]
def l2(p0, p1):
return math.sqrt(sum([(c0 - c1)**2 for (c0, c1) in zip(p0, p1)]))
# Compute the length from each horizontal and vertical line, and return the mean
cc = board.n_cols
cr = board.n_rows
lengths = ([
l2(pt3d[cc * r + 0], pt3d[cc * r + (cc - 1)]) / (cc - 1)
for r in range(cr)
] + [
l2(pt3d[c + 0], pt3d[c + (cc * (cr - 1))]) / (cr - 1)
for c in range(cc)
])
return sum(lengths) / len(lengths)
def handle_stereo(self, msg):
(lmsg, lcmsg, rmsg, rcmsg) = msg
lgray = self.mkgray(lmsg)
rgray = self.mkgray(rmsg)
sc = StereoCalibrator([self.board])
L = self.image_corners(lgray)
R = self.image_corners(rgray)
scm = image_geometry.StereoCameraModel()
scm.fromCameraInfo(lcmsg, rcmsg)
if L and R:
d = [(y0 - y1) for ((_, y0), (_, y1)) in zip(L, R)]
epipolar = math.sqrt(sum([i**2 for i in d]) / len(d))
disparities = [(x0 - x1) for ((x0, y0), (x1, y1)) in zip(L, R)]
pt3d = [scm.projectPixelTo3d((x, y), d) for ((x, y), d) in zip(L, disparities)]
def l2(p0, p1):
return math.sqrt(sum([(c0 - c1) ** 2 for (c0, c1) in zip(p0, p1)]))
# Compute the length from each horizontal and vertical lines, and return the mean
cc = self.board.n_cols
cr = self.board.n_rows
lengths = (
[l2(pt3d[cc * r + 0], pt3d[cc * r + (cc - 1)]) / (cc - 1) for r in range(cr)] +
[l2(pt3d[c + 0], pt3d[c + (cc * (cr - 1))]) / (cr - 1) for c in range(cc)])
dimension = sum(lengths) / len(lengths)
print "epipolar error: %f pixels dimension: %f m" % (epipolar, dimension)
else:
print "no chessboard"
def convert_stereo(self, cp1, cp2):
if cp1.camera_info.P[3] == 0:
left = cp1
right = cp2
else:
left = cp2
right = cp1
u = left.x
v = left.y
disparity = left.x - right.x
stereo_model = image_geometry.StereoCameraModel()
stereo_model.fromCameraInfo(left.camera_info, right.camera_info)
(x, y, z) = stereo_model.projectPixelTo3d((u, v), disparity)
camera_pt = PointStamped()
camera_pt.header.frame_id = left.camera_info.header.frame_id
camera_pt.header.stamp = rospy.Time.now()
camera_pt.point.x = x
camera_pt.point.y = y
camera_pt.point.z = z
self.listener.waitForTransform(self.output_frame,
camera_pt.header.frame_id,
rospy.Time.now(), rospy.Duration(4.0))
output_point = self.listener.transformPoint(self.output_frame,
camera_pt)
return output_point
def chessboard_size(self, lcorners, rcorners, board, msg=None):
"""
Compute the square edge length from two sets of matching undistorted points
given the current calibration.
:param msg: a tuple of (left_msg, right_msg)
"""
# Project the points to 3d
cam = image_geometry.StereoCameraModel()
if msg == None:
msg = self.as_message()
cam.fromCameraInfo(*msg)
disparities = lcorners[:, :, 0] - rcorners[:, :, 0]
pt3d = [
cam.projectPixelTo3d((lcorners[i, 0, 0], lcorners[i, 0, 1]),
disparities[i, 0])
for i in range(lcorners.shape[0])
] ##3dpoints? TODO:
def l2(p0, p1):
return math.sqrt(sum([(c0 - c1)**2 for (c0, c1) in zip(p0, p1)]))
# Compute the length from each horizontal and vertical line, and return the mean
cc = board.n_cols
cr = board.n_rows
lengths = ([
l2(pt3d[cc * r + 0], pt3d[cc * r + (cc - 1)]) / (cc - 1)
for r in range(cr)
] + [
l2(pt3d[c + 0], pt3d[c + (cc * (cr - 1))]) / (cr - 1)
for c in range(cc)
])
return sum(lengths) / len(lengths)
def convertStereo(self, u, v, disparity):
"""
Converts two pixel coordinates u and v along with the disparity to give PointStamped
"""
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(self.info['left'], self.info['right'])
(x, y, z) = stereoModel.projectPixelTo3d((u, v), disparity)
return np.array((x, y, z))
def projectToPixel(pt, info=None):
x, y, z = pt
if info is None:
with open("camera_data/camera_info.p", "rb") as f:
info = pickle.load(f)
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(info['l'], info['r'])
left, right = stereoModel.project3dToPixel((x, y, z))
return (left, right)
def handle_stereo(self, qq):
(lmsg, lcmsg, rmsg, rcmsg) = qq
limg = CvBridge().imgmsg_to_cv(lmsg, "mono8")
rimg = CvBridge().imgmsg_to_cv(rmsg, "mono8")
if 0:
cv.SaveImage("/tmp/l%06d.png" % self.i, limg)
cv.SaveImage("/tmp/r%06d.png" % self.i, rimg)
self.i += 1
scm = image_geometry.StereoCameraModel()
scm.fromCameraInfo(lcmsg, rcmsg)
bm = cv.CreateStereoBMState()
if "wide" in rospy.resolve_name("stereo"):
bm.numberOfDisparities = 160
if 0:
disparity = cv.CreateMat(limg.rows, limg.cols, cv.CV_16SC1)
started = time.time()
cv.FindStereoCorrespondenceBM(limg, rimg, disparity, bm)
print time.time() - started
ok = cv.CreateMat(limg.rows, limg.cols, cv.CV_8UC1)
cv.CmpS(disparity, 0, ok, cv.CV_CMP_GT)
cv.ShowImage("limg", limg)
cv.ShowImage("disp", ok)
cv.WaitKey(6)
self.track(CvBridge().imgmsg_to_cv(lmsg, "rgb8"))
if len(self.tracking) == 0:
print "No markers found"
for code, corners in self.tracking.items():
corners3d = []
for (x, y) in corners:
limr = cv.GetSubRect(limg, (0, y - bm.SADWindowSize / 2, limg.cols, bm.SADWindowSize + 1))
rimr = cv.GetSubRect(rimg, (0, y - bm.SADWindowSize / 2, rimg.cols, bm.SADWindowSize + 1))
tiny_disparity = cv.CreateMat(limr.rows, limg.cols, cv.CV_16SC1)
cv.FindStereoCorrespondenceBM(limr, rimr, tiny_disparity, bm)
if tiny_disparity[7, x] < 0:
return
corners3d.append(scm.projectPixelTo3d((x, y), tiny_disparity[7, x] / 16.))
if 0:
cv.ShowImage("d", disparity)
(a, b, c, d) = [numpy.array(pt) for pt in corners3d]
def normal(s, t):
return (t - s) / numpy.linalg.norm(t - s)
x = PyKDL.Vector(*normal(a, b))
y = PyKDL.Vector(*normal(a, d))
f = PyKDL.Frame(PyKDL.Rotation(x, y, x * y), PyKDL.Vector(*a))
msg = pm.toMsg(f)
# print "%10f %10f %10f" % (msg.position.x, msg.position.y, msg.position.z)
print code, msg.position.x, msg.position.y, msg.position.z
self.broadcast(lmsg.header, code, msg)
def convertStereo(u, v, disparity, info):
"""
Converts two pixel coordinates u and v along with the disparity to give PointStamped
"""
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(info['l'], info['r'])
(x, y, z) = stereoModel.projectPixelTo3d((u, v), disparity)
cameraPoint = PointStamped()
cameraPoint.header.frame_id = info['l'].header.frame_id
cameraPoint.header.stamp = time.time()
cameraPoint.point = Point(x, y, z)
return cameraPoint
def __init__(self, data_path="~/data/"):
'''
self.cam_info = rospy.wait_for_message("/kinect2/sd/camera_info", CameraInfo, timeout=None)
self.cam_model = image_geometry.PinholeCameraModel()
self.cam_model.fromCameraInfo(self.cam_info)
'''
self.data_path = data_path
self.rgb_cam_info = rospy.wait_for_message("/rgb_to_depth/camera_info",
CameraInfo,
timeout=None)
self.depth_cam_info = rospy.wait_for_message("/depth/camera_info",
CameraInfo,
timeout=None)
self.cam_model = image_geometry.StereoCameraModel()
self.cam_model.fromCameraInfo(self.depth_cam_info, self.rgb_cam_info)
def convertStereo(u, v, disparity, info=None):
"""
Converts two pixel coordinates u and v along with the disparity to give PointStamped
"""
stereoModel = image_geometry.StereoCameraModel()
if info is None:
with open("camera_data/camera_info.p", "rb") as f:
info = pickle.load(f)
stereoModel.fromCameraInfo(info['l'], info['r'])
(x, y, z) = stereoModel.projectPixelTo3d((u, v), disparity)
cameraPoint = PointStamped()
cameraPoint.header.frame_id = info['l'].header.frame_id
cameraPoint.header.stamp = rospy.Time.now()
cameraPoint.point = Point(x, y, z)
return cameraPoint
def imageCallback(self, data):
if self.camera is None and self.leftInfo is not None and self.rightInfo is not None:
self.camera = image_geometry.StereoCameraModel()
self.camera.fromCameraInfo(self.leftInfo, self.rightInfo)
elif self.camera is None:
return
# print 'Time between image calls:', time.time() - self.imageTime
# startTime = time.time()
try:
image = self.bridge.imgmsg_to_cv(data.image)
image = np.asarray(image[:,:])
except CvBridgeError, e:
print e
return
def __init__(self):
self.image_pub = rospy.Publisher("/tb3_0/camera/rgb/image_opencv",
Image,
queue_size=10)
self.evader_position_pub = rospy.Publisher("/evader_position",
PoseStamped,
queue_size=10)
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("/tb3_0/rrbot/camera1/image_raw",
Image, self.callback)
self.depth_sub = rospy.Subscriber("/kinect_camera_bot/depth/image_raw",
Image, self.depth_callback)
self.hog = cv2.HOGDescriptor()
self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
self.camera_model = img_geo.StereoCameraModel()
IPython.embed()
def get_points_3d(left_points, right_points, info):
"""
Assumes that corresponding (2-D pixel) points are ordered correctly
in `left_points` and `right_points`. Returns a numpy array of 3D camera points.
https://github.com/BerkeleyAutomation/endoscope_calibration/blob/master/rigid_transformation.py
"""
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(info['l'], info['r'])
points_3d = []
for i in range(len(left_points)):
a = left_points[i]
b = right_points[i]
disparity = np.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
assert disparity == np.linalg.norm(np.array(a) - np.array(b))
(x, y, z) = stereoModel.projectPixelTo3d((a[0], a[1]), disparity)
points_3d.append([x, y, z])
return np.array(points_3d)
def left_pixel_to_robot_prediction(left, params, true_points):
""" Similar to the method in `click_and_crop.py` except that we have a full list of
points from the left camera.
"""
pred_points = []
for left_pt in left:
leftx, lefty = left_pt
left_pt_hom = np.array([leftx, lefty, 1.])
right_pt = left_pt_hom.dot(params['theta_l2r'])
# Copy the code I wrote to convert these pts to camera points.
disparity = np.linalg.norm(left_pt - right_pt)
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(C_LEFT_INFO, C_RIGHT_INFO)
(xx, yy, zz) = stereoModel.projectPixelTo3d((leftx, lefty), disparity)
camera_pt = np.array([xx, yy, zz])
# Now I can apply the rigid body and RF (if desired).
camera_pt = np.concatenate((camera_pt, np.ones(1)))
robot_pt = (params['RB_matrix']).dot(camera_pt)
target = [robot_pt[0], robot_pt[1], robot_pt[2]]
pred_points.append(target)
pred_points = np.array(pred_points)
print(
"\nAssuming we ONLY have the left pixels, we still predict the robot points."
)
print("pred points:\n{}".format(pred_points))
if true_points is not None:
abs_errors = np.abs(pred_points - true_points)
abs_mean_errors = np.mean(abs_errors, axis=0)
print("true points:\n{}".format(true_points))
print("mean abs err: {}".format(abs_mean_errors))
print("mean err: {}".format(np.mean(pred_points - true_points,
axis=0)))
print("Done w/debugging.\n")
def execute(self, goal):
self.targetType = goal.objectType
self.running = True
self.ok = True
if self.leftMsg is not None and self.rightMsg is not None:
self.stereoModel = image_geometry.StereoCameraModel()
self.stereoModel.fromCameraInfo(self.leftMsg, self.rightMsg)
feedback = TrackObjectFeedback()
while running:
if self.server.is_preempt_requested() or self.server.is_new_goal_available():
self.running = False
continue
if self.targetType == TrackObjectGoal.navbar:
#Process navbar stuff
self.feedback = self.navBarFinder.process(self.downImage, self.downModel)
self.server.publish_feedback(self.feedback)
elif self.targetType == TrackObjectGoal.startGate:
self.feedback = self.gatefinder.process(self.leftImage, self.rightImage, self.disparityImage, self.leftModel, self.stereoModel)
self.server.publish_feedback(self.feedback)
#TODO: Fix color thresholds
elif self.targetType == TrackObjectGoal.redBouy:
#process red bouy
self.feedback = self.bouyFinder.process(self.leftImage, self.disparityImage, self.leftModel, self.stereoModel, self.thresholds['red'])
self.server.publish_feedback(self.feedback)
elif self.targetType == TrackObjectGoal.yellowBouy:
#process yellow bouy
self.feedback = self.bouyFinder.process(self.leftImage, self.disparityImage, self.leftModel, self.stereoModel, self.thresholds['yellow'])
self.server.publish_feedback(self.feedback)
elif self.targetType == TrackObjectGoal.greenBouy:
#process green bouy
self.feedback = self.bouyFinder.process(self.leftImage, self.disparityImage, self.leftModel, self.stereoModel, self.thresholds['green'])
self.server.publish_feedback(self.feedback)
self.response.stoppedOk = self.ok
self.server.set_succeeded(self.response)
def determineROI(self, side=None):
"""
Determines the boundaries of a region of interest, based on
the pixel coordinates of the gripper as given by the inverse kinematics of the robot
"""
ROI_width = self.gripper_ROI_size.width
ROI_height = self.gripper_ROI_size.height
pose, timeSinceDetection = self.getEstimatedPose()
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(self.info['left'], self.info['right'])
(u_l, v_l), (u_r,
v_r) = stereoModel.project3dToPixel(pose.position.list)
ROI_left = ((int(u_l - ROI_width), int(v_l - ROI_height)),
(int(u_l + ROI_width), int(v_l + ROI_height)))
ROI_right = ((int(u_r - ROI_width), int(v_r - ROI_height)),
(int(u_r + ROI_width), int(v_r + ROI_height)))
ROI = {'left': ROI_left, 'right': ROI_right}
uv = {'left': (u_l, v_l), 'right': (u_r, v_r)}
if side is None:
return ROI, uv
else:
return ROI[side], uv[side]
topics=['/basler_stereo/right/image_rect_color/compressed']):
if msg.header.stamp == img_msg[0].header.stamp:
img_msg[1] = msg
break
# Read all PSM1 pose messages (instrument TCP wrt. base frame)
psm1_msgs = [
msg for topic, msg, stamp in bag.read_messages(
topics=['/dvrk/PSM1/position_cartesian_current'])
]
# Find PSM1 pose message corresponding (nearest time stamp) to the camera frames
psm1_msg = find_nearest_by_stamp(psm1_msgs, img_msg[0].header.stamp)[1]
# Set up stereo camera model from the image_geometry distributed with ROS
stereo_model = image_geometry.StereoCameraModel()
stereo_model.fromCameraInfo(*cam_info)
# Get robot base to optical (left camera of stereo pair) transformation (the
# 'optical' frame seen wrt. the PSM1 robot 'base' frame)
t_base_optical = msg2tf(
tf_buffer.lookup_transform_core('PSM1_base', 'stereo_optical',
rospy.Time()).transform)
t_optical_base = np.linalg.inv(t_base_optical)
# t_base_tcp = np.array([msg2tf(m.pose) for m in psm1_msgs])
# t_optical_tcp = np.array([t_optical_base.dot(t) for t in t_base_tcp])
t_base_tcp = msg2tf(psm1_msg.pose)
t_optical_tcp = t_optical_base.dot(t_base_tcp)
# de-compress images
# -
tf_listener = tf.TransformListener()
tf_listener.getFrameStrings()
import time
time.sleep(5)
ecm.move_joint(HARDCODED_ECM_POS)
# +
pick_and_place_utils = None
from pick_and_place_utils import get_feat_position_and_img, tf_to_pykdl_frame, PSM_J1_TO_BASE_LINK_TF
import image_geometry
stereo_cam = image_geometry.StereoCameraModel()
stereo_cam.fromCameraInfo(left_camera_info, right_camera_info)
balls, left_frame = get_feat_position_and_img(left_image_msg, right_image_msg, stereo_cam)
ball_pos_cam = balls[0]
print(ball_pos_cam)
plt.imshow(left_frame)
# depth error experiments
# (-0.02772727272727273, 0.009545454545454546, 0.14180015986663413) {-0.02987164259, 0.01018744707, 0.1481492519}
# (-0.028636363636363637, 0.01, 0.14180015986663413) {-0.02869459987, 0.009940028191, 0.1379978657}
# (-0.013333333333333334, 0.010416666666666666, 0.12998347987774794) {-0.01380112767, 0.009659290314, 0.1300171614}
# (0.04038461538461539, 0.008076923076923077, 0.11998475065638273) {-0.009348809719, 0.01096570492, 0.1307697296}
# -
ball_pos_cam = PyKDL.Vector(*ball_pos_cam)
tf_cam_to_jp21 = tf_to_pykdl_frame(tf_listener.lookupTransform('ecm_pitch_link_1', 'camera', rospy.Time()))
ball_pos_jp21 = tf_cam_to_jp21 * ball_pos_cam
def threshRed(self):
THRESH_RED_WIDTH_ROI = 200
THRESH_RED_HEIGHT_ROI = 90
if not self.img.has_key('left') or not self.img.has_key('right'):
return
leftImg = cv.CloneMat(self.img['left'])
rightImg = cv.CloneMat(self.img['right'])
width = cv.GetSize(leftImg)[0]
height = cv.GetSize(leftImg)[1]
estimated_gripper_pose, time_since_detection = self.getEstimatedPose()
gripper_pos = tfx.pose(estimated_gripper_pose).position
tf_tool_to_cam = tfx.lookupTransform('/left_BC',
gripper_pos.frame,
wait=10)
gripper_pos = tf_tool_to_cam * gripper_pos
stereoModel = image_geometry.StereoCameraModel()
stereoModel.fromCameraInfo(self.info['left'], self.info['right'])
(u_l, v_l), (u_r, v_r) = stereoModel.project3dToPixel(gripper_pos.list)
def expandROIRegion(start_ROI,
elapsed_time,
max_elapsed_time=20.,
growth_factor=2.):
elapsed_time = min(elapsed_time, max_elapsed_time)
return int(start_ROI + start_ROI *
(elapsed_time / max_elapsed_time) * growth_factor)
#THRESH_RED_WIDTH_ROI = expandROIRegion(THRESH_RED_WIDTH_ROI, time_since_detection)
#THRESH_RED_HEIGHT_ROI = expandROIRegion(THRESH_RED_HEIGHT_ROI, time_since_detection)
leftImg_lb_width = int(
u_l -
THRESH_RED_WIDTH_ROI) if int(u_l - THRESH_RED_WIDTH_ROI) > 0 else 0
leftImg_ub_width = int(u_l + THRESH_RED_WIDTH_ROI) if int(
u_l + THRESH_RED_WIDTH_ROI) < width else width - 1
leftImg_lb_height = int(v_l - THRESH_RED_HEIGHT_ROI) if int(
v_l - THRESH_RED_HEIGHT_ROI) > 0 else 0
leftImg_ub_height = int(v_l + THRESH_RED_HEIGHT_ROI) if int(
v_l + THRESH_RED_HEIGHT_ROI) < height else height - 1
rightImg_lb_width = int(
u_r -
THRESH_RED_WIDTH_ROI) if int(u_r - THRESH_RED_WIDTH_ROI) > 0 else 0
rightImg_ub_width = int(u_r + THRESH_RED_WIDTH_ROI) if int(
u_r + THRESH_RED_WIDTH_ROI) < width else width - 1
rightImg_lb_height = int(v_r - THRESH_RED_HEIGHT_ROI) if int(
v_r - THRESH_RED_HEIGHT_ROI) > 0 else 0
rightImg_ub_height = int(v_r + THRESH_RED_HEIGHT_ROI) if int(
v_r + THRESH_RED_HEIGHT_ROI) < height else height - 1
if self.print_messages:
print('(height, width) = ({0}, {1})'.format(height, width))
print('time_since_detection: {0}'.format(time_since_detection))
print('leftImg[{0}:{1},{2}:{3}]'.format(leftImg_lb_height,
leftImg_ub_height,
leftImg_lb_width,
leftImg_ub_width))
print('rightImg[{0}:{1},{2}:{3}]'.format(rightImg_lb_height,
rightImg_ub_height,
rightImg_lb_width,
rightImg_ub_width))
if leftImg_lb_width >= leftImg_ub_width or leftImg_lb_height >= leftImg_ub_height or rightImg_lb_width >= rightImg_ub_width or rightImg_lb_height >= rightImg_ub_height:
return
leftImg = leftImg[leftImg_lb_height:leftImg_ub_height,
leftImg_lb_width:leftImg_ub_width]
rightImg = rightImg[rightImg_lb_height:rightImg_ub_height,
rightImg_lb_width:rightImg_ub_width]
if self.show_thresh_red_images:
self.red_roi_pub.publish(self.bridge.cv_to_imgmsg(leftImg))
leftThresh = cv.CreateImage(cv.GetSize(leftImg), 8, 1)
rightThresh = cv.CreateImage(cv.GetSize(rightImg), 8, 1)
leftThresh = threshold(leftImg, leftImg, leftThresh, RED_LOWER,
RED_UPPER)
rightThresh = threshold(rightImg, rightImg, rightThresh, RED_LOWER,
RED_UPPER)
if self.show_thresh_red_images:
self.red_thresh_roi_pub.publish(
self.bridge.cv_to_imgmsg(leftThresh))
leftContours = find_contours(leftThresh, "leftThresh")
rightContours = find_contours(rightThresh, "rightThresh")
foundRed = True
if len(leftContours) > 0 or len(rightContours) > 0:
self.lastFoundRed = rospy.Time.now()
foundRed = True
else:
if self.lastFoundRed is not None and rospy.Time.now(
) - self.lastFoundRed < self.redHistoryDuration:
foundRed = True
else:
foundRed = False
toolPose = tfx.pose([0, 0, 0],
frame=self.tool_frame,
stamp=rospy.Time.now()).msg.PoseStamped()
if foundRed:
self.red_thresh_marker_pub.publish(
createMarker(toolPose, color="red"))
else:
self.red_thresh_marker_pub.publish(
createMarker(toolPose, color="blue"))
self.red_thresh_pub.publish(Bool(foundRed))
import os
import pickle
import sys
import tfx
import time
np.set_printoptions(suppress=True)
# ------------
# dVRK-related
# ------------
ESC_KEYS = [27, 1048603]
camerainfo_p = '/home/davinci0/danielseita/suturing/config/camera_info_matrices/'
C_LEFT_INFO = pickle.load(open(camerainfo_p+'left.p', 'r'))
C_RIGHT_INFO = pickle.load(open(camerainfo_p+'right.p', 'r'))
STEREO_MODEL = image_geometry.StereoCameraModel()
STEREO_MODEL.fromCameraInfo(C_LEFT_INFO, C_RIGHT_INFO)
def camera_pixels_to_camera_coords(left_pt, right_pt, nparrays=False):
""" Given [lx,ly] and [rx,ry], determine [cx,cy,cz]. Everything should be LISTS. """
assert len(left_pt) == len(right_pt) == 2
disparity = np.linalg.norm( np.array(left_pt) - np.array(right_pt) )
(xx,yy,zz) = STEREO_MODEL.projectPixelTo3d( (left_pt[0],left_pt[1]), disparity )
if nparrays:
return np.array([xx,yy,zz])
else:
return [xx,yy,zz]
def init(sleep_time=0):