def test_parameterSet(self):
cam = ct.Camera(ct.RectilinearProjection(), ct.SpatialOrientation())
cam.defaults.elevation_m = 99
assert cam.elevation_m == 99
assert cam.defaults.elevation_m == 99
cam.elevation_m = 10
assert cam.elevation_m == 10
assert cam.defaults.elevation_m == 99
cam.elevation_m = None
assert cam.elevation_m == 99
assert cam.defaults.elevation_m == 99
# test if the parameter sets are liked properly
cam.orientation.elevation_m = 900
assert cam.elevation_m == 900
cam.elevation_m = 800
assert cam.orientation.elevation_m == 800
# test non parameter
cam.foo = 99
assert cam.foo == 99
cam.defaults.foo = 99
assert cam.defaults.foo == 99
self.assertRaises(AttributeError, lambda: cam.bla)
self.assertRaises(AttributeError, lambda: cam.parameters.bla)
self.assertRaises(AttributeError, lambda: cam.projection.bla)
self.assertRaises(AttributeError, lambda: cam.orientation.bla)
self.assertRaises(AttributeError, lambda: cam.defaults.bla)
def startDemonstratorGUI():
cam = ct.Camera(ct.RectilinearProjection(focallength_px=3860, image=[4608, 2592]))
app = QtWidgets.QApplication(sys.argv)
window = Window(cam, Scene9Cubes(cam))
window.show()
app.exec_()
def get_camera_params(project_path, location, display=False):
"""Compute camera parameters using yolo inputs and save it"""
# Paths
yolo_folder = project_path + 'models/yolo_coco/'
image_folder = project_path + 'video_scraping/' + location + '/'
# Get one frame for initialization
frame = plt.imread(image_folder + sorted(os.listdir(image_folder))[-1])
# Intrinsic camera parameters
focal = 6.2 # in mm
sensor_size = (6.17, 4.55) # in mm
frame_size = (frame.shape[1], frame.shape[0]) # (width, height) in pixel
# Initialize the camera
camera = ct.Camera(
ct.RectilinearProjection(focallength_mm=focal,
sensor=sensor_size,
image=frame_size))
# Calibrate using people detection
feet, heads = yolo_collection(yolo_folder, image_folder)
camera.addObjectHeightInformation(
feet, heads, 1.7,
0.3) # Person average height in meter + std information
# Fitting camera parameters
trace = camera.metropolis(
[
# ct.FitParameter("heading_deg", lower=-180, upper=180, value=0),
# ct.FitParameter("roll_deg", lower=-180, upper=180, value=0),
ct.FitParameter("elevation_m", lower=0, upper=100, value=5),
ct.FitParameter("tilt_deg", lower=0, upper=180, value=45)
],
iterations=1e4)
# Save camera parameters
camera.save(project_path + 'camera_params/' + location +
'_camera_params.json')
if display:
# Display calibration information
camera.plotTrace()
plt.tight_layout()
plt.show()
plt.figure('Calibration information', figsize=(15, 10))
plt.subplot(1, 2, 1)
camera.plotFitInformation(frame)
plt.legend()
plt.subplot(1, 2, 2)
camera.getTopViewOfImage(frame, [-10, 10, 0, 20], do_plot=True)
plt.xlabel("x position in m")
plt.ylabel("y position in m")
plt.show()
def test_init_cam(self):
# intrinsic camera parameters
f = 6.2
sensor_size = (6.17, 4.55)
image_size = (3264, 2448)
# initialize the camera
cam = ct.Camera(
ct.RectilinearProjection(focallength_mm=f,
image_width_px=image_size[1],
image_height_px=image_size[0],
sensor_width_mm=sensor_size[1],
sensor_height_mm=sensor_size[0]),
ct.SpatialOrientation())
def test_fitCamParametersFromObjects(self):
# setup the camera
im = np.ones((2592, 4608, 3))
camera = ct.Camera(
ct.RectilinearProjection(focallength_px=3863.64, image=im))
feet = np.array([[1968.73191418, 2291.89125757],
[1650.27266115, 2189.75370951],
[1234.42623164, 2300.56639535],
[927.4853119, 2098.87724083],
[3200.40162013, 1846.79042709],
[3385.32781138, 1690.86859965],
[2366.55011031, 1446.05084045],
[1785.68269333, 1399.83787022],
[889.30386193, 1508.92532749],
[4107.26569943, 2268.17045783],
[4271.86353701, 1889.93651518],
[4007.93773879, 1615.08452509],
[2755.63028039, 1976.00345458],
[3356.54352228, 2220.47263494],
[407.60113016, 1933.74694958],
[1192.78987735, 1811.07247163],
[1622.31086201, 1707.77946355],
[2416.53943619, 1775.68148688],
[2056.81514201, 1946.4146027],
[2945.35225814, 1617.28314118],
[1018.41322935, 1499.63957113],
[1224.2470045, 1509.87120351],
[1591.81599888, 1532.33339856],
[1701.6226147, 1481.58276189],
[1954.61833888, 1405.49985098],
[2112.99329583, 1485.54970652],
[2523.54106057, 1534.87590467],
[2911.95610793, 1448.87104305],
[3330.54617013, 1551.64321531],
[2418.21276457, 1541.28499777],
[1771.1651859, 1792.70568482],
[1859.30409241, 1904.01744759],
[2805.02878512, 1881.00463747],
[3138.67003071, 1821.05082989],
[3355.45215983, 1910.47345815],
[734.28038607, 1815.69614796],
[978.36733356, 1896.36507827],
[1350.63202232, 1979.38798787],
[3650.89052382, 1901.06620751],
[3555.47087822, 2332.50027861],
[865.71688784, 1662.27834394],
[1115.89438493, 1664.09341647],
[1558.93825646, 1671.02167477],
[1783.86089289, 1679.33599881],
[2491.01579305, 1707.84219953],
[3531.26955813, 1729.08486338],
[3539.6318973, 1776.5766387],
[4150.36451427, 1840.90968707],
[2112.48684812, 1714.78834459],
[2234.65444134, 1756.17059266]])
heads = np.array([[1968.45971142, 2238.81171866],
[1650.27266115, 2142.33767714],
[1233.79698528, 2244.77321846],
[927.4853119, 2052.2539967],
[3199.94718145, 1803.46727222],
[3385.32781138, 1662.23146061],
[2366.63609066, 1423.52398752],
[1785.68269333, 1380.17615549],
[889.30386193, 1484.13026407],
[4107.73533808, 2212.98791584],
[4271.86353701, 1852.85753597],
[4007.93773879, 1586.36656606],
[2755.89171994, 1938.22544024],
[3355.91105749, 2162.91833832],
[407.60113016, 1893.63300333],
[1191.97371829, 1777.60995028],
[1622.11915337, 1678.63975025],
[2416.31761434, 1743.29549618],
[2056.67597009, 1910.09072955],
[2945.35225814, 1587.22557592],
[1018.69818061, 1476.70099517],
[1224.55272475, 1490.30510731],
[1591.81599888, 1510.72308329],
[1701.45016126, 1460.88834824],
[1954.734384, 1385.54008964],
[2113.14023137, 1465.41953732],
[2523.54106057, 1512.33125811],
[2912.08384338, 1428.56110628],
[3330.40769371, 1527.40984208],
[2418.21276457, 1517.88006678],
[1770.94524662, 1761.25436746],
[1859.30409241, 1867.88794433],
[2804.69006305, 1845.10009734],
[3138.33130864, 1788.53351052],
[3355.45215983, 1873.21402971],
[734.49504829, 1780.27688131],
[978.1022294, 1853.9484135],
[1350.32991656, 1938.60371039],
[3650.89052382, 1863.97713098],
[3556.44897343, 2278.37901052],
[865.41437575, 1633.53969555],
[1115.59187284, 1640.49747358],
[1558.06918395, 1647.12218082],
[1783.86089289, 1652.74740383],
[2491.20950909, 1677.42878081],
[3531.11177814, 1696.89774656],
[3539.47411732, 1745.49398176],
[4150.01023142, 1803.35570469],
[2112.84669376, 1684.92115685],
[2234.65444134, 1724.86402238]])
camera.addObjectHeightInformation(feet, heads, 0.75, 0.03)
camera.addObjectHeightInformation(feet[0], heads[0], 0.75, 0.03)
camera.addObjectHeightInformation(feet[:2], heads[:2], [0.75, 0.7],
0.03)
horizon = np.array([[418.2195998, 880.253216],
[3062.54424509, 820.94125636]])
camera.addHorizonInformation(horizon, uncertainty=10)
camera.addHorizonInformation(horizon[0], uncertainty=10)
camera.setGPSpos("66°39'53.4\"S 140°00'34.8\"")
gps = ct.gpsFromString("66°39'53.4\"S 140°00'34.8\"")
camera.setGPSpos(gps)
camera.setGPSpos(gps[0], gps[1], 0)
lm_points_px = np.array([[2091.300935, 892.072126],
[2935.904577, 824.364956]])
lm_points_gps = ct.gpsFromString([
("66°39'56.12862''S 140°01'20.39562''", 13.769),
("66°39'58.73922''S 140°01'09.55709''", 21.143)
])
lm_points_space = camera.spaceFromGPS(lm_points_gps)
camera.addLandmarkInformation(lm_points_px, lm_points_space, [3, 3, 5])
camera.addLandmarkInformation(lm_points_px[0], lm_points_space[0],
[3, 3, 5])
trace = camera.metropolis([
ct.FitParameter("elevation_m", lower=0, upper=100, value=20),
ct.FitParameter("tilt_deg", lower=0, upper=180, value=80),
ct.FitParameter("heading_deg", lower=-180, upper=180, value=-77),
ct.FitParameter("roll_deg", lower=-180, upper=180, value=0)
],
iterations=1e3)
print(trace)
camera.fit([
ct.FitParameter("elevation_m", lower=0, upper=100, value=20),
ct.FitParameter("tilt_deg", lower=0, upper=180, value=80),
ct.FitParameter("heading_deg", lower=-180, upper=180, value=-77),
ct.FitParameter("roll_deg", lower=-180, upper=180, value=0)
],
iterations=1e3)
"""
camera.fridge([
ct.FitParameter("elevation_m", lower=0, upper=100, value=20),
ct.FitParameter("tilt_deg", lower=0, upper=180, value=80),
ct.FitParameter("heading_deg", lower=-180, upper=180, value=-77),
ct.FitParameter("roll_deg", lower=-180, upper=180, value=0)
], iterations=1e2)
"""
camera.plotTrace()
camera.plotFitInformation(im)
def updateCameraParameters(self):
"""
update camera dictionary for calculation - uses potentially user modified data from gui
"""
# update current cam parameters
self.cam = dotdict()
self.cam.fov_h_deg = getFloat(self.leFOV_horizontal.text())
self.cam.fov_v_deg = getFloat(self.leFOV_vertical.text())
self.cam.sensor_w_mm = getFloat(self.leSensor_width.text())
self.cam.sensor_h_mm = getFloat(self.leSensor_height.text())
self.cam.focallength_mm = getFloat(self.leFocallength.text())
self.cam.img_w_px = getInteger(self.leImage_width.text())
self.cam.img_h_px = getInteger(self.leImage_height.text())
self.cam.center_x_px = self.cam.img_w_px / 2. + getFloat(
self.leOffsetX.text())
self.cam.center_y_px = self.cam.img_h_px / 2. + getFloat(
self.leOffsetY.text())
self.cam.heading_deg = getFloat(self.leCamPan.text())
self.cam.tilt_deg = getFloat(self.leCamTilt.text())
self.cam.roll_deg = getFloat(self.leCamRoll.text())
self.cam.elevation_m = getFloat(self.leCamElevation.text())
self.cam.pos_x_m = getFloat(self.leCamPosX.text())
self.cam.pos_y_m = getFloat(self.leCamPosY.text())
self.cam.projection = self.cbProjection.itemText(
self.cbProjection.currentIndex())
self.cam.gps_lat = getFloat(self.leCamLat.text())
self.cam.gps_lon = getFloat(self.leCamLon.text())
if self.cam.sensor_h_mm is None or self.cam.sensor_w_mm is None:
self.calcSensorDimensionsFromFOV()
# save parameters as options to DB
self.setOption('DM_focallength_mm', self.cam.focallength_mm)
self.setOption('DM_fov_h_deg', self.cam.fov_h_deg)
self.setOption('DM_fov_v_deg', self.cam.fov_v_deg)
self.setOption('DM_sensor_w_mm', self.cam.sensor_w_mm)
self.setOption('DM_sensor_h_mm', self.cam.sensor_h_mm)
self.setOption('DM_img_w_px', self.cam.img_w_px)
self.setOption('DM_img_h_px', self.cam.img_h_px)
self.setOption('DM_offset_x_px', getFloat(self.leOffsetX.text()))
self.setOption('DM_offset_y_px', getFloat(self.leOffsetY.text()))
print("Camera:")
print(json.dumps(self.cam, indent=4, sort_keys=True))
self.position = dotdict()
self.position.cam_elevation = getFloat(self.leCamElevation.text())
self.position.plane_elevation = getFloat(self.lePlaneElevation.text())
# save parameters to options
self.setOption('DM_cam_elevation', self.position.cam_elevation)
self.setOption('DM_plane_elevation', self.position.plane_elevation)
print("Position:")
print(json.dumps(self.position, indent=4, sort_keys=True))
print(self.cam)
# update camera parameters
# self.camera = ct.CameraTransform(self.cam.focallength_mm, [self.cam.sensor_w_mm, self.cam.sensor_h_mm],[self.cam.img_w_px, self.cam.img_h_px])
orientation = ct.SpatialOrientation(heading_deg=self.cam.heading_deg,
tilt_deg=self.cam.tilt_deg,
roll_deg=self.cam.roll_deg,
elevation_m=self.cam.elevation_m,
pos_x_m=self.cam.pos_x_m,
pos_y_m=self.cam.pos_y_m)
if self.cam.projection == "Cylindrical":
projection = ct.CylindricalProjection(
focallength_mm=self.cam.focallength_mm,
image_width_px=self.cam.img_w_px,
image_height_px=self.cam.img_h_px,
sensor_width_mm=self.cam.sensor_w_mm,
sensor_height_mm=self.cam.sensor_h_mm,
center_x_px=self.cam.center_x_px,
center_y_px=self.cam.center_y_px)
elif self.cam.projection == "Equirectangular":
projection = ct.EquirectangularProjection(
focallength_mm=self.cam.focallength_mm,
image_width_px=self.cam.img_w_px,
image_height_px=self.cam.img_h_px,
sensor_width_mm=self.cam.sensor_w_mm,
sensor_height_mm=self.cam.sensor_h_mm,
center_x_px=self.cam.center_x_px,
center_y_px=self.cam.center_y_px)
else: # default to rectilinear projection
print("Defaulting to rectiliniear")
projection = ct.RectilinearProjection(
focallength_mm=self.cam.focallength_mm,
image_width_px=self.cam.img_w_px,
image_height_px=self.cam.img_h_px,
sensor_width_mm=self.cam.sensor_w_mm,
sensor_height_mm=self.cam.sensor_h_mm,
center_x_px=self.cam.center_x_px,
center_y_px=self.cam.center_y_px)
self.camera = ct.Camera(orientation=orientation, projection=projection)
self.camera.setGPSpos(lat=self.cam.gps_lat,
lon=self.cam.gps_lon,
elevation=self.cam.elevation_m)
def __init__(self, camera_params):
self.bridge = CvBridge()
for camera_id in range(camera_params['n_cameras']):
setattr(self, 'last_image_%02i' % (camera_id, ),
[[None, None, None]])
self.last_model_states = ModelStates()
self.spawned_objects = []
# Camera IDs and corresponding variables holding images
self.camera_type = camera_params['camera_type']
self.update_rate = camera_params['update_rate']
self.image_dims = camera_params['camera_resolution'] + (3, )
self.camera_dict = {'%02i' % (camera_id,): getattr(self, 'last_image_%02i'\
% (camera_id,)) for camera_id in range(camera_params['n_cameras'])}
# ROS Subscribers, Publishers, Services
for camera_id in range(camera_params['n_cameras']):
if self.camera_type in ['rgb', 'both']:
img_callback_rgb = self.__image_callback_wrapper(
camera_id, 'rgb')
topic_name_rgb = '/robot/camera_rgb_%02i' % (camera_id, )
setattr(
self, '__image_rgb_%02i_sub' % (camera_id, ),
rospy.Subscriber(topic_name_rgb,
Image,
img_callback_rgb,
queue_size=1))
if self.camera_type in ['dvs', 'both']:
img_callback_dvs = self.__image_callback_wrapper(
camera_id, 'dvs')
topic_name_dvs = '/robot/camera_dvs_%02i/events' % (
camera_id, )
setattr(
self, '__image_dvs_%02i_sub' % (camera_id, ),
rospy.Subscriber(topic_name_dvs,
EventArray,
img_callback_dvs,
queue_size=1))
self.__set_model_state_pub = rospy.Publisher('/gazebo/set_model_state',
ModelState,
queue_size=1)
rospy.wait_for_service('gazebo/get_model_state', 10.0)
self.__get_model_state_srv = rospy.ServiceProxy(
'gazebo/get_model_state', GetModelState)
# 3D voxel to 2D camera pixel projection parameters (useful for segmentation dataset)
self.cam_transform = []
for camera_id in range(camera_params['n_cameras']):
cam_pose = self.get_object_pose('camera_%02i' % (camera_id))
cam_pos = cam_pose.position
cam_ori = cam_pose.orientation
roll, pitch, yaw = euler_from_quaternion(
[cam_ori.x, cam_ori.y, cam_ori.z, cam_ori.w])
(roll, pitch, yaw) = (angle_in_rad * 180 / np.pi
for angle_in_rad in (roll, pitch, yaw))
cam_projection = ct.RectilinearProjection(
focallength_px=camera_params['focal_length_px'],
image=camera_params['camera_resolution'])
cam_orientation = ct.SpatialOrientation(pos_x_m=cam_pos.x,
pos_y_m=cam_pos.y,
elevation_m=cam_pos.z,
roll_deg=roll,
tilt_deg=90 - pitch,
heading_deg=90 - yaw)
cam_lens = ct.BrownLensDistortion(k1=0.0,
k2=0.0,
k3=0.0,
projection=cam_projection)
self.cam_transform.append(
ct.Camera(projection=cam_projection,
orientation=cam_orientation,
lens=cam_lens))
rospy.init_node('cdp4_data_collection')
rospy.wait_for_service('/gazebo/get_world_properties')
self.__physics_state = rospy.ServiceProxy(
'/gazebo/get_world_properties', GetWorldProperties)
while self.__physics_state().sim_time < 2:
print('Waiting for simulation to be started')
rospy.sleep(2)
self.__path_to_models = os.getenv('HOME') + '/.gazebo/models/'
rospy.wait_for_service('gazebo/set_model_state', 10.0)
self.__set_model_state_srv = rospy.ServiceProxy(
'gazebo/set_model_state', SetModelState)
rospy.wait_for_service('gazebo/spawn_sdf_entity')
self.__spawn_model_srv = rospy.ServiceProxy('/gazebo/spawn_sdf_entity',
SpawnEntity)
rospy.wait_for_service('gazebo/set_visual_properties')
self.__change_color_srv = rospy.ServiceProxy(
'gazebo/set_visual_properties', SetVisualProperties)
def __init__(self):
QtWidgets.QWidget.__init__(self)
# the camera
self.camera = ct.Camera(ct.RectilinearProjection(), lens=ct.BrownLensDistortion())
# the list of loaded images
self.images = []
# widget layout and elements
self.setMinimumWidth(1300)
self.setMinimumHeight(400)
self.setWindowTitle("Camera Calibrator")
# add the main layout
main_layout = QtWidgets.QHBoxLayout(self)
self.setLayout(main_layout)
""" the left pane with the image list """
layout = QtWidgets.QVBoxLayout(self)
main_layout.addLayout(layout)
# the table
self.tableWidget = MyQTable()
layout.addWidget(self.tableWidget)
self.tableWidget.itemSelectionChanged.connect(self.imageSelected)
self.tableWidget.cellDoubleClicked.connect(self.imageDoubleClicked)
self.tableWidget.setColumnCount(3)
self.tableWidget.setHorizontalHeaderLabels(["File", "Processed", "Use"])
self.tableWidget.setMinimumWidth(350)
# the button layout
self.button_layout = QtWidgets.QHBoxLayout()
layout.addLayout(self.button_layout)
# the batch loader
self.input_filename = QtShortCuts.QInputFilename(self.button_layout, None, os.getcwd(), button_text="Add Images",
file_type="Images (*.jpg *.png)", existing=True, just_button=True)
self.input_filename.valueChanged.connect(self.loadBatch)
# clear button
self.button_clear = QtWidgets.QPushButton("Clear Images")
self.button_clear.clicked.connect(self.clearImages)
self.button_layout.addWidget(self.button_clear)
# process button
self.button_start = QtWidgets.QPushButton("Process")
self.button_start.clicked.connect(self.processImages)
self.button_layout.addWidget(self.button_start)
""" the middle pane with the image display """
layout = QtWidgets.QVBoxLayout(self)
main_layout.addLayout(layout)
# view/scene setup
self.view = QExtendedGraphicsView(dropTarget=self)
layout.addWidget(self.view)
self.origin = self.view.origin
self.preview_pixMapItem = QtWidgets.QGraphicsPixmapItem(self.origin)
self.view2 = QExtendedGraphicsView(dropTarget=self)
layout.addWidget(self.view2)
self.origin2 = self.view2.origin
self.preview_pixMapItem2 = QtWidgets.QGraphicsPixmapItem(self.origin2)
self.input_display_undistorted = QtShortCuts.QInputBool(layout, "Display Corrected (D)", True)
self.input_display_undistorted.valueChanged.connect(self.displayImage)
self.progessBar = QtWidgets.QProgressBar()
layout.addWidget(self.progessBar)
""" the right pane with the fit data """
layout = QtWidgets.QVBoxLayout(self)
main_layout.addLayout(layout)
# calibration flags
self.input_fix_center = QtShortCuts.QInputBool(layout, "Fit Center")
self.input_fix_aspect = QtShortCuts.QInputBool(layout, "Fit Aspect Ratio")
self.input_fix_k1 = QtShortCuts.QInputBool(layout, "Fit K1", True)
self.input_fix_k2 = QtShortCuts.QInputBool(layout, "Fit K2", True)
self.input_fix_k3 = QtShortCuts.QInputBool(layout, "Fit K3", True)
# start calibration button
self.button_fit = QtWidgets.QPushButton("Fit Calibration")
self.button_fit.clicked.connect(self.fitCalibration)
layout.addWidget(self.button_fit)
# a label to display the results
self.calibration_result = QtWidgets.QLabel()
layout.addWidget(self.calibration_result)
# a strech
layout.addStretch()
# the plot of the result
self.plot = QtShortCuts.MatplotlibWidget()
layout.addWidget(self.plot)
# the button layout
self.button_layout2 = QtWidgets.QHBoxLayout()
layout.addLayout(self.button_layout2)
# save button
self.button_save = QtShortCuts.QInputFilename(self.button_layout2, None, os.getcwd(),
button_text="Save Calibration",
file_type="Text File (*.txt)", existing=False,
just_button=True)
self.button_save.valueChanged.connect(self.saveCalibration)
self.button_save.setDisabled(True)
# load button
self.button_load = QtShortCuts.QInputFilename(self.button_layout2, None, os.getcwd(),
button_text="Load Calibration",
file_type="Text File (*.txt)", existing=True,
just_button=True)
self.button_load.valueChanged.connect(self.loadCalibration)