def __init__(self, map_name='fleming', bounds=[-5, -5, 5, 5],
plot_robbers=True, map_display_type='probability',
combined_only=True, publish_to_ROS=False):
# Define map properties
# <>TODO: Clean this up- add seperate map creation function?
self.map_name = map_name
if self.map_name == 'fleming':
#self.bounds = [-9.5, -3.33, 4, 3.68]
self.bounds = [0, 0, 5, 5]
else:
self.bounds = bounds # [x_min,y_min,x_max,y_max] in [m]
self.plot_robbers = plot_robbers
self.outer_bounds = [i * 1.1 for i in self.bounds]
self.origin = [0, 0] # in [m]
# <>TODO: Make display type relative to each robber
self.display_type = map_display_type
self.combined_only = combined_only
# Set up ROS elements if using ROS
self.publish_to_ROS = publish_to_ROS
if publish_to_ROS:
from cv_bridge import CvBridge
import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String
self.probability_target = 'Roy'
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("python_probability_map", Image,
queue_size=10)
rospy.Subscriber("robot_probability_map", String,
self.change_published_ax)
# Define map elements
self.objects = {} # For dynamic/static map objects (not robbers/cops)
self.areas = {}
self.cops = {}
self.robbers = {}
self.dynamic_elements = []
self.static_elements = []
self.information_elements = []
self.element_dict = {'dynamic': self.dynamic_elements,
'static': self.static_elements,
'information': self.information_elements}
# Define layers
self.shape_layer = ShapeLayer(self.element_dict, bounds=self.bounds)
self.feasible_layer = FeasibleLayer(bounds=self.bounds)
self.particle_layers = {} # One per robber, plus one combined
self.probability_layers = {} # One per robber, plus one combined
# Set up map
if self.map_name == 'fleming':
set_up_fleming(self) # <>TODO: make a generic 'setup map' function
else:
pass
class Map(object):
"""Environment map composed of multiple elements and layers.
.. image:: img/classes_Map.png
Parameters
----------
mapname : str
The name of the map.
bounds : array_like, optional
Map boundaries as [xmin,ymin,xmax,ymax] in [m].
plot_robbers : list or bool
A list of robbers to plot, True for all, False for None.
map_display_type: {'particle', 'probability'}
Defines which layer to use.
combined_only : bool, optional
Whether to show only the combined plot (as opposed to individual plots
for each robber, plus one combined plot). Defaults to `True`.
publish_to_ROS: bool
Whether to publish an image topic of the map to ROS.
"""
# TODO: @Config Change plot_robbers to just be a string
# TODO: @Refactor Seperate map and interface
def __init__(self, map_name='fleming', bounds=[-5, -5, 5, 5],
plot_robbers=True, map_display_type='probability',
combined_only=True, publish_to_ROS=False):
# Define map properties
# <>TODO: Clean this up- add seperate map creation function?
self.map_name = map_name
if self.map_name == 'fleming':
#self.bounds = [-9.5, -3.33, 4, 3.68]
self.bounds = [0, 0, 5, 5]
else:
self.bounds = bounds # [x_min,y_min,x_max,y_max] in [m]
self.plot_robbers = plot_robbers
self.outer_bounds = [i * 1.1 for i in self.bounds]
self.origin = [0, 0] # in [m]
# <>TODO: Make display type relative to each robber
self.display_type = map_display_type
self.combined_only = combined_only
# Set up ROS elements if using ROS
self.publish_to_ROS = publish_to_ROS
if publish_to_ROS:
from cv_bridge import CvBridge
import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String
self.probability_target = 'Roy'
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("python_probability_map", Image,
queue_size=10)
rospy.Subscriber("robot_probability_map", String,
self.change_published_ax)
# Define map elements
self.objects = {} # For dynamic/static map objects (not robbers/cops)
self.areas = {}
self.cops = {}
self.robbers = {}
self.dynamic_elements = []
self.static_elements = []
self.information_elements = []
self.element_dict = {'dynamic': self.dynamic_elements,
'static': self.static_elements,
'information': self.information_elements}
# Define layers
self.shape_layer = ShapeLayer(self.element_dict, bounds=self.bounds)
self.feasible_layer = FeasibleLayer(bounds=self.bounds)
self.particle_layers = {} # One per robber, plus one combined
self.probability_layers = {} # One per robber, plus one combined
# Set up map
if self.map_name == 'fleming':
set_up_fleming(self) # <>TODO: make a generic 'setup map' function
else:
pass
def add_obj(self, map_obj):
self.objects[map_obj.name] = map_obj
self.static_elements.append(map_obj)
self.feasible_layer.define_feasible_regions(self.static_elements)
def rem_obj(self, map_obj):
self.static_elements.remove(map_obj)
self.feasible_layer.define_feasible_regions(self.static_elements)
del self.objects[map_obj.name]
def add_robot(self, map_obj):
# <>TODO: Modify so it can have relations
self.dynamic_elements.append(map_obj)
def rem_robot(self, map_obj):
self.dynamic_elements.remove(map_obj)
def add_area(self, area):
self.areas[area.name] = area
self.static_elements.append(area)
def rem_area(self, area):
self.static_elements.remove(area)
del self.areas[area.name]
def add_cop(self, cop_obj):
self.dynamic_elements.append(cop_obj)
self.cops[cop_obj.name] = cop_obj
def rem_cop(self, cop_obj):
self.dynamic_elements.remove(cop_obj)
del self.cops[cop_obj.name]
def add_robber(self, robber):
# <>TODO: Make generic imaginary robbers
if self.plot_robbers is True:
robber.visible = True
elif self.plot_robbers is False:
robber.visible = False
elif robber.name not in self.plot_robbers:
robber.visible = False
self.dynamic_elements.append(robber)
self.robbers[robber.name] = robber
def rem_robber(self, robber):
robber.patch.remove()
self.dynamic_elements.remove(robber)
try:
if self.fusion_engine is not None:
if self.display_type == 'particle':
self.rem_particle_layer(robber.name)
elif self.display_type == 'probability':
self.rem_probability_layer(robber.name)
except:
# <>TODO: actually catch other exceptions here
logging.debug('No layer to remove.')
del self.robbers[robber.name]
def found_robber(self, robber):
robber.visible = True
robber.color = 'darkorange'
try:
if self.display_type == 'particle':
self.rem_particle_layer(robber.name)
elif self.display_type == 'probability':
self.rem_probability_layer(robber.name)
except:
# <>TODO: actually catch other exceptions here
logging.debug('No layer to remove.')
def add_particle_layer(self, name, filter_):
self.particle_layers[name] = ParticleLayer(filter_)
def rem_particle_layer(self, name):
self.particle_layers[name].remove()
del self.particle_layers[name]
def add_probability_layer(self, name, filter_):
self.probability_layers[name] = ProbabilityLayer(filter_,
fig=self.fig,
bounds=self.bounds)
def rem_probability_layer(self, name):
self.probability_layers[name].remove()
del self.probability_layers[name]
def plot(self):
# <>TODO: Needs rework
fig = plt.figure(1, figsize=(12, 10))
ax = fig.add_subplot(111)
self.shape_layer.update_plot(update_static=True)
for area in self.areas.values():
ax.add_patch(area.get_patch())
ax.axis('scaled')
ax.set_xlim([self.bounds[0], self.bounds[2]])
ax.set_ylim([self.bounds[1], self.bounds[3]])
ax.set_title('Experimental environment with landmarks and areas')
ax.annotate('Kitchen', [-5, 2.5], weight='bold')
ax.annotate('Billiard Room', [1.5, 2], weight='bold')
ax.annotate('Library', [0.75, -2.25], weight='bold')
ax.annotate('Study', [-4.5, -2.25], weight='bold')
ax.annotate('Dining Room', [-8.75, -1.4], weight='bold')
ax.annotate('Hallway', [-3.5, 0], weight='bold')
plt.show()
def setup_plot(self, fig=None, fusion_engine=None):
"""Create the initial plot for the animation.
"""
logging.info('Setting up plot')
if fig is None:
if plt.get_fignums():
self.fig = plt.gcf()
else:
self.fig = plt.figure(figsize=(14, 10))
else:
self.fig = fig
self.fusion_engine = fusion_engine
self._setup_axes()
self._setup_layers()
def _setup_axes(self):
self.axes = {}
if len(self.robbers) == 1:
name = self.robbers.iterkeys().next()
self.axes[name] = self.fig.add_subplot(111)
pos = self.axes[name].get_position()
print pos
pos = [pos.x0, pos.y0 * 1.2, pos.width, pos.height]
print pos
self.axes[name].set_position(pos)
elif self.combined_only:
self.axes['combined'] = self.fig.add_subplot(111)
else:
num_axes = len(self.robbers) + 1
num_rows = int(math.ceil(num_axes / 2))
i = 0
for robber_name in self.robbers:
ax = plt.subplot2grid((num_rows, 4),
(int(math.floor(i / 2)), (i % 2) * 2),
colspan=2
)
self.axes[robber_name] = ax
i += 1
# Add a plot for the combined estimate
if (num_axes % 2) == 0:
ax = plt.subplot2grid((num_rows, 4), (num_rows - 1, 2),
colspan=2)
else:
ax = plt.subplot2grid((num_rows, 4), (num_rows - 1, 1),
colspan=2)
self.axes['combined'] = ax
# Rescale, setup bounds and title
for ax_name, ax in self.axes.iteritems():
ax.axis('scaled')
ax.set_xlim([self.bounds[0], self.bounds[2]])
ax.set_xlabel('x position (m)')
ax.set_ylim([self.bounds[1], self.bounds[3]])
ax.set_ylabel('y position (m)')
if ax_name == 'combined':
t = ax.set_title('Combined perception of all robots')
else:
t = ax.set_title("Map of {}'s perceived location"
.format(ax_name))
try:
if self.fusion_engine.vel_states is not None:
t.set_y(1.2)
except AttributeError:
logging.debug('No vel states available.')
# plt.tight_layout()
def _setup_layers(self):
# Set up basic layers
self.shape_layers = {}
self.feasible_layers = {}
for ax_name, ax in self.axes.iteritems():
self.shape_layers[ax_name] = ShapeLayer(self.element_dict,
bounds=self.bounds,
ax=ax)
# Set up probability/particle layers
if self.fusion_engine is not None:
filter_ = self.fusion_engine.filters[ax_name]
self.probability_layers[ax_name] = \
ProbabilityLayer(filter_, fig=self.fig, ax=ax,
bounds=self.bounds)
def change_published_ax(self, msg):
self.probability_target = msg.data
def update(self, i=0):
# self.shape_layer.update(i=i)
for ax_name, ax in self.axes.iteritems():
try:
self.shape_layers[ax_name].update(i=i)
# Update probability/particle layers
if self.fusion_engine is not None:
if self.display_type == 'particle':
self.particle_layers[ax_name].update(i=i)
elif self.display_type == 'probability':
self.probability_layers[ax_name].update(i=i)
except KeyError:
logging.debug('Robber already removed.')
if self.publish_to_ROS and ax_name == self.probability_target and \
i % 1 == 0:
import cv2
from cv_bridge import CvBridgeError
extent = ax.get_window_extent().transformed(
self.fig.dpi_scale_trans.inverted())
self.fig.savefig(ax_name + '.png',
bbox_inches=extent.expanded(1.1, 1.2))
img = cv2.imread(ax_name + '.png', 1)
try:
self.image_pub.publish(
self.bridge.cv2_to_imgmsg(img, "bgr8"))
except CvBridgeError, e:
print e