def __init__(self, raytracer='raytracer2', world=None,
min_num_aux=1, aux_per_deg=1):
"""
sensor_type: choose between ImageRangeSensor.RangeFinder, Optics
"""
self.raytracer = TexturedRaytracer(raytracer=raytracer)
self.directions = []
self.spatial_sigma = []
self.sigma = []
self.num_photoreceptors = 0
if world is not None:
self.set_map(world)
self.min_num_aux = min_num_aux
self.aux_per_deg = aux_per_deg
# these are filled in by get_raw_sensor()
self.compiled = False
self.aux_directions = None
self.aux_indices = None
class ImageRangeSensor:
""" This class implements both a rangefinder and a vision sensor
(much of the code is the same).
Dont' use this class directly in the simulations -- use the two
subclasses Optics and Rangefinder
"""
def __init__(self, raytracer='raytracer2', world=None,
min_num_aux=1, aux_per_deg=1):
"""
sensor_type: choose between ImageRangeSensor.RangeFinder, Optics
"""
self.raytracer = TexturedRaytracer(raytracer=raytracer)
self.directions = []
self.spatial_sigma = []
self.sigma = []
self.num_photoreceptors = 0
if world is not None:
self.set_map(world)
self.min_num_aux = min_num_aux
self.aux_per_deg = aux_per_deg
# these are filled in by get_raw_sensor()
self.compiled = False
self.aux_directions = None
self.aux_indices = None
def num_sensels(self):
return self.num_photoreceptors
def render(self, object_state):
self.make_sure_compiled()
position = object_state.get_2d_position()
orientation = object_state.get_2d_orientation()
raw_data = self.raytracer.query_sensor(position, orientation)
proc_data = self.process_raw_data(raw_data)
return proc_data
def add_photoreceptors(self, directions, spatial_sigma, sigma):
n = len(directions)
if not isinstance(spatial_sigma, list):
spatial_sigma = [spatial_sigma] * n
if not isinstance(sigma, list):
sigma = [sigma] * n
self.directions.extend(directions)
self.spatial_sigma.extend(spatial_sigma)
self.sigma.extend(sigma)
# re-order everything according to direction
def permutation_indices(data):
return sorted(range(len(data)), key=data.__getitem__)
self.indices = permutation_indices(self.directions)
self.directions = [self.directions[i] for i in self.indices]
self.spatial_sigma = [self.spatial_sigma[i] for i in self.indices]
self.sigma = [self.sigma[i] for i in self.indices]
self.num_photoreceptors = len(self.directions)
self.num_readings = len(self.directions)
# force reconfiguration of raw sensor
self.compiled = False
def make_sure_compiled(self):
""" We finished composing the sensor """
if not self.compiled:
if len(self.directions) == 0:
raise BVException('You did not specify any receptors for the sensor.')
self.raytracer.set_sensor(self.get_raw_sensor())
self.compiled = True
def get_raw_sensor(self):
# list of list of indices
self.aux_indices = []
self.aux_directions = []
for i, direction in enumerate(self.directions):
spatial_sigma = self.spatial_sigma[i]
num_aux = int(max(ceil(rad2deg(spatial_sigma / self.aux_per_deg)),
self.min_num_aux))
# enforce odd to have a centered ray
if num_aux % 2 == 0:
num_aux += 1
if num_aux == 1:
aux_dirs = [direction]
# special case: linspace(a,b,1) == a
else:
aux_dirs = linspace(-spatial_sigma + direction,
+ spatial_sigma + direction, num_aux)
num_so_far = len(self.aux_directions)
self.aux_directions.extend(aux_dirs)
self.aux_indices.append(range(num_so_far, num_so_far + num_aux))
return {'class': 'sensor', 'directions': self.aux_directions }
def process_raw_data(self, data):
# example {"luminance": [1.0, -1.0, -1.0, -1.0, 1.0], "normal":
# [1.5707960000000001, 3.1415929999999999, 0.0, 3.1415929999999999,
#4.7123889999999999], "region": [0, 0, 0, 0, 0], "surface": [0, 0, 0, 0, 0],
#"readings": [1.0, 1.406641, 1.0, 1.406641, 1.0], "valid": [1, 1, 1, 1, 1],
#"curvilinear_coordinate": [6.9992039999999998, 5.9892620000000001, 5.0,
#4.0107379999999999, 3.0007959999999998]}
proc_data = data.copy()
proc_data['original'] = proc_data['luminance']
# proc_data = {} # if you want to be clean
aux_valid = data['valid']
assert(len(proc_data['luminance']) == len(self.aux_directions))
assert(len(proc_data['valid']) == len(self.aux_directions))
valid = []
for attribute in ['luminance', 'readings']:
values = []
try:
aux_values = array(data[attribute])
except ValueError, e:
print attribute, data[attribute]
raise e
for indices in self.aux_indices:
valid_indices = [ k for k in indices if aux_valid[k] ]
if len(valid_indices) == 0:
values.append(float('nan'))
valid.append(0)
else:
average = aux_values[valid_indices].mean()
values.append(average)
valid.append(1)
proc_data[attribute] = values
proc_data['valid'] = valid
return proc_data