def __init__(self):
Tester.__init__(self, "Navigation")
# tests to run:
# square with Motion module, minimal.launch
# square with Motion module, navigation launch
# expect all to turn out the same, but need to sanity check
#self.motion = Motion()
# flag for a jerky stop
self.jerky = False
# I'm a bit concerned about robot safety if we don't slow things down,
# but I'm also worried it won't be an accurate test if we change the speed
self.walking_speed = 1 # if not self.jerky else .5
# linear test
self.reached_goal = False
# square test
self.reached_corner = [False, False, False, False]
self.cc_square = [(0, 0), (1, 0), (1, 1), (0, 1)]
self.c_square = [(0, 0), (1, 0), (1, -1), (0, -1)]
self.corner_counter = 0
# set up the logger output file
self.filename = None
self.navigation = Navigation(self.jerky)
def __init__(self):
Tester.__init__(self, "Localization")
# set up localization (including map)
landmarks = {0, 1}
landmark_positions = {0: (0, 0), 1: (1, 1)}
landmark_orientations = {0: -pi / 2, 1: pi / 2}
self.localization = Localization({}, {}, {}, landmarks,
landmark_positions,
landmark_orientations)
self.csvtestname = "estimation"
def __init__(self):
Tester.__init__(self, "NavLoc")
# flag for a jerky stop
self.jerky = False
# I'm a bit concerned about robot safety if we don't slow things down,
# but I'm also worried it won't be an accurate test if we change the speed
self.walking_speed = 1 # if not self.jerky else .5
# linear test
self.reached_goal = False
# square test
self.reached_corner = [False, False, False, False]
self.cc_square = [(0, 0), (1, 0), (1, 1), (0, 1)]
self.c_square = [(0, 0), (1, 0), (1, -1), (0, -1)]
self.corner_counter = 0
# set up the logger output file
self.test_name = "path"
# set up points on map
point_ids = MD2.points
locations = MD2.locations
neighbors = MD2.neighbors
# set map location of the landmark
landmarks = MD2.landmarks
landmark_positions = MD2.landmark_pos
landmark_orientations = MD2.landmark_orient
self.navloc = NavLoc(point_ids,
locations,
neighbors,
landmarks,
landmark_positions,
landmark_orientations,
jerky=self.jerky,
walking_speed=self.walking_speed)
# set the destinations
self.destination = [
self.navloc.floorplan.graph['T'].location,
self.navloc.floorplan.graph['R'].location
]
def __init__(self):
Tester.__init__(self)
def __init__(self):
# initalize test class
Tester.__init__(self, "Logger")
def __init__(self):
Tester.__init__(self, "Motion")
# set up basic sensing
self.sensors = Sensors()
self.motion = Motion()
def __init__(self):
Tester.__init__(self, "Sensors")
self.sensors = Sensors()
def __init__(self, branch, hash):
Tester.__init__(self, branch, traceMainScript, ())
self.hash = hash
self.imageDirectory = imageDir + self.branch + "_" + self.hash + "/"
def __init__(self,
deploy_prototxt,
model_file,
mean_file=None,
mean_value=None,
ratio_file=None,
label_file=None,
device_id=-1,
score_thred=0.0,
top_k=-1,
ratio=-1.0,
image_size=256,
raw_scale=255.0,
gray=False):
"""
初始化
"""
Tester.__init__(self)
self.score_thred = score_thred
self.top_k = top_k
self.ratio = ratio
self.gray = gray
self.ratios = None
if ratio_file is not None and os.path.exists(ratio_file):
self.ratios = dict()
for i, line in enumerate(open(ratio_file)):
items = line.strip(" \0\t\r\n").split(";")
self.ratios[i] = float(items[1])
self.labels = None
if label_file is not None:
self.labels = open(label_file).readlines()
self.labels = filter(lambda x: len(x.split(";")) == 2, self.labels)
self.labels = map(lambda x: x.strip().decode("utf-8"), self.labels)
if device_id < 0:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(device_id)
mean = None
# mean: num, channels, height, width
if mean_file and os.path.exists(mean_file):
mean_suffix = os.path.splitext(mean_file)[1]
if mean_suffix == ".binaryproto":
binary_data = open(mean_file, "rb").read()
proto_data = caffe.io.caffe_pb2.BlobProto.FromString(
binary_data)
mean = caffe.io.blobproto_to_array(proto_data)
elif mean_suffix == ".npy":
mean = np.load(mean_file)
elif mean_value is not None:
mean = np.tile(np.array(mean_value),
(1, image_size, image_size, 1))
mean = mean.transpose((0, 3, 1, 2))
if mean is not None and mean.ndim == 4:
mean = mean[0]
assert mean is None or mean.ndim == 3
self.detector = caffe.Detector(deploy_prototxt, model_file, \
image_dims=(image_size, image_size), mean=mean, \
raw_scale=raw_scale, channel_swap=(2, 1, 0) if not self.gray else None)
# thread lock
self.mutex = threading.Lock()
# init over
self.is_init = True
def __init__(self):
Tester.__init__(self, "SafeMotion")
# set up basic sensing
self.motion = SafeMotion(safety_level=1)
