def test_vectors(self):
v1 = Vector((1, 2, 1, 2), 1, 1)
self._event.add_vector(v1, (
'test',
0.55,
))
self.assertEqual(self._event.vectors, [{
'bbox': (2, 4, 1, 2),
'probability': 0.55,
'label': 'test'
}], 'Vectors are invalid')
def next_optimized_waypoint(self, cur_pos, cur_rot, pos_path,
cur_pos_index):
"""
Returns the furthest point from path without an obstacle. Stops at the first position where the laser of
nearest angle (laser angle ~= aimed position angle) detects an obstacle (laser distance < aimed position
distance).
:param cur_pos: current position of the robot
:type cur_pos: Vector
:param cur_rot: current orientation of the robot
:type cur_rot: Quaternion
:param pos_path: loaded path
:type pos_path: list
:param cur_pos_index: index of the current position in the path (-1
:type cur_pos_index: int
"""
lasers_angles = self.get_laser_scan_angles()
lasers = self.get_laser_scan()['Echoes']
max_lookahead_index = min(cur_pos_index + self.__max_lookahead - 1,
len(pos_path) - 1)
# Go through every position on the path starting at the position next to the current one and stopping at
# max_lookahead_index positions further
for i in range(cur_pos_index + 1, max_lookahead_index):
tar_pos = pos_path[i]
# convert potential aimed position to RCS
rcs_tar_pos = pure_pursuit.convert_to_rcs(tar_pos, cur_pos,
cur_rot)
# current robot position in RCS
rcs_origin = Vector(0, 0, 0)
# compute angle between current robot position and aimed position
tar_angle = rcs_origin.get_angle(rcs_tar_pos)
min_ind = 0
min_dist = tar_angle - lasers_angles[0]
# search for the nearest angle in laser_angles
# could be simplified with a calculation instead of this iteration
for j in range(1, len(lasers_angles)):
dist = tar_angle - lasers_angles[j]
if dist < min_dist:
min_dist = dist
min_ind = j
# if the laser hits an obstacle, return the index of the previous position on the path
if lasers[min_ind] < cur_pos.distance_to(tar_pos):
return i - 1
return max_lookahead_index
def orientationPath(self, dict=False):
"""
Parses the orientations in the loaded file into a list of either Quaternion instances or dictionaries depending on the
value of the dict parameter.
:param dict: if set to True, will parse into dictionaries instead of into Quaternion instances
:type dict: bool
"""
if dict:
return [{
'W': p['Pose']['Orientation']['W'],
'X': p['Pose']['Orientation']['X'],
'Y': p['Pose']['Orientation']['Y'],
'Z': p['Pose']['Orientation']['Z']
} for p in self.path]
else:
return [
Quaternion(
p['Pose']['Orientation']['W'],
Vector(p['Pose']['Orientation']['X'],
p['Pose']['Orientation']['Y'],
p['Pose']['Orientation']['Z'])) for p in self.path
]
def apply_one_vector(u):
v = Vector(self.range.dim, 0)
self.op.apply(u._impl, v)
return v
def zero_vector(self):
return WrappedVector(Vector(self.dim, 0))
def _copy_data(self):
self._impl = Vector(self._impl)
def setUp(self):
self._vector = Vector((1, 2, 1, 2), 1, 1)
def greengrass_infinite_infer_run():
try:
# TODO: Parameterize model name
model_name = 'deploy_ssd_resnet50_300'
model_path = "/opt/awscam/artifacts/mxnet_{}_FP16_FUSED.xml".format(
model_name)
model_type = "ssd"
# Trained image shape
input_width = 300
input_height = 300
with open('docs/synset.txt', 'r') as _f:
labels = dict(enumerate(_f, 1))
client.publish(topic=iotTopic, payload="Labels loaded")
# Start thread to write frames to disk
results_thread = FIFO_Thread()
results_thread.start()
# MxNet Model
model = awscam.Model(model_path, {"GPU": 1})
client.publish(topic=iotTopic, payload="Model loaded")
while True:
ret, frame = awscam.getLastFrame()
if ret == False:
raise Exception("Failed to get frame from the stream")
inferred_obj = InferEvent(frame, model_name, model_type)
frameResize = cv2.resize(frame, (input_width, input_height))
# Run inference
output = model.doInference(frameResize)
parsed_results = model.parseResult(model_type, output)['ssd']
# Parse and document vectors
for obj in parsed_results:
if obj['prob'] > 0.5:
vector = Vector((
obj['xmin'],
obj['xmax'],
obj['ymin'],
obj['ymax'],
), inferred_obj.xscale, inferred_obj.yscale)
inferred_obj.add_vector(vector, (
labels[obj['label']],
obj['prob'],
))
cv2.rectangle(frame, (vector.xmin, vector.ymin),
(vector.xmax, vector.ymax), (255, 165, 20),
4)
cv2.putText(
frame, '{}: {:.2f}'.format(labels[obj['label']],
obj['prob']),
(vector.xmin, vector.ymin - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 165, 20), 4)
# Trigger event pipeline
client.publish(topic=iotTopic, payload=str(inferred_obj))
# Update output
global jpeg
ret, jpeg = cv2.imencode('.jpg', frame)
except Exception as e:
client.publish(topic=iotTopic, payload=str(e))
