def main():
app = Application(app_filename="apps/rm/realsense_gmap/realsense_gmap.app.json")
odometry = app.nodes["odometry"].add(Odometry, "Odometry")
app.connect(app.nodes["visual_odometry_tracker"]["StereoVisualOdometry"], "left_camera_pose", odometry, "pose")
app.connect(odometry, "odometry", app.nodes["gmapping"]["GMapping"], "odometry")
gmap = app.nodes['gmapping']["GMapping"]
pprint(gmap.config.get_config())
app.run()
def main():
app = Application(
app_filename=
"apps/rm/realsense_cartographer/realsense_cartographer.app.json")
pose_to_edge = app.nodes["pose_tree_injector"].add(PoseToEdge,
"PoseToEdge")
app.connect(app.nodes["visual_odometry_tracker"]["StereoVisualOdometry"],
"left_camera_pose", pose_to_edge, "pose")
app.connect(pose_to_edge, "edge",
app.nodes["pose_tree_injector"]["PoseMessageInjector"], "pose")
comp = app.nodes['visual_odometry_tracker']["StereoVisualOdometry"]
pprint(comp.config.get_config())
app.run()
def main(args):
app = Application(name="detect_net_inference")
# Load subgraph and get interface node
app.load("packages/detect_net/apps/detect_net_inference.subgraph.json",
prefix="detect_net_inference")
detect_net_inferface = app.nodes["detect_net_inference.subgraph"]\
.components["interface"]
# Load configuration
app.load(args.config)
# Configure detection model
detection_model = app.nodes["detect_net_inference.tensor_r_t_inference"]\
.components["isaac.ml.TensorRTInference"]
if args.detection_model_file_path is not None:
detection_model.config.model_file_path = args.detection_model_file_path
if args.etlt_password is not None:
detection_model.config.etlt_password = args.etlt_password
# Configure detection decoder
decoder = app.nodes["detect_net_inference.detection_decoder"]\
.components["isaac.detect_net.DetectNetDecoder"]
decoder.config.output_scale = [args.rows, args.cols]
if args.confidence_threshold is not None:
decoder.config.confidence_threshold = args.confidence_threshold
if args.nms_threshold is not None:
decoder.config.non_maximum_suppression_threshold = args.nms_threshold
if args.mode == 'cask':
# Load replay subgraph and configure interface node
app.load("packages/record_replay/apps/replay.subgraph.json",
prefix="replay")
replay_interface = app.nodes["replay.interface"].components["output"]
replay_interface.config.cask_directory = args.cask_directory
# Connect the output of the replay subgraph to the detection subgraph
app.connect(replay_interface, "color", detect_net_inferface, "image")
elif args.mode == 'sim':
# Load simulation subgraph and get interface node
app.load("packages/navsim/apps/navsim_training.subgraph.json",\
prefix="simulation")
simulation_interface = app.nodes["simulation.interface"].components[
"output"]
# Connect the output of the simulation with user-specified channel to the detection subgraph
app.connect(simulation_interface, args.image_channel,
detect_net_inferface, "image")
elif args.mode == 'realsense':
app.load_module('realsense')
# Create and configure realsense camera codelet
camera = app.add("camera").add(app.registry.isaac.RealsenseCamera)
camera.config.rows = args.rows
camera.config.cols = args.cols
camera.config.color_framerate = args.fps
camera.config.depth_framerate = args.fps
camera.config.enable_ir_stereo = False
# Connect the output of the camera node to the detection subgraph
app.connect(camera, "color", detect_net_inferface, "image")
elif args.mode == 'v4l':
app.load_module('sensors:v4l2_camera')
# Create and configure V4L camera codelet
camera = app.add("camera").add(app.registry.isaac.V4L2Camera)
camera.config.device_id = 0
camera.config.rows = args.rows
camera.config.cols = args.cols
camera.config.rate_hz = args.fps
# Connect the output of the camera node to the detection subgraph
app.connect(camera, "frame", detect_net_inferface, "image")
elif args.mode == 'image':
app.load_module('message_generators')
# Create feeder node
feeder = app.add("feeder").add(
app.registry.isaac.message_generators.ImageLoader)
feeder.config.color_glob_pattern = args.image_directory
feeder.config.tick_period = "1Hz"
feeder.config.focal_length = [args.focal_length, args.focal_length]
feeder.config.optical_center = [
args.optical_center_rows, args.optical_center_cols
]
feeder.config.distortion_coefficients = [0.01, 0.01, 0.01, 0.01, 0.01]
# Connect the output of the image feeder node to the detection subgraph
app.connect(feeder, "color", detect_net_inferface, "image")
else:
raise ValueError('Not supported mode {}'.format(args.mode))
app.run()
Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
'''
from engine.pyalice import Application
import argparse
import random
import sys
DEMOS = ["demo_1", "demo_2", "demo_3", "demo_4"]
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='flatsim is a flat-world simulator for navigation')
parser.add_argument('--demo',
dest='demo',
help='The scenario which will be used for flatsim')
args, _ = parser.parse_known_args()
demo = args.demo if args.demo is not None else random.choice(DEMOS)
app = Application(name="flatsim")
app.load("packages/flatsim/apps/flatsim.subgraph.json", prefix="flatsim")
app.load("packages/flatsim/apps/{}.json".format(demo))
app.run()