Goran Vuksic
Published © MIT

NVIDIA Jetson Orin Nano powered Pit Droid

Star Wars Pit Droid powered by NVIDIA Jetson Orin Nano and vision AI.

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NVIDIA Jetson Orin Nano powered Pit Droid

Things used in this project

Hardware components

Jetson Orin Nano Developer Kit
NVIDIA Jetson Orin Nano Developer Kit
×1
Digital Servo 25kg (270°)
×2
Trust Exis webcam
×1
Arduino relay 5V
×1

Software apps and online services

Microsoft Azure
Microsoft Azure

Hand tools and fabrication machines

3D Printer (generic)
3D Printer (generic)

Story

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Schematics

LED schema

Code

Pit Droid LED

Python
# Pit Droid LED
# Author: Goran Vuksic

import RPi.GPIO as GPIO
from time import sleep

# set mode to BCM
GPIO.setmode(GPIO.BCM)

# define output pin
output_pin = 18

# GPIO setup
GPIO.setup(output_pin, GPIO.OUT)

# turn LED on
GPIO.output(output_pin, 1)
sleep(2)

# turn LED off
GPIO.output(output_pin, 0)
sleep(2)

# cleanup
GPIO.cleanup()

Pit Droid servo

Python
# Pit Droid servo
# Author: Goran Vuksic

import RPi.GPIO as GPIO
from time import sleep

# set mode to BOARD, pins are by numbers on board
GPIO.setmode(GPIO.BOARD)

# define output pin
output_pin = 33

# GPIO setup
GPIO.setup(output_pin, GPIO.OUT)

# start
servo=GPIO.PWM(33, 50)
servo.start(0)
sleep(1)

# move head left
servo.ChangeDutyCycle(5)
sleep(1)

# move head right
servo.ChangeDutyCycle(10)
sleep(1)

# stop and cleanup
servo.stop()
GPIO.cleanup()

Detectnet-led.py

Python
#!/usr/bin/env python3
#
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#

#######################################################################################
# Pit Droid - detectnet.py is updated to turn on droid lights when person is detected #
#######################################################################################

import sys
import argparse

# import GPIO
import RPi.GPIO as GPIO
from time import sleep

from jetson_inference import detectNet
from jetson_utils import videoSource, videoOutput, Log

# parse the command line
parser = argparse.ArgumentParser(description="Locate objects in a live camera stream using an object detection DNN.", 
                                 formatter_class=argparse.RawTextHelpFormatter, 
                                 epilog=detectNet.Usage() + videoSource.Usage() + videoOutput.Usage() + Log.Usage())

parser.add_argument("input", type=str, default="", nargs='?', help="URI of the input stream")
parser.add_argument("output", type=str, default="", nargs='?', help="URI of the output stream")
parser.add_argument("--network", type=str, default="ssd-mobilenet-v2", help="pre-trained model to load (see below for options)")
parser.add_argument("--overlay", type=str, default="box,labels,conf", help="detection overlay flags (e.g. --overlay=box,labels,conf)\nvalid combinations are:  'box', 'labels', 'conf', 'none'")
parser.add_argument("--threshold", type=float, default=0.5, help="minimum detection threshold to use") 

try:
	args = parser.parse_known_args()[0]
except:
	print("")
	parser.print_help()
	sys.exit(0)

# create video sources and outputs
input = videoSource(args.input, argv=sys.argv)
output = videoOutput(args.output, argv=sys.argv)
	
# load the object detection network
net = detectNet(args.network, sys.argv, args.threshold)

# note: to hard-code the paths to load a model, the following API can be used:
#
# net = detectNet(model="model/ssd-mobilenet.onnx", labels="model/labels.txt", 
#                 input_blob="input_0", output_cvg="scores", output_bbox="boxes", 
#                 threshold=args.threshold)

# set mode to BCM
GPIO.setmode(GPIO.BCM)

# define output pin
output_pin = 18

# GPIO setup
GPIO.setup(output_pin, GPIO.OUT)

# process frames until EOS or the user exits
while True:
    # capture the next image
    img = input.Capture()

    if img is None: # timeout
        continue  
        
    # detect objects in the image (with overlay)
    detections = net.Detect(img, overlay=args.overlay)

    # print the detections
    print("detected {:d} objects in image".format(len(detections)))

    lights = False
    
    for detection in detections:
        # print(detection)
        if int(detection.ClassID) == 1: # person is detected
            lights = True

    if lights:
    	GPIO.output(output_pin, 1)
    else:
        GPIO.output(output_pin, 0)

    # render the image
    output.Render(img)

    # update the title bar
    output.SetStatus("{:s} | Network {:.0f} FPS".format(args.network, net.GetNetworkFPS()))

    # print out performance info
    # net.PrintProfilerTimes()

    # exit on input/output EOS
    if not input.IsStreaming() or not output.IsStreaming():
        break

Pit Droid GitHub

Python code and examples.

Credits

Goran Vuksic

Goran Vuksic

4 projects • 28 followers
Engineering manager, Microsoft AI MVP, cofounder of syntheticAIdata, father, hitchhiker through the galaxy...

Comments

Related channels and tags
  • Internet of Things
  • Machine Learning & AI
  • Microsoft
  • NVIDIA