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dog-detector.py
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#!/usr/bin/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. | |
# | |
# Beckett Porter Nvidia Submission for IDTech AI ML class | |
# This program detects if a dog is in view of the webcam and if so, it yells at it to get off the table | |
# It is designed to have the webcam pointed at a table to prevent a dog from jumping up and eating the food off of it | |
import jetson.inference | |
import jetson.utils | |
import argparse | |
import sys | |
# added playsound library to enable sound playback | |
# from playsound import playsound | |
# 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=jetson.inference.detectNet.Usage() + | |
jetson.utils.videoSource.Usage() + jetson.utils.videoOutput.Usage() + jetson.utils.logUsage()) | |
parser.add_argument("input_URI", type=str, default="", nargs='?', help="URI of the input stream") | |
parser.add_argument("output_URI", 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") | |
is_headless = ["--headless"] if sys.argv[0].find('console.py') != -1 else [""] | |
try: | |
opt = parser.parse_known_args()[0] | |
except: | |
print("") | |
parser.print_help() | |
sys.exit(0) | |
# create video output object | |
output = jetson.utils.videoOutput(opt.output_URI, argv=sys.argv+is_headless) | |
# load the object detection network | |
net = jetson.inference.detectNet(opt.network, sys.argv, opt.threshold) | |
# create video sources | |
input = jetson.utils.videoSource(opt.input_URI, argv=sys.argv) | |
# process frames until the user exits | |
while True: | |
# capture the next image | |
img = input.Capture() | |
# detect objects in the image (with overlay) | |
detections = net.Detect(img, overlay=opt.overlay) | |
# print the detections | |
# added if statement to detect when dog is in frame and then to play the sound file | |
# print("{:s} | Network {:.0f} FPS: detected {:d} objects in image".format(opt.network, net.GetNetworkFPS(), len(detections))) | |
for detection in detections: | |
if detection.ClassID != 17 and detection.ClassID != 18: | |
print("{:s} | Network {:.0f} FPS: detected {:d} objects in image: {:d}".format(opt.network, net.GetNetworkFPS(), len(detections), detection.ClassID)) | |
if detection.ClassID == 17: | |
print("{:s} | Network {:.0f} FPS: detected {:d} objects in image: {:d}: CAT".format(opt.network, net.GetNetworkFPS(), len(detections), detection.ClassID)) | |
if detection.ClassID == 18: | |
print("{:s} | Network {:.0f} FPS: detected {:d} objects in image: {:d}: DOG".format(opt.network, net.GetNetworkFPS(), len(detections), detection.ClassID)) | |
# print(detection) | |
# if detection.ClassID == 18: | |
# playsound('Sierra.mp3') | |
# render the image | |
output.Render(img) | |
# update the title bar | |
output.SetStatus("{:s} | Network {:.0f} FPS".format(opt.network, net.GetNetworkFPS())) | |
# print out performance info | |
# net.PrintProfilerTimes() | |
# exit on input/output EOS | |
if not input.IsStreaming() or not output.IsStreaming(): | |
break | |
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