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Dog-Detection.py

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import jetson.inference

import jetson.utils

import argparse

import sys

# parse the command line

parser = argparse.ArgumentParser(description="Locate objects in a live camera stream using an object detection DNN.")

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")

try:

opt = parser.parse_known_args()[0]

except:

print("")

parser.print_help()

sys.exit(0)

# load the object detection network

net = jetson.inference.detectNet(opt.network, sys.argv, opt.threshold)

# create video sources & outputs

input = jetson.utils.videoSource(opt.input_URI, argv=sys.argv)

output = jetson.utils.videoOutput(opt.output_URI, argv=sys.argv)

while True:

img = input.Capture()

detections = net.Detect(img, overlay=opt.overlay)

# print the detections

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

#Here is where I have to compare the images and download sounds.

for detection in detections:

class_idx = detection.ClassID

print(net.GetClassDesc(class_idx))

if detection.ClassID == 19:

print("dog is at the door")

# 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