detectnet.py

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.",
                                 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)
objects=["unlabeled","person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","trafficlight","firehydrant","streetsign","stopsign","parkingmeter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","hat","backpack","umbrella","shoe","eyeglasses","handbag","tie","suitcase","frisbee","skis","snowboard","sportsball","kite","baseballbat","baseballglove","skateboard","surfboard","tennisracket","bottle","plate","wineglass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hotdog","pizza","donut","cake","chair","couch","pottedplant","bed","mirror","diningtable","window","desk","toilet","door","tv","laptop","mouse","remote","keyboard","cellphone","microwave","oven","toaster","sink","refrigerator","blender","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"]

# 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
        print("detected {:d} objects in image".format(len(detections)))
        classid=[]
        for detection in detections:
                print(detection)
                a=str(detection)
                b=a[35:46]
                for i in b.split():
                        if i.isdigit():
                                classid.append(int(i))


        # 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
print(classid)
l=[]
d={}
for k in classid:
        print(objects[k])
        l.append(objects[k])
for j in l:
        d[j]=l.count(j)
m1=list(d.keys())
m2=list(d.values())
zz=["There are"]
for z in range(len(m1)):
    zz.append(str(m2[z]))
    zz.append(m1[z])
zz.append("before you")
global ccc
if len(zz)==2:
  ccc="please try again, i cant detect any objects before you"
else:
  ccc=str(zz)
print(ccc)
file =open('read.text','w')
file.write(ccc)
file.close()