Jetson Nano 2GB 系列文章（22）： “Hello AI World”图像分类代码

8

img = jetson.utils.loadImage(opt.filename)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

import jetson.inference

import jetson.utils

import argparse

import sys

# parse the command line

parser = argparse.ArgumentParser(description="Classify a live camera stream using an image recognition DNN.",                                 formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.imageNet.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="googlenet", help="pre-trained model to load (see below for options)")

parser.add_argument("--camera", type=str, default="0", help="index of the MIPI CSI camera to use (e.g. CSI camera 0)\nor for VL42 cameras, the /dev/video device to use.\nby default, MIPI CSI camera 0 will be used.")

parser.add_argument("--width", type=int, default=1280, help="desired width of camera stream (default is 1280 pixels)")

parser.add_argument("--height", type=int, default=720, help="desired height of camera stream (default is 720 pixels)")

parser.add_argument('--headless', action='store_true', default=(), help="run without display")

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)

# load the recognition network

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

# create video sources & outputs

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

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

font = jetson.utils.cudaFont()

# process frames until the user exits

while True:

       # capture the next image

       img = input.Capture()

       # classify the image

       class_id, confidence = net.Classify(img)

       # find the object description

       class_desc = net.GetClassDesc(class_id)

       # overlay the result on the image 

       font.OverlayText(img, img.width, img.height, "{:05.2f}% {:s}".format(confidence * 100, class_desc), 5, 5, font.White, font.Gray40)

       # render the image

       output.Render(img)

       # update the title bar

       output.SetStatus("{:s} | Network {:.0f} FPS".format(net.GetNetworkName(), net.GetNetworkFPS()))

       # print out performance info

       net.PrintProfilerTimes()

       # exit on input/output EOS

       if not input.IsStreaming() or not output.IsStreaming():

              break