imagenet-camera.py

#!/usr/bin/python
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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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())

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)")

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 the camera and display
font = jetson.utils.cudaFont()
camera = jetson.utils.gstCamera(opt.width, opt.height, opt.camera)
display = jetson.utils.glDisplay()

# process frames until user exits
while display.IsOpen():
	# capture the image
	img, width, height = camera.CaptureRGBA()

	# classify the image
	class_idx, confidence = net.Classify(img, width, height)

	# find the object description
	class_desc = net.GetClassDesc(class_idx)

	# overlay the result on the image	
	font.OverlayText(img, width, height, "{:05.2f}% {:s}".format(confidence * 100, class_desc), 5, 5, font.White, font.Gray40)
	
	# render the image
	display.RenderOnce(img, width, height)

	# update the title bar
	display.SetTitle("{:s} | Network {:.0f} FPS".format(net.GetNetworkName(), net.GetNetworkFPS()))

	# print out performance info
	net.PrintProfilerTimes()