detectnet-console.py

#!/usr/bin/python
#
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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 an image using an object detection DNN.", 
						   formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.detectNet.Usage())

parser.add_argument("file_in", type=str, help="filename of the input image to process")
parser.add_argument("file_out", type=str, default=None, nargs='?', help="filename of the output image to save")
parser.add_argument("--network", type=str, default="pednet", help="pre-trained model to load, see below for options")
parser.add_argument("--threshold", type=float, default=0.5, help="minimum detection threshold to use")
parser.add_argument("--profile", type=bool, default=False, help="enable performance profiling and multiple runs of the model")
parser.add_argument("--runs", type=int, default=15, help="if profiling is enabling, the number of iterations to run")

try:
	opt, argv = parser.parse_known_args()
except:
	print("")
	parser.print_help()
	sys.exit(0)


# load an image (into shared CPU/GPU memory)
img, width, height = jetson.utils.loadImageRGBA(opt.file_in)

# load the object detection network
net = jetson.inference.detectNet(opt.network, argv, opt.threshold)

# enable model profiling
if opt.profile is True:
	net.EnableLayerProfiler()
else:
	opt.runs = 1

# run model inference
for i in range(opt.runs):
	if opt.runs > 1:
		print("\n/////////////////////////////////////\n// RUN {:d}\n/////////////////////////////////////".format(i))
	
	# detect objects in the image (with overlay)
	detections = net.Detect(img, width, height)

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

	for detection in detections:
		print(detection)
	
	# wait for GPU to complete work
	jetson.utils.cudaDeviceSynchronize()

	# print out timing info
	net.PrintProfilerTimes()

# save the output image with the bounding box overlays
if opt.file_out is not None:
	jetson.utils.saveImageRGBA(opt.file_out, img, width, height)