I have been running the various jetson nano demo programs and when I got to the most recent one, see code at the bottom, I got this error. I am running the programs locally with a screen attached. I tried running it through jupyter, and through the command line. I have rebooted and cleared and restarted the kernal in Jupyter. Until now all of the other examples have worked just fine. I have not reflashed the device yes as I would rather just fix the problem if possible. If someone could help me I would really appreciate it. Thanks!
Exception Traceback (most recent call last)
in
50 font = jetson.utils.cudaFont()
51 camera = jetson.utils.gstCamera(opt.width, opt.height, opt.camera)
—> 52 display = jetson.utils.glDisplay()
53
54 # process frames until user exits
Exception: jetson.utils – failed to create glDisplay device
#!/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=“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()