Running the Live Camera Recognition Demo                  terug naar de inleiding

Realtime classificeren met de camera met:


De schets kan optioneel gebruik maken van 4 commando argumenten

1)  --network vlag voor een classificatie model (default is GoogleNet)

2) --camera vlag voor het type camera (default =  --camera=0 voor de MPI CSI camera dit is de raspberry pi (RPI) V2 camera)

3)  en 4) --width en --height vlaggen van de camera resolutie (default is dit 1280x720)


1) ./

      Maakt gebruik van GoogleNet, de RPI camera en een resolutie van 1280x720

2) ./ --network=resnet-18.

      Maakt gebruik van ResNet-18, de RPI camera en een resolutie van 1280x720

3) ./ --width=640 --height=480
      Maakt gebruik van GoogleNet, de RPI camera en een resolutie van 640x480



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)") 
    opt = parser.parse_known_args()[0] 
# load the recognition network 
net = jetson.inference.imageNet(, sys.argv) 
# create the camera and display 
font = jetson.utils.cudaFont() 
camera = jetson.utils.gstCamera(opt.width, opt.height, 
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