Jetson Inference - Python - DetectNet - Filter class

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

Hi,

I try to do a filter on the class label with Jetson Inference / python / detect-camera.py –network=ssd-inception-v2

But I had not a satisfied result. Do you have an idea ?

Thanks

2

Hi.

I sorry but, I don’t have any idea what you want to do.
Generally, jetson nano + python running really well. Actually I porting George AI to JetsonNano I must say only a few changes in the code and George run really quick.

3

An idea, somebody ?

Thanks

4

Hi.
So here is some code what i use on Jetson Nano with GeorgeAI.

if main_mode == “Pedestrian Detection”:

                 # using a greyscale picture, also for faster detection
               gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
                 # detect people in the image
                 # returns the bounding boxes for the detected objects
               boxes, weights = hog.detectMultiScale(frame, winStride=(8,8) )
               boxes = np.array([[x, y, x + w, y + h] for (x, y, w, h) in boxes])
               for (xA, yA, xB, yB) in boxes:
                    # display the detected boxes in the colour picture
                    imk = cv2.rectangle(frame, (xA, yA), (xB, yB),
                      (0, 255, 0), 2)

                 # Write the output video 
                    frame = imk
                  # Display the resulting frame
5

Hi Tomas_Trnka, #4, and somebody,

Thanks for your reply.

A- I had, this code, from jetson-iference / python / example / detectnet-camera.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 a live camera stream using an object detection DNN.”,
formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.detectNet.Usage())

parser.add_argument(“–network”, type=str, default=“ssd-mobilenet-v2”, help=“pre-trained model to load (see below for options)”)
parser.add_argument(“–overlay”, type=str, default=“box,labels,conf”, help=“detection overlay flags (e.g. --overlay=box,labels,conf)\nvalid combinations are: ‘box’, ‘labels’, ‘conf’, ‘none’”)
parser.add_argument(“–threshold”, type=float, default=0.5, help=“minimum detection threshold to use”)
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 object detection network

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

create the camera and display

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()

# detect objects in the image (with overlay)
detections = net.Detect(img, width, height, opt.overlay)

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

for detection in detections:
	print(detection)

# render the image
display.RenderOnce(img, width, height)

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

# print out performance info
net.PrintProfilerTimes()

########################################################################################

B- How do you do for send a mail (with the image captured) when a object detected ?

Thanks for your code

6

Also see this post: https://devtalk.nvidia.com/default/topic/1065144/jetson-nano/jetson-nano-limiting-the-results-shown-by-the-detectnet-example-/post/5393749/#5393749

You can change the class names you wish to filter out to ‘void’ in the /data/networks/SSD-Inception-v2/ssd_coco_labels.txt file, and it will not return detections to objects that have the class name void.