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Object Detection
Up next we have a realtime object detection camera demo available for C++ and Python:
detectnet-camera.cpp(C++)detectnet-camera.py(Python)
Similar to the previous detectnet-console example, these camera applications use detection networks, except that they process a live video feed from a camera. detectnet-camera accepts 4 optional command-line parameters:
--networkflag setting the classification model (default is PedNet)- See Pre-trained Detection Models Available for the networks available to use.
--cameraflag setting the camera device to use- MIPI CSI cameras are used by specifying the sensor index (
0or1, ect.) - V4L2 USB cameras are used by specifying their
/dev/videonode (/dev/video0,/dev/video1, ect.) - The default is to use MIPI CSI sensor 0 (
--camera=0)
- MIPI CSI cameras are used by specifying the sensor index (
--widthand--heightflags setting the camera resolution (default is1280x720)- The resolution should be set to a format that the camera supports.
- Query the available formats with the following commands:
$ sudo apt-get install v4l-utils $ v4l2-ctl --list-formats-ext
You can combine the usage of these flags as needed, and there are additional command line parameters available for loading custom models. Launch the application with the --help flag to recieve more info, or see the Examples readme.
Below are some typical scenarios for launching the program:
$ ./detectnet-camera # using PedNet, default MIPI CSI camera (1280x720)
$ ./detectnet-camera --network=facenet # using FaceNet, default MIPI CSI camera (1280x720)
$ ./detectnet-camera --camera=/dev/video0 # using PedNet, V4L2 camera /dev/video0 (1280x720)
$ ./detectnet-camera --width=640 --height=480 # using PedNet, default MIPI CSI camera (640x480)$ ./detectnet-camera.py # using PedNet, default MIPI CSI camera (1280x720)
$ ./detectnet-camera.py --network=facenet # using FaceNet, default MIPI CSI camera (1280x720)
$ ./detectnet-camera.py --camera=/dev/video0 # using PedNet, V4L2 camera /dev/video0 (1280x720)
$ ./detectnet-camera.py --width=640 --height=480 # using PedNet, default MIPI CSI camera (640x480)note: for example cameras to use, see these sections of the Jetson Wiki:
- Nano:https://eLinux.org/Jetson_Nano#Cameras
- Xavier:https://eLinux.org/Jetson_AGX_Xavier#Ecosystem_Products_.26_Cameras
- TX1/TX2: developer kits include an onboard MIPI CSI sensor module (0V5693)
Displayed in the OpenGL window are the live camera stream overlayed with the bounding boxes of the detected objects. Note that the SSD-based models currently have the highest performance. Here is one using the coco-dog model:
# C++
$ ./detectnet-camera --network=coco-dog
# Python
$ ./detectnet-camera.py --network=coco-dog
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