2.5. Run a trained neural network on the FPGA board¶

The Blueoil repository contains pre-prepared demonstration scripts to showcase examples of classification and object detection using a DE10-Nano Kit board and a USB camera.

2.5.1. Setup¶

The DE10-Nano: Prepare the board and create a Linux system on a microSD card. (For details, please see Installation.)
USB camera: After setting up the DE10-Nano board, connect the USB camera to the DE10-Nano board. Make sure the camera is recognized by the device.
For macOS: Please install XQuartz so that the demo output can be displayed on your screen.

2.5.2. Preparation¶

After having done the Setup step, you should be able to login to the DE10-Nano via ssh.
```
$ ssh -X root@{DE10-Nano's IP address}
```
Copy the directory generated as output by blueoil convert to the DE10-Nano as /demo. It should contain the following:

demo
 ├── fpga
 │   └── soc_system.rbf
 │   └── preloader-mkpimage.bin
 ├── models
 │   ├── lib
 │   │   └── libdlk_fpga.so
 │   └── meta.yaml
 └── python
     ├── lmnet
     ├── requirements.txt
     └── usb_camera_demo.py

2.5.3. Update FPGA configuration¶

Explore into the demo/fpga directory, and copy soc_system.rbf to the boot partition (/dev/mmcblk0p1).

  $ cd demo/fpga
  $ sudo mount /dev/mmcblk0p1 /media
  $ cp soc_system.rbf /media
  $ dd if=preloader-mkpimage.bin of=/dev/mmcblk0p3 && sync
  $ reboot

2.5.4. Run the demonstration¶

Explore into the demo/python directory, and execute the following commands on the device.

$ cd demo/python
$ export LC_ALL=C # for the first time only
$ sudo pip install -r requirements.txt  # for the first time only
$ python usb_camera_demo.py \
      -m ../models/lib/libdlk_fpga.so \
      -c ../models/meta.yaml

Press [ESC], to stop the demo.