This page provides instructions for building the Vitis-AI Library (v1.3.2) sample applications from source on a ZCU10x evaluation board or KV260 kit running Certified Ubuntu 20.04 LTS for Xilinx Devices.
...
Before building the sample applications, install the required dependency packages:
| Code Block |
|---|
$ sudo apt -y update
$ sudo apt -y install libopencv-dev
$ sudo apt -y install libgoogle-glog-dev |
|
Get the Source Code
All of the source code is available on the Xilinx GitHub. After you clone the Vitis AI repository, switch to the v1.3.2 tag:
| Code Block |
|---|
$ git clone https://github.com/Xilinx/Vitis-AI.git
$ cd Vitis-AI
$ git checkout tags/v1.3.2 |
|
Build the Application Code
...
For example, in order to build the facedetect sample application:
| Code Block |
|---|
$ cd demo/Vitis-AI-Library/samples/facedetect
$ ./build.sh |
|
Download a Model
The sample applications require a compatible machine learning model in order to run. Refer to the readme file in the application directory to determine which models are compatible with each sample application.
For example, to download the densebox_640_360 model and extract in the home directory:
| Code Block |
|---|
$ wget https://www.xilinx.com/bin/public/openDownload?filename=densebox_640_360-zcu102_zcu104-r1.3.1.tar.gz -O ~/densebox_640_360-zcu102_zcu104-r1.3.1.tar.gz
$ tar -xzf ~/densebox_640_360-zcu102_zcu104-r1.3.1.tar.gz -C ~ |
|
Run the Sample Application
...
| Info |
|---|
For help determining which /dev/videoX interface to use for the USB tests, you can use the following command: v4l2-ctl --list-devices
|
| Code Block |
|---|
# Use a jpeg image as input (ex: image.jpeg, result will be in image_result.jpeg)
$ ./test_jpeg_facedetect ~/densebox_640_360/densebox_640_360.xmodel image.jpeg
# Use USB camera as input (2 is the /dev/videoX index that the camera is detected at)
$ ./test_video_facedetect ~/densebox_640_360/densebox_640_360.xmodel 2
# Run the accuracy test - Input is a file with a list of images, results are in results.txt
$ ./test_accuracy_facedetect ~/densebox_640_360/densebox_640_360.xmodel file_list.txt results.txt
# Run the performance test - Input is a file with a list of images, -t: Number of threads, -s: Number of seconds to run
$ ./test_performance_facedetect ~/densebox_640_360/densebox_640_360.xmodel file_list.txt -t 4 -s 10 |
|
There are three ways to specify the model
...
Vitis AI provides a test image archive that can be download to the target and used to run the tests above. To download the sample image package, and extract them to the samples directory in your home directory, use the following commands:
| Code Block |
|---|
wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_library_r1.3.1_images.tar.gz -O ~/vitis_ai_library_r1.3.1_images.tar.gz
tar -xzf vitis_ai_library_r1.3.1_images.tar.gz
|
|