OpenVINO Samples¶
The OpenVINO samples are simple console applications that show how to utilize specific OpenVINO API capabilities within an application. They can assist you in executing specific tasks such as loading a model, running inference, querying specific device capabilities, etc.
If you install OpenVINO™ Runtime, sample applications for С, C++, and Python are created in the following directories:
<INSTALL_DIR>/samples/c<INSTALL_DIR>/samples/cpp<INSTALL_DIR>/samples/python
The applications include:
Speech Sample - Acoustic model inference based on Kaldi neural networks and speech feature vectors.
Hello Classification Sample – Inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API. Input of any size and layout can be set to an infer request which will be pre-processed automatically during inference (the sample supports only images as inputs and supports Unicode paths).
Hello NV12 Input Classification Sample – Input of any size and layout can be provided to an infer request. The sample transforms the input to the NV12 color format and pre-process it automatically during inference. The sample supports only images as inputs.
Hello Query Device Sample – Query of available OpenVINO devices and their metrics, configuration values.
Hello Reshape SSD Sample – Inference of SSD networks resized by ShapeInfer API according to an input size.
Image Classification Sample Async – Inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API (the sample supports only images as inputs).
OpenVINO Model Creation Sample – Construction of the LeNet model using the OpenVINO model creation sample.
Benchmark Application – Estimates deep learning inference performance on supported devices for synchronous and asynchronous modes.
Note that the Python version of the benchmark tool is currently available only through the OpenVINO Development Tools installation. It is not created in the samples directory but can be launched with the following command:
benchmark_app -m <model> -i <input> -d <device>For more information, check the Benchmark Python Tool documentation.
Note
All C++ samples support input paths containing only ASCII characters, except for the Hello Classification Sample, that supports Unicode.
Media Files Available for Samples¶
To run the sample applications, you can use images and videos from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
Samples that Support Pre-Trained Models¶
To run the sample, you can use public or Intel’s pre-trained models from the Open Model Zoo. The models can be downloaded using the Model Downloader.
Build the Sample Applications¶
Build the Sample Applications on Linux¶
The officially supported Linux* build environment is the following:
Ubuntu* 18.04 LTS 64-bit or Ubuntu* 20.04 LTS 64-bit
GCC* 7.5.0 (for Ubuntu* 18.04) or GCC* 9.3.0 (for Ubuntu* 20.04)
CMake* version 3.10 or higher
Note
For building samples from the open-source version of OpenVINO™ toolkit, see the build instructions on GitHub.
To build the C or C++ sample applications for Linux, go to the <INSTALL_DIR>/samples/c or <INSTALL_DIR>/samples/cpp directory, respectively, and run the build_samples.sh script:
build_samples.shOnce the build is completed, you can find sample binaries in the following folders:
C samples:
~/inference_engine_c_samples_build/intel64/ReleaseC++ samples:
~/inference_engine_cpp_samples_build/intel64/Release
You can also build the sample applications manually:
Note
If you have installed the product as a root user, switch to root mode before you continue: sudo -i
Navigate to a directory that you have write access to and create a samples build directory. This example uses a directory named
build:mkdir build
Note
If you run the Image Classification verification script during the installation, the C++ samples build directory is created in your home directory: ~/inference_engine_cpp_samples_build/
Go to the created directory:
cd build
Run CMake to generate the Make files for release or debug configuration. For example, for C++ samples:
For release configuration:
cmake -DCMAKE_BUILD_TYPE=Release <INSTALL_DIR>/samples/cpp
For debug configuration:
cmake -DCMAKE_BUILD_TYPE=Debug <INSTALL_DIR>/samples/cpp
Run
maketo build the samples:make
For the release configuration, the sample application binaries are in <path_to_build_directory>/intel64/Release/; for the debug configuration — in <path_to_build_directory>/intel64/Debug/.
Build the Sample Applications on Microsoft Windows¶
The recommended Windows build environment is the following:
Microsoft Windows 10
Microsoft Visual Studio 2019
CMake version 3.10 or higher
Note
If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14 or higher.
To build the C or C++ sample applications on Windows, go to the <INSTALL_DIR>\samples\c or <INSTALL_DIR>\samples\cpp directory, respectively, and run the build_samples_msvc.bat batch file:
build_samples_msvc.batBy default, the script automatically detects the highest Microsoft Visual Studio version installed on the machine and uses it to create and build a solution for a sample code
Once the build is completed, you can find sample binaries in the following folders:
C samples:
C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_c_samples_build\intel64\ReleaseC++ samples:
C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release
You can also build a generated solution manually. For example, if you want to build C++ sample binaries in Debug configuration, run the appropriate version of the Microsoft Visual Studio and open the generated solution file from the C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\Samples.sln directory.
Build the Sample Applications on macOS*¶
The officially supported macOS* build environment is the following:
macOS* 10.15 64-bit or higher
Clang* compiler from Xcode* 10.1 or higher
CMake* version 3.13 or higher
Note
For building samples from the open-source version of OpenVINO™ toolkit, see the build instructions on GitHub.
To build the C or C++ sample applications for macOS, go to the <INSTALL_DIR>/samples/c or <INSTALL_DIR>/samples/cpp directory, respectively, and run the build_samples.sh script:
build_samples.shOnce the build is completed, you can find sample binaries in the following folders:
C samples:
~/inference_engine_c_samples_build/intel64/ReleaseC++ samples:
~/inference_engine_cpp_samples_build/intel64/Release
You can also build the sample applications manually:
Note
If you have installed the product as a root user, switch to root mode before you continue: sudo -i
Note
Before proceeding, make sure you have OpenVINO™ environment set correctly. This can be done manually by
cd <INSTALL_DIR>/
source setupvars.shNavigate to a directory that you have write access to and create a samples build directory. This example uses a directory named
build:mkdir build
Note
If you ran the Image Classification verification script during the installation, the C++ samples build directory was already created in your home directory: ~/inference_engine_cpp_samples_build/
Go to the created directory:
cd build
Run CMake to generate the Make files for release or debug configuration. For example, for C++ samples:
For release configuration:
cmake -DCMAKE_BUILD_TYPE=Release <INSTALL_DIR>/samples/cpp
For debug configuration:
cmake -DCMAKE_BUILD_TYPE=Debug <INSTALL_DIR>/samples/cpp
Run
maketo build the samples:make
For the release configuration, the sample application binaries are in <path_to_build_directory>/intel64/Release/; for the debug configuration — in <path_to_build_directory>/intel64/Debug/.
Get Ready for Running the Sample Applications¶
Get Ready for Running the Sample Applications on Linux*¶
Before running compiled binary files, make sure your application can find the OpenVINO Runtime libraries. Run the setupvars script to set all necessary environment variables:
source <INSTALL_DIR>/setupvars.sh**(Optional)**: The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:
Open the
.bashrcfile in<user_home_directory>:vi <user_home_directory>/.bashrc
Add this line to the end of the file:
source /opt/intel/openvino_2022/setupvars.sh
Save and close the file: press the Esc key, type
:wqand press the Enter key.To test your change, open a new terminal. You will see
[setupvars.sh] OpenVINO environment initialized.
You are ready to run sample applications. To learn about how to run a particular sample, read the sample documentation by clicking the sample name in the samples list above.
Get Ready for Running the Sample Applications on Windows*¶
Before running compiled binary files, make sure your application can find the OpenVINO Runtime libraries. Use the setupvars script, which sets all necessary environment variables:
<INSTALL_DIR>\setupvars.batTo debug or run the samples on Windows in Microsoft Visual Studio, make sure you have properly configured Debugging environment settings for the Debug and Release configurations. Set correct paths to the OpenCV libraries, and debug and release versions of the OpenVINO Runtime libraries. For example, for the Debug configuration, go to the project’s Configuration Properties to the Debugging category and set the PATH variable in the Environment field to the following:
PATH=<INSTALL_DIR>\runtime\bin;%PATH%where <INSTALL_DIR> is the directory in which the OpenVINO toolkit is installed.
You are ready to run sample applications. To learn about how to run a particular sample, read the sample documentation by clicking the sample name in the samples list above.