Get Started with Samples

To use OpenVINO samples, install OpenVINO using one of the following distributions:

• Archive files (recommended) - Linux | Windows | macOS

• APT or YUM for Linux

• Docker image

• Build from source

If you install OpenVINO Runtime via archive files, sample applications are created in the following directories:

• <INSTALL_DIR>/samples/python

• <INSTALL_DIR>/samples/cpp

• <INSTALL_DIR>/samples/c

Note

If you install OpenVINO without samples, you can still get them directly from the OpenVINO repository.

Before you build samples, refer to the system requirements page and make sure that all the prerequisites have been installed. Next, you can perform the following steps:

1. Build Samples.

2. Select a Sample.

3. Download a suitable model.

4. Download media files used as input, if necessary.

Once you perform all the steps, you can run inference with the chosen sample application to see the results.

Build the Sample Applications

Select a sample you want to use from the OpenVINO Samples page, and follow the instructions below to build it in your operating system.

Note

Some samples may also require OpenCV to run properly. Make sure to install it for use with vision-oriented samples.

Instructions below show how to build sample applications with CMake. If you are interested in building them from source, check the build instructions on GitHub .

Linux

Python

Each Python sample directory contains the requirements.txt file, which you must install before running the sample:

cd <INSTALL_DIR>/samples/python/<SAMPLE_DIR>
python3 -m pip install -r ./requirements.txt

C and C++

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.sh

Once the build is completed, you can find sample binaries in the following folders:

• C samples: ~/openvino_c_samples_build/<architecture>/Release

• C++ samples: ~/openvino_cpp_samples_build/<architecture>/Release where the <architecture> is the output of uname -m, for example, intel64, armhf, or aarch64.

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 .

1. 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 ran the Image Classification verification script during the installation, the C++ samples build directory is created in your home directory: ~/openvino_cpp_samples_build/

2. Go to the created directory:

   cd build
   

3. Run CMake to generate the Make files for release configuration. For example, for C++ samples:

   cmake -DCMAKE_BUILD_TYPE=Release <INSTALL_DIR>/samples/cpp
   

4. Run make to build the samples:

   cmake --build . --parallel
   

For the release configuration, the sample application binaries are in <path_to_build_directory>/<architecture>/Release/; for the debug configuration — in <path_to_build_directory>/<architecture>/Debug/.

Windows

Python

Each Python sample directory contains the requirements.txt file, which you must install before running the sample:

cd <INSTALL_DIR>\samples\python\<SAMPLE_DIR>
python -m pip install -r requirements.txt

C and C++

Note

If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14 or higher.

You can build the C or C++ sample applications on Windows with either PowerShell or Command Prompt.

PowerShell

To build Samples with PowerShell, run the following command:

& <path-to-build-samples-folder>/build_samples.ps1

Command Prompt

To build Samples with CMD, 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.bat

By default, the script automatically detects the highest Microsoft Visual Studio version installed on the system 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\openvino_c_samples_build\<architecture>\Release

• C++ samples: C:\Users\<user>\Documents\Intel\OpenVINO\openvino_cpp_samples_build\<architecture>\Release where the <architecture> is the output of echo PROCESSOR_ARCHITECTURE%, for example, intel64 (AMD64), or arm64.

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\openvino_cpp_samples_build\Samples.sln directory.

macOS

Python

Each Python sample directory contains the requirements.txt file, which you must install before running the sample:

cd <INSTALL_DIR>/samples/python/<SAMPLE_DIR>
python3 -m pip install -r ./requirements.txt

C and C++

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.sh

Once the build is completed, you can find sample binaries in the following folders:

• C samples: ~/openvino_c_samples_build/<architecture>/Release

• C++ samples: ~/openvino_cpp_samples_build/<architecture>/Release

You can also build the sample applications manually. Before proceeding, make sure you have OpenVINO™ environment set correctly. This can be done manually by:

cd <INSTALL_DIR>/
source setupvars.sh

Note

If you have installed the product as a root user, switch to root mode before you continue: sudo -i

1. 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 ran the Image Classification verification script during the installation, the C++ samples build directory was already created in your home directory: ~/openvino_cpp_samples_build/

2. Go to the created directory:

   cd build
   

3. Run CMake to generate the Make files for release configuration. For example, for C++ samples:

   cmake -DCMAKE_BUILD_TYPE=Release <INSTALL_DIR>/samples/cpp
   

4. Run make to build the samples:

   make
   

For the release configuration, the sample application binaries are in <path_to_build_directory>/<architecture>/Release/; for the debug configuration — in <path_to_build_directory>/<architecture>/Debug/.

Sample Application Setup

First, select a sample from the Sample Overview and read the dedicated article to learn how to run it.

Download the Models

You need a model that is specific for your inference task. You can get it from one of model repositories, such as TensorFlow Zoo, HuggingFace, or TensorFlow Hub.

Convert the Model

If Your model requires conversion, check the article for information how to do it.

Download a Media to use

Most of the samples require you to provide an image or a video as input for the model. OpenVINO provides several sample images and videos for you to run code samples and demo applications:

• Sample images and video

• Sample videos

To run the sample applications, you can use images and videos from the media files collection available here . As an alternative, you can get them from sites like Pexels or Google Images .

Run Inference on a Sample

To run the code sample with an input image using the IR model:

1. Set up the OpenVINO environment variables:

   Linux

   source  <INSTALL_DIR>/setupvars.sh
   

   Windows

   <INSTALL_DIR>\setupvars.bat
   

   macOS

   source <INSTALL_DIR>/setupvars.sh
   

Note

OpenVINO environment variables can be set up by running the following command in PowerShell:

. <path-to-setupvars-folder>/setupvars.ps1

2. Go to the code samples release directory created when you built the samples earlier:

   Linux

   cd ~/openvino_cpp_samples_build/intel64/Release
   

   Windows

   cd  %USERPROFILE%\Documents\Intel\OpenVINO\openvino_samples_build\intel64\Release
   

   macOS

   cd ~/openvino_cpp_samples_build/intel64/Release
   

3. Run the code sample executable, specifying the input media file, the IR for your model, and a target device for performing inference:

   Python

   Linux

   python <sample.py file> -m <path_to_model> -i <path_to_media> -d <target_device>
   

   Windows

   python <sample.py file> -m <path_to_model> -i <path_to_media> -d <target_device>
   

   macOS

   python <sample.py file> -m <path_to_model> -i <path_to_media> -d <target_device>
   

   C++

   Linux

   <sample.exe file> -i <path_to_media> -m <path_to_model> -d <target_device>
   

   Windows

   <sample.exe file> -i <path_to_media> -m <path_to_model> -d <target_device>
   

   macOS

   <sample.exe file> -i <path_to_media> -m <path_to_model> -d <target_device>
   

Examples

Running Inference on CPU

The following command shows how to run the Image Classification Code Sample using the dog.bmp file as an input image, the model in IR format from the ir directory, and the CPU as the target hardware:

Note

• Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps, as described earlier on this page.

• Running on GPU is not compatible with macOS.

Python

Linux

python classification_sample_async.py -m ~/ir/googlenet-v1.xml -i ~/Downloads/dog.bmp -d CPU

Windows

python classification_sample_async.py -m %USERPROFILE%\Documents\ir\googlenet-v1.xml -i %USERPROFILE%\Downloads\dog.bmp -d CPU

macOS

python classification_sample_async.py -m ~/ir/googlenet-v1.xml -i ~/Downloads/dog.bmp -d CPU

C++

Linux

./classification_sample_async -i ~/Downloads/dog.bmp -m ~/ir/googlenet-v1.xml -d CPU

Windows

.\classification_sample_async.exe -i %USERPROFILE%\Downloads\dog.bmp -m %USERPROFILE%\Documents\ir\googlenet-v1.xml -d CPU

macOS

./classification_sample_async -i ~/Downloads/dog.bmp -m ~/ir/googlenet-v1.xml -d CPU

When the sample application is complete, you are given the label and confidence for the top 10 categories. The input image and sample output of the inference results is shown below:

Top 10 results:

Image dog.bmp

   classid probability label
   ------- ----------- -----
   156     0.6875963   Blenheim spaniel
   215     0.0868125   Brittany spaniel
   218     0.0784114   Welsh springer spaniel
   212     0.0597296   English setter
   217     0.0212105   English springer, English springer spaniel
   219     0.0194193   cocker spaniel, English cocker spaniel, cocker
   247     0.0086272   Saint Bernard, St Bernard
   157     0.0058511   papillon
   216     0.0057589   clumber, clumber spaniel
   154     0.0052615   Pekinese, Pekingese, Peke

Other Samples

Articles in this section describe all sample applications provided with OpenVINO. They will give you more information on how each of them works, giving you a convenient starting point for your own application.