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README.md

Inference Engine Demos

The Open Model Zoo demo applications are console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases.

The Open Model Zoo includes the following demos:

* Several C++ demos referenced above have simplified implementation in Python*, located in the python_demos directory.

Media Files Available for Demos

To run the demo applications, you can use images and videos from the media files collection available at https://github.com/intel-iot-devkit/sample-videos.

Demos that Support Pre-Trained Models

NOTE: Inference Engine HDDL and FPGA plugins are available in proprietary distribution only.

You can download the pre-trained models using the OpenVINO Model Downloader or from https://download.01.org/opencv/. The table below shows the correlation between models, demos, and supported plugins. The plugins names are exactly as they are passed to the demos with -d option. The correlation between the plugins and supported devices see in the Supported Devices section.

NOTE: MYRIAD below stands for Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units.

Model Demos supported on the model CPU GPU MYRIAD/HDDL HETERO:FPGA,CPU
human-pose-estimation-3d-0001 3D Human Pose Estimation Python* Demo Supported Supported
action-recognition-0001-decoder Action Recognition Demo Supported Supported
action-recognition-0001-encoder Action Recognition Demo Supported Supported
driver-action-recognition-adas-0002-decoder Action Recognition Demo Supported Supported
driver-action-recognition-adas-0002-encoder Action Recognition Demo Supported Supported Supported
person-attributes-recognition-crossroad-0230 Crossroad Camera Demo Supported Supported Supported Supported
person-reidentification-retail-0031 Crossroad Camera Demo Supported Supported Supported Supported
person-reidentification-retail-0076 Crossroad Camera Demo
Multi-Camera Multi-Person Tracking Demo
Supported Supported Supported Supported
person-reidentification-retail-0079 Crossroad Camera Demo
Multi-Camera Multi-Person Tracking Demo
Supported Supported Supported Supported
person-vehicle-bike-detection-crossroad-0078 Crossroad Camera Demo Supported Supported Supported Supported
human-pose-estimation-0001 Human Pose Estimation Demo Supported Supported Supported Supported
image-retrieval-0001 Image Retrieval Python* Demo Supported Supported Supported Supported
semantic-segmentation-adas-0001 Image Segmentation Demo Supported Supported Supported
instance-segmentation-security-0010 Instance Segmentation Demo Supported Supported
instance-segmentation-security-0050 Instance Segmentation Demo Supported Supported
instance-segmentation-security-0083 Instance Segmentation Demo Supported Supported
age-gender-recognition-retail-0013 Interactive Face Detection Demo Supported Supported Supported Supported
emotions-recognition-retail-0003 Interactive Face Detection Demo Supported Supported Supported Supported
face-detection-adas-0001 Interactive Face Detection Demo
Interactive Face Recognition Python* Demo
Supported Supported Supported Supported
face-detection-adas-binary-0001 Interactive Face Detection Demo Supported Supported
face-detection-retail-0004 Interactive Face Detection Demo
Interactive Face Recognition Python* Demo
Supported Supported Supported Supported
facial-landmarks-35-adas-0002 Interactive Face Detection Demo Supported Supported Supported Supported
head-pose-estimation-adas-0001 Interactive Face Detection Demo Supported Supported Supported Supported
license-plate-recognition-barrier-0001 Security Barrier Camera Demo Supported Supported Supported Supported
vehicle-attributes-recognition-barrier-0039 Security Barrier Camera Demo Supported Supported Supported Supported
vehicle-license-plate-detection-barrier-0106 Security Barrier Camera Demo Supported Supported Supported Supported
vehicle-license-plate-detection-barrier-0123 Security Barrier Camera Demo Supported Supported Supported Supported
face-reidentification-retail-0095 Smart Classroom Demo
Interactive Face Recognition Python* Demo
Supported Supported Supported Supported
landmarks-regression-retail-0009 Smart Classroom Demo
Interactive Face Recognition Python* Demo
Supported Supported Supported Supported
person-detection-action-recognition-0005 Smart Classroom Demo Supported Supported Supported Supported
person-detection-action-recognition-teacher-0002 Smart Classroom Demo Supported Supported Supported
single-human-pose-estimation-0001 Single Human Pose Estimation Python* Demo Supported Supported
single-image-super-resolution-1032 Super Resolution Demo Supported Supported Supported
single-image-super-resolution-1033 Super Resolution Demo Supported Supported Supported
text-detection-0003 Text Detection Demo Supported Supported Supported
text-detection-0004 Text Detection Demo Supported Supported Supported
text-recognition-0012 Text Detection Demo Supported Supported
gaze-estimation-adas-0002 Gaze Estimation Demo Supported Supported Supported Supported
head-pose-estimation-adas-0001 Gaze Estimation Demo Supported Supported Supported Supported
facial-landmarks-35-adas-0002 Gaze Estimation Demo Supported Supported Supported Supported
pedestrian-and-vehicle-detector-adas-0001 any demo that supports SSD*-based models, above Supported Supported Supported Supported
pedestrian-detection-adas-0002 any demo that supports SSD*-based models, above Supported Supported Supported Supported
pedestrian-detection-adas-binary-0001 any demo that supports SSD*-based models, above Supported Supported
person-detection-retail-0002 any demo that supports SSD*-based models, above Supported Supported Supported Supported
person-detection-retail-0013 any demo that supports SSD*-based models, above Supported Supported Supported Supported
road-segmentation-adas-0001 any demo that supports SSD*-based models, above Supported Supported Supported Supported
vehicle-detection-adas-binary-0001 any demo that supports SSD*-based models, above Supported Supported
vehicle-detection-adas-0002 any demo that supports SSD*-based models, above Supported Supported Supported Supported

Notice that the FPGA support comes through a heterogeneous execution, for example, when the post-processing is happening on the CPU.

Build the Demo Applications

To be able to build demos you need to source InferenceEngine and OpenCV environment from a binary package which is available as proprietary distribution. Please run the following command before the demos build (assuming that the binary package was installed to <INSTALL_DIR>):

source <INSTALL_DIR>/deployment_tools/bin/setupvars.sh

You can also build demos manually using Inference Engine binaries from the dldt repo. In this case please set InferenceEngine_DIR to a CMake folder you built the dldt project from, for example <dldt_repo>/inference-engine/build. Please also set the OpenCV_DIR variable pointing to the required OpenCV package. The same OpenCV version should be used both for the inference engine and demos build. Please refer to the Inference Engine build instructions for details. Please also add path to built Inference Engine libraries to LD_LIBRARY_PATH (Linux*) or PATH (Windows*) variable before building the demos.

Build the Demo Applications on Linux*

The officially supported Linux* build environment is the following:

  • Ubuntu* 16.04 LTS 64-bit or CentOS* 7.4 64-bit
  • GCC* 5.4.0 (for Ubuntu* 16.04) or GCC* 4.8.5 (for CentOS* 7.4)
  • CMake* version 2.8 or higher.

To build the demo applications for Linux, go to the directory with the build_demos.sh script and run it:

build_demos.sh

You can also build the demo applications manually:

  1. Navigate to a directory that you have write access to and create a demos build directory. This example uses a directory named build:
mkdir build
  1. Go to the created directory:
cd build
  1. Run CMake to generate the Make files for release or debug configuration:
  • For release configuration:
cmake -DCMAKE_BUILD_TYPE=Release <open_model_zoo>/demos
  • For debug configuration:
cmake -DCMAKE_BUILD_TYPE=Debug <open_model_zoo>/demos
  1. Run make to build the demos:
make

For the release configuration, the demo application binaries are in <path_to_build_directory>/intel64/Release/; for the debug configuration — in <path_to_build_directory>/intel64/Debug/.

Build the Demos Applications on Microsoft Windows* OS

The recommended Windows* build environment is the following:

  • Microsoft Windows* 10
  • Microsoft Visual Studio* 2015, 2017, or 2019
  • CMake* version 2.8 or higher

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

To build the demo applications for Windows, go to the directory with the build_demos_msvc.bat batch file and run it:

build_demos_msvc.bat

By 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 demo code. Optionally, you can also specify the preffered Microsoft Visual Studio version to be used by the script. Supported versions are: VS2015, VS2017, VS2019. For example, to build the demos using the Microsoft Visual Studio 2017, use the following command:

build_demos_msvc.bat VS2017

The demo applications binaries are in the C:\Users\<username>\Documents\Intel\OpenVINO\omz_demos_build_build\intel64\Release directory.

You can also build a generated solution by yourself, for example, if you want to build binaries in Debug configuration. Run the appropriate version of the Microsoft Visual Studio and open the generated solution file from the C:\Users\<username>\Documents\Intel\OpenVINO\omz_demos_build\Demos.sln directory.

Build the Native Python* Extension Modules

Some of the Python demo applications require native Python extension modules to be built before they can be run. This requires you to have Python development files (headers and import libraries) installed. To build these modules, follow the instructions for building the demo applications above, but add -DENABLE_PYTHON=ON to either the cmake or the build_demos* command, depending on which you use. For example:

cmake -DCMAKE_BUILD_TYPE=Release -DENABLE_PYTHON=ON <open_model_zoo>/demos

Get Ready for Running the Demo Applications

Get Ready for Running the Demo Applications on Linux*

Before running compiled binary files, make sure your application can find the Inference Engine and OpenCV libraries. If you use a proprietary distribution to build demos, run the setupvars script to set all necessary environment variables:

source <INSTALL_DIR>/bin/setupvars.sh

If you use your own Inference Engine and OpenCV binaries to build the demos please make sure you have added them to the LD_LIBRARY_PATH environment variable.

(Optional): The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:

  1. Open the .bashrc file in <user_home_directory>:
vi <user_home_directory>/.bashrc
  1. Add this line to the end of the file:
source <INSTALL_DIR>/bin/setupvars.sh
  1. Save and close the file: press the Esc key, type :wq and press the Enter key.
  2. To test your change, open a new terminal. You will see [setupvars.sh] OpenVINO environment initialized.

To run Python demo applications that require native Python extension modules, you must additionally set up the PYTHONPATH environment variable as follows, where <bin_dir> is the directory with the built demo applications:

export PYTHONPATH="$PYTHONPATH:<bin_dir>/lib"

You are ready to run the demo applications. To learn about how to run a particular demo, read the demo documentation by clicking the demo name in the demo list above.

Get Ready for Running the Demo Applications on Windows*

Before running compiled binary files, make sure your application can find the Inference Engine and OpenCV libraries. If you use a proprietary distribution to build demos, run the setupvars script to set all necessary environment variables:

<INSTALL_DIR>\bin\setupvars.bat

If you use your own Inference Engine and OpenCV binaries to build the demos please make sure you have added to the PATH environment variable.

To run Python demo applications that require native Python extension modules, you must additionally set up the PYTHONPATH environment variable as follows, where <bin_dir> is the directory with the built demo applications:

set PYTHONPATH=%PYTHONPATH%;<bin_dir>

To debug or run the demos 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 Inference Engine 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>\deployment_tools\inference_engine\bin\intel64\Debug;<INSTALL_DIR>\opencv\bin;%PATH%

where <INSTALL_DIR> is the directory in which the OpenVINO toolkit is installed.

You are ready to run the demo applications. To learn about how to run a particular demo, read the demo documentation by clicking the demo name in the demos list above.

See Also

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