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:
- 3D Human Pose Estimation Python* Demo - 3D human pose estimation demo.
- Action Recognition Python* Demo - Demo application for Action Recognition algorithm, which classifies actions that are being performed on input video.
- Colorization Python* Demo - Colorization demo colorizes input frames.
- Crossroad Camera C++ Demo - Person Detection followed by the Person Attributes Recognition and Person Reidentification Retail, supports images/video and camera inputs.
- Gaze Estimation C++ Demo - Face detection followed by gaze estimation, head pose estimation and facial landmarks regression.
- Human Pose Estimation C++ Demo - Human pose estimation demo.
- Image Inpainting Python Demo - Demo application for GMCNN inpainting network.
- Image Retrieval Python* Demo - The demo demonstrates how to run Image Retrieval models using OpenVINO™.
- Image Segmentation C++ Demo - Inference of image segmentation networks like FCN8 (the demo supports only images as inputs).
- Instance Segmentation Python* Demo - Inference of instance segmentation networks trained in
Detectronormaskrcnn-benchmark. - Interactive Face Detection C++ Demo - Face Detection coupled with Age/Gender, Head-Pose, Emotion, and Facial Landmarks detectors. Supports video and camera inputs.
- Interactive Face Recognition Python* Demo - Face Detection coupled with Head-Pose, Facial Landmarks and Face Recognition detectors. Supports video and camera inputs.
- Mask R-CNN C++ Demo for TensorFlow* Object Detection API - Inference of instance segmentation networks created with TensorFlow* Object Detection API.
- Monodepth Python* Demo - The demo demonstrates how to run monocular depth estimation models.
- Multi-Camera Multi-Person Tracking Python* Demo Demo application for multiple persons tracking on multiple cameras.
- Multi-Channel C++ Demos - Several demo applications for multi-channel scenarios.
- Object Detection for CenterNet Python* Demo - Demo application for CenterNet object detection network.
- Object Detection for Faster R-CNN C++ Demo - Inference of object detection networks like Faster R-CNN (the demo supports only images as inputs).
- Object Detection for SSD C++ Demo - Demo application for SSD-based Object Detection networks, new Async API performance showcase, and simple OpenCV interoperability (supports video and camera inputs).
- Object Detection for YOLO V3 C++ Demo - Demo application for YOLOV3-based Object Detection networks, new Async API performance showcase, and simple OpenCV interoperability (supports video and camera inputs).
- Pedestrian Tracker C++ Demo - Demo application for pedestrian tracking scenario.
- Security Barrier Camera C++ Demo - Vehicle Detection followed by the Vehicle Attributes and License-Plate Recognition, supports images/video and camera inputs.
- Single Human Pose Estimation Python* Demo - 2D human pose estimation demo.
- Smart Classroom C++ Demo - Face recognition and action detection demo for classroom environment.
- Super Resolution C++ Demo - Super Resolution demo (the demo supports only images as inputs). It enhances the resolution of the input image.
- Text Detection C++ Demo - Text Detection demo. It detects and recognizes multi-oriented scene text on an input image and puts a bounding box around detected area.
- Text Spotting Python* Demo - The demo demonstrates how to run Text Spotting models.
- Handwritten Japanese Recognition Python* Demo - The demo demonstrates how to run Handwritten Japanese Recognition models.
* Several C++ demos referenced above have simplified implementation in Python*, located in the python_demos directory.
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.
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 | ||
| instance-segmentation-security-1025 | 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 | ||
| handwritten-japanese-recognition-0001 | Handwritten Japanese Recognition Python* Demo | Supported | 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.
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.shYou 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.
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.shYou can also build the demo applications manually:
- 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- Go to the created directory:
cd build- 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- Run
maketo build the demos:
makeFor 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/.
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.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 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 VS2017The 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.
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>/demosBefore 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.shIf 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:
- Open the
.bashrcfile in<user_home_directory>:
vi <user_home_directory>/.bashrc- Add this line to the end of the file:
source <INSTALL_DIR>/bin/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.
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.
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.batIf 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.