openvino/docs/get_started/get_started_demos.md

Get Started with Sample and Demo Applications

Introduction

This section guides you through a simplified workflow for the Intel® Distribution of OpenVINO™ toolkit using code samples and demo applications. You will perform the following steps:

1. Use the Model Downloader to download suitable models.
2. Convert the models with the Model Optimizer.
3. Download media files to run inference on.
4. Run inference on the sample and see the results:
   • Image Classification Code Sample
   • Security Barrier Camera Demo application

If you installed OpenVINO™ via pip you need to change commands listed below. Details are listed in one of tutorials.

This guide assumes you completed all installation and configuration steps. If you have not yet installed and configured the toolkit:

@sphinxdirective .. tab:: Linux

See :doc:Install Intel® Distribution of OpenVINO™ toolkit for Linux* <openvino_docs_install_guides_installing_openvino_linux>

.. tab:: Windows

See :doc:Install Intel® Distribution of OpenVINO™ toolkit for Windows* <openvino_docs_install_guides_installing_openvino_windows>

.. tab:: macOS

See :doc:Install Intel® Distribution of OpenVINO™ toolkit for macOS* <openvino_docs_install_guides_installing_openvino_macos>

@endsphinxdirective

Build Samples and Demos

If you have already built the demos and samples, you can skip this section. The build will take about 5-10 minutes, depending on your system.

To build OpenVINO samples: @sphinxdirective .. tab:: Linux

Go to the :doc:Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview> and see the "Build the Sample Applications on Linux*" section.

.. tab:: Windows

Go to the :doc:Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview> and see the "Build the Sample Applications on Microsoft Windows* OS" section.

.. tab:: macOS

Go to the :doc:Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview> and see the "Build the Sample Applications on macOS*" section.

@endsphinxdirective

To build OpenVINO demos: @sphinxdirective .. tab:: Linux

Go to the :doc:Open Model Zoo Demos page <omz_demos> and see the "Build the Demo Applications on Linux*" section.

.. tab:: Windows

Go to the :doc:Open Model Zoo Demos page <omz_demos> and see the "Build the Demo Applications on Microsoft Windows* OS" section.

.. tab:: macOS

Go to the :doc:Open Model Zoo Demos page <omz_demos> and see the "Build the Demo Applications on Linux*" section. You can use the requirements from "To build OpenVINO samples" above and adapt the Linux build steps for macOS*.

@endsphinxdirective

Step 1: Download the Models

You must have a model that is specific for your inference task. Example model types are:

• Classification (AlexNet, GoogleNet, SqueezeNet, others): Detects one type of element in an image
• Object Detection (SSD, YOLO): Draws bounding boxes around multiple types of objects in an image
• Custom: Often based on SSD

Options to find a model suitable for the OpenVINO™ toolkit:

• Download public or Intel pre-trained models from the Open Model Zoo using the [Model Downloader tool](@ref omz_tools_downloader)
• Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
• Train your own model with machine learning tools

This guide uses the OpenVINO™ Model Downloader to get pre-trained models. You can use one of the following commands to find a model:

List the models available in the downloader

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
  python3 info_dumper.py --print_all

.. tab:: Windows

.. code-block:: bat

  cd <INSTALL_DIR>\deployment_tools\tools\model_downloader\
  python info_dumper.py --print_all

.. tab:: macOS

.. code-block:: sh

  cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
  python3 info_dumper.py --print_all

@endsphinxdirective

Use grep to list models that have a specific name pattern

   python3 info_dumper.py --print_all | grep <model_name>

Use the Model Downloader to download the models to a models directory. This guide uses <models_dir> and <models_name> as placeholders for the models directory and model name: @sphinxdirective .. tab:: Linux

Don't run downloader with sudo. It will further lead to complications .. code-block:: sh

  python3 downloader.py --name <model_name> --output_dir <models_dir>

.. tab:: Windows

.. code-block:: bat

  python downloader.py --name <model_name> --output_dir <models_dir>

.. tab:: macOS

Don't run downloader with sudo. It will further lead to complications .. code-block:: sh

  python3 downloader.py --name <model_name> --output_dir <models_dir>

@endsphinxdirective

Download the following models to run the Image Classification Sample and Security Barrier Camera Demo applications:

Model Name	Code Sample or Demo App
squeezenet1.1	Image Classification Sample
vehicle-license-plate-detection-barrier-0106	Security Barrier Camera Demo
vehicle-attributes-recognition-barrier-0039	Security Barrier Camera Demo
license-plate-recognition-barrier-0001	Security Barrier Camera Demo

@sphinxdirective .. raw:: html

@endsphinxdirective

To download the SqueezeNet 1.1 Caffe* model to the models folder:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  python3 downloader.py --name squeezenet1.1 --output_dir ~/models

.. tab:: Windows

.. code-block:: bat

  python downloader.py --name squeezenet1.1 --output_dir C:\Users\<USER_ID>\Documents\models

.. tab:: macOS

.. code-block:: sh

  python3 downloader.py --name squeezenet1.1 --output_dir ~/models

@endsphinxdirective

Your screen looks similar to this after the download and shows the paths of downloaded files: @sphinxdirective .. tab:: Linux

.. code-block:: sh

  ###############|| Downloading models ||###############

  ========= Downloading /home/username/models/public/squeezenet1.1/squeezenet1.1.prototxt

  ========= Downloading /home/username/models/public/squeezenet1.1/squeezenet1.1.caffemodel
  ... 100%, 4834 KB, 3157 KB/s, 1 seconds passed

  ###############|| Post processing ||###############

  ========= Replacing text in /home/username/models/public/squeezenet1.1/squeezenet1.1.prototxt =========

.. tab:: Windows

.. code-block:: bat

  ################|| Downloading models ||################

  ========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
  ... 100%, 9 KB, ? KB/s, 0 seconds passed

  ========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel
  ... 100%, 4834 KB, 571 KB/s, 8 seconds passed

  ################|| Post-processing ||################

  ========== Replacing text in C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt

.. tab:: macOS

.. code-block:: sh

  ###############|| Downloading models ||###############

  ========= Downloading /Users/username/models/public/squeezenet1.1/squeezenet1.1.prototxt
  ... 100%, 9 KB, 44058 KB/s, 0 seconds passed

  ========= Downloading /Users/username/models/public/squeezenet1.1/squeezenet1.1.caffemodel
  ... 100%, 4834 KB, 4877 KB/s, 0 seconds passed

  ###############|| Post processing ||###############

  ========= Replacing text in /Users/username/models/public/squeezenet1.1/squeezenet1.1.prototxt =========

@endsphinxdirective

@sphinxdirective .. raw:: html

@endsphinxdirective

@sphinxdirective .. raw:: html

@endsphinxdirective

To download all three pre-trained models in FP16 precision to the models folder in your home folder:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  python3 downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir ~/models --precisions FP16

.. tab:: Windows

.. code-block:: bat

  python downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir C:\Users\<USER_ID>\Documents\models --precisions FP16

.. tab:: macOS

.. code-block:: sh

  python3 downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir ~/models --precisions FP16

@endsphinxdirective

Your screen looks similar to this after the download:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ################|| Downloading models ||################

  ========== Downloading /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
  ... 100%, 204 KB, 183949 KB/s, 0 seconds passed

  ========== Downloading /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
  ... 100%, 1256 KB, 3948 KB/s, 0 seconds passed

  ========== Downloading /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
  ... 100%, 32 KB, 133398 KB/s, 0 seconds passed

  ========== Downloading /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
  ... 100%, 1222 KB, 3167 KB/s, 0 seconds passed

  ========== Downloading /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
  ... 100%, 47 KB, 85357 KB/s, 0 seconds passed

  ========== Downloading /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
  ... 100%, 2378 KB, 5333 KB/s, 0 seconds passed

  ################|| Post-processing ||################

.. tab:: Windows

.. code-block:: bat

  ################|| Downloading models ||################

  ========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml
  ... 100%, 207 KB, 13810 KB/s, 0 seconds passed

  ========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.bin
  ... 100%, 1256 KB, 70 KB/s, 17 seconds passed

  ========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml
  ... 100%, 32 KB, ? KB/s, 0 seconds passed

  ========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.bin
  ... 100%, 1222 KB, 277 KB/s, 4 seconds passed

  ========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml
  ... 100%, 47 KB, ? KB/s, 0 seconds passed

  ========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.bin
  ... 100%, 2378 KB, 120 KB/s, 19 seconds passed

  ################|| Post-processing ||################

.. tab:: macOS

.. code-block:: sh

  ################|| Downloading models ||################

  ========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
  ... 100%, 207 KB, 313926 KB/s, 0 seconds passed

  ========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
  ... 100%, 1256 KB, 2552 KB/s, 0 seconds passed

  ========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
  ... 100%, 32 KB, 172042 KB/s, 0 seconds passed

  ========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
  ... 100%, 1222 KB, 2712 KB/s, 0 seconds passed

  ========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
  ... 100%, 47 KB, 217130 KB/s, 0 seconds passed

  ========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
  ... 100%, 2378 KB, 4222 KB/s, 0 seconds passed

  ################|| Post-processing ||################

@endsphinxdirective

@sphinxdirective .. raw:: html

@endsphinxdirective

Step 2: Convert the Model with Model Optimizer

In this step, your trained models are ready to run through the Model Optimizer to convert them to the IR (Intermediate Representation) format. For most model types, this is required before using the Inference Engine with the model.

Models in the IR format always include an .xml and .bin file and may also include other files such as .json or .mapping. Make sure you have these files together in a single directory so the Inference Engine can find them.

REQUIRED: model_name.xml REQUIRED: model_name.bin OPTIONAL: model_name.json, model_name.mapping, etc.

This tutorial uses the public SqueezeNet 1.1 Caffe* model to run the Image Classification Sample. See the example in the Download Models section of this page to learn how to download this model.

The SqueezeNet1.1 model is downloaded in the Caffe* format. You must use the Model Optimizer to convert the model to IR. The vehicle-license-plate-detection-barrier-0106, vehicle-attributes-recognition-barrier-0039, and license-plate-recognition-barrier-0001 models are downloaded in IR format. You don't need to use the Model Optimizer on them because they are Intel models that have previously been converted. Public models will need converting with Model Optimizer.

Create an <ir_dir> directory to contain the model's Intermediate Representation (IR).

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  mkdir ~/ir

.. tab:: Windows

.. code-block:: bat

  mkdir C:\Users\<USER_ID>\Documents\ir

.. tab:: macOS

.. code-block:: sh

  mkdir ~/ir

@endsphinxdirective

The Inference Engine can perform inference on different precision formats, such as FP32, FP16, or INT8. To generate an IR with a specific precision, run the Model Optimizer with the appropriate --data_type option.

Generic Model Optimizer script:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  cd /opt/intel/openvino/deployment_tools/model_optimizer
  python3 mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>

.. tab:: Windows

.. code-block:: bat

  cd <INSTALL_DIR>\deployment_tools\model_optimizer
  python mo.py --input_model <model_dir>\<model_file> --data_type <model_precision> --output_dir <ir_dir>

.. tab:: macOS

.. code-block:: sh

  cd /opt/intel/openvino/deployment_tools/model_optimizer
  python3 ./mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>

@endsphinxdirective

IR files produced by the script are written to the <ir_dir> directory.

The command with most placeholders filled in and FP16 precision:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  cd /opt/intel/openvino/deployment_tools/model_optimizer
  python3 mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/ir

.. tab:: Windows

.. code-block:: bat

  cd <INSTALL_DIR>\deployment_tools\model_optimizer
  python mo.py --input_model C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel --data_type FP16 --output_dir C:\Users\<USER_ID>\Documents\ir

.. tab:: macOS

.. code-block:: sh

  cd /opt/intel/openvino/deployment_tools/model_optimizer
  python3 mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/ir

@endsphinxdirective

Step 3: Download a Video or Still Photo as Media

Many sources are available from which you can download video media to use the code samples and demo applications. Possibilities include:

• Pexels
• Google Images

As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes several sample images and videos that you can use for running code samples and demo applications: @sphinxdirective .. tab:: Linux

• /opt/intel/openvino_2021/deployment_tools/demo/car.png
• /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp
• Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>_
• Sample videos <https://github.com/intel-iot-devkit/sample-videos>_

.. tab:: Windows

• <INSTALL_DIR>\deployment_tools\demo\car.png
• <INSTALL_DIR>\deployment_tools\demo\car_1.bmp
• Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>_
• Sample videos <https://github.com/intel-iot-devkit/sample-videos>_

.. tab:: macOS

• /opt/intel/openvino_2021/deployment_tools/demo/car.png
• /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp
• Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>_
• Sample videos <https://github.com/intel-iot-devkit/sample-videos>_

@endsphinxdirective

Step 4: Run Inference on the Sample

Run the Image Classification Code Sample

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

1. Set up the OpenVINO environment variables: @sphinxdirective .. tab:: Linux

   .. code-block:: sh

   source /opt/intel/openvino/bin/setupvars.sh

.. tab:: Windows

.. code-block:: bat

  <INSTALL_DIR>\openvino\bin\setupvars.bat

.. tab:: macOS

.. code-block:: sh

  source /opt/intel/openvino/bin/setupvars.sh

@endsphinxdirective

2. Go to the code samples release directory created when you built the samples earlier: @sphinxdirective .. tab:: Linux

   .. code-block:: sh

   cd ~/inference_engine_cpp_samples_build/intel64/Release

.. tab:: Windows

.. code-block:: bat

  cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release

.. tab:: macOS

.. code-block:: sh

  cd ~/inference_engine_cpp_samples_build/intel64/Release

@endsphinxdirective

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

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>

.. tab:: Windows

.. code-block:: bat

  classification_sample_async.exe -i <path_to_media> -m <path_to_model> -d <target_device>

.. tab:: macOS

.. code-block:: sh

  classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>

@endsphinxdirective

@sphinxdirective .. raw:: html

@endsphinxdirective

The following commands run the Image Classification Code Sample using the car.png file from the demo directory as an input image, the model in IR format from the ir directory, and on different hardware devices:

CPU:
@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU

.. tab:: Windows

.. code-block:: bat

  .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d CPU

.. tab:: macOS

.. code-block:: sh

  ./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU

@endsphinxdirective

GPU:

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*.

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU

.. tab:: Windows

.. code-block:: bat

  .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d GPU

@endsphinxdirective

MYRIAD:

Note

: Running inference on VPU devices (Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps, as described earlier on this page.

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD

.. tab:: Windows

.. code-block:: bat

  .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d MYRIAD

.. tab:: macOS

.. code-block:: sh

  ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD

@endsphinxdirective

When the sample application is complete, you see the label and confidence for the top 10 categories on the display. Below is a sample output with inference results on CPU:

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  Top 10 results:

  Image /opt/intel/deployment-tools/demo/car.png

  classid probability label
  ------- ----------- -----
  817     0.8363345   sports car, sport car
  511     0.0946488   convertible
  479     0.0419131   car wheel
  751     0.0091071   racer, race car, racing car
  436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
  656     0.0037564   minivan
  586     0.0025741   half track
  717     0.0016069   pickup, pickup truck
  864     0.0012027   tow truck, tow car, wrecker
  581     0.0005882   grille, radiator grille

  [ INFO ] Execution successful
  
  [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

.. tab:: Windows

.. code-block:: bat

Top 10 results:

Image C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo\car.png

  classid probability label
  ------- ----------- -----
  817     0.8363345   sports car, sport car
  511     0.0946488   convertible
  479     0.0419131   car wheel
  751     0.0091071   racer, race car, racing car
  436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
  656     0.0037564   minivan
  586     0.0025741   half track
  717     0.0016069   pickup, pickup truck
  864     0.0012027   tow truck, tow car, wrecker
  581     0.0005882   grille, radiator grille

  [ INFO ] Execution successful
  
  [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

.. tab:: macOS

.. code-block:: sh

  Top 10 results:

  Image /opt/intel/deployment-tools/demo/car.png

  classid probability label
  ------- ----------- -----
  817     0.8363345   sports car, sport car
  511     0.0946488   convertible
  479     0.0419131   car wheel
  751     0.0091071   racer, race car, racing car
  436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
  656     0.0037564   minivan
  586     0.0025741   half track
  717     0.0016069   pickup, pickup truck
  864     0.0012027   tow truck, tow car, wrecker
  581     0.0005882   grille, radiator grille

  [ INFO ] Execution successful
  
  [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

@endsphinxdirective

@sphinxdirective .. raw:: html

@endsphinxdirective

Run the Security Barrier Camera Demo Application

To run the Security Barrier Camera Demo Application using an input image on the prepared IR models:

1. Set up the OpenVINO environment variables: @sphinxdirective .. tab:: Linux

   .. code-block:: sh

   source /opt/intel/openvino/bin/setupvars.sh

.. tab:: Windows

.. code-block:: bat

  <INSTALL_DIR>\bin\setupvars.bat

.. tab:: macOS

.. code-block:: sh

  source /opt/intel/openvino/bin/setupvars.sh

@endsphinxdirective

2. Go to the demo application build directory: @sphinxdirective .. tab:: Linux

   .. code-block:: sh

   cd ~/inference_engine_demos_build/intel64/Release

.. tab:: Windows

.. code-block:: bat

  cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release

.. tab:: macOS

.. code-block:: sh

  cd ~/inference_engine_demos_build/intel64/Release

@endsphinxdirective

3. Run the demo executable, specifying the input media file, list of model IRs, and a target device for performing inference: @sphinxdirective .. tab:: Linux

   .. code-block:: sh

   ./security_barrier_camera_demo -i <path_to_media> -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_vehicle_attributes model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_license_plate_recognition_model>/license-plate-recognition-barrier-0001.xml -d <target_device>

.. tab:: Windows

.. code-block:: bat

  .\security_barrier_camera_demo.exe -i <path_to_media> -m <path_to_vehicle-license-plate-detection_model_xml> -m_va <path_to_vehicle_attributes_model_xml> -m_lpr <path_to_license_plate_recognition_model_xml> -d <target_device>

.. tab:: macOS

.. code-block:: sh

  ./security_barrier_camera_demo -i <path_to_media> -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_vehicle_attributes model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_license_plate_recognition_model>/license-plate-recognition-barrier-0001.xml -d <target_device>

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CPU: @sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d CPU

.. tab:: Windows

.. code-block:: bat

  .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml -m_va C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml -m_lpr C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml -d CPU

.. tab:: macOS

.. code-block:: sh

  ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d CPU

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GPU:

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*.

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU

.. tab:: Windows

.. code-block:: bat

  .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU

.. tab:: macOS

.. code-block:: sh

  ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU

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MYRIAD:

Note

: Running inference on VPU devices (Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps, as described earlier on this page.

@sphinxdirective .. tab:: Linux

.. code-block:: sh

  ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d MYRIAD

.. tab:: Windows

.. code-block:: bat

  .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d MYRIAD

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Other Demos/Samples

For more samples and demos, you can visit the samples and demos pages below. You can review samples and demos by complexity or by usage, run the relevant application, and adapt the code for your use.

Samples

[Demos](@ref omz_demos)