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:
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:
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:
Go to the Inference Engine Samples page and see the “Build the Sample Applications on Linux*” section.
Go to the Inference Engine Samples page and see the “Build the Sample Applications on Microsoft Windows* OS” section.
Go to the Inference Engine Samples page and see the “Build the Sample Applications on macOS*” section.
To build OpenVINO demos:
Go to the Open Model Zoo Demos page and see the “Build the Demo Applications on Linux*” section.
Go to the Open Model Zoo Demos page and see the “Build the Demo Applications on Microsoft Windows* OS” section.
Go to the Open Model Zoo Demos page 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*.
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
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¶
cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
python3 info_dumper.py --print_all
cd <INSTALL_DIR>\deployment_tools\tools\model_downloader\
python info_dumper.py --print_all
cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
python3 info_dumper.py --print_all
Use 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:
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>
python downloader.py --name <model_name> --output_dir <models_dir>
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>
Download the following models to run the Image Classification Sample and Security Barrier Camera Demo applications:
Model Name |
Code Sample or Demo App |
|---|---|
|
Image Classification Sample |
|
Security Barrier Camera Demo |
|
Security Barrier Camera Demo |
|
Security Barrier Camera Demo |
To download the SqueezeNet 1.1 Caffe* model to the models folder:
python3 downloader.py --name squeezenet1.1 --output_dir ~/models
python downloader.py --name squeezenet1.1 --output_dir C:\Users\<USER_ID>\Documents\models
python3 downloader.py --name squeezenet1.1 --output_dir ~/models
Your screen looks similar to this after the download and shows the paths of downloaded files:
###############|| 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 =========
################|| 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
###############|| 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 =========
To download all three pre-trained models in FP16 precision to the models folder in your home folder:
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
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
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
Your screen looks similar to this after the download:
################|| 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 ||################
################|| 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 ||################
################|| 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 ||################
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).
mkdir ~/ir
mkdir C:\Users\<USER_ID>\Documents\ir
mkdir ~/ir
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:
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>
cd <INSTALL_DIR>\deployment_tools\model_optimizer
python mo.py --input_model <model_dir>\<model_file> --data_type <model_precision> --output_dir <ir_dir>
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>
IR files produced by the script are written to the <ir_dir> directory.
The command with most placeholders filled in and FP16 precision:
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
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
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
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:
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:
/opt/intel/openvino_2021/deployment_tools/demo/car.png/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp
<INSTALL_DIR>\deployment_tools\demo\car.png<INSTALL_DIR>\deployment_tools\demo\car_1.bmp
/opt/intel/openvino_2021/deployment_tools/demo/car.png/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp
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:
Set up the OpenVINO environment variables:
source /opt/intel/openvino/bin/setupvars.sh<INSTALL_DIR>\openvino\bin\setupvars.bat
source /opt/intel/openvino/bin/setupvars.shGo to the code samples release directory created when you built the samples earlier:
cd ~/inference_engine_cpp_samples_build/intel64/Releasecd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release
cd ~/inference_engine_cpp_samples_build/intel64/ReleaseRun the code sample executable, specifying the input media file, the IR for your model, and a target device for performing inference:
classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>
classification_sample_async.exe -i <path_to_media> -m <path_to_model> -d <target_device>
classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>
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:
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
.\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
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
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*.
./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU
.\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
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.
./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
.\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
./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
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:
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
Top 10 results:
Image C:Program Files (x86)Intelopenvino_2021deployment_toolsdemocar.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
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
Run the Security Barrier Camera Demo Application¶
To run the Security Barrier Camera Demo Application using an input image on the prepared IR models:
Set up the OpenVINO environment variables:
source /opt/intel/openvino/bin/setupvars.sh<INSTALL_DIR>\bin\setupvars.bat
source /opt/intel/openvino/bin/setupvars.shGo to the demo application build directory:
cd ~/inference_engine_demos_build/intel64/Releasecd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release
cd ~/inference_engine_demos_build/intel64/ReleaseRun the demo executable, specifying the input media file, list of model IRs, and a target device for performing inference:
./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>
.\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>
./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>
CPU:
./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
.\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
./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
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*.
./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
.\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
./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
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.
./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
.\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