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DOCS: Samples Overview proofreading for 22.2 (#14086)

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@endsphinxdirective
The OpenVINO samples are simple console applications that show how to utilize specific OpenVINO API capabilities within an application. They can assist you in executing specific tasks such as loading a model, running inference, querying specific device capabilities, etc.
The OpenVINO samples are simple console applications that show how to utilize specific OpenVINO API capabilities within an application. They can assist in executing specific tasks, such as loading a model, running inference, querying specific device capabilities, etc.
If you install OpenVINO™ Runtime, sample applications for С, C++, and Python are created in the following directories:
During installation of OpenVINO™ Runtime, sample applications for С, C++ and Python are created in the following directories:
* `<INSTALL_DIR>/samples/c`
* `<INSTALL_DIR>/samples/cpp`
* `<INSTALL_DIR>/samples/python`
The applications include:
- **Speech Sample** - Acoustic model inference based on Kaldi neural networks and speech feature vectors.
- **Speech Sample** - Acoustic model inference based on Kaldi neural networks and speech feature vectors:
- [Automatic Speech Recognition C++ Sample](../../samples/cpp/speech_sample/README.md)
- [Automatic Speech Recognition Python Sample](../../samples/python/speech_sample/README.md)
- **Hello Classification Sample** – Inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API. Input of any size and layout can be set to an infer request which will be pre-processed automatically during inference (the sample supports only images as inputs and supports Unicode paths).
- **Hello Classification Sample** – Inference of image classification networks, such as AlexNet and GoogLeNet, using Synchronous Inference Request API. Input of any size and layout can be set to an infer request which will be pre-processed automatically during inference (the sample supports only images as inputs and supports Unicode paths):
- [Hello Classification C++ Sample](../../samples/cpp/hello_classification/README.md)
- [Hello Classification C Sample](../../samples/c/hello_classification/README.md)
- [Hello Classification Python Sample](../../samples/python/hello_classification/README.md)
- **Hello NV12 Input Classification Sample** – Input of any size and layout can be provided to an infer request. The sample transforms the input to the NV12 color format and pre-process it automatically during inference. The sample supports only images as inputs.
- **Hello NV12 Input Classification Sample** – Input of any size and layout can be provided to an infer request. The sample transforms the input to the `NV12` color format and pre-process it automatically during inference. The sample supports only images as inputs:
- [Hello NV12 Input Classification C++ Sample](../../samples/cpp/hello_nv12_input_classification/README.md)
- [Hello NV12 Input Classification C Sample](../../samples/c/hello_nv12_input_classification/README.md)
- **Hello Query Device Sample** – Query of available OpenVINO devices and their metrics, configuration values.
- **Hello Query Device Sample** – Query of available OpenVINO devices and their metrics, configuration values:
- [Hello Query Device C++ Sample](../../samples/cpp/hello_query_device/README.md)
- [Hello Query Device Python* Sample](../../samples/python/hello_query_device/README.md)
- **Hello Reshape SSD Sample** – Inference of SSD networks resized by ShapeInfer API according to an input size.
- [Hello Reshape SSD C++ Sample**](../../samples/cpp/hello_reshape_ssd/README.md)
- [Hello Reshape SSD Python Sample**](../../samples/python/hello_reshape_ssd/README.md)
- **Image Classification Sample Async** – Inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API (the sample supports only images as inputs).
- [Hello Query Device Python Sample](../../samples/python/hello_query_device/README.md)
- **Hello Reshape SSD Sample** – Inference of SSD networks resized by ShapeInfer API according to an input size:
- [Hello Reshape SSD C++ Sample](../../samples/cpp/hello_reshape_ssd/README.md)
- [Hello Reshape SSD Python Sample](../../samples/python/hello_reshape_ssd/README.md)
- **Image Classification Sample Async** – Inference of image classification networks, such as AlexNet and GoogLeNet, using Asynchronous Inference Request API (the sample supports only images as inputs):
- [Image Classification Async C++ Sample](../../samples/cpp/classification_sample_async/README.md)
- [Image Classification Async Python* Sample](../../samples/python/classification_sample_async/README.md)
- **OpenVINO Model Creation Sample** – Construction of the LeNet model using the OpenVINO model creation sample.
- [Image Classification Async Python Sample](../../samples/python/classification_sample_async/README.md)
- **OpenVINO Model Creation Sample** – Construction of the LeNet model using the OpenVINO model creation sample:
- [OpenVINO Model Creation C++ Sample](../../samples/cpp/model_creation_sample/README.md)
- [OpenVINO Model Creation Python Sample](../../samples/python/model_creation_sample/README.md)
- **Benchmark Application** – Estimates deep learning inference performance on supported devices for synchronous and asynchronous modes.
- **Benchmark Application** – Estimates deep learning inference performance on supported devices for synchronous and asynchronous modes:
- [Benchmark C++ Tool](../../samples/cpp/benchmark_app/README.md)
Note that the Python version of the benchmark tool is currently available only through the [OpenVINO Development Tools installation](../install_guides/installing-model-dev-tools.md). It is not created in the samples directory but can be launched with the following command:
`benchmark_app -m <model> -i <input> -d <device>`
For more information, check the [Benchmark Python Tool](../../tools/benchmark_tool/README.md) documentation.
Keep in mind that the Python version of the benchmark tool is currently available only through the [OpenVINO Development Tools installation](../install_guides/installing-model-dev-tools.md). It is not created in the samples directory but can be launched with the following command:
```sh
benchmark_app -m <model> -i <input> -d <device>
```
> **NOTE**: All C++ samples support input paths containing only ASCII characters, except for the Hello Classification Sample, that supports Unicode.
For more information, see the [Benchmark Python Tool](../../tools/benchmark_tool/README.md) documentation.
> **NOTE**: All C++ samples support input paths that contain only ASCII characters, except for the Hello Classification Sample (it supports Unicode).
## Media Files Available for Samples
To run the sample applications, you can use images and videos from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
To run the sample applications, use images and videos available online in the [test data storage](https://storage.openvinotoolkit.org/data/test_data).
## Samples that Support Pre-Trained Models
To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
To run the sample, use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from Open Model Zoo. The models can be downloaded using [Model Downloader](@ref omz_tools_downloader).
## Build the Sample Applications
### <a name="build-samples-linux"></a>Build the Sample Applications on Linux
The officially supported Linux* build environment is the following:
The supported Linux build environment includes
* Ubuntu* 18.04 LTS 64-bit or Ubuntu* 20.04 LTS 64-bit
* GCC* 7.5.0 (for Ubuntu* 18.04) or GCC* 9.3.0 (for Ubuntu* 20.04)
* CMake* version 3.10 or higher
* Ubuntu 18.04 LTS 64-bit or Ubuntu 20.04 LTS 64-bit,
* GCC 7.5.0 (for Ubuntu 18.04) or GCC 9.3.0 (for Ubuntu 20.04),
* CMake version 3.10 or higher.
> **NOTE**: For building samples from the open-source version of OpenVINO™ toolkit, see the [build instructions on GitHub](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
> **NOTE**: For building samples from the open source version of OpenVINO™ toolkit, see the [build instructions on GitHub](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
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:
```sh
build_samples.sh
```
Once the build is completed, you can find sample binaries in the following folders:
* C samples: `~/inference_engine_c_samples_build/intel64/Release`
* C++ samples: `~/inference_engine_cpp_samples_build/intel64/Release`
Once the build has been completed, sample binaries will be in the following folders:
* C samples - `~/inference_engine_c_samples_build/intel64/Release`
* C++ samples - `~/inference_engine_cpp_samples_build/intel64/Release`
You can also build the sample applications manually:
Also, sample applications can be built manually:
> **NOTE**: If you have installed the product as a root user, switch to root mode before you continue: `sudo -i`
> **NOTE**: If you have installed the product as a root user, switch to root mode (`sudo -i`) before you continue.
1. Navigate to a directory that you have write access to and create a samples build directory. This example uses a directory named `build`:
```sh
mkdir build
```
> **NOTE**: If you run the Image Classification verification script during the installation, the C++ samples build directory is created in your home directory: `~/inference_engine_cpp_samples_build/`
> **NOTE**: If you run the Image Classification verification script during the installation, the C++ samples build directory is created in your home directory - `~/inference_engine_cpp_samples_build/`.
2. Go to the created directory:
2. Go to the newly created directory:
```sh
cd build
```
@@ -129,8 +132,10 @@ cd build
make
```
For the release configuration, the sample application binaries are in `<path_to_build_directory>/intel64/Release/`;
for the debug configuration — in `<path_to_build_directory>/intel64/Debug/`.
The sample application binaries can be found in:
* For release configuration - `<path_to_build_directory>/intel64/Release/`
* For debug configuration - `<path_to_build_directory>/intel64/Debug/`
### <a name="build-samples-windows"></a>Build the Sample Applications on Microsoft Windows
@@ -139,47 +144,47 @@ The recommended Windows build environment is the following:
* Microsoft Visual Studio 2019
* CMake version 3.10 or higher
> **NOTE**: If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14 or higher.
> **NOTE**: Microsoft Visual Studio 2019 requires installation of CMake 3.14 or higher.
To build the C or C++ sample applications on Windows, go to the `<INSTALL_DIR>\samples\c` or `<INSTALL_DIR>\samples\cpp` directory, respectively, and run the `build_samples_msvc.bat` batch file:
To build C or C++ sample applications on Windows, go to the `<INSTALL_DIR>\samples\c` or `<INSTALL_DIR>\samples\cpp` directory, respectively, and run the `build_samples_msvc.bat` batch file:
```sh
build_samples_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 sample code
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 sample code.
Once the build is completed, you can find sample binaries in the following folders:
* C samples: `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_c_samples_build\intel64\Release`
* C++ samples: `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release`
Once the build has been completed, sample binaries will be in the following folders:
* C samples - `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_c_samples_build\intel64\Release`
* C++ samples - `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release`
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
A generated solution can also be built manually. For example, to build C++ sample binaries in Debug configuration, run the suitable version of the
Microsoft Visual Studio and open the generated solution file from the `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\Samples.sln`
directory.
### <a name="build-samples-macos"></a>Build the Sample Applications on macOS*
The officially supported macOS* build environment is the following:
The officially supported macOS build environment is the following:
* macOS* 10.15 64-bit or higher
* Clang* compiler from Xcode* 10.1 or higher
* CMake* version 3.13 or higher
* macOS 10.15 64-bit or higher
* Clang compiler from Xcode 10.1 or higher
* CMake version 3.13 or higher
> **NOTE**: For building samples from the open-source version of OpenVINO™ toolkit, see the [build instructions on GitHub](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
> **NOTE**: To learn how to build samples from the open-source version of OpenVINO™ toolkit, see the [build instructions on GitHub](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
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:
To build 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:
```sh
build_samples.sh
```
Once the build is completed, you can find sample binaries in the following folders:
* C samples: `~/inference_engine_c_samples_build/intel64/Release`
* C++ samples: `~/inference_engine_cpp_samples_build/intel64/Release`
Once the build has been completed, sample binaries will be in the following folders:
* C samples - `~/inference_engine_c_samples_build/intel64/Release`
* C++ samples - `~/inference_engine_cpp_samples_build/intel64/Release`
You can also build the sample applications manually:
The sample applications can also be built manually:
> **NOTE**: If you have installed the product as a root user, switch to root mode before you continue: `sudo -i`
> **NOTE**: If you have installed the product as a root user, switch to root mode (`sudo -i`) before you continue.
> **NOTE**: Before proceeding, make sure you have OpenVINO™ environment set correctly. This can be done manually by
> **NOTE**: Before proceeding, make sure that you have OpenVINO environment set correctly. This can be done manually by
```sh
cd <INSTALL_DIR>/
source setupvars.sh
@@ -191,7 +196,7 @@ 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: `~/inference_engine_cpp_samples_build/`
2. Go to the created directory:
2. Go to the newly created directory:
```sh
cd build
```
@@ -210,43 +215,44 @@ cd build
make
```
For the release configuration, the sample application binaries are in `<path_to_build_directory>/intel64/Release/`;
for the debug configuration — in `<path_to_build_directory>/intel64/Debug/`.
The sample application binaries can be find in:
* For release configuration - `<path_to_build_directory>/intel64/Release/`
* For debug configuration - `<path_to_build_directory>/intel64/Debug/`
## Get Ready for Running the Sample Applications
### Get Ready for Running the Sample Applications on Linux*
### Get Ready for Running the Sample Applications on Linux
Before running compiled binary files, make sure your application can find the
Before running compiled binary files, check if your application can find the
OpenVINO Runtime libraries.
Run the `setupvars` script to set all necessary environment variables:
```sh
source <INSTALL_DIR>/setupvars.sh
```
**(Optional)**: The OpenVINO environment variables are removed when you close the
shell. As an option, you can permanently set the environment variables as follows:
**Optional**: The OpenVINO environment variables are removed when you close the
shell. Optionally, you can permanently set the environment variables as follows:
1. Open the `.bashrc` file in `<user_home_directory>`:
```sh
vi <user_home_directory>/.bashrc
```
2. Add this line to the end of the file:
2. Append the file with the following command-line:
```sh
source /opt/intel/openvino_2022/setupvars.sh
```
3. Save and close the file: press the **Esc** key, type `:wq` and press the **Enter** key.
4. To test your change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
3. Press the **Esc** key, type `:wq` and press the **Enter** key to save and close the file.
4. To test change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
You are ready to run sample applications. To learn about how to run a particular
sample, read the sample documentation by clicking the sample name in the samples
list above.
Sample applications are ready to start. To learn how to run a particular
sample, go to the documentation by clicking the name of a chosen sample from the list above.
### Get Ready for Running the Sample Applications on Windows*
### Get Ready for Running the Sample Applications on Windows
Before running compiled binary files, make sure your application can find the
Before running compiled binary files, check if your application can find the
OpenVINO Runtime libraries.
Use the `setupvars` script, which sets all necessary environment variables:
```sh
@@ -257,18 +263,18 @@ To debug or run the samples 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 OpenVINO Runtime libraries.
For example, for the **Debug** configuration, go to the project's
**Configuration Properties** to the **Debugging** category and set the `PATH`
For example, for the **Debug** configuration, go to
**Configuration Properties** of the project, then select the **Debugging** category and set the `PATH`
variable in the **Environment** field to the following:
```sh
PATH=<INSTALL_DIR>\runtime\bin;%PATH%
```
where `<INSTALL_DIR>` is the directory in which the OpenVINO toolkit is installed.
where `<INSTALL_DIR>` is the installation directory of OpenVINO toolkit.
You are ready to run sample applications. To learn about how to run a particular
You are ready to run sample applications. To learn how to run a particular
sample, read the sample documentation by clicking the sample name in the samples
list above.
## See Also
## Additional Resources
* [OpenVINO™ Runtime User Guide](openvino_intro.md)