Avladimi/cherry pick raspbian gsg (#3654)
* Added Get Started Guide for Raspbian * Updated the TOC xml file * Minor fixes * Implemented review comments * Fixed broken link
This commit is contained in:
Anastasiya Ageeva
committed by
GitHub
GitHub
parent
2e6ea1e290
commit
cc019e0a11
@@ -39,6 +39,7 @@
|
||||
<tab type="user" title="Linux" url="@ref openvino_docs_get_started_get_started_linux"/>
|
||||
<tab type="user" title="Windows" url="@ref openvino_docs_get_started_get_started_windows"/>
|
||||
<tab type="user" title="macOS" url="@ref openvino_docs_get_started_get_started_macos"/>
|
||||
<tab type="user" title="Raspbian" url="@ref openvino_docs_get_started_get_started_raspbian"/>
|
||||
<tab type="user" title="Get Started with OpenVINO via DL Workbench" url="@ref openvino_docs_get_started_get_started_dl_workbench"/>
|
||||
<tab type="user" title="Legal Information" url="@ref openvino_docs_Legal_Information"/>
|
||||
</tab>
|
||||
|
||||
109
docs/get_started/get_started_raspbian.md
Normal file
109
docs/get_started/get_started_raspbian.md
Normal file
@@ -0,0 +1,109 @@
|
||||
# Get Started with OpenVINO™ Toolkit on Raspbian* OS {#openvino_docs_get_started_get_started_raspbian}
|
||||
|
||||
The OpenVINO™ toolkit optimizes and runs Deep Learning Neural Network models on Intel® hardware. This guide helps you get started with the OpenVINO™ toolkit you installed on Raspbian* OS.
|
||||
|
||||
In this guide, you will:
|
||||
* Learn the OpenVINO™ inference workflow.
|
||||
* Build and run sample code using detailed instructions.
|
||||
|
||||
## <a name="openvino-components"></a>OpenVINO™ Toolkit Components
|
||||
On Raspbian* OS, the OpenVINO™ toolkit consists of the following components:
|
||||
* **Inference Engine:** The software libraries that run inference against the Intermediate Representation (optimized model) to produce inference results.
|
||||
* **MYRIAD Plugin:** The plugin developed for inference of neural networks on Intel® Neural Compute Stick 2.
|
||||
|
||||
> **NOTE**:
|
||||
> * The OpenVINO™ package for Raspberry* does not include the [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To convert models to Intermediate Representation (IR), you need to install it separately to your host machine.
|
||||
> * The package does not include the Open Model Zoo demo applications. You can download them separately from the [Open Models Zoo repository](https://github.com/opencv/open_model_zoo).
|
||||
|
||||
In addition, [code samples](../IE_DG/Samples_Overview.md) are provided to help you get up and running with the toolkit.
|
||||
|
||||
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ Toolkit Directory Structure
|
||||
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Raspbian*](../install_guides/installing-openvino-raspbian.md).
|
||||
|
||||
The OpenVINO toolkit for Raspbian* OS is distributed without installer. This document refers to the directory to which you unpacked the toolkit package as `<INSTALL_DIR>`.
|
||||
|
||||
The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/deployment_tools` directory.
|
||||
<details>
|
||||
<summary><strong>Click for the <code>deployment_tools</code> directory structure</strong></summary>
|
||||
|
||||
|
||||
| Directory | Description |
|
||||
|:----------------------------------------|:--------------------------------------------------------------------------------------|
|
||||
| `inference_engine/` | Inference Engine directory. Contains Inference Engine API binaries and source files, samples and extensions source files, and resources like hardware drivers.|
|
||||
| `external/` | Third-party dependencies and drivers.|
|
||||
| `include/` | Inference Engine header files. For API documentation, see the [Inference Engine API Reference](./annotated.html). |
|
||||
| `lib/` | Inference Engine libraries.|
|
||||
| `samples/` | Inference Engine samples. Contains source code for C++ and Python* samples and build scripts. See the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md). |
|
||||
| `share/` | CMake configuration files for linking with Inference Engine.|
|
||||
|
||||
</details>
|
||||
|
||||
## <a name="workflow-overview"></a>OpenVINO™ Workflow Overview
|
||||
|
||||
The OpenVINO™ workflow on Raspbian* OS is as follows:
|
||||
1. **Get a pre-trained model** for your inference task. If you want to use your model for inference, the model must be converted to the `.bin` and `.xml` Intermediate Representation (IR) files, which are used as input by Inference Engine. On Raspberry PI, OpenVINO™ toolkit includes only the Inference Engine module. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
|
||||
|
||||
* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README#model_downloader_usage).
|
||||
<br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_intel_index)
|
||||
|
||||
* Convert a model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:
|
||||
* [Installation Guide for macOS*](../install_guides/installing-openvino-macos.md)
|
||||
* [Installation Guide for Windows*](../install_guides/installing-openvino-windows.md)
|
||||
* [Installation Guide for Linux*](../install_guides/installing-openvino-linux.md)
|
||||
2. **Use the Inference Engine API in the application** to run inference against the Intermediate Representation (optimized model) and output inference results. The application can be an OpenVINO™ sample or your own application.
|
||||
|
||||
## <a name="using-sample"></a>Build and Run Code Samples
|
||||
|
||||
Follow the steps below to run pre-trained Face Detection network using Inference Engine samples from the OpenVINO toolkit.
|
||||
|
||||
1. Create a samples build directory. This example uses a directory named `build`:
|
||||
```sh
|
||||
mkdir build && cd build
|
||||
```
|
||||
2. Build the Object Detection Sample with the following command:
|
||||
```sh
|
||||
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino/deployment_tools/inference_engine/samples/cpp
|
||||
```
|
||||
```sh
|
||||
make -j2 object_detection_sample_ssd
|
||||
```
|
||||
3. Download the pre-trained Face Detection model with the Model Downloader:
|
||||
|
||||
```sh
|
||||
git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
|
||||
cd open_model_zoo/tools/downloader
|
||||
python3 -m pip install -r requirements.in
|
||||
python3 downloader.py --name face-detection-adas-0001
|
||||
```
|
||||
|
||||
4. Run the sample, specifying the model and path to the input image:
|
||||
```sh
|
||||
./armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i <path_to_image>
|
||||
```
|
||||
The application outputs an image (`out_0.bmp`) with detected faced enclosed in rectangles.
|
||||
|
||||
## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples
|
||||
|
||||
Following are some basic guidelines for executing the OpenVINO™ workflow using the code samples:
|
||||
|
||||
1. Before using the OpenVINO™ samples, always set up the environment:
|
||||
```sh
|
||||
source <INSTALL_DIR>/bin/setupvars.sh
|
||||
```
|
||||
2. Have the directory path for the following:
|
||||
- Code Sample binaries
|
||||
- Media: Video or image. Many sources are available from which you can download video media to use the code samples and demo applications, like https://videos.pexels.com and https://images.google.com.
|
||||
- Model in the IR format (.bin and .xml files).
|
||||
|
||||
|
||||
## Additional Resources
|
||||
|
||||
Use these resources to learn more about the OpenVINO™ toolkit:
|
||||
|
||||
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
|
||||
* [OpenVINO™ Toolkit Overview](../index.md)
|
||||
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
|
||||
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
|
||||
* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
|
||||
* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)
|
||||
Reference in New Issue
Block a user