Introduction
NOTE: These steps apply only to Windows* For Linux instructions, see the Linux installation guide
The Intel® Computer Vision SDK (Intel® CV SDK) is a toolkit to accelerate development of computer vision solutions for smart cameras, robotics, autonomous navigation, and retail/industrial automation. It simplifies access to hardware benefits from a range of Intel hardware: CPU, Intel® Integrated Graphics, FPGA, and the Intel® Movidius™ Myriad™ 2 Vision Processing Unit (VPU).
For more information, see the Intel® Computer Vision SDK 2018 Overview.
These instructions describe:
- What is included in the free download
- System requirements
- Software dependencies
- Installing the Intel® CV SDK on Microsoft Windows* 10
- Next steps
What's Included
| Component | Description |
|---|---|
| Deep Learning Module Optimizer | Model import tool. Imports trained models and converts to IR format for use by Deep Learning Inference Engine. This is part of the Intel® Deep Learning Deployment Toolkit. |
| Deep Learning Inference Engine | Unified API to integrate the inference with application logic. This is part of the Intel® Deep Learning Deployment Toolkit. |
| OpenCV version 3.4.1 | OpenCV* community version compiled for Intel® hardware. Includes PVL libraries for computer vision. |
| OpenVX* version 1.1 | Intel's implementation of OpenVX* 1.1 optimized for running on Intel® hardware (CPU, GPU, IPU). |
| Documents and tutorials | https://software.intel.com/en-us/computer-vision-sdk/documentation/view-all |
Where to Download This Release
System Requirements
This guide includes only information related to Microsoft Windows* 10 64-bit. See the Linux installation guide for Linux information and instructions.
Note: Only the CPU and Intel® Integrated Graphics processor options are available. Linux is required for using the FPGA or Intel® Movidius™ Myriad™ 2 VPU options.
Development and Target Platforms
The development and target platforms have the same requirements, but you can select different components during the installation, based on your intended use.
Processor
- 6th-8th Generation Intel® Core™
- Intel® Xeon® v5 family, Intel® Xeon® v6 family
Processor Notes:
- Processor graphics are not included in all processors. See https://ark.intel.com/ for information about your processor.
- A chipset that supports processor graphics is required for Intel® Xeon® processors.
Operating System
Microsoft Windows* 10 64-bit
External Software Dependencies
Install these dependencies before installing the Intel® CV SDK:
- Microsoft Visual Studio* 2015– Required for building Intel® Deep Learning Deployment Toolkit sample and demonstration applications.
- CMake* 3.4 (x64) or higher – Required for building the software
- Python* 3.5 (x64) or higher (including pip*) – Required for running the Model Optimizer
- If your applications will offload computation to Intel® Integrated Graphics, download and install the Intel® Graphics Driver for Windows* [15.65] or higher
Installation Steps
The Windows version of the Intel® Computer Vision SDK (Intel® CV SDK) installation package comes as an easy-to-install executable file.
- Download the Intel® CV SDK. By default, the file is saved to
Downloadsasw_intel_cv_sdk_2018.0.<version>.exe - Go to the
Downloadsfolder. - Double-click
w_intel_cv_sdk_2018.0.<version>.exe. A screen displays with options to choose your installation directory and components:![Components to install]()
- Click Next.
- The next screen warns you about any missing components and the effect the missing component has on installing or using the Intel® CV SDK.
![Windows warning messages]()
- If you are missing a critical component, click Cancel, resolve the issue, and then restart the installation.
- When the installation completes, click Finish to close the wizard and open the Getting Started Guide in a browser.
- Make sure the installation directory is populated with sub-folders. The default installation location is
C:\Intel\computer_vision_sdk_2018.0.<versions>.
Next Steps
IMPORTANT: Before using the Model Optimizer to work with your trained model, make sure your Caffe*, TensorFlow*, or MXNet* framework is prepared for any custom layers you have in place. The next information will put you on the way to doing this.
Learn About the Intel® CV SDK
Before using the Intel® CV SDK, read through the product overview to gain a better understanding of how the product works.
Compile the Extensions Library
Some topology-specific layers, like DetectionOutput used in the SSD*, are delivered in source code that assumes the extensions library is compiled and loaded. The extensions are required for pre-trained models inference. While you can build the library manually, the best way to compile the extensions library is to execute the demo scripts.
Run the Demonstration Applications
To verify the installation, run the demo apps in <INSTALL_FOLDER>\deployment_tools\demo. For demo app documentation, see README.txt in <INSTALL_FOLDER>\deployment_tools\demo.
The demo apps and their functions are:
demo_squeezenet_download_convert_run.bat.This demo Illustrates the basic steps used to convert a model and run it. This enables the Intel® Deep Learning Deployment Toolkit to perform a classification task with the SqueezeNet model. This demo:- Downloads a public SqueezeNet model.
- Installs all prerequisites to run the Model Optimizer.
- Converts the model to an Intermediate Representation.
- Builds the Inference Engine Image Classification Sample from the
<INSTALL_FOLDER>\deployment_tools\inference_engine\samples\classification_sample - Runs the sample using
cars.pngfrom the demo folder. - Shows the label and confidence for the top-10 categories.
demo_security_barrier_camera_sample.bat. This demo shows an inference pipeline using three of the pre-trained models included with the Intel CV SDK. The region found by one model becomes the input to the next. Vehicle regions found by object recognition in the first phase become the input to the vehicle attributes model, which locates the license plate. The region identified in this step becomes the input to a license plate character recognition model. This demo:- Builds the Inference Engine Security Barrier Camera Sample from the
<INSTALL_FOLDER>\deployment_tools\inference_engine\samples\security_barrier_camera_sample. - Runs the sample using
car_1.bmpfrom the demo folder. - Displays the resulting frame with detections rendered as bounding boxes and text.
- Builds the Inference Engine Security Barrier Camera Sample from the
Other Important Information
- See the
<INSTALL_FOLDER>/deployment_tools/inference_engine/samples/folder and the samples overview documentation to learn about the range of samples available for the Inference Engine. - For developer guides, API references, tutorials, and other online documentation, see the Intel® CV SDK documentation.
Legal Information
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