Python video analytics samples with OpenCV*
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Video Analytics Code Samples using OpenCV

This repository contains Python* code samples which us the Open Source Computer Vision (OpenCV) Library. These code samples are a good starting point for developers, across a wide range of markets, who wish to develop more robust computer vision and analytic solutions.

The code samples are mainly in two categories: Diagnostics and Application

Diagnostic Samples:

Sample 01 - Version information and environment variables
Sample 02 - OpenCV build information
Sample 03 - Basic image test - overlay text
Sample 04 - Basic video test - stream and overlay text
Sample 05 - Checks for OpenCL™ availability
Sample 07 - Checks for hardware extension support

Application Samples:

Sample 06 - Video steam and capture image
Sample 08 - Watermarking still image
Sample 09 - Watermarking display stream
Sample 10 - Still image face and eye detection
Sample 11 - Real-time video face detection and tracking
Sample 12 - Real-time people counter

The twelve computer vision code samples in this repository have been optimized using Intel® Integrated Performance Primitives (Intel® IPP) and the Intel® Math Kernel Library (Intel® MKL). By following these code samples with the optimized libraries, you will be able to see performance improvements over the basic installation of OpenCV.

Supported Intel® hardware platforms


Intel® NUC NUC6i7KYK with Intel® Core™ i7 (codename Skylake)

Development Environments


Microsoft Windows® 10 + Microsoft Visual Studio* 2015


Ubuntu* 16.04.2 + Anaconda + Intel® Distribution for Python*
Microsoft Windows 10 + Anaconda + Intel® Distribution for Python*




Intel® SDK for OpenCL™ Applications:

Intel® Media SDK:


Important: OpenCV v3.2.0 release can use vendor-provided OpenVX* and LAPACK/BLAS including Intel® Math Kernel Libraries for acceleration. Do not refer to outdated Intel® INDE documentation but directly refer to OpenCV documentation within the OpenCV v3.2.0 release.