Rhonda Software

Highest quality full cycle software development.

Expert in areas of Computer Vision, Multimedia, Messaging, Networking and others. Focused on embedded software development. Competent in building cross-platform solutions and distributed SW systems.

Offer standalone custom solutions as well as integration of existing products. Opened for outsourcing services.

Visit us at: http://www.rhondasoftware.com

Compiling OpenCV for Android using NDK 3

Posted on : 22-04-2010 | By : Alexander Permyakov | In : OpenCV

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Build platform: Ubuntu 9.10
Target platform: Android

Download and prepare OpenCV library source code.

1. Download the latest version of OpenCV (http://sourceforge.net/project/showfiles.php?group_id=22870).

2. As build platform is Linux, select linux version (for example OpenCV2.1.0.tat.bz).

FAQ: OpenCV Haartraining

Posted on : 10-11-2009 | By : rhondasw | In : OpenCV

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Hi All, before posting your question, please look at this FAQ carefully! Also you can read OpenCV haartraining article.  If you are sure, there is no answer to your question, feel free to post comment.  Also please, put comments about improvement of this post.  This post will be updated, if needed.

Audience Measurement (face tracker, gender recognition, attention recognition, etc)

Posted on : 05-10-2009 | By : Aleksey Kodubets | In : Demo, Demo video, Demo videos, OpenCV, YouTube

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am-3.avi

This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).

Fast & Furious face detection with OpenCV

Posted on : 18-06-2009 | By : rhondasw | In : OpenCV

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In OpenCV/Samples there is  facedetect program.  This program can detect  faces on images and video.  It’s very fun, but its speed leaves much to be desired =(.  Of course  with OpenMP,  it works  faster; on Intel Core Duo 2.7GHZ, it works fast;  but will it work fast on ARM? I have big doubts.  I compiled facedetect without OpenMP and on average it takes 600 ms for 640×480 resolution to find one face.   I wanted to find out, if it’s possible to improve this time by software means or not…  After some investigations, code refactoring and improvements, facedetect started to work 2.5 time faster, even on ARM.  Of course, without big quality loss =)

Parallel world of OpenCV (HaarTraining)

Posted on : 03-06-2009 | By : rhondasw | In : OpenCV

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If you want to generate cascade with OpenCV training tools, you should be ready for waiting plenty of time. For example, on training set: 3000 positive / 5000 negative, it takes about 6 days! to get cascade for face detection.  I wanted to generate many cascades with different training sets, also I added my own features to standart OpenCV’s ones  and refactor algorithms a little bit.  So waiting for 6 days to understand, that your cascade does nothing good =) was really anoying.  To reduce time, I chose paralleling methods.

“Fixing” the OpenCV’s implementation of Viola-Jones algorithm

Posted on : 10-04-2009 | By : rhondasw | In : OpenCV

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Today’s story is about improving performance of OpenCV library on the ARM-based platforms.

As you already know (from here or from here or may be even from here), face detection algorithm implemented by OpenCV library doesn’t work perfectly on ARM processors. Science doesn’t know for certain why this happens. There might be several possible reasons. One of our assumption was missing of hardware support for floating point operations. So we tried to translate Viola-Jones algorithm from floating point to fixed point. And that’s how we did this…