Fun Computer Vision opencv tutorials and ..

























Opencv Simple instalation.

From Opencv 3.0 Cmake to Opencv 3.1 prebuild Libs

I wrote about opencv instalation since the release of version 3 and Visual Studio 2015.. In early build of opencv 3.0 there is no precompiled library for VS2015 and I wrote about instalation using CMAKE here . This is still usefull when you need non standard capabilities. You can manage build process and target specific functions and properties. Opencv version 3.1 was shipped with prebuild libs for Visual studio 2015 and everithing is much more simple.. Instation and basic setup is discussed in tutorial here .


Install Opencv, Visual studio 2015 with NUGET

The most simple way without setting the global enviromental variables and staff like location of headers and libs is use the NUGET packages.. 

You can install in nuget package console.. DONT be afraid of CONSOLE. PLEASE. 

1.Open Nuget console 

2. Create empty Visual C++ project

3. Add a source File and write some code.. 

4. Add NUGET package

5. Compile and have a fun.. RLY simple in less than 4 minutes.. 


Again please dont be afraid of console again.. This is much more simple than anaything.. 


Opencv Nuget Console options 

After the PM> you can write commands for Nuger console.. And install Opencv is rly simple. For default configuration like in prebuild libs just use.. 


Opencv Default Build 3.1.0

PM>  Install-Package opencvdefault

Enabled advanced CPU instructions. With OpenMP. No TBB/IPP. vc12/vc14, x64/x86 available.

PM>  Install-Package opencv3.1

AND DONE !!

FOLLOW IMAGES TO CREATE AND INSTALL NUGET IN VS PROJECT


Opencv NUGET console


Opencv install


Opencv 3.1

Opencv Installation NUGET

Opencv Install

opencv 3,1 nuget


opencv 3.1 nuget instal visual studio


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My iceland video tracking example

Video tracking stabilization opencv

i know that, I did not write anything interesting for a long time.. I am working on it..





Transparent mask tutorial is Here







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Microsoft cognitive-services VS Google Vision API


Which image api recognition engine is bigger gentleman ?


Check the result.. This is just a funny comparison.. I did not mean this rude. You get it, when you check the result of this outstanding api..  Microsoft has realy truly stunning results. 


Microsoft cognitive-services VS Google Vision API


Microsoft cognitive-services image clasification result



{ "text": "a beautiful woman standing on a beach", "confidence": 0.6798031586203954 }

There is much more information about the image and they are impresive.. A beautiful woman.. Ok lets check google result,, 

Google vision api More neutral 

and let say, More or less ok.. 

"description": "clothing",  "description": "vacation", ¨description": "beauty", 
  "description": "photo shoot",  "description": "sun tanning",  "description": "sports",
 "description": "volleyball",

Microsoft cognitive-services VS Google Vision API

Microsoft cognitive-services image clasification result


{ "text": "a woman walking down a beach next to the ocean", "confidence": 0.5507436156974482 } ] }

Google vision api More neutral 

"description": "clothing", "description": "vacation", "description": "black hair", "description": "beauty", "description": "sea", "description": "model", "description": "swimwear",  "description": "supermodel",  "description": "photo shoot",


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I will be back soon. Tutorials and more about Computer vision, technology and more


Blog techblog computer vision

Fotka zveřejněná uživatelem @vladakuc,
Fotka zveřejněná uživatelem @vladakuc,

Fotka zveřejněná uživatelem @vladakuc,

Median flow tracker code coming soon


  • Simple median flow tracker base od goodFeaturesToTrack
  •  calcOpticalFlowPyrLK
  • TODO complete forward backwards tracking and feature filtering. I already have forward, backwards LK tracker, without filtering (just prepared NORM computation).
  • TODO complete redetection. White rectangles are redetection with online learning by RTrees opencv 3.1 support. Rtrees work great. I only need better features. Do you have any advice or tips?
  • From the initial Rectangle is generated several positive examples and negatives from the rest of image. Rtrees learning and prediction can run very fast. I still haven't got a good features. Feature extracting took some time to. 
  • TODO Forward backward + redetection. 
  • 300 lines of code 
Final result will be 

TLD tracker

I would like to release this in 4 next tutorials. 

Forward backwards median flow tracker

Rtrees learning ( new features ) and prediction with single scale detection sliding window

Complete TLD tracker

Slow Multi target TLD tracker




Share please

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Testing Alchemy Vision  




Alchemy.ai is powerful computer vision API that provide advance image understanding for business to make a better decisions. Alchemy Vision extract text from images, recognize some brands and put them in the content. This deep learning Watson based technology is great for social media monitoring, analysis and target ads for example on Stratigram. Some of the result on Alchemy Vision outperform other products in this category.
Cloud based computer vision with advanced image analysis  is available through Rest API that anyone can create application with this capabilities on Mobile phones, tablets and personal computers.


Target segment is

  • Brand monitoring on social media
  • Organization of images
  • Customer suggestions 
  • Profile markets
Look at the results

I am testing a lots of products like this. The Alchemy is the most advanced with better results than others products like this. AlchemyAPI is part of Watson cloud.

Brand and logo recognition


alchemy.ai coputer vision


alchemy.ai coputer vision


Text within Image


intelkelloggpuma, cred, ups, atletico, ibm, gucci, disadvantages, gap, redbullgoogle, son, kayla, caterpillar, cartier, isa, ros, cam, timemicrosoftwii

People and logo image finding

Alchemy Vision

Tags

  • Person
Text within image
Gender
  • Female Age  18-24 
This is incredible!! Result
  • Celebrity
  • Mila Kunis
  • People-celebrities-Mila Kunis 0.978
  • Person-Actor-Celebrity-Film-Actor-TVActor
For example there is no one famous on that picture :)
Alchemy Vision



Gender
  • Male
  • Female
  • Male
  • Male
  • Female
  • Female
Age
  • 35-44
  • 35-44
  • 45-54
  • 35-44
  • 18-24
  • 55-64
alchemy.ai coputer vision
Results
Tags
  • Person 0.9

Text within image
Gender
  • Female  Age 35-44 


Simular images
alchemy.ai coputer vision

Image understanding 



alchemy.ai coputer vision
Results
Tags
  • Car 0.961
  • Ford   0.5     OK,, This is a Lexus.

Word hierarchy
  • vehicle-cars-ford
  • Male  in age 35-44    

Action recognition

                      alchemy.ai coputer vision


Results
Tags
Word Hierarchy
  • activities sports hockey
  • activities sport
  • people
  • events sports nhl
Gender
  • Male
Age
  • 18-24

People activity analysis



alchemy.ai coputer vision
Tags 
  • Persons
Text Within Image

Social media analysis

Alchemy Vision


Tags
  • Person
Gender
  • Female
  • Female
  • Male
Age
  • 18-24
  • 18-24
  • >64
Simular pictures


Alchemy Vision







Alchemy Vision

Tags

  • Person

Text within image

  • Trump
  • Steaks

Male Age 55-64

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Opencv 3.1 Tutorial Optical flow (calcOpticalFlowFarneback)

Farneback Optical flow Share this for more tutorials and computer vision post from me.. Thanks best Vladimir Optical Fl...

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