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OpenCV 4.5 simple optical flow GPU tutorial cuda::FarnebackOpticalFlow

This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o
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Microsoft cognitive-services VS Google Vision API

Microsoft cognitive-services VS Google Vision API


Simple comparison of result of Microsoft cognitive-services VS Google Vision API. Check the results achieved by 2 somehow simular example.. 

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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Comments

  1. Tom AndersonJuly 29, 2016 at 5:49 AM

    Quite different types of results. For many applications, I might prefer Google's, but the natural sounding sentences from the Microsoft approach could be useful if a sentence about the picture was needed.

    ReplyDelete
  2. James harperJuly 13, 2017 at 2:35 AM

    She completely knows the requirement of getting workforce healthy and well balanced if you want for them to successfully combat the tests of work. Kinesiologie

    ReplyDelete

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