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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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Opencv 3.3.0 released information

As a big fan of OPENCV 3.3.0 There is what is new!

Some my notes about new released. Based on changelog and released notes. 

Deep neural network module is now accelerated with improved performance also moved into the main repository branch under opencv/modules/dnn. This module is also no more available in contrib branch. There was improved loading models from Troch and TensorFlow and many performance improvements. 

Support of
  • Caffe 1
  • TensorFlow
  • Torch/PyTorch
Interesting is available layer types list 
  • AbsVal
  • AveragePooling
  • BatchNormalization
  • Concatenation
  • Convolution (including dilated convolution)
  • Crop
  • Deconvolution, a.k.a. transposed convolution or full convolution
  • DetectionOutput (SSD-specific layer)
  • Dropout
  • Eltwise (+, *, max)
  • Flatten
  • FullyConnected
  • LRN
  • LSTM
  • MaxPooling
  • MaxUnpooling
  • MVN
  • NormalizeBBox (SSD-specific layer)
  • Padding
  • Permute
  • Power
  • PReLU (including ChannelPReLU with channel-specific slopes)
  • PriorBox (SSD-specific layer)
  • ReLU
  • RNN
  • Scale
  • Shift
  • Sigmoid
  • Slice
  • Softmax
  • Split
  • TanH

And even more

Advanced core performance AVX, AVX2, SSE and NEON
New Python and C++ samples DNN C++ and Python api


Another mainly performance improvements is 15% speed according to IPPICV from 2015.12 to 2017.2 version upgrade

Opencv C++ 11 ready 

I am interesting about news C++ 11 support. This should speed up development a bit. Tested on fedora distribution and Opencv should be build with -DENABLE_CXX11=ON. I will try on windows. I do not expect any problem. Build Opencv 3.3 with Visual Studio 2017 and CXX11 support.
Examples like

parallel_for_
And dummy auto containers :)
auto A = Mat_<double>({0, -1, 0, -1, 5, -1, 0, -1, 0}).reshape(1, 3);

Opencv hardware-accelerated video encoding/decoding

This is big in my eyes. Continue doing this.. Encoding and decoding of raw H.264 and MPEG1/2 video streams is supported, media containers are not supported yet.

Big thanks to all contributors of OPENCV 3.3.0 to doing this great lib even better. 

 

 


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