Opencv 3.3.0 released information
Some my notes about new releases. 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
- 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
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
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.
The very good information and images that you use on your blog are real.
It looks really good.
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