renamed-opencv-python-inference-engine

How does this fork differ from upstream? Upstream makes OpenCV import-able as cv2, while this fork makes it available as renamed_cv2. The reason why the module is renamed is to avoid conflicting with the 'opencv-contrib-python' package, which among other submodules, makes cv2.aruco available for detecting ARUCO markers. Since 'opencv-contrib-python' doesn't have Intel's inference engine compiled in, you would need upstream's package 'opencv-python-inference-engine', which gives you cv2.dnn.readNet(). If you installed both packages, only one of the cv2s would resolve and you'd lose access to either cv2.aruco or cv2.dnn. Therefore, this repo introduces 'renamed-opencv-python-inference-engine' package, which gives you renamed_cv2.dnn.readNet().

Another option would have been to compile one OpenCV python package with both the contrib submodules and the deep learning submodule compiled in. However, since I don't intend to make releases as regularly as the official OpenCV org does, and the default OpenCV python package doesn't include the deep learning submodule, this compromise allows users to access the latest OpenCV python w/contrib libraries from cv2, while being able to access the deep learning submodule through the renamed_cv2 module.

Installing from pip3

Remove previously installed versions of cv2

pip3 install renamed-opencv-python-inference-engine

Examples of usage

Please see the examples.ipynb in the tests folder.

You will need to preprocess data as a model requires and decode the output. A description of the decoding should be in the model documentation with examples in open-vino documentation, however, in some cases, the original article may be the only information source. Some models are very simple to encode/decode, others are tough (e.g., PixelLink in tests).

Downloading intel models

The official way is awkward because you need to git clone the whole model_zoo (details)

Better to find a model description here and download manually from here

Description

Why

I needed an ability to fast deploy a small package that able to run models from Intel's model zoo and use Movidius NCS. Well-known opencv-python can't do this. The official way is to use OpenVINO, but it is big and clumsy (just try to use it with python venv or fast download it on cloud instance).

Limitations

• Package comes without contrib modules.
• You need to add udev rules if you want working MYRIAD plugin.
• It was tested on Ubuntu 18.04, Ubuntu 18.10 as Windows 10 Subsystem and Gentoo.
• It will not work for Ubuntu 16.04 and below (except v4.1.0.4).
• I had not made builds for Windows or MacOS.
• It built with ffmpeg and v4l support (ffmpeg libs included).
• No GTK/QT support -- use matplotlib for plotting your results.
• It is 64 bit.

Main differences from opencv-python-headless

• Usage of AVX2 instructions
• No JPEG 2000, WEBP, OpenEXR support
• TBB used as a parallel framework
• Inference Engine with MYRIAD plugin

Main differences from OpenVINO

• No model-optimizer
• No ITT
• No IPP
• No Intel Media SDK
• No OpenVINO IE API
• No python2 support (it is dead)
• No Gstreamer (use ffmpeg)
• No GTK (+16 MB and a lot of problems and extra work to compile Qt\GTK libs from sources.)

For additional info read cv2.getBuildInformation() output.

Versioning

YYYY.MM.DD, because it is the most simple way to track opencv/openvino versions.

Compiling from source

You will need ~7GB RAM and ~10GB disk space

I am using Ubuntu 18.04 (python 3.6) multipass instance: multipass launch -c 6 -d 10G -m 7G 18.04.

Requirements

From opencv, dldt, ffmpeg, and ngraph

# We need newer `cmake` for dldt (fastest way I know)
# >=cmake-3.16
sudo apt remove --purge cmake
hash -r
sudo snap install cmake --classic

# nasm for ffmpeg
# libusb-1.0-0-dev for MYRIAD plugin
sudo apt update
sudo apt install build-essential git pkg-config python3-dev nasm python3 virtualenv libusb-1.0-0-dev chrpath shellcheck

# for ngraph
# the `dldt/_deps/ext_onnx-src/onnx/gen_proto.py` has `#!/usr/bin/env python` string and will throw an error otherwise
sudo ln -s  /usr/bin/python3 /usr/bin/python

Preparing

git clone https://github.com/hello-binit/renamed-opencv-python-inference-engine
cd renamed-opencv-python-inference-engine
# git checkout dev
./download_all_stuff.sh

Compilation

cd build/ffmpeg
./ffmpeg_setup.sh &&
./ffmpeg_premake.sh &&
make -j6 &&
make install

cd ../dldt
./dldt_setup.sh &&
make -j6

# NB: check `-D INF_ENGINE_RELEASE` value
# should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
cd ../opencv
./opencv_setup.sh &&
make -j6

Wheel creation

# get all compiled libs together
cd ../../
cp build/opencv/lib/python3/cv2.cpython*.so create_wheel/renamed_cv2/cv2.so

cp dldt/bin/intel64/Release/lib/*.so create_wheel/renamed_cv2/
cp dldt/bin/intel64/Release/lib/*.mvcmd create_wheel/renamed_cv2/
cp dldt/bin/intel64/Release/lib/plugins.xml create_wheel/renamed_cv2/
cp dldt/inference-engine/temp/tbb/lib/libtbb.so.2 create_wheel/renamed_cv2/

cp build/ffmpeg/binaries/lib/*.so create_wheel/renamed_cv2/

# change RPATH
cd create_wheel
for i in  renamed_cv2/*.so; do chrpath -r '$ORIGIN' $i; done

# final .whl will be in /create_wheel/dist/
# NB: check version in the `setup.py`
../venv/bin/python3 setup.py bdist_wheel

The .whl will be in /create_wheel/dist. In order to be able to install it on other version of Python besides the one with which it was compiled, the .whl file will need to be renamed. For example:

mv renamed_opencv_python_inference_engine-2022.1.5-cp36-cp36m-linux_x86_64.whl renamed_opencv_python_inference_engine-2022.1.5-py3-none-manylinux1_x86_64.whl

Optional things to play with

• dldt build instruction
• dldt cmake flags
• opencv cmake flags

NB: removing QUIET from find_package() in project Cmake files, could help to solve some problems -- сmake will start to log them.

GTK2

Make next changes in opencv-python-inference-engine/build/opencv/opencv_setup.sh:

1. change string -D WITH_GTK=OFF \ to -D WITH_GTK=ON \
2. export PKG_CONFIG_PATH=$ABS_PORTION/build/ffmpeg/binaries/lib/pkgconfig:$PKG_CONFIG_PATH -- you will need to add absolute paths to .pc files. On Ubuntu 18.04 they here: /usr/lib/x86_64-linux-gnu/pkgconfig/:/usr/share/pkgconfig/:/usr/local/lib/pkgconfig/:/usr/lib/pkgconfig/

Exporting PKG_CONFIG_PATH for ffmpeg somehow messes with default values.

It will add ~16MB to the package.

Integrated Performance Primitives

Just set -D WITH_IPP=ON in opencv_setup.sh.

It will give +30MB to the final cv2.so size. And it will boost some opencv functions.

Official Intel's IPP benchmarks (may ask for registration)

MKL

You need to download MKL-DNN release and set two flags:-D GEMM=MKL , -D MKLROOT (details)

OpenVino comes with 30MB libmkl_tiny_tbb.so, but you will not be able to compile it, because it made from proprietary MKL.

Our opensource MKL-DNN experiment will end with 125MB libmklml_gnu.so and inference speed compatible with 5MB openblas (details).

CUDA

I did not try it. But it cannot be universal, it will only work with the certain combination of GPU+CUDA+cuDNN for which it will be compiled for.

• Compile OpenCV’s ‘dnn’ module with NVIDIA GPU support
• Use OpenCV’s ‘dnn’ module with NVIDIA GPUs, CUDA, and cuDNN

OpenMP

It is possible to compile OpenBLAS, dldt and OpenCV with OpenMP. I am not sure that the result would be better than now, but who knows.