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.github/workflows/main.yml

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- dev-*
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jobs:
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build_autotools:
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runs-on: ubuntu-18.04
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strategy:
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matrix:
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container: ["ridgerun/r2inference-ubuntu-18.04:v0.1.5", "ridgerun/r2inference-ubuntu-16.04:v0.3.1"]
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container:
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image: ${{ matrix.container }}
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env:
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PREFIX: /usr/lib/x86_64-linux-gnu/
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CFLAGS: -Werror
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CXXFLAGS: -Werror
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steps:
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- uses: actions/checkout@v2
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- name: Build backend
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run: |
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tar -C /usr/local -xzf /root/r2inference/backends/tensorflow/v1.15.0/libtensorflow-cpu-linux-x86_64-1.15.0.tar.gz
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ldconfig
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- name: Checkout r2i repo
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uses: actions/checkout@master
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with:
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repository: RidgeRun/r2inference
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- name: Build r2i
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run: |
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NOCONFIGURE=1 ./autogen.sh
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./configure --disable-docs --enable-tensorflow
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make
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make install
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- name: Checkout gst-inference repo
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uses: actions/checkout@v2
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with:
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submodules: true
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- name: Build gst-inference
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run: |
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CFLAGS="${{ env.CFLAGS }}"
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CXXFLAGS="${{ env.CXXFLAGS }}"
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NOCONFIGURE=1 ./autogen.sh
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./configure --disable-docs --prefix $PREFIX --libdir $PREFIX
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make
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- name: Check gst-inference
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env:
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LD_LIBRARY_PATH: /usr/local/lib/
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run: make check VERBOSE=1
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- name: Install gst-inference
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run: |
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make install
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- name: Check gst-inference install
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env:
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LD_LIBRARY_PATH: /usr/local/lib/
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run: gst-inspect-1.0 inference
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- name: Run GStreamer pipeline
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env:
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ROOT: /root/r2inference/resources/InceptionV1_TensorFlow
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LD_LIBRARY_PATH: /usr/local/lib/
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run: |
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GST_DEBUG=2,inferencebin:6 gst-launch-1.0 filesrc location=$ROOT/Egyptian_cat.jpg ! jpegparse ! jpegdec ! \
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inferencebin arch='inceptionv1' model-location=$ROOT/graph_inceptionv1_tensorflow.pb \
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backend='tensorflow' input-layer='input' output-layer='InceptionV1/Logits/Predictions/Reshape_1' \
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labels=$ROOT/imagenet_labels.txt crop=false overlay=true filter=-1 ! \
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fakesink silent=false sync=false async=false -v
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build_meson:
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runs-on: ubuntu-18.04
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strategy:
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run: |
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CFLAGS="${{ env.CFLAGS }}"
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CXXFLAGS="${{ env.CXXFLAGS }}"
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NOCONFIGURE=1 ./autogen.sh
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./configure --disable-docs --enable-tensorflow
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make
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make install
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meson build --prefix $PREFIX -Denable-tensorflow=true -Denable-docs=disabled
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ninja -C build
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ninja -C build install
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- name: Checkout gst-inference repo
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uses: actions/checkout@v2
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with:

.gitignore

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\#*
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*.orig
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# Autotools cruft
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aclocal.m4
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autom4te.cache/
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compile
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config.guess
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config.h
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config.h.in
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config.status
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config.sub
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configure
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install-sh
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missing
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Makefile
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Makefile.in
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depcomp
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libtool
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ltmain.sh
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m4/
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.deps/
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.libs/
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stamp-h*
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autoregen.sh
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gst-inference-*.pc
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# Build outputs
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.deps/
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.libs/
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*.la
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*.lo
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*.o
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# Git conflict files
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# Examples
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tests/examples/classification/classification
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tests/examples/detection/detection
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tests/examples/embedding/embedding

.gitmodules

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AUTHORS

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ChangeLog

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MAINTAINERS

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Makefile.am

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NEWS

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README.md

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<a href="https://developer.ridgerun.com/wiki/index.php?title=GstInference"><img src="https://developer.ridgerun.com/wiki/images/thumb/9/92/GstInference_Logo_with_name.jpeg/600px-GstInference_Logo_with_name.jpeg" height="400" width="400"></a>
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GstInference | Coral from Google
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:-------------------------:|:-------------------------:
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[<img src="https://developer.ridgerun.com/wiki/images/thumb/9/92/GstInference_Logo_with_name.jpeg/600px-GstInference_Logo_with_name.jpeg" height="400" width="400">](https://developer.ridgerun.com/wiki/index.php?title=GstInference) | [<img src="https://developer.ridgerun.com/wiki/images/6/62/Works_with_coral_svg.svg" height="400" width="400">](https://coral.ai/products/#prototyping-products)
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# GstInference
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This repo uses **[R²Inference](https://github.com/RidgeRun/r2inference)**, an abstraction layer in C/C++ for a variety of machine learning frameworks. With R²Inference a single C/C++ application may work with models on different frameworks. This is useful to execute inference taking advantage of different hardware resources such as CPU, GPU, or AI optimized acelerators.
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GstInference provides several example elements for common applications, such as [`Inception v4`](ext/r2inference/gstinceptionv4.c) for image classification, [`TinyYOLO v2`](ext/r2inference/gsttinyyolov2.c) for object detection, and [`FaceNet`](ext/r2inference/gstfacenetv1.c) for face recognition. Examples are provided for performing inference on any GStreamer video stream.
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GstInference provides several example elements for common applications, such as [`Inception v4`](ext/r2inference/gstinceptionv4.c) for image classification and [`TinyYOLO v2`](ext/r2inference/gsttinyyolov2.c) for object detection. Examples are provided for performing inference on any GStreamer video stream.
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<img src="https://developer.ridgerun.com/wiki/images/thumb/4/4f/GstInference-examples.jpeg/800px-GstInference-examples.jpeg" width="800">
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We provide GStreamer [example pipelines](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_pipelines) for all our suported platforms,architectures and backends.
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We also provide [example applications](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_Applications) for classification, detection and face recognition.
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Our [smart lock](tests/examples/face_detection/README.md) example can get you started with a real security camera application.
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We also provide [example applications](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_Applications) for classification and detection.
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We also provide example trained models on our [model zoo](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Model_Zoo)

REQUIREMENTS

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