Docker Image along with v2.1.0 (#309)

* Delete .vscode * Delete configure * CLI.OperatorAPI * CLI.Finished + Update.Inference.Doc * Refine installation format Fix FAKE Compatible => OpenCV3.2 CI => Compile OpenCV from source. Accelerate CI Accelerate CI * Fix imshow logic * Remove old docker codes * Add docker files * Enhance Docker * Refine Dockerfile * Optimize.Appearance * New README * Center.Align * LOGO * LOGO * Refine.Doc
tensorlayer · Aug 30, 2020 · c4df544 · c4df544
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diff --git a/.dockerignore b/.dockerignore
@@ -1 +1,4 @@
-docker/*.deb
+.git
+*build*
+data
+3rdparty
diff --git a/.gitignore b/.gitignore
@@ -47,5 +47,6 @@ docs/make.bat
 examples/user_codes/*.cpp
 debug.*
 
+!logo.png
 !docs/Makefile
-!docs/markdown/images/*
+!docs/markdown/images/*
diff --git a/Dockerfile b/Dockerfile
@@ -0,0 +1,50 @@
+# Dockerfile
+
+# docker build .
+
+# Based on CUDA11.0 & CuDNN8
+FROM nvidia/cuda:10.2-devel-ubuntu18.04
+
+# Install Non-GPU Dependencies.
+RUN apt update --allow-unauthenticated && version="7.0.0-1+cuda10.2" ; \
+    apt install -y \
+    libnvinfer7=${version} libnvonnxparsers7=${version} libnvparsers7=${version} \
+    libnvinfer-plugin7=${version} libnvinfer-dev=${version} libnvonnxparsers-dev=${version} \
+    libnvparsers-dev=${version} libnvinfer-plugin-dev=${version} python-libnvinfer=${version} \
+    python3-libnvinfer=${version} && \
+    apt-mark hold \
+    libnvinfer7 libnvonnxparsers7 libnvparsers7 libnvinfer-plugin7 libnvinfer-dev libnvonnxparsers-dev libnvparsers-dev libnvinfer-plugin-dev python-libnvinfer python3-libnvinfer
+
+# Install OpenCV Dependencies
+RUN apt install -y software-properties-common || apt install -y software-properties-common && \
+    add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main" && \
+    APT_DEPS="git cmake libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev x264 v4l-utils python3-dev python3-pip libcanberra-gtk-module libcanberra-gtk3-module" && \
+    apt install -y $APT_DEPS || apt install -y $APT_DEPS && \
+    python3 -m pip install numpy
+
+# Compile OpenCV
+RUN git clone --branch 4.4.0 https://github.com/opencv/opencv.git && \
+    cd opencv && mkdir build && cd build && \
+    cmake .. -DCMAKE_BUILD_TYPE=Release \
+             -DCMAKE_INSTALL_PREFIX=/usr/local \
+             -DWITH_TBB=ON \
+             -DWITH_V4L=ON \
+             -DBUILD_TESTS=OFF \
+             -DBUILD_OPENCV_PYTHON3=OFF && \
+    make -j && make install
+
+# Install HyperPose Dependencies
+RUN apt install -y python3-dev python3-pip subversion libgflags-dev
+
+COPY . /hyperpose
+
+# Download related data
+RUN for file in $(find /hyperpose/scripts -type f -iname 'download*.sh'); do sh $file; done
+
+# Build Repo
+RUN cd hyperpose && mkdir build && cd build && \
+    cmake .. -DCMAKE_BUILD_TYPE=Release && make -j
+
+WORKDIR /hyperpose/build
+
+ENTRYPOINT ["./hyperpose-cli"]
diff --git a/README.md b/README.md
@@ -1,10 +1,29 @@
-# HyperPose
+</a>
+<p align="center">
+    <img src="./docs/markdown/images/logo.png", width="800">
+</p>
 
-[![Documentation Status](https://readthedocs.org/projects/hyperpose/badge/?version=latest)](https://hyperpose.readthedocs.io/en/latest/?badge=latest)
+<p align="center">
+    <a href="https://readthedocs.org/projects/hyperpose/badge/?version=latest" title="Docs Building"><img src="https://readthedocs.org/projects/hyperpose/badge/?version=latest"></a>
+    <a href="https://github.com/tensorlayer/hyperpose/actions?query=workflow%3ACI" title="Build Status"><img src="https://github.com/tensorlayer/hyperpose/workflows/CI/badge.svg"></a>
+    <a href="https://hub.docker.com/repository/docker/tensorlayer/hyperpose" title="Docker"><img src="https://img.shields.io/docker/image-size/tensorlayer/hyperpose"></a>
+    <a href="https://drive.google.com/drive/folders/1w9EjMkrjxOmMw3Rf6fXXkiv_ge7M99jR?usp=sharing" title="PreTrainedModels"><img src="https://img.shields.io/badge/trained%20models-GoogleDrive-brightgreen.svg"></a>
+    <a href="https://en.cppreference.com/w/cpp/17" title="CppStandard"><img src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B"></a>
+    <a href="https://github.com/tensorlayer/hyperpose/graphs/commit-activity" title="Maintenance"><img src="https://img.shields.io/badge/maintained%3F-YES-brightgreen.svg"></a>
+    <a href="https://github.com/tensorlayer/tensorlayer/blob/master/LICENSE.rst" title="TensorLayer"><img src="https://img.shields.io/github/license/tensorlayer/tensorlayer">
+</p>
 
-HyperPose is a library for building human pose estimation systems that can efficiently operate in the wild.
+---
 
-> **News**: The PoseProposal inference model is released! See the HyperPose models on [Google Drive](https://drive.google.com/drive/folders/1w9EjMkrjxOmMw3Rf6fXXkiv_ge7M99jR?usp=sharing).
+<p align="center">
+    <a href="#Features">Features</a> •
+    <a href="#Documentation">Documentation</a> •
+    <a href="#Quick-Start-with-Docker">Quick-Start with Docker</a> •
+    <a href="#Performance">Performance</a> •
+    <a href="#License">License</a>
+</p>
+
+HyperPose is a library for building human pose estimation systems that can efficiently operate in the wild.
 
 ## Features
 
@@ -13,42 +32,63 @@ HyperPose has two key features, which are not available in existing libraries:
 - **Flexible training platform**: HyperPose provides flexible Python APIs to build many useful pose estimation models (e.g., OpenPose and PoseProposalNetwork). HyperPose users can, for example, customize data augmentation, use parallel GPUs for training, and replace deep neural networks (e.g., changing from ResNet to MobileNet), thus building models specific to their real-world scenarios.
 - **High-performance pose estimation**: HyperPose achieves real-time pose estimation though a high-performance pose estimation engine. This engine implements numerous system optimizations: pipeline parallelism, model inference with TensorRT, CPU/GPU hybrid scheduling, and many others. This allows HyperPose to run 4x faster than OpenPose and 10x faster than TF-Pose.
 
-## Get Started
+## Documentation
+
+You can install HyperPose(Python Training Library, C++ inference Library) and learn its APIs through [HyperPose Documentation](https://hyperpose.readthedocs.io/en/latest/).
 
-You can install HyperPose and learn its APIs through [Documentation](https://hyperpose.readthedocs.io/en/latest/).
+## Quick-Start with Docker
 
-## Example
+The official docker image is on [DockerHub](https://hub.docker.com/r/tensorlayer/hyperpose).
 
-We provide an example to show human pose estimation achieved by HyperPose. You need to install CUDA Toolkit 10+, TensorRT 7+, OpenCV 3.2+ and gFlags (cmake version), and enable C++ 17 support. Once the prerequisite are met, run the following script:
+Make sure you have [docker](https://docs.docker.com/get-docker/) with [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) functionality installed. 
+
+> Also note that your nvidia driver should be [compatible](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#support-title) with CUDA10.2.
 
 ```bash
-sudo apt -y install git cmake build-essential subversion libgflags-dev libopencv-dev
-git clone https://github.com/tensorlayer/hyperpose.git && cd hyperpose
-sh scripts/download-test-data.sh                       # Install data for examples.
-sh scripts/download-tinyvgg-model.sh                   # Install tiny-vgg model.
-mkdir build && cd build
-cmake .. -DCMAKE_BUILD_TYPE=Release && cmake --build . # Build library && examples.
-./hyperpose-cli                                        # The ouput images will be in the build folder.
+# [Example 1]: Doing inference on given video, copy the output.avi to the local path. 
+docker run --name quick-start --gpus all tensorlayer/hyperpose --runtime=stream
+docker cp quick-start:/hyperpose/build/output.avi .
+docker rm quick-start
+
+
+# [Example 2](X11 server required to see the imshow window): Real-time inference.
+# You may need to install X11 server locally:
+# sudo apt install xorg openbox xauth
+xhost +; docker run --rm --gpus all -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tensorlayer/hyperpose --imshow
+
+
+# [Example 3]: Camera + imshow window
+xhost +; docker run --name pose-camera --rm --gpus all -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --device=/dev/video0:/dev/video0 tensorlayer/hyperpose --source=camera --imshow
+# To quit this image, please type `docker kill pose-camera` in another terminal.
+
+
+# [Dive into the image]
+xhost +; docker run --rm --gpus all -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --device=/dev/video0:/dev/video0 --entrypoint /bin/bash tensorlayer/hyperpose
+# For users that cannot access a camera or X11 server. You may also use:
+# docker run --rm --gpus all -it --entrypoint /bin/bash tensorlayer/hyperpose
 ```
 
+> For more details, please check [here](https://hyperpose.readthedocs.io/en/latest/markdown/quick_start/prediction.html#table-of-flags-for-hyperpose-cli).
+
 ## Performance
 
-We compare the prediction performance of HyperPose with [OpenPose 1.6](https://github.com/CMU-Perceptual-Computing-Lab/openpose) and [TF-Pose](https://github.com/ildoonet/tf-pose-estimation). We implement the OpenPose algorithms with different configurations in HyperPose. The test-bed has Ubuntu18.04, 1070Ti GPU, Intel i7 CPU (12 logic cores). The test video is Crazy Updown Funk ([YouTube](https://www.youtube.com/watch?v=2DiQUX11YaY)). The HyperPose models (in the ONNX or Uff formats) are available [here](https://github.com/tensorlayer/pretrained-models/tree/master/models/hyperpose).
+We compare the prediction performance of HyperPose with [OpenPose 1.6](https://github.com/CMU-Perceptual-Computing-Lab/openpose) and [TF-Pose](https://github.com/ildoonet/tf-pose-estimation). We implement the OpenPose algorithms with different configurations in HyperPose. The test-bed has Ubuntu18.04, 1070Ti GPU, Intel i7 CPU (12 logic cores). 
 
-| HyperPose Configuration  | DNN Size | DNN Input Shape | HyerPose | Baseline |
+| HyperPose Configuration  | DNN Size | Input Size | HyerPose | Baseline |
 | --------------- | ------------- | ------------------ | ------------------ | --------------------- |
 | OpenPose (VGG)   | 209.3MB       | 656 x 368            | 27.32 FPS           | 8 FPS (OpenPose)          |
 | OpenPose (TinyVGG)  | 34.7 MB       | 384 x 256          | 124.925 FPS         | N/A                   |
 | OpenPose (MobileNet) | 17.9 MB       | 432 x 368          | 84.32 FPS           | 8.5 FPS (TF-Pose)         |
 | OpenPose (ResNet18)  | 45.0 MB       | 432 x 368          | 62.52 FPS           | N/A                  |
 
-As we can see, HyperPose is the only library that can achieve **real-time** human pose estimation.
-
 </a>
 <p align="center">
-    <img src="https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/media/dance_foot.gif?raw=true", width="360">
+    <img src="./docs/markdown/images/demo-xbd.gif", width="600">
 </p>
 
+<p align="center">
+    新宝岛 with HyperPose(Lightweight OpenPose model)
+</p>
 ## License
 
 HyperPose is open-sourced under the [Apache 2.0 license](https://github.com/tensorlayer/tensorlayer/blob/master/LICENSE.rst).

diff --git a/deps/apt.txt b/deps/apt.txt
diff --git a/deps/check.sh b/deps/check.sh
diff --git a/docker/.gitignore b/docker/.gitignore
diff --git a/docker/Dockerfile.builder-cpu b/docker/Dockerfile.builder-cpu
diff --git a/docker/Dockerfile.builder-cpu-ubuntu18 b/docker/Dockerfile.builder-cpu-ubuntu18
diff --git a/docker/Dockerfile.builder-gpu b/docker/Dockerfile.builder-gpu
diff --git a/docker/Dockerfile.tensorrt b/docker/Dockerfile.tensorrt
diff --git a/docker/Makefile b/docker/Makefile
diff --git a/docker/sources.list.bionic.ustc b/docker/sources.list.bionic.ustc
diff --git a/docker/sources.list.ustc b/docker/sources.list.ustc
diff --git a/docs/index.rst b/docs/index.rst
@@ -6,21 +6,26 @@
 Welcome to HyperPose's Documentation!
 =====================================
 
-Get Started
+Installation
 -----------
 .. toctree::
    :maxdepth: 1
 
-   markdown/design/design
    markdown/install/prediction
    markdown/install/training
+
+Get Started
+-----------
+.. toctree::
+   :maxdepth: 1
+
    markdown/quick_start/prediction
    markdown/quick_start/training
 
 Tutorials
 -------------
 .. toctree::
-    :maxdepth: 2
+    :maxdepth: 1
 
     markdown/tutorial/prediction
     markdown/tutorial/training
@@ -41,6 +46,13 @@ Performance and Supports
     markdown/performance/supports
     markdown/performance/prediction
 
+Insights
+------------------------
+.. toctree::
+    :maxdepth: 1
+
+    markdown/design/design
+
 Frequently Asked Questions(FAQs)
 --------------------------------
 .. toctree::

diff --git a/docs/markdown/images/demo-xbd.gif b/docs/markdown/images/demo-xbd.gif
diff --git a/docs/markdown/images/logo.png b/docs/markdown/images/logo.png