Merge branch 'master' into add_pythoncheck

.github/workflows/python2.yml

@@ -0,0 +1,18 @@
+# generated by `./generate_action_config.py noetic`
+# jsk_travis
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    name: check_python2
+
+    container: ubuntu:20.04
+
+    steps:
+      - name: Chcekout
+        uses: actions/checkout@v2
+      - name: Check Python2
+        run: |
+          apt update -q && apt install -y -q python2
+          python2 -m compileall .

.travis.yml

@@ -34,6 +34,7 @@ env:
     - ROSDEP_ADDITIONAL_OPTIONS='-n -r -v --ignore-src' # run rosdep without -q
   matrix:
     - CHECK_PYTHON3_COMPILE=true
+    - CHECK_PYTHON2_COMPILE=true
     - ROS_DISTRO=melodic DOCKER_IMAGE_JENKINS="ros-ubuntu:18.04-base" BEFORE_SCRIPT="sudo pip uninstall -y fcn chainer chainercv decorator cupy-cuda91; sudo ln -sf /bin/echo /usr/local/bin/pip; sudo ln -sf /bin/echo /usr/local/bin/pip2" TEST_PKGS="jsk_recognition_msgs" # check https://github.com/jsk-ros-pkg/jsk_recognition/pull/2533
     - ROS_DISTRO=indigo
     - ROS_DISTRO=indigo CATKIN_TOOLS_CONFIG_OPTIONS="--install" TEST_PKGS='jsk_recognition_msgs' # to skip test
@@ -52,6 +53,7 @@ before_script:
   - if [[ "$ROS_DISTRO" == "melodic" && "$DOCKER_IMAGE_JENKINS" == "" ]]; then export export DOCKER_IMAGE_JENKINS='ros-ubuntu:18.04-pcl'; fi
   - if [[ "$ROS_DISTRO" == "noetic" && "$DOCKER_IMAGE_JENKINS" == "" ]]; then export export DOCKER_IMAGE_JENKINS='ros-ubuntu:20.04-pcl'; fi
 script:
+  - if [ "${CHECK_PYTHON2_COMPILE}" == "true" ]; then python2 -m compileall .; exit $?; fi
   - if [ "${CHECK_PYTHON3_COMPILE}" == "true" ]; then bash -c "ret=0; trap 'ret=1' ERR; python3 -m compileall .; 2to3 -w -f except -f execfile -f has_key -f import -f raw_input -f zip .; git diff --exit-code . :^.travis > /dev/null; echo Exitting with \$ret; exit \$ret"; exit $?; fi
   # use https://github.com/ros-infrastructure/rosdep/pull/694 to respect version_lt for python pip, some package requries python3
   - export BEFORE_SCRIPT="sudo pip install numpy==1.16.6; sudo pip install fcn chainercv chainer-mask-rcnn==0.3.0 decorator==4.4.2 chainer==6.7.0 cupy-cuda91; $BEFORE_SCRIPT"

doc/imagesift/nodes/imagesift.rst

@@ -9,23 +9,23 @@ Extract sift features from input image.
 Subscribing Topic
 -----------------
 
-- ``image`` (``sensor_msgs/Image``)
+- ``/image`` (``sensor_msgs/Image``)
 
    Input image. This triggers output ``Feature0D``.
 
--  ``camera_info`` (``sensor_msgs/CameraInfo``)
+-  ``/camera_info`` (``sensor_msgs/CameraInfo``)
 
    Input camera_info.
 
 
 Publishing Topic
 ----------------
 
--  ``Feature0D`` (``posedetection_msgs/Feature0D``)
+-  ``/Feature0D`` (``posedetection_msgs/Feature0D``)
 
    This appears with input ``image``.
 
--  ``ImageFeature0D`` (``posedetection_msgs/ImageFeature0D``)
+-  ``/ImageFeature0D`` (``posedetection_msgs/ImageFeature0D``)
 
    This appears with both inputs ``image`` and ``camera_info``.
 

doc/install_chainer_gpu.rst

@@ -1,86 +1,195 @@
-Install Chainer with GPU Support
-================================
+Install Chainer/PyTorch with GPU Support
+========================================
 
-This documentation describes how to install Chainer with GPU suppport.
+This documentation describes how to install Chainer/PyTorch with GPU suppport.
 
 
 Requirements
 ------------
 
 - Nvidia GPU (ex. K80, TitanX, GTX 1080Ti).
-- Ubuntu (ex. 14.04, 16.04).
+- Ubuntu (ex. 14.04, 16.04, 18.04).
 
   You can check whether your PC has a GPU by ``lspci | grep -i nvidia``.
 
+Version Compatibilities for 18.04
+---------------------------------
+
+(Recommended) Use CUDA 9.1 from Official ubuntu repository (https://packages.ubuntu.com/bionic/nvidia-cuda-dev)
+
+- Chainer
+
+  - chainer == 6.7.0 (last version supoprting python2. See https://github.com/chainer/chainer/releases/tag/v6.7.0)
+  - cupy-cuda91 == 6.7.0 (chainer v6.7.0 requires cupy/cudnn for hardware acceleration support https://docs.chainer.org/en/v6.7.0/install.html)
+
+- PyTorch
+
+  - pytorch == 1.1.0 (Latest pytorch version supporting CUDA 9.1 https://download.pytorch.org/whl/cu90/torch_stable.html)
+  - CUDA >= 9.0 (Minimum required version for PyTorch 1.1.0 https://pytorch.org/get-started/previous-versions/#v110)
+
+(Experimental) Use CUDA 10.2 from Nvidia Developer's site (https://developer.nvidia.com/cuda-10.2-download-archive)
+
+- Chainer
+
+  - chainer == 6.7.0 (last version supoprting python2. See https://github.com/chainer/chainer/releases/tag/v6.7.0)
+  - cupy >=6.7.0,<7.0.0 (chainer v6.7.0 requires cupy/cudnn for hardware acceleration support https://docs.chainer.org/en/v6.7.0/install.html)
+  - cuDNN < 8 (cupy 6.7.0 requires cuDNN v5000= and <=v7999)
+  - CUDA 10.2 (cuDNN v7.6.5 requires CUDA 10.2 https://developer.nvidia.com/rdp/cudnn-archive)
+
+- PyTorch
+
+  - pytorch >= 1.4.0
+  - CUDA >= 9.2 (Minimum required version for PyTorch https://pytorch.org/get-started/previous-versions/#v140)
+  - Driver Version >= 396.26 (From CUDA Toolkit and Corresponding Driver Versions in https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html)
+
 Install CUDA
 ------------
 
-- Download deb file from https://developer.nvidia.com/cuda-downloads?target_os=Linux::
+- Ubuntu 14.04 : Download deb file from https://developer.nvidia.com/cuda-downloads?target_os=Linux::
 
+```bash
     # If you'd like to use CUDA8.0 on Ubuntu 14.04.
     wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64-deb
     mv cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64-deb cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb
     sudo dpkg -i cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb
     sudo apt-get update
     sudo apt-get install cuda
+```
+
+  - Add below to your `~/.bashrc`::
 
+    ```bash
+    # setup cuda & cudnn
+    export LD_LIBRARY_PATH=/usr/local/lib:/usr/lib:$LD_LIBRARY_PATH
+    export LIBRARY_PATH=/usr/local/lib:/usr/lib:$LIBRARY_PATH
+    export CPATH=/usr/include:$CPATH
+    export CFLAGS=-I/usr/include
+    export LDFLAGS="-L/usr/local/lib -L/usr/lib"
+    if [ -e /usr/local/cuda ]; then
+      export CUDA_PATH=/usr/local/cuda
+      export PATH=$CUDA_PATH/bin:$PATH
+      export CPATH=$CUDA_PATH/include:$CPATH
+      export LD_LIBRARY_PATH=$CUDA_PATH/lib64:$CUDA_PATH/lib:$LD_LIBRARY_PATH
+      export CFLAGS=-I$CUDA_PATH/include
+      export LDFLAGS="-L$CUDA_PATH/lib64 -L$CUDA_PATH/lib"
+    fi
+    ```
+
+
+- Ubuntu 16.04 : Download deb file from https://developer.nvidia.com/cuda-downloads?target_os=Linux::
+
+```bash
     # If you'd like to use CUDA9.2 on Ubuntu 16.04.
     # Choose the green buttons on the web page like x86_64 -> Ubuntu -> version -> deb (network).
     # Excute 1-3 and then, change step 4 as follows:
     sudo apt install cuda-9-2
+```
+
+- Ubuntu 18.04 : You can use CUDA 9.1 by deafult
+
+  ```bash
+  sudo apt install nvidia-cuda-toolkit
+  sudo apt install nvidia-cuda-dev
+
+- (Experimental) Ubuntu 18.04 : CUDA 10.2 is the latest version which supports `jsk_perception`. Download deb file from https://developer.nvidia.com/cuda-downloads?target_os=Linux::
+
+    ```bash
+    # If you'd like to use CUDA10.2 on Ubuntu 18.04.
+    # goto https://developer.nvidia.com/cuda-10.2-download-archive
+    # Choose the green buttons on the web page like x86_64 -> Ubuntu -> version -> deb (network).
+    # Excute all steps, but change the last step as follows:
+    sudo apt install cuda-10-2
+    ```
+
+  - If you install CUDA from nvidia, Make sure to uninstall CUDA tools from packages.ubuntu.com
+
+    ```bash
+    sudo apt remove nvidia-cuda-toolkit
+    sudo apt remove nvidia-cuda-dev
+    ```
+
+  - Also set environment variables to ~/.bashrc
+
+    ```bash
+    # set PATH for cuda 10.0 installation
+    if [ -d "/usr/local/cuda-10.2/bin/" ]; then
+        export PATH=/usr/local/cuda-10.2/bin${PATH:+:${PATH}}
+        export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+        export CFLAGS=-I/usr/local/cuda-10.2/include
+    fi
+    ```
 
 - After rebooting, you can see the memory usage of your GPU by ``nvidia-smi``
 
 Install CUDNN
 -------------
 
+- If you install `pip install cupy-cuda91`, you do not need to install CUDNN manually. (c.f. https://github.com/jsk-ros-pkg/jsk_visualization/issues/809). Thus, default 18.04 user can use CUDA 9.1 and `cupy-cuda91==6.7.0` for `chainer==6.7.0` and you can SKIP this section.
+
+  Installing CUDNN manually only requires for experimental user who install CUDA 10.2 manually.
+
 - You need to login at https://developer.nvidia.com/cudnn
 - Go to cuDNN Download and choose version
 - Download deb files of cuDNN Runtime Library and cuDNN Developer Library
 
-::
-
+```bash
    # If you'd like to install cuDNN for CUDA9.2 on Ubuntu 16.04
    # Download cuDNN v7.3.1 Runtime Library for Ubuntu16.04 (Deb)
    sudo dpkg -i libcudnn7_7.3.1.20-1+cuda9.2_amd64.deb
    # Download cuDNN v7.3.1 Developer Library for Ubuntu16.04 (Deb)
    sudo dpkg -i libcudnn7-dev_7.3.1.20-1+cuda9.2_amd64.deb
+   # Download cuDNN v7.6.5 Developer Library for Ubuntu18.04 (Deb)
+   sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.2_amd64.deb
+   sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.2_amd64.deb
+```
 
 Install Chainer
 ---------------
 
-::
-
-    pip install chainer
-
+```bash
+    sudo pip install chainer==6.7.0
+```
 
 Install Cupy
 ------------
 
-- Add below to your `~/.bashrc`::
+- (Default) Chainer 6.7.0 requires CuPy 6.7.0 and if you have CUDA 9.1, you can use CuPy pre-compiled binary package.
+
+
+  - Pre-compiled Install Cupy for CUDA 9.1 ::
+
+    ```bash
+    sudo pip install cupy-cuda91==6.7.0
+    ```
+
+- (Experimental) If you have newer CUDA version. You need to install CuPy with source distribution. This requires CUDNN before you run `pip install cupy` .
+
+  - Source Install Cupy for CUDA 10.2 ::
+
+    ```bash
+    sudo pip install -vvv cupy --no-cache-dir
+    ```
 
-    # setup cuda & cudnn
-    export LD_LIBRARY_PATH=/usr/local/lib:/usr/lib:$LD_LIBRARY_PATH
-    export LIBRARY_PATH=/usr/local/lib:/usr/lib:$LIBRARY_PATH
-    export CPATH=/usr/include:$CPATH
-    export CFLAGS=-I/usr/include
-    export LDFLAGS="-L/usr/local/lib -L/usr/lib"
-    if [ -e /usr/local/cuda ]; then
-      export CUDA_PATH=/usr/local/cuda
-      export PATH=$CUDA_PATH/bin:$PATH
-      export CPATH=$CUDA_PATH/include:$CPATH
-      export LD_LIBRARY_PATH=$CUDA_PATH/lib64:$CUDA_PATH/lib:$LD_LIBRARY_PATH
-      export CFLAGS=-I$CUDA_PATH/include
-      export LDFLAGS="-L$CUDA_PATH/lib64 -L$CUDA_PATH/lib"
-    fi
 
-- Install Cupy::
+Install PyTorch
+---------------
+
+- 18.04 provides CUDA 9.1 by defualt. To install PyTorch compatible with this version, download following wheel from https://download.pytorch.org/whl/cu90/torch_stable.html, and install manually.
+
+```bash
+   sudo pip install torch-1.1.0-cp27-cp27mu-linux_x86_64.whl
+   sudo pip install torchvision-0.3.0-cp27-cp27mu-manylinux1_x86_64.whl
+```
 
-    sudo bash
-    pip install -vvv cupy --no-cache-dir
+- (Experimental) If you manually install CUDA 10.2 manually, you can use latest PyTorch.
 
+```bash
+   sudo pip install torch==1.4.0
+```
 
-Try Samples
+- See https://github.com/jsk-ros-pkg/jsk_recognition/pull/2601#issuecomment-876948260 for more info.
+
+Try Chainer Samples
 -----------
 
 You can try to run samples to check if the installation succeeded::
@@ -89,6 +198,13 @@ You can try to run samples to check if the installation succeeded::
     roslaunch jsk_perception sample_people_pose_estimation_2d.launch GPU:=0
     roslaunch jsk_perception sample_regional_feature_based_object_recognition.launch GPU:=0
 
+Try PyTorch Samples
+-----------
+
+You can try to run samples to check if the installation succeeded::
+
+    roslaunch jsk_perception sample_hand_pose_estimation_2d.launch gpu:=0
+
 Trouble Shooting
 ----------------
 

doc/jsk_perception/nodes/aws_auto_checkin_app.rst

@@ -0,0 +1,83 @@
+aws_auto_cehckin_app.py
+=========================
+
+What is this?
+-------------
+
+.. image:: https://d1.awsstatic.com/Solutions/Solutions%20Category%20Template%20Draft/Solution%20Architecture%20Diagrams/auto-check-in-app-architecture.8baa84b79c2294d035c7b9cee323d7c9ba53a43a.png
+
+Face recognition using Amazon Rekognition, see
+https://aws.amazon.com/solutions/implementations/auto-check-in-app/
+for more info.
+
+Subscribing Topic
+-----------------
+
+
+* ``~image`` (``sensor_msgs/Image``)
+
+  Raw image.
+
+* ``~face_roi`` (``opencv_apps/FaceArrayStamped``)
+
+  Rectangles on the face of input image. Use ROI value.
+  ```
+        msg.faces[].face.x      : X coordinates of the center of the face image in the ~image input
+        msg.faces[].face.y      : Y coordinates of the center of the face image in the ~image input
+        msg.faces[].face.width  : Width of the face image
+        msg.faces[].face.height : Height of the face iamge
+  ```
+
+Publishing Topic
+----------------
+
+* ``~face_name`` (``opencv_apps/FaceArrayStamped``)
+
+  Publish recognized face name as well as face image. The face.{x,y,width,height} corresponds to input `face_roi`, that means x, y is the center of face rectangle.
+
+Parameters
+----------
+
+* ``~use_window`` (Bool, default: ``False``)
+
+  Show input image on the window, if it is true.
+
+* ``~env_path`` (String, default: ``env.json``)
+
+  Json file for environment variables to run aws auto-checkin app. You
+  can find how to generate this file on
+  https://aws.amazon.com/jp/builders-flash/202004/auto-checkin-app/.
+  In addition to that, you need to add "UserName" and "UserPassword"
+  ```
+  {
+    "Region": "%%REGION%%",
+    "ApiEndpoint" : "%%REST_API_ID%%.execute-api.%%REGION%%.amazonaws.com/prod/rekognize_face",
+    "CognitoUserPoolId": "%%COGNITO_USER_POOL_ID%%",
+    "CognitoUserPoolClientId": "%%COGNITO_USER_POOL_CLIENT_ID%%",
+    "FaceAreaThreshold": 1e4,
+    "FaceMarginRatio": 0.2,
+    "FaceSimilarityThreshold": 90,
+    "CroppedImageWidth": 540,
+    "CroppedImageHeight": 540,
+    "NameTtlSec": 10,
+    "UseDeepLeaningForDetector": true,
+    "UserName": "%%YOUR_USER_NAME%%",
+    "UserPassword": "%%YOUR_PASSWORD%%"
+  }
+  ```
+
+Sample
+------
+
+.. code-block:: bash
+
+  roslaunch jsk_perception sample_aws_auto_checkin_app.launch use_window:=true
+
+
+For JSK user, Download `env.json` file from
+[Gdrive](https://drive.google.com/file/d/1WUrRxPtT0ZuRx-IqjGwDBqeR5vZVTkB1/view?usp=sharing)
+and put this under `/tmp` directory to run sample code.
+
+To add new people to face database, add face image file to [Amazon
+S3](https://console.aws.amazon.com/s3),
+`auto-check-in-gapp-register...` buckets