[OSS] - [ROS2OV_Team1] Fix typo and grammatical errors

.ci_local_test/README.md

@@ -1,8 +1,8 @@
 
 
-The Jenkinsfile Introduce:
+Jenkins file Introduction:
 
-1. The jenkins matchine would scan the ROS2_Openvion project regularly.
+1. The Jenkins machine would scan the ROS2_OpenVINO project regularly.
 
 it would trigger test when scan the PR or other change.
 

.ci_local_test/ros2_openvino_toolkit_test/docker_run.sh

@@ -1,43 +1,33 @@
 #!/bin/bash
 
 export DISPLAY=:0
-
 export work_dir=$PWD
 
-
-function run_container() {
-
-    docker images | grep ros2_openvino_docker
-
-    if [ $? -eq 0 ]
-    then
-        echo "the image of ros2_openvino_docker:01 existence"
+function run_container() 
+{ 
+    if docker images -q ros2_openvino_docker:01 &>/dev/null; then
+        echo "The container ros2_openvino_docker:01 image exists"
         docker rmi -f ros2_openvino_docker:01
     fi
 
     docker ps -a | grep ros2_openvino_container
-    if [ $? -eq 0 ]
-    then
+    if docker ps -aq -f name=ros2_openvino_container; then
+        echo "The container ros2_openvino_container exists. Removing the container..."
         docker rm -f ros2_openvino_container
     fi
 
     # Removing some docker image ..
     # Using jenkins server ros2_openvino_toolkit code instead of git clone code.
-    cd $work_dir && sed -i '/RUN git clone -b ros2/d' Dockerfile
+    cd "$work_dir" && sed -i '/RUN git clone -b ros2/d' Dockerfile
     # add the jpg for test.
-    cd $work_dir && sed -i '$i COPY jpg /root/jpg' Dockerfile
-
-    cd $work_dir && docker build --build-arg ROS_PRE_INSTALLED_PKG=galactic-desktop --build-arg VERSION=galactic  -t ros2_openvino_docker:01 .
-    cd $work_dir && docker images
-    docker run -i --privileged=true --device=/dev/dri -v $work_dir/ros2_openvino_toolkit:/root/catkin_ws/src/ros2_openvino_toolkit  -v $HOME/.Xauthority:/root/.Xauthority -e GDK_SCALE  -v $work_dir/test_cases:/root/test_cases --name ros2_openvino_container  ros2_openvino_docker:01 bash -c "cd /root/test_cases && ./run.sh galactic"
+    cd "$work_dir" && sed -i '$i COPY jpg /root/jpg' Dockerfile
 
+    cd "$work_dir" && docker build --build-arg ROS_PRE_INSTALLED_PKG=galactic-desktop --build-arg VERSION=galactic  -t ros2_openvino_docker:01 .
+    cd "$work_dir" && docker images
+    docker run -i --privileged=true --device=/dev/dri -v "$work_dir"/ros2_openvino_toolkit:/root/catkin_ws/src/ros2_openvino_toolkit  -v "$HOME"/.Xauthority:/root/.Xauthority -e GDK_SCALE  -v "$work_dir"/test_cases:/root/test_cases --name ros2_openvino_container  ros2_openvino_docker:01 bash -c "cd /root/test_cases && ./run.sh galactic"
 }
 
-run_container
-if [ $? -ne 0 ]
-then
-  echo "Test fail"
-  exit -1
+if ! run_container; then 
+  echo "Test failed"
+  exit 1
 fi
-
-

.ci_local_test/ros2_openvino_toolkit_test/test_cases/ros2_openvino_tool_model_download.sh

@@ -3,17 +3,16 @@
 mkdir -p /opt/openvino_toolkit/models
 #apt install -y python-pip
 apt install -y python3.8-venv
-cd ~ && python3 -m venv openvino_env && source openvino_env/bin/activate
+cd ~ && python3 -m venv openvino_env
+#shellcheck source=/dev/null
+source openvino_env/bin/activate
 python -m pip install --upgrade pip
-pip install openvino-dev[tensorflow2,onnx]==2022.3
-
+pip install "openvino-dev[tensorflow2,onnx]==2022.3"
 
 #Download the optimized Intermediate Representation (IR) of model (execute once)
 cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list && omz_downloader --list download_model.lst -o /opt/openvino_toolkit/models/
-
 cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list && omz_converter --list convert_model.lst -d /opt/openvino_toolkit/models/ -o /opt/openvino_toolkit/models/convert
 
-
 #Copy label files (execute once)
 cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/face_detection/face-detection-adas-0001.labels /opt/openvino_toolkit/models/intel/face-detection-adas-0001/FP32/
 cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/face_detection/face-detection-adas-0001.labels /opt/openvino_toolkit/models/intel/face-detection-adas-0001/FP16/
@@ -27,4 +26,3 @@ cp /opt/openvino_toolkit/models/convert/public/mask_rcnn_inception_resnet_v2_atr
 
 cd /root/test_cases/ && ./yolov5_model_download.sh
 cd /root/test_cases/ && ./yolov8_model_download.sh
-

.ci_local_test/ros2_openvino_toolkit_test/test_cases/run.sh

@@ -7,10 +7,12 @@ then
 else
     export ros2_branch=$1
 fi
-source /root/test_cases/config.sh $ros2_branch
+#shellcheck source=/dev/null
+source /root/test_cases/config.sh "$ros2_branch"
 
 cd /root/catkin_ws && colcon build --symlink-install
-cd /root/catkin_ws && source ./install/local_setup.bash
+# shellcheck source=/dev/null 
+source ./install/local_setup.bash
 
 apt-get update
 # apt-get install -y ros-$ros2_branch-diagnostic-updater
@@ -31,6 +33,5 @@ result=$?
 echo "Test ENV:" && df -h && free -g
 if [ $result -ne 0 ]
 then
-        exit -1
+        exit 1
 fi
-

.ci_local_test/ros2_openvino_toolkit_test/test_cases/yolov5_model_download.sh

@@ -5,34 +5,32 @@ cd /root && git clone https://github.com/ultralytics/yolov5.git
 
 #Set Environment for Installing YOLOv5
 
-cd yolov5
+cd yolov5 || exit
 python3 -m venv yolo_env            # Create a virtual python environment
+# shellcheck source=/dev/null 
 source yolo_env/bin/activate        # Activate environment
 pip install -r requirements.txt     # Install yolov5 prerequisites
 pip install wheel
 pip install onnx
 
 # Download PyTorch Weights
-mkdir -p /root/yolov5/model_convert && cd /root/yolov5/model_convert
+mkdir -p /root/yolov5/model_convert && cd /root/yolov5/model_convert || exit
 wget https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt
 
-cd /root/yolov5
+cd /root/yolov5 || exit
 python3 export.py --weights model_convert/yolov5n.pt --include onnx
 
-
 #2. Convert ONNX files to IR files
-cd /root/yolov5/
+cd /root/yolov5/ || exit
 python3 -m venv ov_env                      # Create openVINO virtual environment
+# shellcheck source=/dev/null 
 source ov_env/bin/activate                  # Activate environment
 python -m pip install --upgrade pip         # Upgrade pip
-pip install openvino[onnx]==2022.3.0        # Install OpenVINO for ONNX
-pip install openvino-dev[onnx]==2022.3.0    # Install OpenVINO Dev Tool for ONNX
-
+pip install "openvino[onnx]==2022.3.0"        # Install OpenVINO for ONNX
+pip install "openvino-dev[onnx]==2022.3.0"    # Install OpenVINO Dev Tool for ONNX
 
-cd /root/yolov5/model_convert
+cd /root/yolov5/model_convert || exit
 mo --input_model yolov5n.onnx
 
-
 mkdir -p  /opt/openvino_toolkit/models/convert/public/yolov5n/FP32/
 sudo cp yolov5n.bin yolov5n.mapping yolov5n.xml /opt/openvino_toolkit/models/convert/public/yolov5n/FP32/
-

.ci_local_test/ros2_openvino_toolkit_test/test_cases/yolov8_model_download.sh

@@ -2,23 +2,21 @@
 
 #Pip install the ultralytics package including all requirements in a Python>=3.7 environment with PyTorch>=1.7.
 
-mkdir -p yolov8 && cd yolov8
+mkdir -p yolov8 && cd yolov8 || exit
 pip install ultralytics
 apt install python3.8-venv
 python3 -m venv openvino_env
+# shellcheck source=/dev/null 
 source openvino_env/bin/activate
 
-
 #Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
 # export official model
 yolo export model=yolov8n.pt format=openvino
 yolo export model=yolov8n-seg.pt format=openvino
 
-
 # Move to the Recommended Model Path
 mkdir -p  /opt/openvino_toolkit/models/convert/public/FP32/yolov8n
 mkdir -p  /opt/openvino_toolkit/models/convert/public/FP32/yolov8n-seg
 
 cp yolov8n_openvino_model/* /opt/openvino_toolkit/models/convert/public/FP32/yolov8n
 cp yolov8n-seg_openvino_model/* /opt/openvino_toolkit/models/convert/public/FP32/yolov8n-seg
-

README.md

@@ -24,7 +24,7 @@
 # Overview
 ## ROS2 Version Supported
 
-|Branch Name|ROS2 Version Supported|Openvino Version|OS Version|
+|Branch Name|ROS2 Version Supported|OpenVINO Version|OS Version|
 |-----------------------|-----------------------|--------------------------------|----------------------|
 |[ros2](https://github.com/intel/ros2_openvino_toolkit/tree/ros2)|Galactic, Foxy, Humble|V2022.1, V2022.2, V2022.3|Ubuntu 20.04, Ubuntu 22.04|
 |[dashing](https://github.com/intel/ros2_openvino_toolkit/tree/dashing)|Dashing|V2022.1, V2022.2, V2022.3|Ubuntu 18.04|
@@ -50,12 +50,12 @@
 |**OS**|Mandatory|We only tested this project under Ubuntu distros. It is recommended to install the corresponding Ubuntu Distro according to the ROS distro that you select to use. **For example: Ubuntu 18.04 for dashing, Ubuntu 20.04 for Foxy and Galactic, Ubuntu 22.04 for Humble.**|
 |**ROS2**|Mandatory|We have already supported active ROS distros (Humble, Galactic, Foxy and Dashing (deprecated)). Choose the one matching your needs. You may find the corresponding branch from the table above in section [**ROS2 Version Supported**](#ros2-version-supported).|
 |**OpenVINO**|Mandatory|The version of OpenVINO toolkit is decided by the OS and ROS2 distros you use. See the table above in Section [**ROS2 Version Supported**](#ros2-version-supported).|
-|**Realsense Camera**|Optional|Realsense Camera is optional, you may choose these alternatives as the input: Standard Camera, ROS Image Topic, Video/Image File or RTSP camera.|
+|**RealSense Camera**|Optional|RealSense Camera is optional, you may choose these alternatives as the input: Standard Camera, ROS Image Topic, Video/Image File or RTSP camera.|
 
 # Introduction
 ## Design Architecture
 <p><details><summary>Architecture Design</summary>
-From the view of hirarchical architecture design, the package is divided into different functional components, as shown in below picture. 
+From the view of hierarchical architecture design, the package is divided into different functional components, as shown in below picture. 
 
 ![OpenVINO_Architecture](./data/images/design_arch.PNG "OpenVINO RunTime Architecture")
 
@@ -83,16 +83,16 @@ See more from [here](https://github.com/openvinotoolkit/openvino) for Intel Open
 <details>
 <summary>ROS Input & Output</summary>
 
-- **Diversal Input resources** are data resources to be infered and analyzed with the OpenVINO framework.
-- **ROS interfaces and outputs** currently include _Topic_ and _service_. Natively, RViz output and CV image window output are also supported by refactoring topic message and inferrence results.
+- **Diverse Input resources** are data resources to be inferred and analyzed with the OpenVINO framework.
+- **ROS interfaces and outputs** currently include _Topic_ and _service_. Natively, RViz output and CV image window output are also supported by refactoring topic message and inference results.
 </details>
 </p>
 
 <p>
 <details>
 <summary>Optimized Models</summary>
 
-- **Optimized Models** provided by Model Optimizer component of Intel® OpenVINO™ toolkit. Imports trained models from various frameworks (Caffe*, Tensorflow*, MxNet*, ONNX*, Kaldi*) and converts them to a unified intermediate representation file. It also optimizes topologies through node merging, horizontal fusion, eliminating batch normalization, and quantization. It also supports graph freeze and graph summarize along with dynamic input freezing.
+- **Optimized Models** provided by Model Optimizer component of Intel® OpenVINO™ toolkit. Imports trained models from various frameworks (Caffe*, TensorFlow*, MxNet*, ONNX*, Kaldi*) and converts them to a unified intermediate representation file. It also optimizes topologies through node merging, horizontal fusion, eliminating batch normalization, and quantization. It also supports graph freeze and graph summarize along with dynamic input freezing.
 </details>
 </p>
 </details></p>
@@ -103,15 +103,15 @@ From the view of logic implementation, the package introduces the definitions of
 
 ![Logic_Flow](./data/images/impletation_logic.PNG "OpenVINO RunTime Logic Flow")
 
-Once a corresponding program is launched with a specified .yaml config file passed in the .launch file or via commandline, _**parameter manager**_ analyzes the configurations about pipeline and the whole framework, then shares the parsed configuration information with pipeline procedure. A _**pipeline instance**_ is created by following the configuration info and is added into _**pipeline manager**_ for lifecycle control and inference action triggering.
+Once a corresponding program is launched with a specified .yaml config file passed in the .launch file or via command line, _**parameter manager**_ analyzes the configurations about pipeline and the whole framework, then shares the parsed configuration information with pipeline procedure. A _**pipeline instance**_ is created by following the configuration info and is added into _**pipeline manager**_ for lifecycle control and inference action triggering.
 
 The contents in **.yaml config file** should be well structured and follow the supported rules and entity names. Please see [yaml configuration guidance](./doc/quick_start/yaml_configuration_guide.md) for how to create or edit the config files.
 
 <p>
 <details>
 <summary>Pipeline</summary>
 
-**Pipeline** fulfills the whole data handling process: initiliazing Input Component for image data gathering and formating; building up the structured inference network and passing the formatted data through the inference network; transfering the inference results and handling output, etc.
+**Pipeline** fulfills the whole data handling process: initializing Input Component for image data gathering and formatting; building up the structured inference network and passing the formatted data through the inference network; transfering the inference results and handling output, etc.
 </details>
 </p>
 
@@ -235,7 +235,7 @@ For the snapshot of demo results, refer to the following picture.
 
 # Installation and Launching
 ## Deploy in Local Environment
-* Refer to the quick start document for [getting_started_with_ros2](./doc/quick_start/getting_started_with_ros2_ov2.0.md) for detailed installation & lauching instructions.
+* Refer to the quick start document for [getting_started_with_ros2](./doc/quick_start/getting_started_with_ros2_ov2.0.md) for detailed installation & launching instructions.
 * Refer to the quick start document for [yaml configuration guidance](./doc/quick_start/yaml_configuration_guide.md) for detailed configuration guidance.
 
 ## Deploy in Docker
@@ -274,7 +274,7 @@ For the snapshot of demo results, refer to the following picture.
 * Report questions, issues and suggestions, using: [issue](https://github.com/intel/ros2_openvino_toolkit/issues).
 
 # More Information
-* ROS2 OpenVINO discription written in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw 
+* ROS2 OpenVINO description written in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw 
 
 ###### *Any security issue should be reported using process at https://01.org/security*
 

data/labels/emotions-recognition/FP32/emotions-recognition-retail-0003.labels

@@ -1,5 +1,5 @@
-neutual
+neutral
 happy
 sad
-supprise
+surprise
 anger

doc/quick_start/getting_started_with_Dashing_Ubuntu18.04.md

@@ -74,7 +74,7 @@ sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_segmentation/fr
 sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehicle-license-plate-detection-barrier-0106.labels /opt/openvino_toolkit/models/vehicle-license-plate-detection/output/intel/vehicle-license-plate-detection-barrier-0106/FP32	
 ```
 
-* If the model (tensorflow, caffe, MXNet, ONNX, Kaldi)need to be converted to intermediate representation (For example the model for object detection)
+* If the model (TensorFlow, caffe, MXNet, ONNX, Kaldi)need to be converted to intermediate representation (For example the model for object detection)
 * ssd_mobilenet_v2_coco
   ```
   cd /opt/openvino_toolkit/models/
@@ -94,7 +94,7 @@ sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehic
   sudo python3 /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/converter.py --name=yolo-v2-tf --mo /opt/intel/openvino_2021/deployment_tools/model_optimizer/mo.py
   ```
 
-* Before launch, check the parameter configuration in ros2_openvino_toolkit/sample/param/xxxx.yaml, make sure the paramter like model path, label path, inputs are right.
+* Before launch, check the parameter configuration in ros2_openvino_toolkit/sample/param/xxxx.yaml, make sure the parameter like model path, label path, inputs are right.
   * run face detection sample code input from StandardCamera.
   ```
   ros2 launch dynamic_vino_sample pipeline_people.launch.py
@@ -129,7 +129,7 @@ sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehic
   ```
 
 # More Information
-* ROS2 OpenVINO discription writen in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw
+* ROS2 OpenVINO description written in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw
 
 ###### *Any security issue should be reported using process at https://01.org/security*
 

doc/quick_start/getting_started_with_ros2_ov2.0.md

@@ -11,7 +11,7 @@ For ROS2 foxy and galactic on ubuntu 20.04:
 
   * Install Intel® OpenVINO™ Toolkit Version: 2022.3.</br>
   Refer to: [OpenVINO_install_guide](https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_apt.html#doxid-openvino-docs-install-guides-installing-openvino-apt)
-    * Install from an achive file. Both runtime and development tool are needed, `pip` is recommended for installing the development tool.</br>
+    * Install from an archive file. Both runtime and development tool are needed, `pip` is recommended for installing the development tool.</br>
     Refer to: [OpenVINO_devtool_install_guide](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html)
 
   * Install Intel® RealSense™ SDK.</br>
@@ -54,7 +54,7 @@ source ./install/local_setup.bash
 ## 3. Running the Demo
 ### Install OpenVINO 2022.3 by PIP
 OMZ tools are provided for downloading and converting models of open_model_zoo in ov2022.</br>
-Refer to: [OMZtool_guide](https://pypi.org/project/openvino-dev/)
+Refer to: [OMZ-tool_guide](https://pypi.org/project/openvino-dev/)
 
 * See all available models
 ```
@@ -67,7 +67,7 @@ cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list
 omz_downloader --list download_model.lst -o /opt/openvino_toolkit/models/
 ```
 
-* If the model (tensorflow, caffe, MXNet, ONNX, Kaldi) need to be converted to intermediate representation (such as the model for object detection):
+* If the model (TensorFlow, caffe, MXNet, ONNX, Kaldi) need to be converted to intermediate representation (such as the model for object detection):
 ```
 cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list
 omz_converter --list convert_model.lst -d /opt/openvino_toolkit/models/ -o /opt/openvino_toolkit/models/convert
@@ -85,7 +85,7 @@ cd ~/openvino/thirdparty/open_model_zoo/tools/model_tools
 sudo python3 downloader.py --list download_model.lst -o /opt/openvino_toolkit/models/
 ```
 
-* If the model (tensorflow, caffe, MXNet, ONNX, Kaldi) need to be converted to Intermediate Representation (such as the model for object detection):
+* If the model (TensorFlow, caffe, MXNet, ONNX, Kaldi) need to be converted to Intermediate Representation (such as the model for object detection):
 ```
 cd ~/openvino/thirdparty/open_model_zoo/tools/model_tools
 sudo python3 converter.py --list convert_model.lst -d /opt/openvino_toolkit/models/ -o /opt/openvino_toolkit/models/convert
@@ -102,7 +102,7 @@ sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_segmentation/fr
 sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehicle-license-plate-detection-barrier-0106.labels /opt/openvino_toolkit/models/intel/vehicle-license-plate-detection-barrier-0106/FP32
 ```
 
-* Check the parameter configuration in ros2_openvino_toolkit/sample/param/xxxx.yaml before lauching, make sure parameters such as model_path, label_path and input_path are set correctly. Please refer to the quick start document for [yaml configuration guidance](./yaml_configuration_guide.md) for detailed configuration guidance.
+* Check the parameter configuration in ros2_openvino_toolkit/sample/param/xxxx.yaml before launching, make sure parameters such as model_path, label_path and input_path are set correctly. Please refer to the quick start document for [yaml configuration guide](./yaml_configuration_guide.md) for detailed configuration guidance.
   * run face detection sample code input from StandardCamera.
   ```
   ros2 launch openvino_node pipeline_people.launch.py
@@ -129,7 +129,7 @@ sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehic
   ```
 
 # More Information
-* ROS2 OpenVINO discription writen in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw
+* ROS2 OpenVINO description written in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw
 
 ###### *Any security issue should be reported using process at https://01.org/security*