Update 'README.md'

README.md

@@ -4,38 +4,72 @@ This repository contains code for detecting heat pipes in the greenhouse as well
 
 Platform: ROS 2, Humble, Ubuntu 22.04
 
-## How to build the workspace?
-```
-$ git clone https://tea.der-space.de/apoorva/greenhouse.git
-```
 ## How to install dependencies??
+- Install `git`
 ```
-
+sudo apt install git
-sudo apt-get install ros-humble-$(PACKAGE_NAME)
-
 ```
-
+- Install `ROS 2 Humble` on `Ubuntu 22.04` by following https://docs.ros.org/en/foxy/Installation/Ubuntu-Install-Debians.html
+- Install `colcon build` by following https://docs.ros.org/en/foxy/Tutorials/Beginner-CLI-Tools/Configuring-ROS2-Environment.html
+```
+sudo apt install python3-colcon-common-extensions
+```
+- Instal `pip` for python packages
+```
+sudo apt install python3-pip
+```
+- Now clone the repository
+```
+git clone https://tea.der-space.de/apoorva/greenhouse.git
+```
+- Install `ultralytics` for yolov3 package
+```
+pip install ultralytics
+```
+- For `yolov3_ros`, there are a bunch of other requirements. Go to `yolov3` folder and install using following commands:
+```
+cd ~/greenhouse/yolov3
+pip install -r requirements.txt
+```
+- Go to `ros2_ws` inside `greenhouse`. Make sure you only have `src` folder.
+```
+cd ~/greenhouse/ros2_ws
+ls
+src
+```
+- Inside `ros2_ws` folder, start building individual packages in the below sequence to avoid errors.
+```
+colcon build --packages-select pipe_msgs
+colcon build --packages-select pcl_ros
+colcon build --allow-overriding pcl_ros
+colcon build --packages-select pcl_conversions
+colcon build --allow-overriding pcl_conversions
+colcon build --packages-select find-pose
+colcon build --packages-select yolov3_ros
+. install/setup.bash
+```
+- The code should be ready to launch as explained in [How to run Live Detection?](#how-to-run-live-detection)
 This section explains what each module is responsible for.
 ## perception_pcl
 This module is responsible for providing `pcl_conversions` and `pcl_ros` modules in `ros 2`.
 To build, run the following command:
 ```
-$ cd ros2_ws/
+cd ros2_ws/
-$ colcon build --packages-select perception_pcl
+colcon build --packages-select perception_pcl
-$ . install/setup.bash
+. install/setup.bash
 ```
 
 ## pipe_msgs
 This module contains ros msgs for storing information about the detected object's bounding box.
 ```
-$ cd ros2_ws/
+cd ros2_ws/
-$ colcon build --packages-select pipe_msgs
+colcon build --packages-select pipe_msgs
-$ . install/setup.bash
+. install/setup.bash
 ```
 To check if msgs are built properly, run following command
 ```
-$ ros2 interface show pipe_msgs/msg/BoundingBox
+ros2 interface show pipe_msgs/msg/BoundingBox
-
+```
 The output will be:
 float64 probability
 int64 xmin
@@ -44,7 +78,7 @@ int64 xmax
 int64 ymax
 int16 id
 string class_id
-```
+
 
 ## find-pose
 This ROS module is responsible for determining the position of the detected objects.
@@ -58,9 +92,9 @@ Output:
 How to build and run?
 This package is dependent on custom `pcl_conversion` and `pcl_ros` module. Make sure you have built those before building this package.
 ```
-$ colcon build --packages-select find-pose
+colcon build --packages-select find-pose
-$ . install/setup.bash
+. install/setup.bash
-$ ros2 launch find-pose find-pose-node.launch.py
+ros2 launch find-pose find-pose-node.launch.py
 ```
 All the topics can be remapped in the launch file.
 
@@ -73,7 +107,7 @@ The following input/ros topics are needed:
 
 ROS Paramater Input:
 - best_weights: String that is the path to the best weights file of yolov3 detection
-Defaults: `'src/pipe_weights.pt'`
+Defaults: `'src/pipe_weights.pt'` inside `ros2_ws` folder
 
 The following are the output topics:
 - /detection_image: The RGB Image topic with bounding box drawn on it for visualization and debugging purpose
@@ -84,9 +118,10 @@ The following are the output topics:
 
 How to build and run?
 ```
-$ colcon build --packages-select yolov3_ros
+colcon build --packages-select yolov3_ros
-$ . install/setup.bash
+. install/setup.bash
-$ ros2 launch yolov3_ros pipe_detection.launch.py
+cd greenhouse/ros2_ws/
+ros2 launch yolov3_ros pipe_detection.launch.py
 ```
 All the topics can be remapped in the launch file. The path to best_weights can also be changed inside the launch file.
 Launch file is stored in `yolov3_ros/launch/`.
@@ -106,7 +141,7 @@ drive.mount("/content/gdrive")
 ```
 - Upload the yolov3 code and cd into the location of code
 ```
-%cd /content/gdrive/MyDrive/yolov3
+cd /content/gdrive/MyDrive/yolov3
 ```
 - Run the training script
 ```
@@ -127,12 +162,12 @@ This ROS module converts the rosbag data to images for yolo training purpose etc
 - Create folder called `stereo` inside convert_2_img module.
 - Run following command to launch the node. Currently, this node listens for `/camera/color/image_raw` topic.
 ```
-$ cd ros2_ws/src/convert_2_img
+cd ros2_ws/src/convert_2_img
-$ python3 convert_2_img/convert_to_img.py
+python3 convert_2_img/convert_to_img.py
 ```
 - Play the rosbag in another terminal
 ```
-$ ros2 bag play bag/bag.db
+ros2 bag play bag/bag.db
 ```
 - Once bag has finished playing, the images will be stored inside `stereo` folder.
 
@@ -140,8 +175,8 @@ $ ros2 bag play bag/bag.db
 This module is used to label images for yolo. The pre-defined custom classes file was changed to use new labels. This file is stored in `cd labelImg/data/predefined_classes.txt`
 To launch the gui, run
 ```
-$ cd labelImg
+cd labelImg
-$ python3 labelImg.py 
+python3 labelImg.py 
 ```
 More details: https://github.com/heartexlabs/labelImg
 
@@ -181,28 +216,29 @@ You have the following required data in forms of ros topic:
 How do you get this data?
 - This data can come from a pre recorded Rosbag. If this is the case, do following:
 ```
-$ ros2 bag play bag-folder/bag-name.db3
+ros2 bag play bag-folder/bag-name.db3
 ```
 - This data can come directly from camera's (D455/ZED2I) ROS Node: Launch your node in a terminal. 
 - If your camera topics have different names than the default topic names mentioned above, update/remap ONLY the `pipe_detection.launch.py` script stored in `ros2_ws/src/yolov3_ros/launch` folder.
 - Once you have updated the launch file, build the code again using `colcon build --packages-select yolov3_ros` and proceedas mentioned below.
 
 Steps to run:
-1. Open a terminal.
+- Open a terminal.
-2. Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
+- Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
-3. Launch node for object detection:
+- Launch node for object detection:
 ```
-$ ros2 launch yolov3_ros pipe_detection.launch.py
+ros2 launch yolov3_ros pipe_detection.launch.py
 ```
-This node will output two topics: `/bboxes` and `/detection_image`.
+- This node will output two topics: `/bboxes` and `/detection_image`.
-4. Launch node for pose estimation:
+- Make sure that you run this launch file from `greenhouse/ros2_ws/` folder since the path of weights is relative (`/src/pipe_weights.pt`) inside the launch file.
+- Launch node for pose estimation:
 ```
-$ ros2 launch find-pose find-pose-node.launch.py
+ros2 launch find-pose find-pose-node.launch.py
 ```
-This node will output TF topics between `/camera_link` and `/${detected_object_name}`.
+- This node will output TF topics between `/camera_link` and `/${detected_object_name}`.
-5. Open RVIZ by running `$ rviz2`. Change `fixed frame` from `map` to `camera_link`.
+- Open RVIZ by running `$ rviz2`. Change `fixed frame` from `map` to `camera_link`.
-5a. Go to `Add` button. Under `By Display type`, select `tf`. Once tf is added, select required frames like `camera_link` and `l_trail` to see the tfs.
+    - Go to `Add` button. Under `By Display type`, select `tf`. Once tf is added, select required frames like `camera_link` and `l_trail` to see the tfs.
-5b. To see the current image with detected object, Go to `Add` Button. Under `By Topic`, select topci called `detection_image`.
+    - To see the current image with detected object, Go to `Add` Button. Under `By Topic`, select topci called `detection_image`.
-You can add other topics as per the need and topic names. 
+    -You can add other topics as per the need and topic names. 
-6. You can open launch files to update/remap topic name if different camera is being used. 
+- You can open launch files to update/remap topic name if different camera is being used. 
-7. You can also update ros parameter from launch file. Currently, the `pipe_weights.pt` file is the one used. This file can be changed and you can update the parameter name `best_weights` inside the `pipe_detection.launch.py` file.
+- You can also update ros parameter from launch file. Currently, the `pipe_weights.pt` file is the one used. This file can be changed and you can update the parameter name `best_weights` inside the `pipe_detection.launch.py` file.