updated with dependencies

README.md

@@ -4,17 +4,52 @@ 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?
+## How to install dependencies??
+- Install `git`
+```
+$ sudo apt install git
+```
+- 
+- 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
 ```
-## How to install dependencies??
+- Install `ultralytics` for yolov3 package
 ```
-
-sudo apt-get install ros-humble-$(PACKAGE_NAME)
-
+$ 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`.
@@ -73,7 +108,7 @@ 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
@@ -86,6 +121,7 @@ How to build and run?
 ```
 $ colcon build --packages-select yolov3_ros
 $ . install/setup.bash
+$ 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.
@@ -188,21 +224,22 @@ $ ros2 bag play bag-folder/bag-name.db3
 - 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.
-2. Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
-3. Launch node for object detection:
+- Open a terminal.
+- Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
+- Launch node for object detection:
 ```
 $ ros2 launch yolov3_ros pipe_detection.launch.py
 ```
 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
 ```
 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`.
-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.
-5b. To see the current image with detected object, Go to `Add` Button. Under `By Topic`, select topci called `detection_image`.
+- Open RVIZ by running `$ rviz2`. Change `fixed frame` from `map` to `camera_link`.
+-- 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.
+-- 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. 
-6. 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 open launch files to update/remap topic name if different camera is being used. 
+- 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.