ROS2 (As Ease As Possible)

Goal of the project is to build computer vision projects easier on ROS

Computer vision is an important application of ROS2, as it allows robots and other autonomous systems to understand and interact with their environment. Here are some common topics related to computer vision in ROS2:

Image processing: Process images from cameras and other sensors. These libraries enable developers to implement various computer vision algorithms, such as object detection, segmentation, and tracking.

Depth sensing: In addition to cameras, many robots use depth sensors, such as LIDAR or depth cameras, to perceive their environment. ROS2 provides several libraries, such as depth_image_proc and pointcloud2, to process and analyze depth data.

Perception algorithms: Various perception algorithms that can be used for object detection, tracking, and recognition.

Integration with other ROS2 components: Computer vision algorithms in ROS2 are often used in conjunction with other ROS2 components, such as navigation, manipulation, and planning. ROS2 provides various communication and messaging protocols, such as ROS2 Topics and ROS2 Services, that enable developers to integrate these components and create complex systems.

tested on

## ros distro
abc@xyz:~$ rosversion -d
humble

## ubuntu version
abc@xyz:~$ lsb_release -a
No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 22.04.1 LTS
Release:	22.04
Codename:	jammy

## python version
abc@xyz:~$ python3 --version
Python 3.10.6

my ros_ws

### steps for setting up
git clone https://github.com/bharath5673/ros_ws.git
cd ros_ws
colcon build
cd ..

1. turtlebot

### steps for turtleot
source ros_ws/install/setup.bash
ros2 run turtlesim turtlesim_node & ros2 run my_robot_controller turtle_controller

2. opencv-python

### steps for opencv

source ros_ws/install/setup.bash
ros2 run test_opencv run_test

3. mediapipe

pip install mediapipe

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