Deep Learning D415 Repo

This repository contains code for working with the Intel RealSense D415 camera in ROS 2 for deep learning applications.

Development Environment

• ROS 2 Humble
• Ubuntu 22.04
• Python 3.10
• RealSense SDK v2.56.5

Setup Instructions

1. Clone the Repository

cd ~
git clone --recursive git@github.com:hrc-pme/Deep_learning_d415_ROS2.git

Note: The --recursive flag is required to clone submodules properly

2. Update Repository and Submodules

cd ~/Deep_Learning_d415_ROS2
git pull
git submodule sync --recursive
git submodule update --init --recursive

3. Copy the usb rule to machine

cd ~/Deep_learning_d415_ROS2
sudo cp .99-realsense-libusb.rules /etc/udev/rules.d/99-realsense-libusb.rules

Start Docker Container

Choose the appropriate Docker setup based on your hardware:

• Use GPU version if you have a compatible graphics card
• Use CPU version for systems without GPU

1. GPU Version

Before starting with GPU container check out your CUDA version first.

The default CUDA version will be 12.4.

# For GPU systems:
# Check your CUDA version first
nvidia-smi

If you want to change your CUDA version Check on this repo. CUDA Version

If you have the correct version, you can open the container.

cd ~/Deep_Learning_d415_ROS2
chmod +x run_gpu.sh           # give the permission (you will only do one time)
./run_gpu.sh                  # default CUDA version 12.4

2. CPU Version

If you are using VMware or any virtual machine, you only can use CPU version.

# For CPU systems:
cd ~/Deep_Learning_d415_ROS2
chmod +x run_cpu.sh           # give the permission (you will only do one time)
./run_cpu.sh

In both container the user ID will be

user: hrc 
passward: 111111

Build ROS 2 Workspace

Inside the Docker container:

cd  ~/Workspace/ros2_ws
colcon build --symlink-install
# Or simply use `cb` instead of typing the full command.

Note: The alias cb had been defined as alias cb='colcon build --symlink-install

RealSense D415 Camera Setup

1. Find Camera Serial Number

rs-enumerate-devices -s

Expected Output

Device Name                   Serial Number       Firmware Version
Intel RealSense D415          241222061237        5.16.0.1

2. Configure Camera Serial Number

1. Open the launch file:

   ~/Deep_Learning_d415_ROS2/ros2_ws/src/realsense-ros/realsense2_camera/launch/rs_launch.py
   

2. Update the serial number:

   {'name': 'serial_no', 'default': "'YOUR_CAMERA_SERIAL_NUMBER'", 'description': 'choose device by serial number'},

3. Rebuild the workspace:

   cb

3. Launch Camera

In Terminal 1:

ros2 launch realsense2_camera rs_launch.py

4. Visualize Camera Feed

Turn on the Rviz2 to visualize the image.

In Terminal 2:

cd ~/Deep_Learning_d415_ROS2
./run_[your_version].sh       #./run_gpu.sh OR ./run_cpu.sh 

rviz2

OR you can open rosbridge visualize in foxglove

ros2 launch rosbridge_server rosbridge_websocket_launch.xml

Then in RViz2:

1. Click "Add"
2. Navigate to: By topic → /camera → /camera → /color → /image_raw → /Image

5. Verify Camera Topics

Check if camera topics are being published:

ros2 topic list

Expected topics:

/camera/camera/color/camera_info
/camera/camera/color/image_raw
/camera/camera/color/image_raw/compressed
/camera/camera/color/image_raw/compressedDepth
/camera/camera/color/image_raw/theora
...

Recording ROS Bags

1. Configure Recording Settings

Edit the configuration file:

~/Workspace/ros2_ws/src/bag_recorder/config/bag_recorder.yaml

Default configuration:

topics:
  - /tf_static
  - /camera/camera/color/image_raw/compressed

output_dir: ~/Workspace/Outputs/rosbags/test

2. Start Recording

We will start to record a rosbg with camera topics.

Choose one of these methods:

Using GUI will be easier to record mulit bags.

Option 1: Using GUI

ros2 launch bag_recorder record_with_ui.launch.py

Option 2: Using Terminal

ros2 launch bag_recorder record.launch.py
(Press Ctrl + C to stop recording)

Convert ROSBags to MP4 Video

Edit the configuration file:

~/Workspace/Script/config/bags2mp4.yaml

Default configuration:

jobs:
  - input_paths:                      # Paths to bag folders
      - "/home/hrc/Workspace/Outputs/rosbags/test"
    output_root: "/home/hrc/Workspace/Outputs/mp4/test"       # Output MP4 folder
    topic: "/camera/camera/color/image_raw/compressed"   
    # Image topic   If omit → auto-pick if only one; error if multiple

NOTES: If Image topic omit → auto-pick if only one; error if multiple

Convert ROSBags to MP4 Video

cd ~/Workspace/Script
python3 bag2mp4.py

Keep Going for Detection

Detection