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Application of Vision Language Models with ROS 2 workshop

We explore how robots can perceive and understand their environment through the powerful combination of image understanding and natural language processing. This repository dives deep into the fascinating world of vision-language models for robotics applications, specifically utilizing the powerful Intel OpenVINO Toolkit.

This repository is presented as a workshop at the ROS meetup Lagos

Prerequisites

  • Ubuntu 22.04 or newer
  • ROS 2 Humble or newer
  • Python 3
  • Intel OpenVINO toolkit

Hardware Requirements

Please note that to run the code in this repository, you will need a device compatible with the Intel OpenVINO Toolkit. This typically includes Intel CPUs, Intel Neural Compute Sticks, or other Intel hardware supporting OpenVINO.

Create a ROS 2 colcon workspace

mkdir -p ~/ros2_ws/src

Create & Setup Python Virtual Environment

cd ~/ros2_ws

virtualenv -p python3 ./vlm-venv                      
source ./vlm-venv/bin/activate

# Make sure that colcon doesn’t try to build the venv
touch ./vlm-venv/COLCON_IGNORE

Install Python dependencies

pip install timm --extra-index-url https://download.pytorch.org/whl/cpu  # is needed for torch

pip install "openvino>=2024.1" "torch>=2.1" opencv-python supervision transformers yapf pycocotools addict "gradio>=4.19" tqdm

Add your Python virtual environment package path

Make sure to update <<YOUR_USER_NAME>> with your system username.

export PYTHONPATH='/home/<<YOUR_USER_NAME>>/ros2_ws/vlm-venv/lib/python3.10/site-packages'

Clone this repository inside the 'src' folder of your workspace

cd ~/ros2_ws/src

git clone https://github.com/nilutpolkashyap/vlms_with_ros2_workshop.git

Download weights and required packages

cd ~/ros2_ws/src/vlms_with_ros2_workshop

python3 download_weights.py

Download OpenVINO IR models from Google Drive

Download the zip file from the Google Drive link here

Place the contents of the zip file inside the 'openvino_irs' directory in the following path

~/ros2_ws/src/vlms_with_ros2_workshop/ros2_vlm/ros2_vlm/modules/openvino_irs

Build and source the workspace

cd ~/ros2_ws
colcon build --symlink-install

source ~/ros2_ws/install/setup.bash

Object detection and masking with GroundedSAM (GroundingDINO + SAM)

GroundedSAM tackles object detection and segmentation. It integrates various open-world models, allowing to not just detect objects but also understand their specific regions. This can empower robots to act on specific parts (e.g., grasping a cup's handle) based on textual instructions or visual cues.

GroundedSAM

Run the GroundedSAM node

ros2 run ros2_vlm grounded_sam --ros-args -p device:='CPU' -p video_source:=/dev/video2 -p isSegment:=False -p detectionList:="["eyes", "person", "hair"]"

ROS 2 CLI Arguments

  • device - Inference Device (e.g. CPU, GPU, NPU)
  • video_source - Video source to get the image frame
  • isSegment - To run Segment Anything model (True/False)
  • detectionList - List of objects to detect

Check out more in the GroundedSAM OpenVINO Notebook

Visual Question Answering using BLIP

BLIP bridges the gap between vision and language. It analyzes images and extracts meaningful information, generating captions describing the scene or answering questions about it. This lets robots not only "see" their environment but also understand its context and respond to natural language instructions effectively.

Visual Question Answering

Run the Blip Visual QnA node

ros2 run ros2_vlm blip_visual_qna --ros-args -p device_name:="GPU.0" -p question:="What is in the image?" -p image_path:="/home/nilutpol/ai_ws/src/blip_qna_code/demo2.jpg"

ROS 2 CLI Arguments

  • device_name - Inference Device (e.g. CPU, GPU, NPU)
  • question - Question for the blip model
  • image_path - Path to the image source

Check out more in the BLIP Visual Question Answering OpenVINO Notebook

Resources