Introduction

A tool to anonymize images in ros2 bags. The tool combines GroundingDINO, OpenCLIP and SegmentAnything to anonymize images in rosbags

Installation

Clone the repository

git clone https://github.com/autowarefoundation/autoware_rosbag2_anonymizer.git
cd rosbag2_anonymizer

Download the pretrained weights

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GroundingDINO_SwinB.cfg.py
wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth

Install ros2 mcap dependencies if you will use mcap files

sudo apt install ros-humble-rosbag2-storage-mcap

Install autoware_rosbag2_anonymizer package

python3 -m pip install .

Configuration

Define prompts in the validation.jsonfile. The tool will use these prompts to detect objects. You can add your prompts as dictionaries under the prompts key. Each dictionary should have two keys:

• prompt: The prompt that will be used to detect the object. This prompt will be blurred in the anonymization process.
• should_inside: This is a list of prompts that object should be inside. If the object is not inside the prompts, the tool will not blur the object.

{
    "prompts": [
        {
            "prompt": "license plate",
            "should_inside": ["car", "bus", "..."]
        },
        {
            "prompt": "human face",
            "should_inside": ["person", "human body", "..."]
        }
    ]
}

You should set your configuration in the configuration files under config folder according to the usage. Following instructions will guide you to set each configuration file.

config/anonymize_with_unified_model.yaml

rosbag:
  input_bag_path: "path/to/input.bag" # Path to the input ROS2 bag file with 'mcap' or 'sqlite3' extension
  output_bag_path: "path/to/output/folder" # Path to the output ROS2 bag folder
  output_save_compressed_image: True # Save images as compressed images (True or False)
  output_storage_id: "sqlite3" # Storage id for the output bag file (`sqlite3` or `mcap`)

grounding_dino:
  box_threshold: 0.1 # Threshold for the bounding box (float)
  text_threshold: 0.1 # Threshold for the text (float)
  nms_threshold: 0.1 # Threshold for the non-maximum suppression (float)

open_clip:
  score_threshold: 0.7 # Validity threshold for the OpenCLIP model (float

yolo:
  confidence: 0.15 # Confidence threshold for the YOLOv8 model (float)

bbox_validation:
  iou_threshold: 0.9 # Threshold for the intersection over union (float), if the intersection over union is greater than this threshold, the object will be selected as inside the validation prompt

blur:
  kernel_size: 31 # Kernel size for the Gaussian blur (int)
  sigma_x: 11 # Sigma x for the Gaussian blur (int)

config/yolo_create_dataset.yaml

rosbag:
  input_bags_folder: "path/to/input/folder" # Path to the input ROS2 bag files folder

dataset:
  output_dataset_folder: "path/to/output/folder" # Path to the output dataset folder
  output_dataset_subsample_coefficient: 25 # Subsample coefficient for the dataset (int)

grounding_dino:
  box_threshold: 0.1 # Threshold for the bounding box (float)
  text_threshold: 0.1 # Threshold for the text (float)
  nms_threshold: 0.1 # Threshold for the non-maximum suppression (float)

open_clip:
  score_threshold: 0.7 # Validity threshold for the OpenCLIP model (float

bbox_validation:
  iou_threshold: 0.9 # Threshold for the intersection over union (float), if the intersection over union is greater than this threshold, the object will be selected as inside the validation prompt

config/yolo_train.yaml

dataset:
  input_dataset_yaml: "path/to/input/data.yaml" # Path to the config file of the dataset, which is created in the previous step

yolo:
  epochs: 100 # Number of epochs for the YOLOv8 model (int)
  model: 'yolov8x.pt' # Select the base model for YOLOv8 ('yolov8x.pt' 'yolov8l.pt', 'yolov8m.pt', 'yolov8n.pt')

config/yolo_anonymize.yaml

rosbag:
  input_bag_path: "path/to/input.bag" # Path to the input ROS2 bag file with 'mcap' or 'sqlite3' extension
  output_bag_path: "path/to/output/folder" # Path to the output ROS2 bag folder
  output_save_compressed_image: True # Save images as compressed images (True or False)
  output_storage_id: "sqlite3" # Storage id for the output bag file (`sqlite3` or `mcap`)

yolo:
  model: "path/to/yolo/model" # Path to the trained YOLOv8 model file (`.pt` extension) (you can download the pre-trained model from releases)
  config_path: "path/to/input/data.yaml" # Path to the config file of the dataset, which is created in the previous step
  confidence: 0.15 # Confidence threshold for the YOLOv8 model (float)

blur:
  kernel_size: 31 # Kernel size for the Gaussian blur (int)
  sigma_x: 11 # Sigma x for the Gaussian blur (int)

Usage

The tool provides two options to anonymize images in rosbags.

Option 1: Anonymize with Unified Model

You should provide a single rosbag and tool anonymize images in rosbag with a unified model. The model is a combination of GroundingDINO, OpenCLIP, YOLOv8 and SegmentAnything. If you don't want to use pre-trained YOLOv8 model, you can follow the instructions in the second option to train your own YOLOv8 model.

You should set your configuration in config/anonymize_with_unified_model.yaml file.

python3 main.py config/anonymize_with_unified_model.yaml --anonymize_with_unified_model

Option 2: Anonymize Using the YOLOv8 Model Trained on a Dataset Created with the Unified Model

Step 1: Create an initial dataset with the unified model. You can provide multiple rosbags to create a dataset. You should set your configuration in config/yolo_create_dataset.yaml file. After running the following command, the tool will create a dataset in YOLO format.

python3 main.py config/yolo_create_dataset.yaml --yolo_create_dataset

Step 2: The dataset which is created in the first step has some missing labels. You should label the missing labels manually.

Step 3: After labeling the missing labels, you should split the dataset into train and validation sets. If the labeling tool you used does not provide a split option, you can use the following command to split the dataset.

Give the path to the dataset folder which is created in the first step.

autoware-rosbag2-anonymizer-split-dataset /path/to/dataset

Step 4: Train the YOLOv8 model with the dataset. You should set your configuration in config/yolo_train.yaml file.

python3 main.py config/yolo_train.yaml --yolo_train

Step 5: Anonymize images in rosbags with the trained YOLOv8 model. You should set your configuration in config/yolo_anonymize.yaml file. If you want to anonymize your ROS2 bag file with only YOLOv8 model, you should use following command. But we recommend to use the unified model for better results. You can follow the Option 1 for the unified model with the YOLOv8 model trained by you.

python3 main.py config/yolo_anonymize.yaml --yolo_anonymize