feat: add autoware_rosbag2_anonymizer usage

docs/datasets/.pages

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 nav:
   - index.md
+  - Data Anonymization: data-anonymization

docs/datasets/data-anonymization/index.md

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+# Rosbag2 Anonymizer
+
+## Overview
+
+Autoware provides a tool ([autoware_rosbag2_anonymizer](https://github.com/autowarefoundation/autoware_rosbag2_anonymizer)) to anonymize ROS 2 bag files.
+This tool is useful when you want to share your data with Autoware community but want to keep the privacy of the data.
+
+With this tool you can blur any object (faces, license plates, etc.) in your bag files, and you can get a new bag file
+with the blurred images.
+
+## Installation
+
+### Clone the repository
+
+```bash
+git clone https://github.com/autowarefoundation/autoware_rosbag2_anonymizer.git
+cd autoware_rosbag2_anonymizer
+```
+
+### Download the pretrained models
+
+```bash
+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
+
+wget https://github.com/autowarefoundation/autoware_rosbag2_anonymizer/releases/download/v0.0.0/yolov8x_anonymizer.pt
+wget https://github.com/autowarefoundation/autoware_rosbag2_anonymizer/releases/download/v0.0.0/yolo_config.yaml
+```
+
+### Install ROS 2 mcap dependencies if you will use mcap files
+
+!!! warning
+
+    Be sure you have installed the ROS 2 on your system.
+
+```bash
+sudo apt install ros-humble-rosbag2-storage-mcap
+```
+
+### Install `autoware_rosbag2_anonymizer` tool
+
+Before installing the tool, you should update the pip package manager.
+
+```bash
+python3 -m pip install pip -U
+```
+
+Then, you can install the tool with the following command.
+
+```bash
+python3 -m pip install .
+```
+
+## Configuration
+
+Define prompts in the `validation.json` file. 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.
+
+```json
+{
+  "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`
+
+```yaml
+rosbag:
+  input_bags_folder: "/path/to/input_bag_folder" # Path to the input folder which contains ROS 2 bag files
+  output_bags_folder: "/path/to/output_folder" # Path to the output ROS 2 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`
+
+```yaml
+rosbag:
+  input_bags_folder: "/path/to/input_bag_folder" # Path to the input ROS 2 bag files folder
+
+dataset:
+  output_dataset_folder: "/path/to/output/dataset" # 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`
+
+```yaml
+dataset:
+  input_dataset_yaml: "path/to/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`
+
+```yaml
+rosbag:
+  input_bag_path: "/path/to/input_bag/bag.mcap" # Path to the input ROS 2 bag file with 'mcap' or 'sqlite3' extension
+  output_bag_path: "/path/to/output_bag_file" # Path to the output ROS 2 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 ROS 2 bag files.
+
+!!! warning
+
+    If your ROS 2 bag file includes custom message types from Autoware or any other packages, you should source the
+    their workspaces before running the tool.
+
+    You can source Autoware workspace with the following command.
+    ```bash
+    source /path/to/your/workspace/install/setup.bash
+    ```
+
+### 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.
+
+```bash
+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 a Dataset
+
+Create an initial dataset with the unified model. You can provide multiple ROS 2 bag files to create a dataset. After
+running the following command, the tool will create a dataset in YOLO format.
+
+You should set your configuration in config/yolo_create_dataset.yaml file.
+
+```bash
+python3 main.py config/yolo_create_dataset.yaml --yolo_create_dataset
+```
+
+#### Step 2: Manually Label the Missing Labels
+
+The dataset which is created in the first step has some missing labels. You should label the missing labels manually.
+You can use the following example tools to label the missing labels:
+
+- [label-studio](https://github.com/HumanSignal/label-studio)
+- [Roboflow](https://roboflow.com/) (You can use the free version)
+
+#### Step 3: Split the Dataset
+
+Split the dataset into training and validation sets. Give the path to the dataset folder which is created in the first
+step.
+
+```bash
+autoware-rosbag2-anonymizer-split-dataset /path/to/dataset/folder
+```
+
+#### Step 4: Train the YOLOv8 Model
+
+Train the YOLOv8 model with the dataset which is created in the first step.
+
+You should set your configuration in config/yolo_train.yaml file.
+
+```bash
+python3 main.py config/yolo_train.yaml --yolo_train
+```
+
+#### Step 5: Anonymize Images in ROS 2 Bag Files
+
+Anonymize images in ROS 2 bag files with the trained YOLOv8 model. If you want to anonymize your ROS 2 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.
+
+You should set your configuration in config/yolo_anonymize.yaml file.
+
+```bash
+python3 main.py config/yolo_anonymize.yaml --yolo_anonymize
+```
+
+## Troubleshooting
+
+- **Error 1**: `torch.OutOfMemoryError: CUDA out of memory`
+
+```bash
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU 0 has a total capacity of 10.87 GiB of which 1010.88 MiB is free. Including non-PyTorch memory, this process has 8.66 GiB memory in use. Of the allocated memory 8.21 GiB is allocated by PyTorch, and 266.44 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+```
+
+This error occurs when the GPU memory is not enough to run the model. You can add the following environment variable to
+avoid this error.
+
+```bash
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+```
+
+## Share Your Anonymized Data
+
+After anonymizing your data, you can share your anonymized data with the Autoware community. If you want to share your
+data with the Autoware community, you should create an issue and pull request to
+the [Autoware Documentation repository](https://github.com/autowarefoundation/autoware-documentation).
+
+## Citation
+
+```bibtex
+@article{liu2023grounding,
+  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
+  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
+  journal={arXiv preprint arXiv:2303.05499},
+  year={2023}
+}
+```
+
+```bibtex
+@article{kirillov2023segany,
+  title={Segment Anything},
+  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
+  journal={arXiv:2304.02643},
+  year={2023}
+}
+```
+
+```bibtex
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```