mkdir -p $HOME/code/ cd $HOME/code/  

- Set environment variable `PointNetGPD_FOLDER` in your `$HOME/.bashrc` file. export PointNetGPD_FOLDER=$HOME/code/PointNetGPD  

1. Install `pcl-tools` via `sudo apt install pcl-tools`. 2. An example for create a virtual environment: `conda create -n pointnetgpd python=3.10 numpy ipython matplotlib opencv mayavi -c conda-forge` 3. Make sure in your Python environment do not have same package named ```meshpy``` or ```dexnet```. 4. Install PyTorch: https://pytorch.org/get-started/locally/

1. Clone this repository: cd $HOME/codegit clone https://github.com/lianghongzhuo/PointNetGPD.git  

2. Install our requirements in `requirements.txt`     cd $PointNetGPD_FOLDER     pip install -r requirements.txt 3. Install our modified meshpy (Modify from [Berkeley Automation Lab: meshpy](https://github.com/BerkeleyAutomation/meshpy))     cd $PointNetGPD_FOLDER/meshpy     python setup.py develop  

4. Install our modified dex-net (Modify from [Berkeley Automation Lab: dex-net](https://github.com/BerkeleyAutomation/dex-net))     cd $PointNetGPD_FOLDER/dex-net     python setup.py develop 5. Modify the gripper configurations to your own gripper     vim $PointNetGPD_FOLDER/dex-net/data/grippers/robotiq_85/params.json

1.下载作者生成好的数据集: https://tams.informatik.uni-hamburg.de/research/datasets/PointNetGPD_grasps_dataset.zip 2.修改文件夹名字为`ycb_grasp`并移动到`$PointNetGPD_FOLDER/PointNetGPD/data/`

1. 下载YCB数据集：(http://ycb-benchmarks.s3-website-us-east-1.amazonaws.com/). 1.1 用命令下载数据集(https://github.com/lianghongzhuo/ycb-tools).     cd $PointNetGPD_FOLDER/data     git clone https://github.com/lianghongzhuo/ycb-tools     cd ycb-tools     python download_ycb_dataset.py rgbd_512 2. 数据集里内容格式：Manage your dataset at: `$PointNetGPD_FOLDER/PointNetGPD/data`     Every object should have a folder, structure like this:     ```     ├002_master_chef_can     |└── google_512k     |    ├── nontextured.obj (generated by pcl-tools)     |    ├── nontextured.ply     |    ├── nontextured.sdf (generated by SDFGen)     |└── rgbd     |    ├── *.jpg     |    ├── *.h5     |    ├── ...     ├003_cracker_box     └004_sugar_box     ...     ``` 3. Install SDFGen from [GitHub](https://github.com/jeffmahler/SDFGen.git):     cd $PointNetGPD_FOLDER     git clone https://github.com/jeffmahler/SDFGen.git     cd SDFGen && mkdir build && cd build && cmake .. && make 4. Install [Open3D](http://www.open3d.org/docs/latest/getting_started.html)     pip install open3d 5. Generate `nontextured.sdf` file and `nontextured.obj` file using `pcl-tools` and `SDFGen` by running:     cd $PointNetGPD_FOLDER/dex-net/apps     python read_file_sdf.py 6. Generate dataset by running the code:     cd $PointNetGPD_FOLDER/dex-net/apps     python generate-dataset-canny.py [prefix]     where `[prefix]` is optional, it will add a prefix on the generated files.

- Visualization grasps可视化抓取     cd $PointNetGPD_FOLDER/dex-net/apps     python read_grasps_from_file.py     Note:     - This file will visualize the grasps in `$PointNetGPD_FOLDER/PointNetGPD/data/ycb_grasp/` folder

- Visualization object normal可视化法线     cd $PointNetGPD_FOLDER/dex-net/apps     python Cal_norm.py This code will check the norm calculated by `meshpy` and `pcl` library.

1. YCB数据准备:     cd $PointNetGPD_FOLDER/PointNetGPD/data

    Make sure you have the following files, The links to the dataset directory should add by yourself:     ```     ├── google2cloud.csv  (Transform from google_ycb model to ycb_rgbd model)     ├── google2cloud.pkl  (Transform from google_ycb model to ycb_rgbd model)     └── ycb_grasp  (generated grasps)     ```

2.从RGBD图像中生成点云 you may change the number of process running in parallel if you use a shared host with others     cd $PointNetGPD_FOLDER/PointNetGPD     python ycb_cloud_generate.py Note: Estimated running time at our `Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz` dual CPU with 56 Threads is 36 hours. Please also remove objects beyond the capacity of the gripper.

    cd $PointNetGPD_FOLDER/PointNetGPD  

    Launch a tensorboard for monitoring     tensorboard --log-dir ./assets/log --port 8080  

    and run an experiment for 200 epoch     python main_1v.py --epoch 200 --mode train --batch-size x (x>1)  

    File name and corresponding experiment:     ```     main_1v.py        --- 1-viewed point cloud, 2 class——单视角点云+2类     main_1v_mc.py     --- 1-viewed point cloud, 3 class——单视角点云+3类     main_1v_gpd.py    --- 1-viewed point cloud, GPD——单视角点云+GPD     main_fullv.py     --- Full point cloud, 2 class——整个点云+2类     main_fullv_mc.py  --- Full point cloud, 3 class——整个点云+3类     main_fullv_gpd.py --- Full point cloud, GPD——整个点云+GPD     ```

For GPD experiments, you may change the input channel number by modifying `input_chann` in the experiment scripts(only 3 and 12 channels are available)

1. Get UR5 robot state:

    Goal of this step is to publish a ROS parameter tell the environment whether the UR5 robot is at home position or not.

    cd $PointNetGPD_FOLDER/dex-net/apps     python get_ur5_robot_state.py 2. Run perception code:     This code will take depth camera ROS info as input, and gives a set of good grasp candidates as output.     All the input, output messages are using ROS messages.     cd $PointNetGPD_FOLDER/dex-net/apps     python kinect2grasp.py

    arguments:     -h, --help                 show this help message and exit     --cuda                     using cuda for get the network result     --gpu GPU                  set GPU number     --load-model LOAD_MODEL    set witch model you want to use (rewrite by model_type, do not use this arg)     --show_final_grasp         show final grasp using mayavi, only for debug, not working on multi processing     --tray_grasp               not finished grasp type     --using_mp                 using multi processing to sample grasps     --model_type MODEL_TYPE    selet a model type from 3 existing models     ```