ReUseX

TODO:

• Add instructions for installing Dependencies
  • Add instructions for installing OpenCV and PCL
• Add an explanation of the project structure
• Implement Regiongrowing based on pcl::RegionGrowing< PointT, NormalT >
  See 10.1016/j.autcon.2022.104250` for reference.
• Separate read, register, and merge functions to enable working with larger datasets, that don't fit in memory.
• Fix pointer issues. see
• Implement Buffer protocol for PointCloud to enable direct access to the data from Python. see

Notes

Scanning app: StrayScanner

Speckle; an open data platform for AEC.

Speckle Automate (Video); a CI/CD like flow for triggering custom functions on Speckle data.

Some bench marking:

Number of frames: 2500 -> ( start: 0; step: 4; end: 9996 ) 6.58% 
[polyscope] Backend: openGL3_glfw -- Loaded openGL version: 3.3.0 NVIDIA 535.154.05
[============================================================] (2500/2500) 100% -     0s -  48 threads - (Loading data)
Total time: 00:02:10s (Loading data)
[============================================================] (2500/2500) 100% -     0s -  48 threads - (Preprocessing)
Total time: 00:02:06s (Preprocessing)
[============================================================] (2500/2500) 100% -     0s -   1 threads - (Running Inference)
Total time: 00:01:15s (Running Inference)
[============================================================] (2500/2500) 100% -     0s -  48 threads - (Postprocessing)
Total time: 00:00:00s (Postprocessing)
[============================================================] (2500/2500) 100% -     0s -  48 threads - (Computing normals)
Total time: 00:03:33s (Computing normals)
[============================================================] (2500/2500) 100% -     0s -  48 threads - (Saving clouds)
Done

Description

This is a tool for processing scan data captured with an iPhone / iPad in the context of mapping buildings. The goal is to segment and label objects in the point cloud and reconstruct a simplified 3D model on a room-by-room detail scale.

Project structure:

graph TD;
    A[Main Library]
    B[Python Module]
    C[Unit Testing]
    D[CLI ?]


    A --> B;
    A --> C;
    A --> D;

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Classes Diagram:

classDiagram

Dataset
Dataset : string  path
Dataset : size_t  n_frames
Dataset : int     rgb_width
Dataset : int     rgb_hight
Dataset : int     depth_width
Dataset : int     depth_hight
Dataset : Dataset(path, [Field, ..])
Dataset : operator[](size_t index)


FramData
FramData : get<Field>() -> Field

Dataset --|> FramData : get data per frame

Yolo
Yolo : Yolo(path, bool use_gpu)
Yolo : Detect(Mat img)
Yolo : Preprocess(Mat img) -> Mat
Yolo : Postprocess(vector<Mat> blob, Vec4d params, Mat srcImg) -> vector<OutputParams>

OutputParams
OutputParams : int id
OutputParams : float confidence
OutputParams : Rect box
OutputParams : RotatedRect rotatedBox
OutputParams : Mat boxMask
OutputParams : Rotate(Size size)
OutputParams : Scale(Size size_in, Size size_out)

Yolo --|> OutputParams : Detect

PointCloud
PointCloud : PointCloud()
PointCloud : read(string path) -> PointCloud
PointCloud : write(string path) -> PointCloud


PointT
PointT: float x
PointT: float y
PointT: float z
PointT: float rgb
PointT: float normal_x
PointT: float normal_y
PointT: float normal_z
PointT: float curvature
PointT: int confidence
PointT: int semantic
PointT: int instance
PointT: int label

PointT: getPos () -> pos3
PointT: getColor () -> color3
PointT: getNormal () -> vec3
PointT: operator<< (ostream& os, const PointT& p) -> ostream&

PointCloud --|> PointT

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Flow Diagram:

flowchart LR
    A[Load Dataset]
    B[Yolo Preprocess]
    C[Yolo Detect]
    D[Yolo Postprocess]
    E[Register Frams]
    F[Move Frames]
    G[Loop closures?]
    H[Save Frames]

    A-->B;
    B-->C;
    C-->D;
    A-->E;
    E-->F;
    F-->G;
    G-->H;
    D-->H;


    I[Load Frames]
    J[Merge Frames / Downsample]
    K[Save Point Cloud]

    I-->J;
    J-->K;


    L[Load Point Cloud]
    N[Clustering / Instance Segmentation]
    M[Region Growing]
    O[Refinement]
    P[Cell Complex]
    Q[Ray Tracing]
    R[Markov Clustering]
    S[Polygonal Surface Reconstruction]
    T[Save Results]

    L-->M;
    L-->N;
    M-->O;
    O-->P;
    P-->Q;
    Q-->R;
    R-->S;
    S-->T;
    N-->T;

    U[Load Results]
    V[Speckle]

    U-->V;

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Build instructions:

Clone repository recursively:

git clone --recursive https://github.com/linkarkitektur/linkml_cpp
# --recursive is used to clone submodules in (extern)

Install dependencies: OpenCV and PCL have been compiled from source and are not included but need to be installed separately.

Dependencies:

• PCL (Manually compiled with CUDA support)
• OpenCV (Manually compiled with CUDA support)
• CGAL
  • Eigen3 (Required by CGAL and Markov Clustering)
  • TBB (Threading Building Blocks for GGAL parralelization)
  • SCIP (Fallback LIP solver in CGAL)
  • Gurobi (Commercial, but free for academic use)
• Embree
• HDF5 (Currently not used, but included in the project)

Generally, it would_ be great to have all dependencies self-contained in the project in the extern folder as submodules.

OpenCV

Open CV needs to be compiled with CUDA support for inference on the GPU. The branch needs to be 4.x and commit 0c6fc763f4 has been confirmed to work.

Dependencies:
- CUDA
- cuda-toolkit ([Instructions](https://developer.nvidia.com/cuda-downloads))
- cuDNN (8.9.7) ([Arechive](https://developer.nvidia.com/rdp/cudnn-archive))

*There were probably more dependencies, but I don't remember them. Let's extend the list when we get there*

**Note**: Nvidia's instruction installs the [current](https://developer.nvidia.com/cudnn-downloads) cuDNN version, which is (9.0.0). This does **not** seem compatible with [OpenCV](https://github.com/opencv/opencv/issues/24983). Here are the archives for [cuDNN 8.9.7](https://developer.nvidia.com/rdp/cudnn-archive)

```shell
mkdir opencv && cd opencv

git clone git@github.com:opencv/opencv_contrib.git
got clone git@github.com:opencv/opencv.git
cd opencv

mkdir build && cd build

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules -D WITH_CUDA=ON ..

make -j48 #Number of cores

sudo make install

# Most of the flags below show be enable by using -D WITH_CUDA=ON

#cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D CUDA_FAST_MATH=1 -D ENABLE_FAST_MATH=1 -D WITH_GTK=ON -D WITH_TBB=ON -D WITH_FFMPEG=ON -D INSTALL_PYTHON_EXAMPLES=OFF  -D OPENCV_DNN_CUDA=ON -D CUDA_GENERATION=Auto -D CUDA_FAST_MATH=ON  -D WITH_NVCUVID=OFF -D WITH_NVCUVENC=OFF  ..

PCL

Maybe we don't need the new version of PCL, but I compiled it from source while looking for some features (CUDA acceleration) that I didn't end up using.

Again, not sure what all the dependencies are, but those are the ones that are listed online.

Dependencies:

• Boost (also required by GCAL)
• Eigen (also required by CGAL)
• Flann
• VTK
• QHull
• OpenNI
• OpenNI2
• CUDA

Official Instructions here.

git clone git@github.com:PointCloudLibrary/pcl.git
cd pcl
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..

# I'm not sure if there were any other flags, but I think I used the defaults.
# Otherwise let's add them here.

Export Yolo v8.1 model

from ultralytics import YOLO

model = YOLO('yolov8x-seg.pt') 
model.export(format="onnx", simplify=True)

Debugging

I have found a way in VSCode where I can launch a Python process and attach a debugger to it and then gdb to the C++ process. Alternatively, now that the data loading is also part of the C++ code, we could also just call it a regular CLI application.

launch.json with the Python C++ Debugger pluging.

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "My (gdb) Attach",
            "type": "cppdbg",
            "request": "attach",
            "processId": "",
            "setupCommands": [
                {
                    "description": "Enable pretty-printing for gdb",
                    "text": "-enable-pretty-printing",
                    "ignoreFailures": false
                },
                {
                    "description": "Set Disassembly Flavor to Intel",
                    "text": "-gdb-set disassembly-flavor intel",
                    "ignoreFailures": true
                }
            ]
        },
    ]
}

Example

from linkml_py import *

dataset = Dataset("path_to_folder")
parse_dataset(dataset, "./out_put_folder/", step=5)

# Load all the frames into a collection of point clouds. 
clouds = PointCloudsOnDisk("./out_put_folder/")

# Annotate the frames, and use the dataset to provide access to the full-resolution images.
clouds.annotate("./yolov8x-seg.onnx", dataset)

# Register the individual frames to eachother
clouds.register()

# Merge and downsample the cloud
cloud = clouds.filter().merge().downsample(0.02)

# Cluster the semantic annotations into instances
cloud.clustering().save("path_for_saving.pcd")

# Create a solid model
cloud.solidify()

# Display the point cloud
cloud..display()

Example of how to access the data in the point cloud from Python: As long as fields are continuous in memory, structured_to_unstructured will provide a view of the data rather than a copy.

from numpy.lib.recfunctions import structured_to_unstructured

cloud = PointCloud("path_to_file.pcd")
data = np.asarray(cloud)

# View of position values
structured_to_unstructured(data[["x","y","z"]])

# View of normal values
structured_to_unstructured(data[["normal_x","normal_y","normal_z"]])

# View of RGB values
data["rgb"].view("u4").reshape(-1, 1).view("u1")[:, :3]