Metadata-Version: 2.3
Name: nclt-dataloader
Version: 0.1.0
Summary: Efficient and user-friendly point cloud data loader for the NCLT Dataset, supporting multiple coordinate systems and numpy compatibility.
Keywords: kaist,pointcloud,lidar,slam,autonomous driving,dataset
Author: HopeCollector
Author-email: HopeCollector <cmw0249@gmail.com>
License: MIT License
         
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Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: numpy>=2.3.2
Requires-Dist: scipy>=1.16.1
Requires-Python: >=3.12
Project-URL: Documentation, https://github.com/HopeCollector/nclt-dataloader#readme
Project-URL: Homepage, https://github.com/HopeCollector/nclt-dataloader
Project-URL: Source, https://github.com/HopeCollector/nclt-dataloader
Description-Content-Type: text/markdown

# NCLT Point Cloud Data Loader

> This is an English translation of the original Chinese documentation (`README_CN.md`).

> **Dataset Homepage**: [NCLT Dataset](https://robots.engin.umich.edu/nclt/index.html#top)

## Project Overview

This project provides an efficient and easy-to-use point cloud data loader for the NCLT dataset. It supports multiple coordinate frame conversions, iteration, slicing, and is suitable for autonomous driving, SLAM, 3D reconstruction, and related scenarios.

## Features

- **Multiple Coordinate Frames Supported**
  - `BASE_LINK`: Vehicle local coordinate frame
  - `MAP`: Global map coordinate frame
  - `MAP_FRD`: Global FRD coordinate frame
  - `WORLD`: World coordinate frame
  - `WORLD_NED`: NED world coordinate frame
- **Iterator & Slicing**: Supports iteration, slicing, and random access
- **Numpy Compatible**: Directly reads from numpy arrays
- **Dataset Length**: Supports the `len()` method

## Installation

It is recommended to use [uv](https://github.com/astral-sh/uv) or pip to install dependencies:

```bash
uv pip install -e .
```

Or use pip directly:

```bash
pip install nclt-dataloader
```

## Quick Start

```python
from nclt_dataloader import PointCloudLoader, FrameID

loader = PointCloudLoader("/path/to/nclt/2012-01-08", frame_id=FrameID.MAP)
pc, pose = loader[0]  # Read the first point cloud and its pose

# Iterate over the first 10 frames
for pc, pose in loader[:10]:
    # Process point cloud and pose
    pass

# Get dataset length
print(len(loader))
```

## C++/pybind11 Example

This project supports direct invocation of the Python loader from C++ via pybind11, suitable for integration with C++ projects.  
Sample code is provided in `example/cpp/main.cc`. Make sure the `root` path points to a valid NCLT dataset directory.

### Build & Run Steps

1. **Activate Python virtual environment** (ensure `nclt-dataloader` and `pybind11` are installed)
2. Edit line 18 in `main.cc` to set the `root` variable to your dataset path, e.g. `/ws/data/nclt/2012-05-26`
3. Build the project:
   ```bash
   cd example/cpp
   cmake -S . -B build
   cmake --build build
   ```
4. Run the example:
   ```bash
   ./build/embed_loader
   ```

The output will show the dataset length and the shapes of the point cloud and pose.

### Main Code Logic

- Start the Python interpreter  
  ```cpp
  #include <pybind11/embed.h>
  namespace py = pybind11;
  py::scoped_interpreter guard{};
  ```
- Import the `nclt_dataloader` module  
  ```cpp
  py::module_ kd = py::module_::import("nclt_dataloader");
  py::object PointCloudLoader = kd.attr("PointCloudLoader");
  py::object FrameID = kd.attr("FrameID");
  ```
- Create a `PointCloudLoader` instance  
  ```cpp
  const char* root = "/ws/data/nclt/2012-05-26";
  py::object loader =
    PointCloudLoader(root, py::arg("frame_id") = FrameID.attr("MAP"));
  ```
- Get dataset length  
  ```cpp
  std::size_t n = loader.attr("__len__")().cast<std::size_t>();
  std::cout << "dataset length = " << n << "\n";
  ```
- Read point cloud and pose, print their shapes  
  ```cpp
  py::tuple item0 = loader[py::int_(0)];
  py::array_t<float> cld = item0[0].cast<py::array_t<float>>();
  py::array_t<double> pose = item0[1].cast<py::array_t<double>>();
  std::cout << "points shape = (" << cld.shape(0) << ", " << cld.shape(1) << ")\n";
  std::cout << "pose shape   = (" << pose.shape(0) << ", " << pose.shape(1) << ")\n";
  ```
- Iterate over the first 10 frames (optional)  
  ```cpp
  for (py::size_t i = 0; i < 10 && i < n; ++i) {
    py::tuple it = loader[py::int_(i)];
    // You can process point cloud and pose here
  }
  ```

See `example/cpp/main.cc` for details.

## Coordinate Frame Description

- `BASE_LINK`: Vehicle local coordinate frame, point cloud transformed by calibration matrix
- `MAP`: Global map coordinate frame, point cloud transformed by global pose
- `MAP_FRD`: Global FRD coordinate frame
- `WORLD`: World coordinate frame
- `WORLD_NED`: NED world coordinate frame

You can switch coordinate frames via the `frame_id` parameter or property:

```python
loader.frame_id = FrameID.WORLD
pc, pose = loader[0]
```

## API Reference

- [`PointCloudLoader`](src/nclt_dataloader/pointcloud.py): Main loader class, supports indexing, slicing, iteration, etc.
- [`FrameID`](src/nclt_dataloader/pointcloud.py): Coordinate frame enum type
- [`get_lidar_files`](src/nclt_dataloader/basic.py): Get point cloud file list
- [`read_lidar`](src/nclt_dataloader/basic.py): Read a single frame point cloud
- [`load_calib_matrix`](src/nclt_dataloader/basic.py): Read calibration matrix
- [`load_global_poses`](src/nclt_dataloader/basic.py): Read global poses
- [`transform_point_cloud`](src/nclt_dataloader/basic.py): Point cloud coordinate transformation

## Data Format

- Point cloud data shape: `[N, 5]`, representing `[x, y, z, intensity, label]`
- Pose matrix shape: `[4, 4]`

## Dependencies

- numpy

Development/testing dependencies (see [pyproject.toml](pyproject.toml)):
- pypcd4
- matplotlib
- pytest
- ipykernel
- ruff

## Testing

Unit tests are included. Run:

```bash
uv pip install .[dev]
uv run pytest
```

## Changelog

- **v0.1.0**: Initial version, basic functionality implemented
