Lane detection code using CLRerNet model

GEMstack/offboard/lane_detection/README.md

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+# Lane Detection and 3D Reconstruction with CLRerNet
+
+This project detects 2D lane lines from camera images using [CLRerNet](https://github.com/naver/clrernet) and projects them to 3D ground coordinates using Inverse Perspective Mapping (IPM). The final 3D lane points are saved in `.ply` and `.npy` formats for visualization and downstream tasks.
+
+## Features
+
+- Lane detection using MMDetection-compatible CLRerNet
+- Image undistortion using camera intrinsics
+- 2D to 3D projection using inverse camera model
+- Per-image 2D visualization with overlayed lane predictions
+- Exports `.ply` point cloud and `.npy` array of all 3D lanes
+
+## Installation
+
+First, clone this repository and install dependencies:
+
+```bash
+git clone https://github.com/naver/clrernet.git
+cd clrernet
+conda create -n mmlab python=3.10 -y
+conda activate mmlab
+pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
+pip install -U openmim
+mim install "mmcv>=2.0.0rc4,<2.2.0"
+pip install mmdet
+pip install -r requirements.txt
+pip install "numpy<2"
+conda install -c conda-forge cudatoolkit-dev
+cd libs/models/layers/nms
+pip install -e .
+```
+
+Then, make sure your project directory has the following structure:
+download the checkpoints and put them under a folder called 'checkpoints'
+```
+your_project/
+├── lane_detection.py          # The main lane detection script
+├── clrernet/                      # The cloned CLRerNet repo
+    └── checkpoints                # The checkpoint folder
+├── image_data/
+│   └── sample1.jpg                # Example input images
+└── output/
+    └── ...                        # Outputs will be written here
+```
+
+## Configuration
+
+Ensure your `settings.py` file provides the following parameters for each camera type used:
+
+```python
+settings = {
+    "calibration": {
+        "front_right_camera": {
+            "intrinsics": {
+                "focal": [fx, fy],
+                "center": [cx, cy],
+                "skew": 0.0,
+                "distort": [k1, k2, p1, p2, k3]
+            },
+            "extrinsics": {
+                "rotation": [[...], [...], [...]],
+                "position": [x, y, z]
+            }
+        },
+        ...
+    }
+}
+```
+
+## Usage
+
+Run the lane detection and projection pipeline:
+
+```bash
+python run_lane_detection.py \
+    --image_folder image_data \
+    --output_folder output \
+    --output_file_prefix sample \
+    --camera_type front_right \
+    --config CLRerNet/configs/lane_detection.py \
+    --checkpoint CLRerNet/checkpoints/lane_detection.pth \
+    --device cuda:0
+```
+
+### Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--image_folder` | Folder with input images |
+| `--output_folder` | Output directory for saving results |
+| `--output_file_prefix` | Prefix for output filenames |
+| `--camera_type` | Camera ID: `fr`, `fl`, `rr`, `rl` |
+| `--config` | Path to the CLRerNet config file |
+| `--checkpoint` | Path to model checkpoint |
+| `--device` | PyTorch device (e.g., `cuda:0`, `cpu`) |
+
+## Output
+
+- `output/image_name_with_detected_lanes.png`: Visualizations with 2D lane overlays
+- `output/sample_all_lanes_3d.ply`: 3D point cloud of projected lane points
+- `output/sample_all_lanes_3d.npy`: Numpy array of all 3D lane points
+
+## Acknowledgments
+
+- [CLRerNet](https://github.com/naver/clrernet)
+- [OpenCV](https://opencv.org/)
+- [Open3D](http://www.open3d.org/)
+
+
+**Author**: Henry Yi  
+**License**: MIT