### Run Demo Evaluation

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Executes a demo evaluation using provided example JSON files. Navigate to the example directory first.

```bash
cd example
bash eval_demo.sh
```

--------------------------------

### Install Project Requirements

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Install all necessary Python packages listed in the requirements.txt file.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install OpenCV

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Clones the OpenCV repository, builds it with Release type, and installs it to /usr/local. Ensure you have sufficient build tools and permissions.

```bash
git clone https://github.com/opencv/opencv.git
cd ~/opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j7
sudo make install
```

--------------------------------

### Install Progress Library

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Command to install the progress library using pip.

```shell
pip install progress
```

--------------------------------

### Install PyTorch

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Install PyTorch version 2.0.0 or higher using pip. Ensure torchvision and torchaudio are also installed.

```bash
pip install torch torchvision torchaudio torchdata
```

--------------------------------

### Example Evaluation Command

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

An example of how to execute the evaluation script with specific paths and parameters for dataset, results, images, test list, lane width, IoU, and output.

```bash
./evaluate \
-a ./Dataset/OpenLane/lane3d_v2.0/ \
-d ./Evaluation/PersFormer/result_dir/ \
-i ./Dataset/OpenLane/images/ \
-l ./Evaluation/PersFormer/test_list.txt \
-w 30 \
-t 0.3 \
-o ./Evaluation/PersFormer/ \

```

--------------------------------

### Install MMCV

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Install MMCV version 2.0.0 or higher. This requires installing openmim first.

```bash
pip install -U openmim
mim install mmcv
```

--------------------------------

### Example 3D Lane Evaluation Command

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

An example of how to run the evaluation script with specific directory paths for dataset, predictions, and the test list file.

```bash
python eval_3D_lane.py \
--dataset_dir=./Dataset/OpenLane/lane3d_v2.0/ \
--pred_dir=./Evaluation/PersFormer/result_dir/ \
--test_list=./Evaluation/PersFormer/test_list.txt \

```

--------------------------------

### Install OpenLane Evaluation Tool

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Navigates to the OpenLane evaluation tool directory and compiles it using make. Remember to specify your OpenCV include and library paths in the Makefile.

```bash
cd /path/to/project/tools/metrics/lane/openlane/lane2d
# specify your opencv path in ./Makefile#L40,41 (OPENCV_INCLUDE and OPENCV_LIB_PATH)
make
```

--------------------------------

### Basic Spinner Usage

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Provides an example of using a spinner for tasks with an unknown number of steps.

```python
from progress.spinner import Spinner

spinner = Spinner('Loading ')
while state != 'FINISHED':
    # Do some work
    spinner.next()
```

--------------------------------

### Progress Bar as a Context Manager

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Shows how to use a progress bar with a 'with' statement for automatic handling of start and finish.

```python
from progress.bar import Bar

with Bar('Processing', max=20) as bar:
    for i in range(20):
        # Do some work
        bar.next()
```

--------------------------------

### Create Python Environment

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Use conda to create a new Python environment for the project. Activate it before proceeding with other installations.

```bash
conda create -n hdmapnet python=3.10
conda activate hdmapnet
```

--------------------------------

### Finetune with Pretrained Model

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Finetunes the model using a pretrained checkpoint. Place the checkpoint file in the specified log directory and use --resume --load-model-only flags.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4 --resume --load-model-only
```

--------------------------------

### Basic Progress Bar Usage

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Demonstrates the fundamental way to use a progress bar by manually calling next() and finish().

```python
from progress.bar import Bar

bar = Bar('Processing', max=20)
for i in range(20):
    # Do some work
    bar.next()
bar.finish()
```

--------------------------------

### Resume Training from Checkpoint

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Resumes a distributed training process from a saved checkpoint. Ensure the checkpoint path is correctly specified.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4 --resume
```

--------------------------------

### Run CondLSTR2DRes34 Model on OpenLane Dataset

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Launches the testing script for the CondLSTR2DRes34 model on the OpenLane dataset using distributed training. Ensure you have the correct paths for datasets, checkpoints, and logs.

```bash
python -m torch.distributed.launch --nproc_per_node=4 tools/test.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --test-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4
```

--------------------------------

### Training Debug

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Initiates a training run for lane detection with specified parameters. Ensure dataset and log directories are correctly set.

```python
python tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint -b 4 -j 4
```

--------------------------------

### Creating a Custom Progress Bar Subclass

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Demonstrates how to create a custom progress bar by subclassing 'Bar' and overriding default attributes.

```python
class FancyBar(Bar):
    message = 'Loading'
    fill = '*'
    suffix = '%(percent).1f%% - %(eta)ds'
```

--------------------------------

### Multi-GPU Training

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Launches distributed training across multiple GPUs using torch.distributed.launch. Specify the number of GPUs and their IDs.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4
```

--------------------------------

### Run 3D Lane Evaluation Script

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Command to execute the 3D lane evaluation script. Ensure you are in the 'lane3d' directory and provide paths to the dataset, prediction results, and test list.

```bash
cd lane3d
python eval_3D_lane.py --dataset_dir $dataset_dir --pred_dir $pred_dir --test_list $test_list
```

--------------------------------

### Set OpenCV Library Path

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Environment variable export command to resolve 'libopencv not found' errors by specifying the path to the OpenCV library.

```bash
export LD_LIBRARY_PATH=/path/to/opencv/lib64/:$LD_LIBRARY_PATH
```

--------------------------------

### Training with AMP

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Enables Automated Mixed Precision (AMP) for distributed training to potentially speed up training and reduce memory usage. Use the '-p amp' flag.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4 -p amp
```

--------------------------------

### Evaluation Debug

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Runs an evaluation process for lane detection, similar to training but with the --eval flag. Ensure dataset and log directories are correctly set.

```python
python tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint -b 4 -j 4 --eval
```

--------------------------------

### Custom Progress Bar with Calculated Suffix

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Shows how to create a custom progress bar with a suffix that includes a calculated property like remaining hours.

```python
class SlowBar(Bar):
    suffix = '%(remaining_hours)d hours remaining'
    @property
    def remaining_hours(self):
        return self.eta // 3600
```

--------------------------------

### Iterating with Progress Bar

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Illustrates using the 'iter' method to wrap an iterable and automatically update the progress bar.

```python
for i in Bar('Processing').iter(it):
    # Do some work
```

--------------------------------

### Test/Visualization Debug

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Executes a test run for lane detection, generating visualizations. Specify output directory for test results.

```python
python tools/test.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --test-dir /path/to/output -b 4 -j 4
```

--------------------------------

### Configure CUDA Environment Variables

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Set CUDA environment variables for CUDA 11.7. Ensure these are added to your ~/.bashrc and sourced.

```bash
export CUDA_HOME=/usr/local/cuda-11
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
```

--------------------------------

### Evaluate OpenLane 2D Predictions

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Runs the OpenLane 2D evaluation script. This command requires the directory containing the model predictions and the directory with the ground truth annotations. The --translate flag is used for translation.

```bash
python tools/metrics/lane/openlane/openlane_2d.py --pred-dir /path/to/output --anno-dir /path/to/datasets/openlane --translate
```

--------------------------------

### Customizing Progress Bar Appearance

Source: https://github.com/czyczyyzc/condlstr/blob/master/utils/progress/README.rst

Explains how to customize the fill character and suffix message of a progress bar during instantiation.

```python
bar = Bar('Loading', fill='@', suffix='%(percent)d%%')
```

--------------------------------

### Multi-GPU Test/Visualization

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Runs distributed testing and visualization across multiple GPUs. Specify the output directory for test results.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/test.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --test-dir /path/to/output --gpu-ids 0,1,2,3 -b 4 -j 4
```

--------------------------------

### Evaluation Script Command

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Command to run the lane detection evaluation script. Specify dataset, results, images, test list, lane width, IoU threshold, and output file.

```bash
cd lane2d
./evaluate -a $dataset_dir  -d $result_dir -i $image_dir -l $test_list -w $w_lane -t $iou -o $output_file
```

--------------------------------

### Result JSON Structure

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Defines the expected structure for result JSON files, including image file path and lane line details with category and pixel coordinates.

```json
{
    "file_path":                            <str> -- image path
    "lane_lines": [
        {
            "category":                     <int> -- lane category
            "uv":                           <float> [2, n] --  2D lane points in pixel coordinate
        },
        ...
    ]
}
```

--------------------------------

### Multi-GPU Evaluation

Source: https://github.com/czyczyyzc/condlstr/blob/master/README.md

Performs distributed evaluation across multiple GPUs. Ensure the correct number of processes and GPU IDs are specified.

```python
python -m torch.distributed.launch --nproc_per_node=4 tools/train.py -a CondLSTR2DRes34 -d openlane -v 2d -c 21 -t lane_det_2d --data-dir /path/to/datasets/  --logs-dir /path/to/checkpoint --gpu-ids 0,1,2,3 -b 4 -j 4 --eval
```

--------------------------------

### 3D Lane Prediction Result JSON Structure

Source: https://github.com/czyczyyzc/condlstr/blob/master/tools/metrics/lane/openlane/README.md

Defines the expected structure for JSON files containing 3D lane prediction results, including camera intrinsic/extrinsic matrices, image path, and lane line details with coordinates and category.

```json
{
    "intrinsic":                            <float> [3, 3] -- camera intrinsic matrix
    "extrinsic":                            <float> [4, 4] -- camera extrinsic matrix
    "file_path":                            <str> -- image path
    "lane_lines": [
        {
            "xyz":                          <float> [3, n] -- x,y,z coordinates of sampled points in vehicle coordinate
            "category":                     <int> -- lane category
        }，
        ...                                 (k lanes in `lane_lines` dict)
    ]
}
```

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