lazy5/DMPR-PS

DMPR-PS: A Novel Approach for Parking-Slot Detection Using Directional Marking-Point Regression

DMPR-PS

This is the implementation of DMPR-PS using PyTorch.

Requirements

• PyTorch
• CUDA (optional)
• Other requirements
  pip install -r requirements.txt

Pre-trained weights

The pre-trained weights could be used to reproduce the number in the paper.

Inference

• Image inference

  python inference.py --mode image --detector_weights $DETECTOR_WEIGHTS --inference_slot
  

• Video inference

  python inference.py --mode video --detector_weights $DETECTOR_WEIGHTS --video $VIDEO --inference_slot
  

  Argument DETECTOR_WEIGHTS is the trained weights of detector.
  Argument VIDEO is path to the video.
  View config.py for more argument details.

Prepare data

1. Download ps2.0 from here, and extract.

2. Download the labels, and extract.
   (In case you want to label your own data, you can use directional_point branch of my labeling tool MarkToolForParkingLotPoint.)

3. Perform data preparation and augmentation:

   python prepare_dataset.py --dataset trainval --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --output_directory $OUTPUT_DIRECTORY
   python prepare_dataset.py --dataset test --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --output_directory $OUTPUT_DIRECTORY
   

   Argument LABEL_DIRECTORY is the directory containing json labels.
   Argument IMAGE_DIRECTORY is the directory containing jpg images.
   Argument OUTPUT_DIRECTORY is the directory where output images and labels are.
   View prepare_dataset.py for more argument details.

Train

python train.py --dataset_directory $TRAIN_DIRECTORY

Argument TRAIN_DIRECTORY is the train directory generated in data preparation.
View config.py for more argument details (batch size, learning rate, etc).

Evaluate

• Evaluate directional marking-point detection

  python evaluate.py --dataset_directory $TEST_DIRECTORY --detector_weights $DETECTOR_WEIGHTS
  

  Argument TEST_DIRECTORY is the test directory generated in data preparation.
  Argument DETECTOR_WEIGHTS is the trained weights of detector.
  View config.py for more argument details (batch size, learning rate, etc).

• Evaluate parking-slot detection

  python ps_evaluate.py --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --detector_weights $DETECTOR_WEIGHTS
  

  Argument LABEL_DIRECTORY is the directory containing testing json labels.
  Argument IMAGE_DIRECTORY is the directory containing testing jpg images.
  Argument DETECTOR_WEIGHTS is the trained weights of detector.
  View config.py for more argument details.

Citing DMPR-PS

If you find DMPR-PS useful in your research, please consider citing:

@inproceedings{DMPR-PS,
Author = {Junhao Huang and Lin Zhang and Ying Shen and Huijuan Zhang and Shengjie Zhao and Yukai Yang},
Booktitle = {2019 IEEE International Conference on Multimedia and Expo (ICME)},
Title = {{DMPR-PS}: A novel approach for parking-slot detection using directional marking-point regression},
Month = {Jul.},
Year = {2019},
Pages = {212-217}
}