DMPR-PS/README.md

DMPR-PS

This is the implementation of DMPR-PS using PyTorch.

Requirements

• CUDA
• PyTorch
• OpenCV
• NumPy
• Pillow
• Visdom (optional)
• Matplotlib (optional)

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
  

• Video inference

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

  DETECTOR_WEIGHTS is the trained weights of detector.
  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.

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
   

   LABEL_DIRECTORY is the directory containing json labels.
   IMAGE_DIRECTORY is the directory containing jpg images.
   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

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
  

  TEST_DIRECTORY is the test directory generated in data preparation.
  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
  

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