97 lines
3.4 KiB
Markdown
97 lines
3.4 KiB
Markdown
# DMPR-PS
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This is the implementation of DMPR-PS using PyTorch.
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## Requirements
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* PyTorch
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* CUDA (optional)
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* Other requirements
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`pip install -r requirements.txt`
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## Pre-trained weights
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The [pre-trained weights](https://drive.google.com/open?id=1OuyF8bGttA11-CKJ4Mj3dYAl5q4NL5IT) could be used to reproduce the number in the paper.
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## Inference
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* Image inference
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```(shell)
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python inference.py --mode image --detector_weights $DETECTOR_WEIGHTS
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```
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* Video inference
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```(shell)
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python inference.py --mode video --detector_weights $DETECTOR_WEIGHTS --video $VIDEO
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```
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Argument `DETECTOR_WEIGHTS` is the trained weights of detector.
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Argument `VIDEO` is path to the video.
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View `config.py` for more argument details.
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## Prepare data
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1. Download ps2.0 from [here](https://cslinzhang.github.io/deepps/), and extract.
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2. Download the [labels](https://drive.google.com/open?id=1o6yXxc3RjIs6r01LtwMS_zH91Tk9BFRB), and extract.
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(In case you want to label your own data, you can use [`directional_point` branch of my labeling tool **MarkToolForParkingLotPoint**](https://github.com/Teoge/MarkToolForParkingLotPoint/tree/directional_point).)
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3. Perform data preparation and augmentation:
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```(shell)
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python prepare_dataset.py --dataset trainval --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --output_directory $OUTPUT_DIRECTORY
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python prepare_dataset.py --dataset test --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --output_directory $OUTPUT_DIRECTORY
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```
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Argument `LABEL_DIRECTORY` is the directory containing json labels.
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Argument `IMAGE_DIRECTORY` is the directory containing jpg images.
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Argument `OUTPUT_DIRECTORY` is the directory where output images and labels are.
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View `prepare_dataset.py` for more argument details.
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## Train
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```(shell)
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python train.py --dataset_directory $TRAIN_DIRECTORY
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```
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Argument `TRAIN_DIRECTORY` is the train directory generated in data preparation.
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View `config.py` for more argument details (batch size, learning rate, etc).
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## Evaluate
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* Evaluate directional marking-point detection
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```(shell)
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python evaluate.py --dataset_directory $TEST_DIRECTORY --detector_weights $DETECTOR_WEIGHTS
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```
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Argument `TEST_DIRECTORY` is the test directory generated in data preparation.
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Argument `DETECTOR_WEIGHTS` is the trained weights of detector.
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View `config.py` for more argument details (batch size, learning rate, etc).
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* Evaluate parking-slot detection
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```(shell)
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python ps_evaluate.py --label_directory $LABEL_DIRECTORY --image_directory $IMAGE_DIRECTORY --detector_weights $DETECTOR_WEIGHTS
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```
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Argument `LABEL_DIRECTORY` is the directory containing testing json labels.
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Argument `IMAGE_DIRECTORY` is the directory containing testing jpg images.
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Argument `DETECTOR_WEIGHTS` is the trained weights of detector.
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View `config.py` for more argument details.
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## Citing DMPR-PS
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If you find DMPR-PS useful in your research, please consider citing:
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```()
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@inproceedings{DMPR-PS,
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Author = {Junhao Huang and Lin Zhang and Ying Shen and Huijuan Zhang and Shengjie Zhao and Yukai Yang},
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Booktitle = {2019 IEEE International Conference on Multimedia and Expo (ICME)},
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Title = {{DMPR-PS}: A novel approach for parking-slot detection using directional marking-point regression},
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Month = {Jul.},
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Year = {2019},
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Pages = {212-217}
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}
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```
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