GMM-SLIC-RustDetction

Official implementation for "Automatic Rust Segmentation Using Gaussian Mixture Model and Superpixel Segmentation" published in IEEE HDIS 2022 Conference.

This is a novel rust segmentation approach based on the Gaussian mixture model (GMM) and SLIC superpixel segmentation.

Training code, testing code, and testing images are provided. For the training images and the pre-trained models (including GMMmodel_RGB.pkl, GMMmodel_HSV.pkl, and GMMmodel_Combine.pkl), please contact [email protected].

For commercial use, please also contact [email protected].

Citation

Please cite our IEEE HDIS 2022 paper when using this code:

@INPROCEEDINGS{9991284,
  author={Yang, Jingyuan and Luo, Yangzhixin and Zhou, Zhixiang and Li, Nan and Fu, Ruiqing and Zhang, Xiaoguang and Ding, Ning},
  booktitle={2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS)}, 
  title={Automatic Rust Segmentation Using Gaussian Mixture Model and Superpixel Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={71-78},
  doi={10.1109/HDIS56859.2022.9991284}}

Preparation

Install required dependencies:

pip install -r requirements.txt

Make a new directory ./model under the working directory for the trained models that will be generated by training:

cd GMM-SLIC-RustDetction # go to the working directory that you put this project in
mkdir model

Make a new directory ./output under the working directory for the output images that will be generated by testing:

mkdir output

Usage of Training/Testing Code

The files under training/ directory are used to train the GMM model, with the following features:

• training/train_GMM_RGB.py: Training code for GMM model using RGB features.
• training/train_GMM_HSV.py: Training code for GMM model using HSV features.
• training/train_GMM_Combine.py: Training code for GMM model using RGB+HSV features.

The files under testing/ directory are used to test the GMM model with SLIC superpixel segmentation, and generate output images, with the following features:

• testing/test_RGB_SLIC.py: Testing code with SLIC segmentation for GMM model using RGB features.
• testing/test_HSV_SLIC.py: Testing code with SLIC segmentation for GMM model using HSV features.
• testing/test_Combine_SLIC.py: Testing code with SLIC segmentation for GMM model using RGB+HSV features.

Training

Train GMM model using RGB features:

python training/train_GMM_RGB.py --train_path=<your-training-images-directory-path>

Train GMM model using HSV features:

python training/train_GMM_HSV.py --train_path=<your-training-images-directory-path>

Train GMM model using RGB+HSV features:

python training/train_GMM_Combine.py --train_path=<your-training-images-directory-path>

Example:

python training/train_GMM_HSV.py --train_path="./train_images"

Testing

Test with SLIC segmentation for GMM model using RGB features:

python testing/test_RGB_SLIC.py --test_img_path=<your-testing-image-path>

Test with SLIC segmentation for GMM model using HSV features:

python testing/test_HSV_SLIC.py --test_img_path=<your-testing-image-path>

Test with SLIC segmentation for GMM model using RGB+HSV features:

python testing/test_Combine_SLIC.py --test_img_path=<your-testing-image-path>

Example:

python testing/test_HSV_SLIC.py --test_img_path="./test_images/test2.png"

Output

Training Output

The trained model .pkl file (according to which training file you ran) will be generated under ./model in the working directory:

• GMMmodel_RGB.pkl: The trained model using RGB features.
• GMMmodel_HSV.pkl: The trained model using HSV features.
• GMMmodel_Combine.pkl: The trained model using RGB+HSV features.

Testing Output

All the output images will be generated under ./output in the working directory, including (take running testing/test_HSV_SLIC.py as example):

• HeatMap_GMM_HSV.png: The heat image (visualized probabilities) predicted by the GMM model.
• SLIC_Black_HSV.png: The SLIC superpixel segmentation output (black background) of the original input image.
• SLIC_White_HSV.png: The SLIC superpixel segmentation output (white background) of the original input image.
• SLIC_HSV.png: The SLIC superpixel segmentation output (original background) of the original input image.
• Final_Img_HSV.png: The final segmentation output with rust detected in red and original background.
• Binary_Img_HSV.png: The binary segmentation output with rust detected in red and background in white.

Sample

Testing Input

Input_Img.png

Testing Output

HeatMap_GMM_HSV.png
  
SLIC_Black_HSV.png      SLIC_White_HSV.png

SLIC_HSV.png
  
Final_Img_HSV.png      Binary_Img_HSV.png

Remarks

1. We have supported the image formats including PNG, JPG, JPEG, png, jpg, jpeg for the training and testing images.
2. If your environment does not support GUI display (like when running on remote servers), just comment out all the lines of cv2.imshow(...), cv2.waitKey(0), cv2.destroyAllWindows().
3. Notice that do not comment out the lines of cv2.imwrite(...), otherwise you will not get the output images under ./output in the working directory.