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py-XVis

Python implementation for XVis Toolbox release with the book Computer Vision for X-Ray Testing. Originally implemented in Matlab by Domingo Mery for the first edition of the book. This package is part of the second edition of the book Computer Vision for X-Ray Testing (November 2020).

Requirements

  • Python 3.6 or higher
  • numpy < 1.19
  • matplotlib >= 3.3.2
  • scipy >= 1.5.2
  • pyqt5 >= 5.15.1
  • pybalu >= 0.2.9
  • opencv-python = 3.4.2
  • opencv-contrib-python = 3.4.2
  • tensorflow >= 2.3.1
  • scikit-learn >= 0.23.2
  • scikit-image >= 0.17.2
  • pandas >= 1.1.2

Instalation

The package is part of the Python Index (PyPi). Installation is available by pip:

pip install pyxvis

Interactive Examples

All examples in the Book have been implemented in Jupyter Notebooks tha run on Google Colab.

Chapter 01: X-ray Testing Open In Colab

  • Example 1.1: Displaying X-ray images
  • Example 1.2: Dual Energy
  • Example 1.3: Help of PyXvis functions

Chapter 02: Images for X-ray Testing Open In Colab

  • Example 2.1: Displaying an X-ray image of GDXray

Chapter 03: Geometry in X-ray Testing Open In Colab

  • Example 3.1: Euclidean 2D transformation
  • Example 3.2: Euclidean 3D transformation
  • Example 3.3: Perspective projection
  • Example 3.4: Cubic model for distortion correction
  • Example 3.5: Hyperbolic model for imaging projection
  • Example 3.6: Geometric calibration
  • Example 3.7: Epipolar geometry
  • Example 3.8: Trifocal geometry
  • Example 3.9: 3D reconstruction

Chapter 04: X-ray Image Processing Open In Colab

  • Example 4.1: Aritmetic average of images
  • Example 4.2: Contrast enhancement
  • Example 4.3: Shading correction
  • Example 4.4: Detection of defects using median filtering
  • Example 4.5: Edge detection using gradient operation
  • Example 4.6: Edge detection with LoG
  • Example 4.7: Segmentation of bimodal images
  • Example 4.8: Welding inspection using adaptive thresholding
  • Example 4.9: Region growing
  • Example 4.10: Defects detection using LoG approach
  • Example 4.11: Segmentation using MSER
  • Example 4.12: Image restoration

Chapter 05: X-ray Image Representation Open In Colab

  • Example 5.1: Geometric features
  • Example 5.2: Elliptical features
  • Example 5.3: Invariant moments
  • Example 5.4: Intenisty features
  • Example 5.5: Defect detection usin contrast features
  • Example 5.6: Crossing line profiles (CLP)
  • Example 5.7: SIFT
  • Example 5.8: Feature selection
  • Example 5.9: Example using intenisty features
  • Example 5.10: Example using geometric features

Chapter 06: Classification in X-ray Testing Open In Colab

  • Example 6.1: Basic classification example
  • Example 6.2: Minimal distance (dmin)
  • Example 6.3: Bayes
  • Example 6.4: Mahalanobis, LDA and QDA
  • Example 6.5: KNN
  • Example 6.6: Neural networks
  • Example 6.7: Support Vector Machines (SVM)
  • Example 6.8: Training and testing many classifiers
  • Example 6.9: Hold-out
  • Example 6.10: Cross-validation
  • Example 6.11: Confusion matrix
  • Example 6.12: ROC and Precision-Recall curves
  • Example 6.13: Example with intensity features
  • Example 6.14: Example with geometric features

Chapter 07: Deep Learing in X-ray Testing

  • Example 7.1: Basic neural networks (from skratch) Open In Colab

  • Example 7.2: Neural network using sklearn Open In Colab

  • Example 7.3: Convolutional Neural Network Open In Colab

  • Example 7.4: Pre-trained models Open In Colab

  • Example 7.5: Fine tunning Open In Colab

  • Example 7.6: Generative Adversarial Networks (GANs) Open In Colab

  • Example 7.7: Object detection using YOLOv3 Open In Colab

  • Example 7.8: Object detection using YOLOv4 Open In Colab

  • Example 7.9: Object detection using YOLOv5 Open In Colab

  • Example 7.10: Object detection using EfficientDet Open In Colab

  • Example 7.11: Object detection using RetinaNet Open In Colab

  • Example 7.12: Object detection using DETR Open In Colab

  • Example 7.13: Object detection using SSD Open In Colab

Chapter 08: Simulation in X-ray Testing Open In Colab

  • Example 8.1: Basic simulation using voxels
  • Example 8.2: Simulation of defects using mask
  • Example 8.3: Simulation of ellipsoidal defects
  • Example 8.4: Superimposition of threat objects

Chapter 09: Applications in X-ray Testing

  • Example 9.1: Defect detection in castings Open In Colab

  • Example 9.2: Defect detection in welds Open In Colab

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Official Python implementation for XVis Toolbox release with the book Computer Vision for X-Ray Testing.

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