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Description:

pyxvis 0.1.0a9

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

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

Example 2.1: Displaying an X-ray image of GDXray

Chapter 03: Geometry in X-ray Testing

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

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

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 se;ection
Example 5.9: Example using intenisty features
Example 5.10: Example using geometric features

Chapter 06: Classification in X-ray Testing

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)

Example 7.2: Neural network using sklearn

Example 7.3: Convolutional Neural Network

Example 7.4: Pre-trained models

Example 7.5: Fine tunning

Example 7.6: Generative Adversarial Networks (GANs)

Example 7.7: Object detection using YOLOv3

Example 7.8: Object detection using YOLOv4

Example 7.9: Object detection using YOLOv5

Example 7.10: Object detection using EfficientDet

Example 7.11: Object detection using RetinaNet

Example 7.12: Object detection using DETR

Example 7.13: Object detection using SSD

Chapter 08: Simulation in X-ray Testing

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

Example 9.2: Defect detection in welds