defect_detection

This packge provides a basic API to implement defect detection algorithms. Those can be tuned in order to automatically detect any defects in a PCB or other components.

Requirement

The following packages are required :

• numpy
• opencv-python
• torch
• scikit-learn

Recommended python version >= 3.8

Installation

To install the latest stable release from PyPI :

pip install defect_detection

For developper who wants to work with a local and editable version :

git clone https://github.com/lovaslin/defect_detection.git
cd defect_detection
pip install -e .

For the local install, you should of course run the commands using a clean python environment. I recommend to use venv to setup a pip-friendly environemnt.

Available features

• Dataset creation

The provided defect_detection.generate_dataset function can be used to generate a dataset suitable for training and/or testing models. A list of the source image file names must be provided together with preprocessing and data augmetation parameters.

The specification of the function arguments are available in the doctring.

• Input batch loading

An batch of images can be loaded from disk directly using the defect_detection.load_batch function. Optionnaly, it is possible produce a noisy version of the input batch than can be used e.g. for training a new model. The batch of images is returned as a torch tensor stored on the required device.

If the data was already loaded as a numpy array, it is possible to convert it to a torch tensor using the defect_detection.get_tensor function. The option to generate a noisy version is also available.

• New model training

The defect_detection.deepAE_train function provides a basic training loop to traina new unsupervised defect detection model. It is recommended to use a dataset generated with the defect_detection.generate_dataset to perform the training (but not mandatory). Note that a file containing the specification of the model structure hiperparameters must be provided. The trained model will be saved on disk and the training and validation loss functions will be returned after completion of the training.

It is also possible write a custom training loop using the built-in AE_cls.batch_train method to compute the loss and update model parameters.

• Load a existing model for application

The defect_detection.deepAE_load function allows to load a previouly trained model from the disk. By default, the model will set for application only (no training functionality available).

Once a model is loaded, it is possible to compute both the per pixel anomaly score map and loss using the built-in AE_cls.batch_apply method.