|
|
| """
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| This file contains a list of python script and their purpuse and status
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| """
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| """
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| 0_environment.yml
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| This file includes a setup of the python environment requried to run the scripts.
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| Please note that installation of GPU resources are not added. The model wil
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| automatically try to run the script on the GPU (if installed) and will otherwise
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| use the CPU.
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|
|
| """
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|
|
| """
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| 1a_dataset_statistics.py
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| This is used to compute statistics of the dataset.
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| 1. Calculates Total Pixel Area (Resolution * Image Count).
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| 2. Calculates "No Defect" (Background) pixel counts.
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| 3. Calculates Pixel Percentages for all categories.
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| 4. Maintains the TA, TB, TC split.
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| """
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|
|
| """
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| 1b_histogram_plot.py
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| This script reads the segmented masks and plots histograms of the defect size
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| distribution. It generates:
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| 1. Individual plots for all datasets and individual plots for the tunnels
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| TA, TB, TC.
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| 2. A combined subplot figure comparing TA, TB, and TC.
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|
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| The user chose which defect that should be plotted.
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| """
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|
|
|
|
| """
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| 1c_create_classification.py
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| this scripts reads the csv files that contain information about images with and
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| withou cracks. Based on this, three classification datasets are created in the
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| folder "3_classification", i.e. TA, TB and TC. Each folder contains the
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| subfolder "crack" and "no_crack"
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| """
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|
|
| """
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| 2_train_CNN.py
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|
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| This script trains a UNet segmentaiton model for a single detection class.
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| The user defines the "Session_Name" which is the output folder for the saved
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| model, plots and metrics.
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| The user use the Global Configuration to adjust parameters. This includdes:
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| - A weight factor is included for imbalanced datasets.
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| - Data used for Traning, Evaluation and Testing is based on csv files.
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| - The script creates masks used for the fastai packaage which use 1 for defect
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| and 0 for background. The user defines the pixel value for the class they want
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| to train the model for.
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| - Model training parameters are easily adjusted.
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| - Output includes plots of top 5 best and worst predictions of cracks and
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| txt files with a summary of the metrics
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| """
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|
|
| """
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| 2b_plot_training.py
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| This script reads the csv output from training and creates a plot of training
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| and validation loss in one plot and IoU and F1-score in a second plot.
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| User only needs to change "TRAINING_DATA" to correct training set.
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| """
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|
|
| """
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| 3_evaluate_CNN.py
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| This script loads a pre-trained model and evaluate its performance on a list
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| of datasets. The output is a .txt file with metrics. Naming of the file is based on
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| the SESSION_NAME and metrics for each eavluation is added in the txt file in
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| sequence, i.e. the metrics for all evaluation using the same model is stored in
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| the same file.
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| """ |
