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