sgtlib 3.4.2

A software tool for graph theory analysis of microscopy images.

StructuralGT

A software tool that allows graph theory analysis of nanostructures. This is a modified version of StructuralGT initially proposed by Drew A. Vecchio, DOI: 10.1021/acsnano.1c04711.

Installation

1. Install as software

• Download link: https://forms.gle/UtFfkGGqRoUjzeL47
• Install and enjoy.
• 5 minute YouTube tutorial: https://www.youtube.com/watch?v=bEXaIKnse3g
• We would love to hear from you, please give us feedback.

2. Install via pip

• Install Python version 3.13 on your computer.
• Execute the following commands:

pip install sgtlib

3. Install via source code

Therefore, please follow the manual installation instructions provided below:

• Install Python version 3.13 on your computer.
• Git Clone the branch DicksonOwuor-GUI from this repo: https://github.com/compass-stc/StructuralGT.git
• Extract the source code folder named 'structural-gt' and save it to your preferred location on your PC.
• Open a terminal application such as CMD.
• Navigate to the location where you saved the 'structural-gt' folder using the terminal.
• Execute the following commands:

cd structural-gt
pip install --upgrade pip
pip install -r requirements.txt
pip install .

3. Usage

3(a) Executing GUI App

To run the GUI version, please follow these steps:

• Open a terminal application such as CMD.
• Execute the following command:

StructuralGT

3(b) Executing Terminal App

Before executing StructuralGT-cli, you need to specify these parameters:

• image file path or image directory/folder: [required and mutually exclusive] you can set the file path using -f path-to-image or set the directory path using -d path-to-folder. If the directory path is set, StructuralGT will compute the GT metrics of all the images simultaneously,
• configuration file path: [required] you can set the path to config the file using -c path-to-config. To make it easy, find the file sgt_configs.ini (in the ''root folder'') and modify it to capture your GT parameters,
• type of GT task: [required] you can either 'extract graph' using -t 1 or compute GT metrics using -t 2,
• output directory: [optional] you can set the folder where the GT results will be stored using -o path-to-folder,
• allow auto-scaling : [optional] allows StructuralGT to automatically scale images to an optimal size for computation. You can disable this using -s 0.

Please follow these steps to execute:

• Open a terminal application such as CMD.
• Execute the following command:

StructuralGT-cli -d datasets/ -c datasets/sgt_configs.ini -o results/ -t 2

OR

StructuralGT-cli -f datasets/InVitroBioFilm.png -c datasets/sgt_configs.ini -t 2

OR

StructuralGT-cli -f datasets/InVitroBioFilm.png -c datasets/sgt_configs.ini -t 1

3(c) Using Library API

To use StructuralGT library:

• Make sure you install via pip
• Create a Python script or Jupyter Notebook and import modules as shown:

import matplotlib.pyplot as plt
from StructuralGT import modules as sgt

# set paths
img_path = "path/to/image"
cfg_file = "path/to/sgt_configs.ini"   # Optional: leave blank

# Define a function for receiving progress updates
def print_updates(progress_val, progress_msg):
    print(f"{progress_val}: {progress_msg}")

# Create a Network object
ntwk_obj, _ = sgt.ImageProcessor.create_imp_object(img_path, config_file=cfg_file)

# Apply image filters according to cfg_file
ntwk_obj.add_listener(print_updates)
ntwk_obj.apply_img_filters()
ntwk_obj.remove_listener(print_updates)

# View images
sel_img_batch = ntwk_obj.get_selected_batch()
bin_images = [obj.img_bin for obj in sel_img_batch.images]
mod_images = [obj.img_mod for obj in sel_img_batch.images]
plt.imshow(bin_images[0])
plt.axis('off')  # Optional: Turn off axis ticks and labels for a cleaner image display
plt.title('Binary Image')
plt.show()

plt.imshow(mod_images[0])
plt.axis('off')  # Optional: Turn off axis ticks and labels for a cleaner image display
plt.title('Processed Image')
plt.show()

# Extract graph
ntwk_obj.add_listener(print_updates)
ntwk_obj.build_graph_network()
ntwk_obj.remove_listener(print_updates)

# View graph
net_images = [sel_img_batch.graph_obj.img_ntwk]
plt.imshow(net_images[0])
plt.axis('off')  # Optional: Turn off axis ticks and labels for a cleaner image display
plt.title('Graph Image')
plt.show()

# Compute graph theory metrics
compute_obj = sgt.GraphAnalyzer(ntwk_obj)
sgt.GraphAnalyzer.safe_run_analyzer(compute_obj, print_updates)
print(compute_obj.output_df)

# Save in PDF
sgt.GraphAnalyzer.write_to_pdf(compute_obj)

References

• Drew A. Vecchio, Samuel H. Mahler, Mark D. Hammig, and Nicholas A. Kotov ACS Nano 2021 15 (8), 12847-12859. DOI: 10.1021/acsnano.1c04711.