# The Coverage Calculator
Now available as a web-app on StreamLit!

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://geezacoleman-gilgaidetection-streamlit-app-wn203j.streamlit.app/)

The CoverageCalculator allows you to upload images and perform two-class, colour-based segmentation. Using [this thread on Twitter](https://twitter.com/cropmad/status/1637700842727022593) for inspiration, the app was made to allow a user to threshold images and 
determine the area of an image that comes from each of the two classes. It was built in a few hours with a lot of help from ChatGPT.

![image](https://user-images.githubusercontent.com/51358498/226951394-16d4bf7e-e0ce-4946-aad3-2b69730098d1.png)

## A Tkinter-based user interface
To use the tkinter GUI, clone this repository, install the requirements (numpy and OpenCV) and then run `python gui.py` 
on your command line. It should bring up a window like the one below. There are default colour settings included
in the `parameters.json` file, but if you want to change those, you'll need to run `gilgai_detection.py` separately.

![gui](gui_example.png)

Once it is open, select the directory of images you would like to process, click process. It will save the percent cover
of each to a csv file and display a segmented image. You can toggle between the images using the Back/Forward 
buttons.

## Non-GUI based

This example of Gilgai 'detection' uses simple HSV thresholding for wheat and Gilgai/non-wheat areas with OpenCV and numpy. 
The percentage cover of each class in the image is calculated using total pixels in the image and displayed 
on the image. 

![image](example_output.png)

When the windows are closed with 'Q' the slider values are saved and will be loaded on reopening. Use the `gilgai_detection.py`
file to set and store the values first and then the `process_directory.py` file to use the stored values to process and
save the results from an entire directory to CSV.