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README.md

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 Collection of work aiming to detect ellipses in SEM images. This work was done in a summer
 research project from May 2022 to August 2022 under the supervision of Dr. Paul Barclay.
 
+This will mainly be an archival tool of the progress I make on this. Commits will mainly
+be various updates as I understand and discover different things. 
+
+## Table of Contents
+* [Installation](#installation)
+* [Usage](#usage)
+* [Creating Ground Truth Images](#creating-ground-truth-images)
+* [Development](#development)
+
+## Installation
+
+Installation is broken up into two section: the Python packages and the AAMED algorithm.
+If the AAMED algorithm is not needed, one can skip installing it.
+
+### Installing Python Packages
+
+The Anaconda version of Python was used and tested in this work. Instruction for such 
+will be given. If needed, first create a new environment with the desired Python 
+version. Versions 3.8-3.10 have been tested.
+```
+$ conda create -n ellipse python=3.8
+``` 
+Next, install the required packages
+```
+$ conda install -n ellipse opencv=4.5.5 numpy pandas matplotlib tifffile ipykernel
+```
+
+OpenCV version 4.5.5 is specified since the provided AAMED binaries were compiled with
+this version. Other mismatched versions may work, but to be sure, it is best to compile
+the AAMED algorithm for that version if the functionality is desired. See the next 
+section for details.
+
+### Installing AAMED
+
+In order to use the AAMED algorithm, the AAMED binaries should be downloaded and placed 
+into the `./ellipsefinder/methods` folder. The binaries are provided for AAMED in the 
+releases section on the right side. Provided are versions for the Anaconda distribution 
+of Python 3.8-3.10 for Windows and Linux compiled for OpenCV 4.5.5. The system 
+distribution in Linux should also work. For other distributions, one will need to build 
+the AAMED binary themselves. 
+
+Cython and a C++ compiler are needed for building. See the 
+[AAMED GitHub page](https://github.com/Li-Zhaoxi/AAMED) for building details.
+
+It is not necessary to install the AAMED binaries. If they are not found, an error will
+be shown, but the code can be used normally without access to the AAMED algorithm.
+
+## Usage
+
+The main code is in `./ellipsefinder` and it consists of the main `find_ellipses.py`
+file and the `methods` folder. In the `find_ellipses.py` file is all the functions
+wrapping the functionality of the various ellipse finding algorithms in the `methods`
+folder.
+
+Most of the testing of the algorithms I did with the Jupyter notebooks in 
+[`./notebooks`](./notebooks/). It is a bit unorganized, but examples of using 
+[`find_ellipses.py`](./ellipsefinder/find_ellipses.py) are best shown in
+[`ellipses.ipynb`](./notebooks/ellipses.ipynb).
+
+In general, a couple different sections should be written. First, for the images that
+we have used, we open then and remove any banners that are present. Second, we set
+up any filtering that we want done on the output results. Next, run our selected
+detection algorithm in the standard way seen in the examples. Finally, we can calculate
+some things with results and save all the output image.
+
+## Creating Ground Truth Images
+
+To compare the accuracy of the ellipse finding methods, and for possible use in future 
+machine learning methods, ground truth images/masks were created using 
+[ImageJ](https://imagej.net/software/fiji/downloads). For the details in creating the
+selection regions of interest (ROIs) and masks, see the 
+[ImageJ GT Steps.md](./ImageJ%20GT%20Steps.md) file.
+
+Three files are created from this method: the ground truth mask, the ROI zip file, and 
+the CSV file of the selection regions. I tested out both extraction the ellipses from 
+the mask and creating the ellipse from the CSV selection region data. Both are okay, 
+using `./ellipsefinder/format_roi_csv.py` with the CSV data is probably better.
+
+## Development
+
+I tried to make adding new future methods relatively easy. All the current ellipse
+finder algorithms implement the abstract base class (ABC) `finder.py` which has the 
+various functions needed to get going. Bare minimum, the two abstract methods 
+`preprocess()` and `extract()` should be implemented. `preprocess()`, if needed, should 
+return a preprocessed image ready for the extraction process to start. `extract()` 
+should actually implement the extraction process and return the DataFrame with the found
+ellipses' details. Is this the best way to organize this process? I don't know, but I
+thought it worked well for me.
+
+The ABC also has a few convenience functions that can be utilized when creating a new
+extraction method, as well as a default function to plot the found ellipses onto the
+original image.
+
+A standard that I have used when storing the data in the DataFrame is storing the angle
+of the ellipse with the commonplace counterclockwise positive direction starting from
+the x-axis. In the OpenCV methods I have used, it adapts a clockwise positive direction
+starting from the x-axis as its standard. This is something to keep in mind when
+creating the `extract()` function and storing results.