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-# PFE_Classification
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+# PFE_Classification
+
+## Table of Contents
+
+1. [General Info](#general-info)
+2. [Technologies](#technologies)
+3. [Installation](#installation)
+4. [Usage](#usage)
+5. [Collaboration](#collaboration)
+
+## General Info
+
+This project is part of our Master 2 Computer Science final year project at the university of Bordeaux. It is a generic classification application allowing to perform pre-processing on a loaded database, data augmentation, launch classification model training and classify an image with a loaded pre-trained model.
+
+## Technologies
+
+[OpenCV](https://gist.github.com/umardx/dfce1dc74d925629a9eb8ad409740b47) : Version 3.2.0
+Debian GNU/Linux 10 (buster)
+[QT](https://www.qt.io/download) : Version 5.0
+
+## Installation
+
+Retrieve the repository :
+$ git clone git@github.com:Florian33850/PFE_Classification.git
+
+CMake is used to build the project.
+Create a build directory :
+```
+$ mkdir build
+$ cd build
+```
+```
+$ cmake ..
+$ make
+```
+To obtain the project's code coverage execute the following command:
+
+```
+$ make && make gcov && make lcov && firefox lcoverage/index.html
+```
+
+Launch the application :
+```
+$ ./application_viewer
+```
+## Usage
+
+First tab : 
+- Possibility to load one or more images with File > ImageSelection or a database as a folder with File > LymeDatabase
+- Addition of pre-processing which is carried out on the original images.
+- Save all images with active pre-processing.
+
+Second tab:
+- Possibility to load images as before (if not already done in the previous tab).
+- Addition of one or more data increases. Each data augmentation preview is done separately from each other.
+- Currently saving the images is only done on the last data augmentation previewed.
+
+Third tab:
+- Possibility to choose a classification method between deep learning and random forest.
+- Then you have to choose a python file (where it should be specified, according to the the following convention how the arguments needed in the application are passed): 
+       - DeepLearning: arg[1] = width of images, arg[2] = height of images, arg[3] = training set, arg[4] = testing set, arg[5] = number of epochs
+       - RandomForest : arg[1] = width of images, arg[2] = height of images, arg[3] = training set, arg[4] = number of trees
+Arguments can be passed in the python code with the import sys and the method sys.argv[n°] prefixed with the desired type (int, str, ...).
+- Once the training start, a message is displayed to inform that a training is in progress and another message is displayed when it finished.
+- The standard output of the python file is displayed in the application.
+
+Fourth tab :
+- Same method as for the previous tab, except that a prediction file must be passed as a parameter and should be already writtent in Python by the user.
+- Possibility of passing a pre-trained model (arg[1]), an image to be classified (arg[2]) and a label file (arg[3]) as parameters to the python file.
+
+## Collaboration
+
+Florian Dayre, Elodie Gaudry, Hugo Lecomte, Hugo Trarieux.