Windows paths are dumb af

README.md

@@ -40,8 +40,8 @@ This repository contains all files for the Image recognition course of HU Electr
 
 ---
 
-### How to:
-#### Use the virtual environment
+## How to:
+### Use the virtual environment
 1. Make sure you have the Python extension in VSCode
 2. Create a virtual environment using VSCode by entering the Command Palette, selecting "Python: Create Environment..." and choosing venv.
 3. VSCode will automatically include the venv in the integrated terminal, if you want to open it in another terminal, use the appropriate activation script in the `.venv` folder
@@ -53,7 +53,7 @@ $ ./.venv/Scripts/activate(.bat/.ps1)
 $ pip install -r ./requirements.txt
 ```
 
-#### Create a dataset
+### Create a dataset
 1. Rename all images to include a tag and unique id, seperated by an underscore '_'
     - e.g. `accasia_1210262`
 2. Put all images into `./res/dataset`
@@ -62,7 +62,7 @@ $ pip install -r ./requirements.txt
 $ python ./src/experiments/dataset.py
 ```
 
-#### Run CVSuite (for the first time)
+### Run CVSuite (for the first time)
 1. Create `config.json` in the `./src/config/` folder and copy the contents of the template
 2. Edit `config.json` to fit your system, use full paths 
     - `path` should point to the dataset directory
@@ -74,13 +74,13 @@ $ python ./src/experiments/dataset.py
 $ python ./src/suite.py
 ``` 
 
-#### Train and save a KNN model
+### Train and export a KNN model
 1. Open CVSuite and select the desired training set
 2. Press 'Run analysis for entire dataset(!)', this will export a CSV file with all preprocessed data in the `./out` directory
     - Based on your system configuration, this might take a while
 3. Run the CVSuiteTestKNN CLI tool:
 ```sh
-$ python .\src\helpers\test\knn.py -i .\out\result-(date/time).csv -o .\out\models\model_knn.yaml 
+$ python ./src/helpers/test/knn.py -i ./out/result-(date/time).csv -o ./out/models/model_knn.yaml 
 ```
 4. Edit your `config.json` to include the newly created model