Tree Recogniser 7000

This repository contains all files for the Image recognition course of HU Electrical Engineering year 3.

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Directories and files:

.
├── example/                                        (training assignments course)
├── out/
│   ├── img                                         (images exported using CVSuite)/
│   │   └── (tag)_(preprocessor)_(date/time).png
│   ├── log                                         (preprocessor export from CVSuite)/
│   │   └── result_(date/time).csv
│   └── models                                      (exported OpenCV ML models for usage in CVSuite tests)/
│       └── model_(name).yaml
├── res/
│   ├── dataset                                     (dataset for CVSuite, see README)/
│   │   ├── testing
│   │   ├── training
│   │   ├── validation
│   │   └── *.png
│   ├── essay/                                      (export photo's and graphs for report)
│   ├── trees/                                      (initial dataset)
│   └── *.png                                       (photos required by assignments in .example/)
├── src/
│   ├── config/
│   │   └── config.json                             (CVSuite config, alter config.template.json to desired settings)
│   ├── experiments/                                (standalone python scripts for experimentation)
│   ├── helpers/
│   │   ├── gui/
│   │   │   └── main.ui                             (pygubu ui configuration)
│   │   ├── test/                                   (ML test classes for CVSuite)
│   │   └── *.py                                    (other CVSuite helper classes)
│   └── suite.py                                    (CVSuite main script)
├── README.md                                       (this file)
└── requirements.txt                                (pip install file)

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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

$ ./.venv/Scripts/activate(.bat/.ps1)

4. Install required packages using pip

$ pip install -r ./requirements.txt

Fix relative imports

1. Install the local package as editable using pip:

$ pip install -e .

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
3. Run the dataset tool:

$ python ./src/experiments/dataset.py

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
   • models should point to trained ML models in YAML format
   • out should point to the respective folders in the ./out folder
   • size determines the display size in the suite
3. Run CVSuite:

$ python ./src/suite.py

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:

$ python ./src/helpers/test/knn.py -i ./out/result-(date/time).csv -o ./out/models/ 

4. Edit your config.json to include the newly created model

📝 Please note:
The KNN Training script also generates the scaler required to make the decision tree model

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Arne van Iterson
Tom Selier