Create a Python AI Custom Drawings Classifier

Table of Contents

1. Introduction
2. Building a Custom Drawing Classifier
3. Project Setup
4. Creating the Graphical User Interface
5. Storing and Loading the Images
6. Training the Model
7. Making Predictions
8. Rotating the Model
9. Saving and Loading the Model
10. Saving and Loading Everything
11. On Closing the Application
12. Conclusion

Introduction

In this tutorial, we will learn how to build a custom drawing classifier in Python. This application uses machine learning to classify custom drawings into one of three classes. We will start by setting up the project and defining the classes. Next, we will Create a graphical user interface where we can draw and save samples for training the model. After that, we will train the model using the training data. Once the model is trained, we can make predictions on new drawings. We will also implement functionality to rotate the model and save and load the model and project data. Lastly, we will handle the closing of the application and offer options to save the work. This tutorial is targeted towards intermediate Python programmers, as it involves writing a significant amount of code and using machine learning algorithms and graphical user interface. Let's get started!

Building a Custom Drawing Classifier

To begin, we will build a custom drawing classifier in Python. This application allows us to classify drawings into custom-defined classes. We will create a graphical user interface where we can draw and save samples for training the model. Then, we will train the model using the training data and make predictions on new drawings. We will also implement functionality to rotate the model and save and load the model and project data.

Project Setup

Before we dive into the tutorial, let's set up the project. We need to install some external Python packages: numpy, pillow, opencv-python, and scikit-learn. You can use the pip command to install these packages. Once the packages are installed, we can import them in our Python script. We will also import the pickle and os.path modules from the Core Python library. These modules will be used for storing and loading data. Next, we will import the necessary modules from the tkinter library for creating the graphical user interface. Lastly, we will import the machine learning models we will use: linear support Vector classifier, k-neighbors classifier, logistic regression, decision tree classifier, random forest classifier, and Gaussian naive Bayes classifier.

Creating the Graphical User Interface

To create the graphical user interface for our custom drawing classifier, we will use the tkinter library. We will define a class called DrawingClassifier, which will have methods for initializing the graphical user interface (initGUI) and prompting the user to enter project and class names (classesPrompt). We will also define methods for handling button clicks, such as saving samples, clearing the canvas, changing the model, training the model, and making predictions.

Storing and Loading the Images

To store and load the images, we will create methods in the DrawingClassifier class. When saving samples, we will get the image data from the canvas, save it to a temporary file, resize it, and then move it to the respective class directory. We will also update the class counters to keep track of the number of instances for each class. When loading samples, we will iterate over the class directories, load the images, and reshape them to match the model's input shape.

Training the Model

Next, we will train the model using the training data. We will get the image and class data from the class directories and reshape the images to match the model's input shape. Then, we will use the scikit-learn classifiers to train the model on the data. After training, we will display a success message to indicate that the model has been trained.

Making Predictions

Once the model is trained, we can make predictions on new drawings. When the user clicks the "Predict" button, we will get the image data from the canvas, preprocess it, and pass it to the model for prediction. We will display the predicted class in a message box to the user.

Rotating the Model

To allow for model rotation, we will define a method in the DrawingClassifier class called rotateModel. This method will rotate the model to the next classifier in the sequence. For example, if the Current model is a linear support Vector classifier, we will rotate it to a k-neighbors classifier. We will update the status label to display the current model.

Saving and Loading the Model

To save and load the model, we will define two methods in the DrawingClassifier class: saveModel and loadModel. When the user clicks the "Save Model" button, we will prompt them to choose a file path and save the model to that location. When the user clicks the "Load Model" button, we will prompt them to choose a file path and load the model from that location.

Saving and Loading Everything

In addition to saving and loading the model, we will also implement functionality to save and load everything, including the classes, samples, counters, and model. We will store the data in a dictionary and save it as a pickle file. When the user clicks the "Save Everything" button, we will prompt them to choose a file path and save everything to that location. When the user opens the application, we will check if there is a saved project and load it if it exists.

On Closing the Application

Lastly, we will handle the closing of the application. When the user tries to close the application, we will prompt them to save their work if they haven't already. We will display a message box with options to save, discard, or cancel the closing. If the user chooses to save, we will call the saveEverything method. If the user chooses to discard, we will exit the application without saving. If the user cancels the closing, we will do nothing and allow them to Continue working.

Conclusion

In this tutorial, we have learned how to build a custom drawing classifier in Python. We used machine learning algorithms from the scikit-learn library to train the model and make predictions on new drawings. We created a graphical user interface using the tkinter library, allowing users to draw and save samples for training the model. We also implemented functionality to rotate the model, save and load the model, and save and load the project data. This project is a great example for intermediate Python programmers who are interested in machine learning and graphical user interfaces. With this application, you can classify your custom drawings into defined classes and make accurate predictions.