In this tutorial, we will build a custom drawing classifier in Python using machine learning. The application will allow us to classify our drawings into three custom-defined classes. We will provide sample drawings to train the model and use it to predict the class of new drawings.
Contents
Introduction
Have you ever wondered if you could build your own drawing classifier using machine learning? In this tutorial, we will guide you step by step on how to create your own custom drawing classifier in Python. This project will not only allow you to classify your drawings accurately but also provide you with a deeper understanding of machine learning and graphical user interface (GUI) development.
Creating the Project
First, let’s create a new project by defining the project name and the three classes we want to classify. For example, we can have classes like “Triangle,” “Circle,” and “Square”. The project name could be “Shapes”.
Designing the Graphical User Interface (GUI)
To draw and interact with the application, we will create a graphical user interface (GUI) using the Tkinter library. The GUI will consist of a canvas where we can draw and a set of buttons for each class to label the drawings.
Saving and Loading Samples
To train the model, we need to store the sample drawings in their respective class directories. We will create directories for each class and save the sample drawings as PNG files. This will allow us to easily load the training data during the training phase.
Training the Model
Once we have the sample drawings, we can train our model using various classification algorithms such as linear support vector classifier, k-neighbors classifier, logistic regression, decision tree classifier, random forest classifier, or Gaussian naive Bayes. We can choose the appropriate classifier based on our requirements and accuracy.
Making Predictions
After training the model, we can use it to predict the class of new drawings. We will load the new drawing, preprocess it, and pass it through the trained model. The model will output the predicted class label for the drawing.
Saving and Loading the Model
To save the trained model, we will use the pickle library to serialize the model and store it as a file. This allows us to load the model later on for further prediction or analysis.
Changing the Model
To improve the classification accuracy or experiment with different classifiers, we can switch between different models on the fly. We will provide a button to change the model, allowing us to explore different algorithms and compare their performance.
Saving and Loading Everything
To save the entire project, including the classes, sample drawings, and the model, we will create a function to save everything to a file. This will allow us to resume our work from where we left off or share the project with others.
Handling Closing Event
To provide a good user experience, we will handle the closing event of the application. If the user has made any changes or provided any sample drawings, we will prompt them to save their work before closing the application.
FAQs
Q: Is this project suitable for beginners in Python?
A: This project is more targeted towards intermediate Python programmers who have an understanding of machine learning and graphical user interface development.
Q: Can I use different classifiers for this project?
A: Yes, you can experiment with different classifiers such as neural networks or other classification algorithms. The code provided is modular, allowing you to easily add or remove classifiers according to your preference.
Q: Can I save my work and resume it later?
A: Yes, you can save your project, including the classes, sample drawings, and the trained model. This allows you to continue your work from where you left off.
Conclusion
In this tutorial, we have learned how to build a custom drawing classifier in Python using machine learning. We have covered the steps involved in training the model, making predictions, and saving and loading the project. By following this tutorial, you can create your own drawing classifier and explore the exciting world of machine learning and graphical user interface development.
