This is a simple project that uses a convolutional neural network (CNN) to classify images of flowers into different categories. The project consists of two Python files:
model_trainer.py: This file contains the code to train the CNN model using the flower images.app.py: This file contains a GUI application that allows the user to select an image of a flower and get a prediction of the flower's category.
The dataset used in this project contains images of five different types of flowers:
- Daisy
- Dandelion
- Rose
- Sunflower
- Tulip
The images are stored in the flowers directory, which is organized into subdirectories for each flower type.
To train the model, simply run the model_trainer.py file. The script will load the images from the flowers directory, preprocess them, and train the CNN model using TensorFlow. The trained model will be saved to a file named flower_classifier.
To run the GUI application, simply run the app.py file. The application will open a window that allows the user to select an image of a flower. Once the user selects an image, the application will preprocess the image, make a prediction using the trained CNN model, and display the predicted flower category.
This project requires the following Python packages:
- TensorFlow
- Pillow
- tkinter
To install these packages, you can use pip:
pip install tensorflow pillow tkinter
This is a simple project that demonstrates how to use a CNN to classify images of flowers. The project can be easily extended to include other types of images or to improve the accuracy of the model.