license: mit
pipeline_tag: image-classification
WOW Spell Identifier
This model is a specialized image classification model trained to identify World of Warcraft (WoW) spell images. It accepts a 56x56 pixel image as input and outputs the probabilities for various spell IDs.
Model Description
The WOW Spell Identifier is designed to assist gamers by automating the recognition of spell icons during gameplay. By processing 56x56 pixel images, the model predicts the likelihood of each spell ID, enabling the integration of this functionality into gaming tools for acessibility.
Model Details
- Developer: Luiz Hemerly
- Model Type: Convolutional Neural Network (CNN) for Image Classification
- Framework: TensorFlow/Keras
- Input: 56x56 pixel image
- Output: Probabilities for spell IDs
- License: MIT
Usage
The model can be integrated into gaming interfaces or used in standalone applications to enhance the gaming experience by providing real-time spell identification.
import tensorflow as tf
# Load the trained model
model = tf.keras.models.load_model('path_to_model/spell_identifier_model.h5')
# Load the list of spell IDs from classes.txt
with open('path_to_classes/classes.txt', 'r') as file:
classes = file.read().splitlines()
# Function to preprocess the input image
def preprocess_image(image_path):
# Implement preprocessing steps (e.g., resizing, normalization)
pass
# Function to predict the spell ID
def predict_spell_id(image_path):
processed_image = preprocess_image(image_path)
predictions = model.predict(processed_image)
predicted_class_index = np.argmax(predictions)
predicted_spell_id = classes[predicted_class_index]
return predicted_spell_id
# Example usage
spell_id = predict_spell_id('path_to_image/spell_image.png')
print(spell_id)
In this example, classes.txt contains the list of spell IDs corresponding to the model’s output classes. The predict_spell_id function processes the image, makes a prediction, and then maps the predicted class index to the actual spell ID using the classes list.
Contributions
Contributions to the model and its development are welcome.
License
This project is licensed under the MIT License.