Gra-SI/nn.py

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import os
import glob
import PIL
from PIL import Image
import tensorflow as tf
import pickle
from tensorflow import keras
from keras import layers
from keras.models import Sequential
import pathlib
class NeuralN:
# @staticmethod
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def predict(self,image):
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data_dir = pathlib.Path('zdjecia')
saved_model_path = pathlib.Path('trained_model.h5')
class_names_path = pathlib.Path("class_names.pkl")
image_count = sum(len(files) for _, _, files in os.walk(data_dir))
print(image_count)
# ORK_ARCHER = list(glob.glob('C:\\mobs_photos\\ORK_ARCHER'))
# im = PIL.Image.open(ORK_ARCHER[0])
# im.show()
if os.path.exists(saved_model_path):
model = tf.keras.models.load_model(saved_model_path)
print("Saved model loaded")
with open(class_names_path, 'rb') as f:
class_names = pickle.load(f)
print("Class names loaded.")
else:
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(180, 180),
batch_size=32)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(180, 180),
batch_size=32)
# test_ds = tf.keras.utils.image_dataset_from_directory(
# data_dir,
# seed=123,
# image_size=(180, 180),
# batch_size=32)
class_names = train_ds.class_names
print(class_names)
num_classes = len(class_names)
model = Sequential([
layers.Rescaling(1. / 255, input_shape=(180, 180, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'])
model.summary()
epochs = 1
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
model.save("trained_model.h5")
print("Model trained and saved.")
with open(class_names_path, 'wb') as f:
pickle.dump(train_ds.class_names, f)
print("Class names saved.")
# loaded_model = tf.keras.models.load_model("trained_model.h5")
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax()])
#image_path = image
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image_path = pathlib.Path('zdjecia\ORK_ARCHER\ork_archer (942).jpg')
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image = Image.open(image_path)
# Preprocess the image
image = image.resize((180, 180)) # Resize to match the input size of the model
image_array = tf.keras.preprocessing.image.img_to_array(image)
image_array = image_array / 255.0 # Normalize pixel values
# Add an extra dimension to the image array
image_array = tf.expand_dims(image_array, 0)
# Make the prediction
predictions = probability_model.predict(image_array)
# Convert the predictions to class labels
predicted_label = class_names[predictions[0].argmax()]
#actions = {
# 'ORK_MELEE': 'fight',
# 'ORK_ARCHER': 'change_dir',
# 'SAURON': 'change_dir'
#}
# Get the action for the predicted character
#action = actions.get(predicted_label, 'unknown')
# Print the predicted label
print(predicted_label)
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return predicted_label#, action