neural network (incomplete)

a_star.py

@@ -6,7 +6,7 @@ MAX_ROWS = 15
 MAX_COLS = 25
 
 mapping = {
-    'd': 10,
+    'd': 2,
     's': 20,
     'r': 999
 }

decision_tree.py

@@ -27,7 +27,7 @@ def treelearn():
     # visualize and display decision tree
     data = tree.export_graphviz(decision_tree, out_file=None, feature_names=attributes)
     graph = pydotplus.graph_from_dot_data(data)
-    graph.write_png(os.path.join('resources', 'decision_tree.png'))
+    # graph.write_png(os.path.join('resources', 'decision_tree.png'))
     img = pltimg.imread(os.path.join('resources', 'decision_tree.png'))
     imgplot = plt.imshow(img)
 

neural.py

@@ -0,0 +1,58 @@
+import tensorflow as tf
+import glob
+import numpy as np
+import os
+
+from PIL import Image
+from sklearn.model_selection import train_test_split
+from tensorflow import keras
+
+images = []
+labels = []
+
+class_names = ['fragile', 'priority', 'skull']
+
+image_dir = 'C:\\Users\\mateu\\Desktop\\SI projekt\\resources'
+image_size = (32, 32)
+
+for class_index, class_name in enumerate(class_names):
+    class_path = os.path.join(image_dir, class_name)
+    file_names = os.listdir(class_path)
+    print(class_path)
+    for file_name in file_names:
+        image_path = os.path.join(class_path, file_name)
+        image = Image.open(image_path).convert('L')
+        image = image.resize(image_size)
+        image_array = np.array(image) / 255.0
+        images.append(image_array)
+        labels.append(class_index)
+
+images = np.array(images)
+labels = np.array(labels)
+
+X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
+
+num_classes = 3
+y_train_encoded = keras.utils.to_categorical(y_train, num_classes)
+y_test_encoded = keras.utils.to_categorical(y_test, num_classes)
+
+model = keras.Sequential([
+    keras.layers.Flatten(input_shape=image_size),
+    keras.layers.Dense(64, activation='relu'),
+    keras.layers.Dense(num_classes, activation='softmax')
+])
+
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+batch_size = 32
+epochs = 10
+
+model.fit(X_train, y_train_encoded, batch_size=batch_size, epochs=epochs,
+          validation_data=(X_test, y_test_encoded))
+
+test_loss, test_acc = model.evaluate(X_test, y_test_encoded)
+print('Test accuracy:', test_acc)
+
+predictions = model.predict(X_test)
+
+model.save('resources/model.h5')

test.py

@@ -0,0 +1,25 @@
+import numpy as np
+from PIL import Image
+from tensorflow import keras
+
+saved_model_path = 'resources/model.h5'
+model = keras.models.load_model(saved_model_path)
+
+
+image_path = 'resources/indeks-fragile.png'
+image_size = (32, 32)
+
+image = Image.open(image_path).convert('L')
+image = image.resize(image_size)
+image_array = np.array(image) / 255.0
+input_data = np.expand_dims(image_array, axis=0)
+
+
+predictions = model.predict(input_data)
+predicted_class = np.argmax(predictions, axis=1)
+
+
+class_names = ['fragile', 'priority', 'skull']
+predicted_label = class_names[predicted_class[0]]
+
+print('Predicted class:', predicted_label)