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+# DSZI_Survival - Sieć Neuronowa
+### Autor: Jonathan Spaczyński
+
+## Cel zastosowania w projekcie
+W projekcie DSZI_Survival sieć neuronowa użyta jest do podejmowania decyzji przez agenta.
+Decyzja polega na rozpoznawaniu zdjęć owoców (jabłka i gruszki). W przypadku nie rozpoznania owocu przez
+agenta, dochodzi do zatrucia i agent umiera/przegrywa.
+
+## Przygotowanie danych
+
+* **Krok 1** Przechowywane zdjęcia owoców muszą przejść przez proces zamiany zdjęcia (.jpg) na dane, które
+mogą być wykorzystane przez sieć neuronową.
+
+```python
+CATEGORIES = ["Apple", "Pear"]
+IMG_SIZE = 64
+
+training_data = []
+
+
+def create_training_data():
+    for category in CATEGORIES:
+        path = os.path.join(DATADIR, category)
+        class_num = CATEGORIES.index(category)
+        for img in os.listdir(path):
+            try:
+                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
+                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
+                training_data.append([new_array, class_num])
+            except Exception as e:
+                pass
+```
+zdjęcia są przechowywane w tablicy training_data wraz z klasyfikacją (class_num) odpowiadającą jakim typem owocu jest zdjęcie
+
+* **Krok 2** Bardzo ważnym krokiem jest pomieszanie danych. W przeciwnym wypadku nasz model po ciągłym otrzymywanie danych
+reprezentujących tylko jedną kategorię owoców mógłby się wyuczyć, aby tylko zgadywać tą kategorię.
+```python
+random.shuffle(training_data)
+```
+* **Krok 3** Ostatnim krokiem jest zaktualizowanie danych w taki sposób żeby były z przedziału
+od 0 d 255 (reprezentacja koloru danego pixela)
+```python
+X = X / 255.0
+```
+## Kilka słów na temat danych
+* **Ilość Danych** Do trenowania modelu wykorzystałem 8568 zdjęć gruszek i jabłek 
+z czego mniej więcej połowa danych była jednym z typów ww. owoców, a druga połowa
+reprezentowała pozostałą kategorią
+* **Dane wykorzystane do obliczenia skutecznośći** stanowiły małą i oddzielną część danych wykorzystanych do trenowania.
+## Model
+* **Dane wejściowe:** Dane o kształcie 64x64 reprezentujące pixele w zdjęciach owoców 
+* **Warstwa ukryta:** Składająca się z 128 "neuronów" wykorzystującą sigmoid jako funkcję aktywacyjną 
+* **Warstwa wyjściowa:** Składająca się z 2 "neuronów" reprezentujących gruszkę i jabłko
+* **Stała ucząca:** 0.001
+
+```python
+model = tf.keras.Sequential([
+    tf.keras.layers.Flatten(input_shape=(64, 64)),
+    tf.keras.layers.Dense(128, activation=tf.nn.sigmoid),
+    tf.keras.layers.Dense(2, activation=tf.nn.sigmoid)
+    ])
+
+model.compile(tf.keras.optimizers.Adam(lr=0.001),
+              loss="sparse_categorical_crossentropy",
+              metrics=["accuracy"])
+```
+## Osiągniecia modelu
+* **Trafność:** 86.4%
+* **Strata:** 0.312
+
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diff --git a/mygraph.png b/mygraph.png
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diff --git a/src/AI/NeuralNetwork/load_images.py b/src/AI/NeuralNetwork/load_images.py
new file mode 100644
index 0000000..3e11ac9
--- /dev/null
+++ b/src/AI/NeuralNetwork/load_images.py
@@ -0,0 +1,54 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import cv2
+import random
+import pickle
+
+DATADIR = "/home/paul/Desktop/TEST"
+CATEGORIES = ["Apple", "Pear"]
+IMG_SIZE = 64
+
+training_data = []
+
+
+def create_training_data():
+    for category in CATEGORIES:
+        path = os.path.join(DATADIR, category)
+        class_num = CATEGORIES.index(category)
+        for img in os.listdir(path):
+            try:
+                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
+                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
+                training_data.append([new_array, class_num])
+            except Exception as e:
+                pass
+
+
+create_training_data()
+print("amount of training data: " + str(len(training_data)))
+random.shuffle(training_data)
+
+X = []
+y = []
+
+for features, label in training_data:
+    X.append(features)
+    y.append(label)
+X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
+y = np.array(y)
+
+
+pickle_out = open("X_test.pickle.pickle", "wb")
+pickle.dump(X, pickle_out)
+pickle_out.close()
+
+pickle_out = open("y_test.pickle.pickle", "wb")
+pickle.dump(y, pickle_out)
+pickle_out.close()
+
+
+"""
+pickle_in = open("X.pickle", "rb")
+X = pickle.load(pickle_in)
+"""
\ No newline at end of file
diff --git a/src/AI/NeuralNetwork/model.h5 b/src/AI/NeuralNetwork/model.h5
new file mode 100644
index 0000000..f1c4c2c
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diff --git a/src/AI/NeuralNetwork/predict_image.py b/src/AI/NeuralNetwork/predict_image.py
new file mode 100644
index 0000000..67e3eb9
--- /dev/null
+++ b/src/AI/NeuralNetwork/predict_image.py
@@ -0,0 +1,79 @@
+import os
+from random import randint
+import cv2
+from tensorflow.keras.models import load_model
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class NN:
+    def __init__(self):
+        self.model = load_model('src/AI/NeuralNetwork/model.h5')
+        self.IMG_SIZE = 64
+        self.game_images = []
+        self.CATEGORIES = ["Apple", "Pear"]
+        self.X = []
+        self.y = []
+        self.load_images()
+        self.images_used = []
+
+    def show_picture(self, picture_id):
+        plt.imshow(self.game_images[picture_id][0])
+        plt.savefig("mygraph.png")
+        #print("this is: " + self.CATEGORIES[self.game_images[picture_id][1]])
+
+        show_img = cv2.imread("mygraph.png")
+        cv2.imshow('image', show_img)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    def load_images(self):
+        DATADIR = "data/AI_data/Nn_images"
+        for category in self.CATEGORIES:
+            path = os.path.join(DATADIR, category)
+            class_num = self.CATEGORIES.index(category)
+            for img in os.listdir(path):
+                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
+                new_array = cv2.resize(img_array, (self.IMG_SIZE, self.IMG_SIZE))
+                self.game_images.append([new_array, class_num])
+
+        for features, label in self.game_images:
+            self.X.append(features)
+            self.y.append(label)
+        self.X = np.array(self.X).reshape(-1, self.IMG_SIZE, self.IMG_SIZE, 1)
+        self.X = self.X / 255.0
+
+    def predict_image(self):
+        while True:
+            random_num = randint(0, len(self.game_images) - 1)
+            if random_num not in self.images_used:
+                self.images_used.append(random_num)
+                break
+        self.show_picture(random_num)
+        predictions = self.model.predict(self.X)
+        print("this is my prediction: " + self.CATEGORIES[np.argmax(predictions[random_num])])
+        if np.argmax(predictions[random_num]) == self.y[random_num]:
+            return True, self.CATEGORIES[np.argmax(predictions[random_num])]
+        else:
+            return False, self.CATEGORIES[np.argmax(predictions[random_num])]
+
+"""
+pm = CNN()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+print(pm.predict_image())
+print()
+
+
+
+"""
\ No newline at end of file
diff --git a/src/AI/NeuralNetwork/train_model.py b/src/AI/NeuralNetwork/train_model.py
new file mode 100644
index 0000000..e2ac45a
--- /dev/null
+++ b/src/AI/NeuralNetwork/train_model.py
@@ -0,0 +1,33 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
+import pickle
+from tensorflow.keras.models import load_model
+import numpy as np
+
+X = pickle.load(open("X.pickle", "rb"))
+y = pickle.load(open("y.pickle", "rb"))
+
+X_test = pickle.load(open("X_test.pickle", "rb"))
+y_test = pickle.load(open("y_test.pickle", "rb"))
+
+X = X / 255.0
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Flatten(input_shape=(64, 64)),
+    tf.keras.layers.Dense(128, activation=tf.nn.relu),
+    tf.keras.layers.Dense(2, activation=tf.nn.softmax)
+    ])
+
+model.compile(optimizer="adam",
+              loss="sparse_categorical_crossentropy",
+              metrics=["accuracy"])
+
+model.fit(X, y, epochs=5)
+test_loss, test_acc = model.evaluate(X_test, y_test)
+
+print("Accuracy: " + str(test_acc))
+print("Loss: " + str(test_loss))
+
+model.save('model.h5')
+
diff --git a/src/game/Game.py b/src/game/Game.py
index f3e84ec..8e6f46e 100644
--- a/src/game/Game.py
+++ b/src/game/Game.py
@@ -5,7 +5,8 @@ from pathlib import Path
 import os
 from random import sample
 from random import shuffle
-
+import pickle
+import cv2
 import pygame
 
 from src.AI.Affinities import Affinities
@@ -16,6 +17,8 @@ from src.AI.DecisionTrees.projectSpecificClasses.SurvivalClassification import S
 from src.AI.GA import geneticAlgorithm
 from src.AI.GaTravelingForHerbs.GeneticAlgorithm import GeneticAlgorithm
 from src.AI.GaTravelingForHerbs.Traveling import Traveling, START_COORD, END_COORD
+from src.AI.NeuralNetwork.predict_image import NN
+from src.entities.Enums import Classifiers
 from src.entities.Player import Player
 from src.game.EventManager import EventManager
 from src.game.Map import Map
@@ -101,6 +104,11 @@ class Game:
                 filesPath) + os.sep + "data" + os.sep + "AI_data" + os.sep + "dt_exmpls" + os.sep + "dt_examples"
             dtExampleManager = ExamplesManager(examplesFilePath)
             dtExampleManager.generateExamples()
+
+        elif argv[1] == "NN":
+            print("Running Neural Network for image classification")
+            self.run_neural_network(filesPath)
+
         # Traveling ga algorithm
         elif argv[1] == "ga_travel":
             self.travelRun(filesPath)
@@ -109,6 +117,112 @@ class Game:
             print("Invalid game mode. \n Possible options: test, ga")
             exit(1)
 
+    def run_neural_network(self, filesPath, pauseAfterDecision=False):
+
+        image_predictor = NN()
+
+        self.running = True
+        print("Initializing screen, params: " + str(self.config["window"]) + "...", end=" ")
+
+        # Vertical rotation is unsupported due to UI layout
+        if self.config["window"]["height"] > self.config["window"]["width"]:
+            print("The screen cannot be in a vertical orientation. Exiting...")
+            exit(1)
+
+        # Read examples to decision tree learning
+        examplesFilePath = str(
+            filesPath) + os.sep + "data" + os.sep + "AI_data" + os.sep + "dt_exmpls" + os.sep + "dt_examples"
+        examplesManager = ExamplesManager(examplesFilePath)
+        examples = examplesManager.readExamples()
+
+        # Create decision tree
+        survivalDecisionTree = SurvivalDT(DT.inductiveDecisionTreeLearning(examples,
+                                                                           AttrDefs.allAttributesDefinitions,
+                                                                           SurvivalClassification.FOOD,
+                                                                           SurvivalClassification))
+
+        print("\nDecision tree: \n")
+        DecisionTree.printTree(survivalDecisionTree.entityPickingDecisionTree, 0)
+        print()
+
+        # Initialize timers
+        # Virtual timer to track in-game time
+        self.ingameTimer = Timer()
+        self.ingameTimer.startClock()
+
+        # Initialize screen
+        self.screen = Screen(self, self.config["window"])
+        print("OK")
+
+        self.initializeMap(filesPath)
+
+        # Initialize the player
+        self.player = Player((15, 15), self.map.tileSize, Affinities(0.3, 0.6, 0.1, 0.5))
+        self.map.addEntity(self.player, DONTADD=True)
+
+        pause = False
+        decisionsMade = 0
+
+        # main loop without user input
+        while True:
+
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    exit(0)
+                elif event.type == pygame.KEYDOWN:
+                    if event.key == pygame.K_SPACE:
+                        pause = not pause
+
+            if pause or not self.running:
+                pass
+
+            else:
+                # Tick the timers
+                self.ingameTimer.updateTime(self.pgTimer.tick())
+                self.screen.ui.updateTime()
+
+                # If player is dead write information to console and break main loop
+                if not self.player.alive:
+                    self.screen.ui.updateOnDeath(self.player)
+                    self.screen.ui.console.printToConsole(
+                        "Score: {}".format(str(decisionsMade + self.player.movePoints)))
+                    self.screen.ui.console.printToConsole("Decisions made {}. Movements made {}.".
+                                                          format(decisionsMade, self.player.movePoints))
+                    self.spritesList.update()
+                    self.spritesList.draw(self.screen.pygameScreen)
+                    pygame.display.flip()
+                    self.running = False
+
+                # Choose target for player using decision tree
+                if self.player.movementTarget is None:
+                    pickedEntity = survivalDecisionTree.pickEntity(self.player, self.map)
+                    if pickedEntity.classifier == Classifiers.FOOD:
+                        result = image_predictor.predict_image()
+                        self.screen.ui.console.printToConsole("I think it is a: " + result[1])
+                        if result[0]:
+                            self.screen.ui.console.printToConsole("I was right")
+                            pass
+                        else:
+                            self.screen.ui.console.printToConsole("I was wrong")
+                            self.player.alive = False
+                            continue
+
+                    self.player.gotoToTarget(survivalDecisionTree.pickEntity(self.player, self.map), self.map)
+                    decisionsMade += 1
+                    if pauseAfterDecision:
+                        pause = True
+
+                self.screen.ui.updateBarsBasedOnPlayerStats(self.player.statistics)
+
+                # Call update() method for each entity
+                self.spritesList.update()
+
+                # Draw all sprites
+                self.spritesList.draw(self.screen.pygameScreen)
+
+                # Flip the display
+                pygame.display.flip()
+
     def initializePygame(self):
         """
         Initializes all pygame members.