neural network config

.gitignore

@@ -4,4 +4,5 @@
 __pycache__
 
 #PyCharm
-.idea/
+.idea/
+AI_brain/model.h5

AI_brain/image_recognition.py

@@ -5,18 +5,19 @@ from tensorflow import keras
 import cv2
 import random
 
-#You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
+# You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
-#Change the path to model + to datasets (string 12 + strings 35,41,47,53)
+# Change the path to model + to datasets (string 12 + strings 35,41,47,53)
+
 
 class VacuumRecognizer:
-    model = keras.models.load_model('AI_brain\model.h5') #Neuron model path
+    model = keras.models.load_model("AI_brain\model.h5")  # Neuron model path
 
     def recognize(self, image_path) -> str:
-        class_names = ['Banana', 'Cat', 'Earings', 'Plant']
+        class_names = ["Banana", "Cat", "Earings", "Plant"]
 
         img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
         cv2.waitKey(0)
-        img = (np.expand_dims(img, 0))
+        img = np.expand_dims(img, 0)
 
         predictions = self.model.predict(img)[0].tolist()
 
@@ -31,31 +32,43 @@ class VacuumRecognizer:
         return class_names[predictions.index(max(predictions))]
 
     def get_random_dir(self, type) -> str:
-        if type == 'Plant':
+        if type == "Plant":
-            plant_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant' #Plant dataset path
+            plant_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant"  # Plant dataset path
             plant_dirs = os.listdir(plant_image_paths)
-            full_path = plant_image_paths + '\\' + plant_dirs[random.randint(0, len(plant_dirs)-1)]
+            full_path = (
+                plant_image_paths
+                + "\\"
+                + plant_dirs[random.randint(0, len(plant_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Earings':
+        elif type == "Earings":
-            earnings_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings' #Earings dataset path
+            earnings_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings"  # Earings dataset path
             earning_dirs = os.listdir(earnings_image_paths)
-            full_path = earnings_image_paths + '\\' + earning_dirs[random.randint(0, len(earning_dirs)-1)]
+            full_path = (
+                earnings_image_paths
+                + "\\"
+                + earning_dirs[random.randint(0, len(earning_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Banana':
+        elif type == "Banana":
-            banana_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana' #Banana dataset path
+            banana_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana"  # Banana dataset path
             banana_dirs = os.listdir(banana_image_paths)
-            full_path = banana_image_paths + '\\' + banana_dirs[random.randint(0, len(banana_dirs)-1)]
+            full_path = (
+                banana_image_paths
+                + "\\"
+                + banana_dirs[random.randint(0, len(banana_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Cat':
+        elif type == "Cat":
-            cat_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat' #Cat dataset path
+            cat_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat"  # Cat dataset path
             cat_dir = os.listdir(cat_image_paths)
 
 
-#For testing the neuron model
+# For testing the neuron model
-'''image_paths = []
+"""image_paths = []
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana')
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat')
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings')
@@ -65,4 +78,4 @@ uio = VacuumRecognizer()
 for image_path in image_paths:
     dirs = os.listdir(image_path)
     for i in range(3):
-        print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))'''
+        print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))"""

config.ini

@@ -9,4 +9,4 @@ NumberOfEarrings = 3
 NumberOfPlants = 5
 
 [NEURAL_NETWORK]
-is_nural_network_off = True
+is_neural_network_off = True

main.py

@@ -3,6 +3,9 @@ from random import randint
 import pygame
 import configparser
 
+config = configparser.ConfigParser()
+config.read("config.ini")
+
 from domain.commands.random_cat_move_command import RandomCatMoveCommand
 from domain.commands.vacuum_move_command import VacuumMoveCommand
 from domain.entities.cat import Cat
@@ -13,17 +16,15 @@ from domain.entities.earring import Earring
 from domain.entities.docking_station import Doc_Station
 from domain.world import World
 from view.renderer import Renderer
-from AI_brain.image_recognition import VacuumRecognizer
+
+if not config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
+    from AI_brain.image_recognition import VacuumRecognizer
 
 # from AI_brain.movement import GoAnyDirectionBFS, State
 # from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
 from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
 
 
-config = configparser.ConfigParser()
-config.read("config.ini")
-
-
 class Main:
     def __init__(self):
         tiles_x = 10
@@ -145,7 +146,7 @@ class Main:
 
 
 def generate_world(tiles_x: int, tiles_y: int) -> World:
-    if config.getboolean("NEURAL_NETWORK", "is_nural_network_off"):
+    if config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
         world = World(tiles_x, tiles_y)
         for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
             temp_x = randint(0, tiles_x - 1)
@@ -165,15 +166,16 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
         world.add_entity(Earring(5, 5))
         world.add_entity(Earring(4, 6))
     else:
-        def world_adder(x,y,object,style=None):
+
+        def world_adder(x, y, object, style=None):
             print(object)
-            if object == 'Plant':
+            if object == "Plant":
                 world.add_entity(Entity(x, y, f"PLANT{randint(1, 3)}"))
-            if object == 'Earings':
+            if object == "Earings":
                 world.add_entity(Earring(x, y))
-            if object == 'Banana':
+            if object == "Banana":
                 world.add_entity(Garbage(temp_x, temp_y))
-            if object == 'Cat' and config.getboolean("APP", "cat"):
+            if object == "Cat" and config.getboolean("APP", "cat"):
                 world.add_entity(Cat(x, y))
 
         neural_network = VacuumRecognizer()
@@ -188,22 +190,21 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
         for _ in range(config.getint("CONSTANT", "NumberOfPlants")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Plant')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Plant")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
         for _ in range(config.getint("CONSTANT", "NumberOfEarrings")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Earings')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Earings")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
         for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Banana')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Banana")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
-
     for x in range(world.width):
         for y in range(world.height):
             if world.is_garbage_at(x, y):