model for image classification from file

ForkliftAgent.py

@@ -103,7 +103,7 @@ class ForkliftAgent(AgentBase):
                 stations = dict(self.graph.packingStations)
                 if i.real_type == ItemType.SHELF:
                     packing_station = stations[PatchType.packingA]
-                elif i.real_type == ItemType.REFRIGERATOR:
+                elif i.real_type == ItemType.FRIDGE:
                     packing_station = stations[PatchType.packingB]
                 elif i.real_type == ItemType.DOOR:
                     packing_station = stations[PatchType.packingC]

GameModel.py

@@ -21,8 +21,6 @@ from decision.ActionType import ActionType
 from pathfinding.PathfinderOnStates import PathFinderOnStates, PathFinderState
 from util.PathByEnum import PathByEnum
 from util.PathDefinitions import GridLocation, GridWithWeights
-from imageClasification.Classificator import image_clasification
-
 
 
 class Phase(Enum):
@@ -190,13 +188,13 @@ class GameModel(Model):
 
     def recognise_item(self, item: Item):
         # TODO IMAGE PROCESSING
-        val = image_clasification(self.picture_visualization.img, self.classificator)
+        val = self.classificator.image_clasification(self.picture_visualization.img)
         print("VAL: {}".format(val))
 
         if val == ItemType.DOOR:
             item.guessed_type = ItemType.DOOR
-        elif val == ItemType.REFRIGERATOR:
-            item.guessed_type = ItemType.REFRIGERATOR
+        elif val == ItemType.FRIDGE:
+            item.guessed_type = ItemType.FRIDGE
         elif val == ItemType.SHELF:
             item.guessed_type = ItemType.SHELF
 

data/enum/ItemType.py

@@ -4,4 +4,4 @@ from enum import Enum
 class ItemType(Enum):
     DOOR = "door"
     SHELF = "shelf"
-    REFRIGERATOR = "refrigerator"
+    FRIDGE = "fridge"

imageClasification/Classificator.py

@@ -1,23 +0,0 @@
-import numpy as np
-import tensorflow as tf
-
-
-def image_clasification(image_path, model):
-    # loaded_model = keras.models.load_model("my_model")
-    class_names = ['door', 'refrigerator', 'shelf']
-
-    img = tf.keras.utils.load_img(
-        image_path, target_size=(180, 180)
-    )
-    img_array = tf.keras.utils.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)  # Create a batch
-
-    predictions = model.predict(img_array)
-    score = tf.nn.softmax(predictions[0])
-    # print(
-    #     "This image most likely belongs to {} with a {:.2f} percent confidence."
-    #         .format(class_names[np.argmax(score)], 100 * np.max(score))
-    # )
-    return class_names[np.argmax(score)]
-
-

imageClasification/usedAssets/TrainClassificator.py

@@ -9,7 +9,7 @@ from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
 
 
-class ClassificatorInside():
+class Classificator():
 
     def __init__(self, data_dir: str) -> None:
         super().__init__()

main.py

@@ -2,7 +2,6 @@ import random
 
 from mesa.visualization.ModularVisualization import ModularServer
 from mesa.visualization.modules import CanvasGrid
-from tensorflow import keras
 
 from ForkliftAgent import ForkliftAgent
 from GameModel import GameModel
@@ -10,6 +9,7 @@ from PatchAgent import PatchAgent
 from PatchType import PatchType
 from PictureVisualizationAgent import PictureVisualizationAgent
 from data.enum.Direction import Direction
+from imageClasification.Classificator import Classificator
 from util.PathDefinitions import GridWithWeights
 from visualization.DisplayAttributeElement import DisplayAttributeElement
 from visualization.DisplayItemListAttribute import DisplayItemListAttributeElement
@@ -88,13 +88,13 @@ if __name__ == '__main__':
     ordersText = DisplayOrderList("orderList")
     fulfilled_orders = DisplayOrderList("fulfilled_orders")
 
-    loaded_model = keras.models.load_model("./imageClasification/my_model")
+    classificator = Classificator("imageClasification/images")
 
     server = ModularServer(GameModel,
                            [grid, readyText, provided_itesm, recognised_items, ordersText,
                             fulfilled_orders],
                            "Automatyczny Wózek Widłowy",
-                           dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=loaded_model))
+                           dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=classificator))
 
     server.port = 8888
     server.launch()

util/PathByEnum.py

@@ -9,7 +9,7 @@ class PathByEnum:
         if item == ItemType.DOOR:
             a = str(random.randint(1, 10))
             return "item_images/door/drzwi" + a + ".jpg"
-        if item == ItemType.REFRIGERATOR:
+        if item == ItemType.FRIDGE:
             a = str(random.randint(1, 10))
             return "item_images/refrigerator/lodowka" + a + ".jpg"
         if item == ItemType.SHELF: