movement.py

@@ -2,7 +2,7 @@ import joblib
 from sklearn.calibration import LabelEncoder
 from agentActionType import AgentActionType
 import time
-from garbage import GarbageType, RecognizedGarbage
+from garbage import Garbage, GarbageType, RecognizedGarbage
 from garbageCan import GarbageCan
 from turnCar import turn_left_orientation, turn_right_orientation
 from garbageTruck import GarbageTruck
@@ -14,6 +14,12 @@ import pygame
 from bfs import find_path_to_nearest_can
 from agentState import AgentState
 
+import tensorflow as tf
+from keras.models import load_model
+import keras.utils as image
+from keras.optimizers import Adam
+import numpy as np
+
 
 def collect_garbage(game_context: GameContext) -> None:
     while True:
@@ -30,11 +36,12 @@ def collect_garbage(game_context: GameContext) -> None:
     pass
 
 def _recognize_garbage(dust_car: GarbageTruck, can: GarbageCan) -> None:
-    loaded_model = joblib.load('machine_learning/model.pkl')
+    tree_model = joblib.load('machine_learning/model.pkl')
+    optimizer = Adam(learning_rate=0.001)
+    neural_model = load_model('machine_learning/neuralModel.h5', compile=False)
+    neural_model.compile(optimizer=optimizer)
     for garbage in can.garbage:
-        attributes = [garbage.shape, garbage.flexibility, garbage.does_smell, garbage.weight, garbage.size, garbage.color, garbage.softness, garbage.does_din]
-        encoded = attributes_to_floats(attributes)
-        predicted_class = loaded_model.predict([encoded])[0]
+        predicted_class = predict_class(garbage, tree_model, neural_model)
         garbage_type: GarbageType = None
         if predicted_class == 'PAPER':
             garbage_type = GarbageType.PAPER
@@ -50,6 +57,35 @@ def _recognize_garbage(dust_car: GarbageTruck, can: GarbageCan) -> None:
         recognized_garbage = RecognizedGarbage(garbage, garbage_type)
         dust_car.sort_garbage(recognized_garbage)
 
+def predict_class(garbage: Garbage, tree_model, neural_model) -> str:
+    if garbage.img is None:
+        return predict_class_from_tree(garbage, tree_model)
+    return predict_class_from_neural_model(garbage, neural_model)
+
+def predict_class_from_tree(garbage: Garbage, tree_model) -> str:
+    attributes = [garbage.shape, garbage.flexibility, garbage.does_smell, garbage.weight, garbage.size, garbage.color, garbage.softness, garbage.does_din]
+    encoded = attributes_to_floats(attributes)
+    return tree_model.predict([encoded])[0]
+
+def predict_class_from_neural_model(garbage: Garbage, neural_model) -> str:
+    img = image.load_img(garbage.img, target_size=(150, 150))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.
+
+    predictions = neural_model.predict(img_array)
+    prediction = np.argmax(predictions[0])
+    if prediction == 0:
+        return "BIO"
+    if prediction == 1:
+        return "GLASS"
+    if prediction == 2:
+        return "MIXED"
+    if prediction == 3:
+        return "PAPER"
+    if prediction == 4:
+        return "PLASTIC_AND_METAL"
+
 def attributes_to_floats(attributes: list[str]) -> list[float]:
     output: list[float] = []
     if attributes[0] == 'Longitiudonal':

startup.py

@@ -36,12 +36,19 @@ def create_city() -> City:
     streets = create_streets()
     trashcans = create_trashcans()
     bumps = create_speed_bumps()
-    garbage_pieces = create_garbage_pieces()
+    garbage_pieces = create_garbage_pieces_witout_imgs()
     garbage_pieces_counter = 0
     for s in streets:
         city.add_street(s)
     for t in trashcans:
-        for i in range(4):
+        for _ in range(4):
+            t.add_garbage(garbage_pieces[garbage_pieces_counter])
+            garbage_pieces_counter = garbage_pieces_counter + 1
+        city.add_can(t)
+    garbage_pieces = create_garbage_pieces_with_images()
+    garbage_pieces_counter = 0
+    for t in trashcans:
+        for _ in range(3):
             t.add_garbage(garbage_pieces[garbage_pieces_counter])
             garbage_pieces_counter = garbage_pieces_counter + 1
         city.add_can(t)
@@ -50,16 +57,21 @@ def create_city() -> City:
     return city
 
 
-def create_garbage_pieces() -> List[Garbage]:
+def create_garbage_pieces_witout_imgs() -> List[Garbage]:
     garbage_pieces = []
     with open('machine_learning/garbage_infill.csv', 'r') as file:
         lines = file.readlines()
         for line in lines[1:]:
             param = line.strip().split(',')
             garbage_pieces.append(
-                Garbage('img', param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7].strip()))
+                Garbage(None, param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7].strip()))
     return garbage_pieces
 
+def create_garbage_pieces_with_images() -> list[Garbage]:
+    garbage_pieces = []
+    for i in range(1, 22):
+        garbage_pieces.append(Garbage('machine_learning/photos_not_from_train_set/' + str(i) + '.jpg', None, None, None, None, None, None, None, None))
+    return garbage_pieces
 
 def create_streets() -> List[Street]:
     streets = []