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Added recognition of content in boxes using neural network

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s407409 2021-06-15 23:14:10 +02:00
parent 63c9ebaf0d
commit fe3f9bf4fe
5 changed files with 145 additions and 21 deletions
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nn_model.h5 Normal file
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2
server.py
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@ -2,7 +2,7 @@ from src.agent.map.gameMap import GameMap
from mesa.visualization.modules import CanvasGrid
from mesa.visualization.modules.TextVisualization import TextElement
from mesa.visualization.ModularVisualization import ModularServer
from src.decisiontree import create_model
#from src.decisiontree import create_model
from src.direction import Direction
from collections import defaultdict

118
src/agent/hero.py
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@ -1,17 +1,29 @@
import random
import pandas as pd
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconverter
from rpy2.robjects.packages import importr
#import rpy2.robjects as robjects
#from rpy2.robjects.packages import importr
#from rpy2.robjects import pandas2ri
#from rpy2.robjects.conversion import localconverter
#from rpy2.robjects.packages import importr
from src.agent.model import *
from src.agent.state import AgentState
from src.direction import Direction
from src.items.armory import WM9
from src.treesearch.actionsInterpreter import ActionInterpreter
from src.agent.model.dice.dice import roll_the_dice
from src.treesearch.bfs import BFS
from src.nominalize import nominalize
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
class Player(Creature):
def __init__(self, unique_id, model, n, s, a, w, max_hp, hp, weapon, arm, g, w2, w3, list_of_chests):
@ -40,6 +52,75 @@ class Player(Creature):
self.opponent=None
self.strategy="PASS"
def identify_content(self, chest):
dataset_url = "https://drive.google.com/uc?export=download&id=1b6w1FbupRmgVC-q9Lpdlg5OBK_gRKEUy"
data_dir = tf.keras.utils.get_file(fname='loot', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
#image_count = len(list(data_dir.glob('*/*.jpg')))
#print(image_count)
batch_size = 32
img_height = 180
img_width = 180
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
#print(class_names)
normalization_layer = layers.experimental.preprocessing.Rescaling(1. / 255)
normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are now in `[0,1]`.
#print(np.min(first_image), np.max(first_image))
#num_classes = 3
# Recreate the exact same model, including its weights and the optimizer
new_model = tf.keras.models.load_model('nn_model.h5')
# Show the model architecture
#new_model.summary()
# loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)
# print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))
object_url = chest.type
object_path = tf.keras.utils.get_file(chest.file_name, origin=object_url)
img = keras.preprocessing.image.load_img(
object_path, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = new_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)]
def predict_strategy(self, opponent):
testcase = pd.DataFrame({'p_strength': nominalize(self.get_strength(),10),
'p_agility':nominalize(self.get_agility(), 10),
@ -169,12 +250,14 @@ class Player(Creature):
print("Player died :/")
def open_chest(self, chest):
self.gold = self.gold + chest.gold
print("------Chest opened. Gold inside:", chest.gold, "-----")
chest.gold = 0
self.opened_chests += 1
self.has_goal_chest = False
chest.model.grid.remove_agent(chest)
if chest.type == 1:
ch_gold = 3 * roll_the_dice(6)
self.gold = self.gold + ch_gold
print("------Chest opened. Gold inside:", ch_gold, "-----")
elif chest.type == 2:
self.weapon1 = WM9
else:
self.health = 0
# self.direction = 0 # po osiągnięciu jednego celu 'restartuje sie' na szukanie ścieżki do kolejnego -- NIE ZEROWAĆ OBROTU - to psuje goldState w bfs!!!
# if isinstance(chest.loot,Armor):
# buffer = self.armor
@ -247,7 +330,18 @@ class Player(Creature):
cellmates = self.model.grid.get_cell_list_contents([self.pos])
if len(cellmates) > 1:
if isinstance(cellmates[0], Box):
self.open_chest(cellmates[0])
decision = self.identify_content(cellmates[0])
print("Content of chest: "+cellmates[0].address)
if (decision == 'coins') or (decision == 'weapons'):
print("I will open this chest!")
self.open_chest(cellmates[0])
print("Type of opened chest: ", cellmates[0].type)
else:
print("Probably a trap - chest skipped!")
print("Type of skipped chest: ", cellmates[0].type)
self.opened_chests += 1
self.has_goal_chest = False
cellmates[0].model.grid.remove_agent(cellmates[0])
else:
self.opponent = cellmates[0]
self.strategy=self.predict_strategy(self.opponent)

11
src/agent/map/gameMap.py
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@ -12,12 +12,14 @@ from src.items.armory import *#WM1, A1, WR1, S1, WM2, A2
from mesa.time import RandomActivation
from mesa.space import MultiGrid
import random
# from mesa.datacollection import DataCollector
x = 10
y = 10
step_counter = 0
boxes_number = 5
boxes_number = 6
creatures_number = 35
class GameMap(Model):
@ -34,7 +36,8 @@ class GameMap(Model):
y = self.random.randrange(self.grid.height)
self.grid.place_agent(self.player, (x, y))
for i in range(self.boxes_number):
box = Box(i, self)
r_type = random.randrange(1, 4)
box = Box(i, self, r_type)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
if self.grid.is_cell_empty((x, y)):
@ -70,7 +73,7 @@ class GameMap(Model):
self.schedule.add(creature)
else:
pass
self.datacollector=DataCollector(model_reporters={"HP":self.player.health, "Gold":self.player.gold, "Position (x, y)":self.player.pos}) #informacje o stanie planszy, pozycja agenta
#self.datacollector=DataCollector(model_reporters={"HP":self.player.health, "Gold":self.player.gold, "Position (x, y)":self.player.pos}) #informacje o stanie planszy, pozycja agenta
#other data: position, strength & other parameters
def get_list_of_chests(self):
return self.listOfChests
@ -84,5 +87,5 @@ class GameMap(Model):
def step(self):
self.schedule.step()
self.datacollector.collect(self)
#self.datacollector.collect(self)
#print(str(self.datacollector.model_reporters))

35
src/agent/model/box.py
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@ -1,14 +1,41 @@
from mesa import Agent
from .dice.dice import roll_the_dice
import random
#from .dice.dice import roll_the_dice
golds = [("https://drive.google.com/uc?export=download&id=1fWeew0jXZ1lZBmv6CG5viLGloJAex6ao",'moneta'),
("https://drive.google.com/uc?export=download&id=1UrXbbfJhfCuDZSnxund7sVk40QMS3R2Q", 'moneta2'),
("https://drive.google.com/uc?export=download&id=1qH0OP4X1NQqpHtUkwD8SryJtKCKcZzVe", 'moneta3'),
("https://drive.google.com/uc?export=download&id=1b9tZf639mEWgiWq_EYyqjeKYPEMi7dX9", 'moneta4'),
("https://drive.google.com/uc?export=download&id=1z9jt-j3aS1fRUgVA_t1zR7TS5QzuXW5b", 'moneta5')]
weapons = [("https://drive.google.com/uc?export=download&id=1TA-ObC33FaiHmgQ6i71Kcb32VrHsKUd1", 'miecz'),
("https://drive.google.com/uc?export=download&id=1oyv15FSPJ84xx1tQLJW8Wlbb1JUx2xCy", 'miecz2'),
("https://drive.google.com/uc?export=download&id=1Ha0eeLRLcidrMAN1P59V9zB8uSQq3GM5", 'miecz3'),
("https://drive.google.com/uc?export=download&id=1GetUWnglUtqWqcK4sd5HsdVuaUpU5Ec_", 'miecz4'),
("https://drive.google.com/uc?export=download&id=1RImo84OykYICvwfLycDEb5tr4tPbGVy1", 'miecz5')]
traps = [("https://drive.google.com/uc?export=download&id=1G-AxY712V-eT2ylW0VXn4o2V_4pvy7lz", 'kwiat'),
("https://drive.google.com/uc?export=download&id=1i7MwzJRPBZ-KrCDqhT5RCXLstLlWCsx9", 'kwiat2'),
("https://drive.google.com/uc?export=download&id=1zF7wQuG1gtQ796m6TM08FOUOVK8s_qhT", 'kwiat3'),
("https://drive.google.com/uc?export=download&id=1qwrIThsoKg44b57JXvbXe--TIplacd-i", 'kwiat4'),
("https://drive.google.com/uc?export=download&id=1YsqdQaLyD8Es4w09p4Zdw2CufNkl_avN", 'kwiat5')]
chest_names=['c'+str(i)+'.jpg' for i in range(1,10)]
class Box(Agent):
def __init__(self, unique_id, model):
def __init__(self, unique_id, model, type):
super().__init__(unique_id, model)
self.gold = 3 * roll_the_dice(6)
#self.gold = 3 * roll_the_dice(6)
self.isBox = True
self.isCreature = False
self.isPlayer = False
self.type = type
r_img = random.randrange(0,4)
if type == 1:
self.address = golds[r_img][0]
self.file_name = golds[r_img][1]
elif type == 2:
self.address = weapons[r_img][0]
self.file_name = weapons[r_img][1]
else:
self.address = traps[r_img][0]
self.file_name = traps[r_img][1]
def step(self):
pass