najbliższe pole

This commit is contained in:
Tomasz Adamczyk 2021-06-01 13:11:07 +02:00
parent 3849244fbc
commit 6d69fcaa2c
69 changed files with 25 additions and 28 deletions

View File

@ -20,7 +20,6 @@
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@ -37,7 +36,6 @@
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@ -233,6 +231,8 @@
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@ -319,6 +319,6 @@
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</project>

View File

@ -54,7 +54,7 @@ FARMLAND_DRY_COST = 3
FARMLAND_WET = pygame.image.load(os.path.join('resources/images', 'farmland_wet.png'))
FARMLAND_WET = pygame.transform.scale(FARMLAND_WET, (BLOCK_SIZE, BLOCK_SIZE))
FARMLAND_WET_COST = 1
FPS = 10
FPS = 1
POTATOES_ADULT_COST = 4
POTATOES_GROW_COST = 10
POTATOES_GROW_TIME = 5

View File

@ -4,6 +4,7 @@ from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
import glob
import graph
import os
import pathlib
import torch
@ -95,15 +96,12 @@ def create_neural_network(): #tworzenie sieci neuronowej
model.load_state_dict(checkpoint)
model.eval()
return classes, model
def predfield(cart_direction, cart_x, cart_y, classes, model): #zwraca najbliższe miejsce pola z wyrośniętą rośliną na podstawie wykrywania obrazu
def predfield(classes, istate, model): #zwraca najbliższe miejsce pola z wyrośniętą rośliną na podstawie wykrywania obrazu
pred_path = os.path.join('resources/neural_network/sliced/') #ścieżka do obrazków do sprawdzenia
pred_dict = {}
images_path = glob.glob(pred_path + '/*.png')
cart_x = int(cart_x) #x'owa wózka
cart_y = int(cart_y) #y'owa wózka
additional_rotate_moves = 0
x = None #x'owa pola
y = None#y'kowa pola
y = None #y'kowa pola
min = None
for i in images_path: #dodajemy pocięte obrazki do listy i ustawiamy im przewidywaną metkę
pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
@ -122,7 +120,8 @@ def predfield(cart_direction, cart_x, cart_y, classes, model): #zwraca najbliżs
y = 9
else:
y = y - 1
min = abs(cart_x - x) + abs(cart_y - y) + additional_rotate_moves
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
print(min)
else:
temp_x = img_name[18]
temp_y = img_name[15]
@ -136,15 +135,13 @@ def predfield(cart_direction, cart_x, cart_y, classes, model): #zwraca najbliżs
temp_y = 9
else:
temp_y = temp_y - 1
if abs(cart_x - temp_x) + abs(cart_y - temp_y) + additional_rotate_moves < min:
if len((graph.graphsearch([], [], (temp_x, temp_y), istate, graph.succ))) < min:
min = len((graph.graphsearch([], [], (temp_x, temp_y), istate, graph.succ)))
x = temp_x
y = temp_y
min = abs(cart_x - x) + abs(cart_y - y) + additional_rotate_moves
if x == None and y == None: #jeżeli nie ma wyrośniętej rośliny to zwróć False
if x == None and y == None: #jeżeli nie ma wyrośniętej rośliny to zwróć fałsz
return False
else:
print(x, y)
print(min)
return x, y
def prediction1(classes, img_path, model, transformer): #zwraca predykcję dla danego obrazka
image = Image.open(img_path).convert('RGB')

6
py.py
View File

@ -38,11 +38,11 @@ def main():
pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/sliced/', 'screen.jpg')) #zrzut obecnego ekranu
image_slicer.slice(os.path.join('resources/neural_network/sliced/', 'screen.jpg'), 100) #pocięcie ekranu na sto części
os.remove('resources/neural_network/sliced/screen.jpg')
if neuralnetwork.predfield(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE, classes, model) is not False: #jeżeli istnieje jakaś dojrzała roślina
random_movement = False
istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne miejsce)
if neuralnetwork.predfield(classes, istate, model) is not False: #jeżeli istnieje jakaś dojrzała roślina
random_movement = False
if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
move_list = (astar.graphsearch([], astar.f, [], neuralnetwork.predfield(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE, classes, model), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar
move_list = (astar.graphsearch([], astar.f, [], neuralnetwork.predfield(classes, istate, model), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar
else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch
else:

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