mniej screenshotów

This commit is contained in:
Tomasz Adamczyk 2021-06-01 12:39:57 +02:00
parent 1e3035f838
commit 3849244fbc
72 changed files with 68 additions and 52 deletions

View File

@ -2,27 +2,18 @@
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<SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1622539089888" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> <SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1622543817734" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
</component> </component>
</project> </project>

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

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@ -95,32 +95,57 @@ def create_neural_network(): #tworzenie sieci neuronowej
model.load_state_dict(checkpoint) model.load_state_dict(checkpoint)
model.eval() model.eval()
return classes, model return classes, model
def predfield(classes, model): #zwraca miejsce pola z wyrośniętą rośliną na podstawie wykrywania obrazu def predfield(cart_direction, cart_x, cart_y, classes, 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_path = os.path.join('resources/neural_network/sliced/') #ścieżka do obrazków do sprawdzenia
pred_dict = {} pred_dict = {}
images_path = glob.glob(pred_path + '/*.png') 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 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 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) pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
for img_name, field in pred_dict.items(): for img_name, field in pred_dict.items():
if field != "random": #jeżeli metka nie jest 'random' to przypisz do x'a i y'a miejsce wyrośniętej rośliny if field != "random": #jeżeli metka nie jest 'random' to przypisz do x'a i y'a miejsce wyrośniętej rośliny
x = img_name[15] if x is None and y is None:
y = img_name[18] x = img_name[18]
x = int(x) y = img_name[15]
y = int(y) x = int(x)
if x == 0: y = int(y)
x = 9 if x == 0:
x = 9
else:
x = x - 1
if y == 0:
y = 9
else:
y = y - 1
min = abs(cart_x - x) + abs(cart_y - y) + additional_rotate_moves
else: else:
x = x - 1 temp_x = img_name[18]
if y == 0: temp_y = img_name[15]
y = 9 temp_x = int(temp_x)
else: temp_y = int(temp_y)
y = y - 1 if temp_x == 0:
temp_x = 9
else:
temp_x = temp_x - 1
if temp_y == 0:
temp_y = 9
else:
temp_y = temp_y - 1
if abs(cart_x - temp_x) + abs(cart_y - temp_y) + additional_rotate_moves < min:
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 False
return False return False
else: else:
return y, x print(x, y)
print(min)
return x, y
def prediction1(classes, img_path, model, transformer): #zwraca predykcję dla danego obrazka def prediction1(classes, img_path, model, transformer): #zwraca predykcję dla danego obrazka
image = Image.open(img_path).convert('RGB') image = Image.open(img_path).convert('RGB')
image_tensor = transformer(image).float() image_tensor = transformer(image).float()

25
py.py
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@ -26,6 +26,7 @@ def main():
tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
decision = [0] #początkowa decyzja o braku powrotu do stacji (0) decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
classes, model = neuralnetwork.create_neural_network() #uczenie sieci neuronowej classes, model = neuralnetwork.create_neural_network() #uczenie sieci neuronowej
random_movement = False
run = True run = True
while run: #pętla główna programu while run: #pętla główna programu
clock.tick(definitions.FPS) clock.tick(definitions.FPS)
@ -33,18 +34,22 @@ def main():
if event.type == pygame.QUIT: if event.type == pygame.QUIT:
run = False run = False
map1.draw_window(cart1, cart1_rect) map1.draw_window(cart1, cart1_rect)
pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/sliced/', 'screen.jpg')) #zrzut obecnego ekranu if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
image_slicer.slice(os.path.join('resources/neural_network/sliced/', 'screen.jpg'), 100) #pocięcie ekranu na sto części pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/sliced/', 'screen.jpg')) #zrzut obecnego ekranu
os.remove('resources/neural_network/sliced/screen.jpg') image_slicer.slice(os.path.join('resources/neural_network/sliced/', 'screen.jpg'), 100) #pocięcie ekranu na sto części
if not move_list and neuralnetwork.predfield(classes, model) is not False: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina os.remove('resources/neural_network/sliced/screen.jpg')
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(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
if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole random_movement = False
move_list = (astar.graphsearch([], astar.f, [], neuralnetwork.predfield(classes, model), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar 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)
else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch 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
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:
random_movement = True
elif move_list: #jeżeli move_list nie jest pusta elif move_list: #jeżeli move_list nie jest pusta
cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
else: if random_movement is not False:
cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu
decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK) decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)

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