def __init__(self):
self.utils = bot_utils.DebugUtils()
self.browns_affected = []
self.board = []
self.reco = Reco()
#Grab and crop the Game area
self.box = (CONFIG.SCREEN_PADDING[X]+1, CONFIG.SCREEN_PADDING[Y]+1,
CONFIG.SCREEN_PADDING[X] + CONFIG.SCREEN_SIZE[W], CONFIG.SCREEN_PADDING[Y] + CONFIG.SCREEN_SIZE[H])
self.strategies = []
self.strategies.append(LaserAbilityStrategy("LaserAbilityStrategy"))
self.strategies.append(NoAbilityStrategy("NoAbilityStrategy"))
self.strategies.append(ColorBlastAbilityStrategy("ColorBlastAbilityStrategy"))
self.im_end = Image.open(os.getcwd() + '\\testEndGame.png')
self.reco.train()
self.utils.get_characteristics()
class BotApplication:
def __init__(self):
self.utils = bot_utils.DebugUtils()
self.browns_affected = []
self.board = []
self.reco = Reco()
#Grab and crop the Game area
self.box = (CONFIG.SCREEN_PADDING[X]+1, CONFIG.SCREEN_PADDING[Y]+1,
CONFIG.SCREEN_PADDING[X] + CONFIG.SCREEN_SIZE[W], CONFIG.SCREEN_PADDING[Y] + CONFIG.SCREEN_SIZE[H])
self.strategies = []
self.strategies.append(LaserAbilityStrategy("LaserAbilityStrategy"))
self.strategies.append(NoAbilityStrategy("NoAbilityStrategy"))
self.strategies.append(ColorBlastAbilityStrategy("ColorBlastAbilityStrategy"))
self.im_end = Image.open(os.getcwd() + '\\testEndGame.png')
self.reco.train()
self.utils.get_characteristics()
def can_start_move(self, image1, image2):
count = 0
pixels1 = image1.load()
pixels2 = image2.load()
min_x, min_y, max_x, max_y = -1, -1, -1, -1
if len(self.browns_affected) > 0:
## Get the browns bounding box
min_x = CONFIG.SCREEN_GAME_BOARD_OFFSETS[X] + min(self.browns_affected, key=lambda t: t[0])[X] * CONFIG.PICTURE_SIZE[X]
max_y = CONFIG.SCREEN_SIZE[H]
max_x = CONFIG.SCREEN_GAME_BOARD_OFFSETS[X] + (max(self.browns_affected, key=lambda t: t[0])[X] +1) * CONFIG.PICTURE_SIZE[X]
min_y = CONFIG.SCREEN_GAME_BOARD_OFFSETS[Y] + min(self.browns_affected, key=lambda t: t[1])[Y] * CONFIG.PICTURE_SIZE[Y]
for i in xrange(image1.size[0]):
for j in xrange(image1.size[1]):
# Do not take into account pixel differences in the brown pieces animation
if min_x < i < max_x and min_y < j < max_y:
continue
other_pixel = pixels2[i, j]
if pixels1[i, j] != other_pixel:
count += 1
return count < CONFIG.ANIMATION_PIXEL_THRESHOLD
def do_recognition(self):
self.board = []
self.browns_affected = []
win32api.SetCursorPos((CONFIG.SCREEN_PADDING[W] + CONFIG.MOUSE_OFFSETS_IDLE[X],
CONFIG.SCREEN_PADDING[H] - CONFIG.MOUSE_OFFSETS_IDLE[Y]))
screen_shot = ImageGrab.grab(self.box)
screen_shot.save("real.png",'PNG')
for x in xrange(0, CONFIG.GAME_BOARD_SIZE[W]):
new_row = []
for y in xrange(0, CONFIG.GAME_BOARD_SIZE[H]):
#1. Cut the tile
tile = self.utils.get_tile_from_image(screen_shot, x, y)
#2. Classify the tile
class_id = self.reco.predict(tile)
if class_id != -1:
####print "({0},{1}) --> {2}".format(x, y, CONFIG.TEMPLATE_NAMES[class_id])
new_row.append(class_id)
if class_id == CONFIG.IDX_BROWN:
self.browns_affected.append((x, y))
####else:
####print "({0},{1}) --> NIMIC".format(x, y)
self.board.append(new_row)
self.utils.print_board_image(self.board, "reco.png")
def do_step(self):
solutions = []
for strategy in self.strategies:
solution = strategy.solve(self.board)
solutions.append(solution)
print "Solutia data de strategia {0} are scorul {1} este Start ({2},{3}) End {4} ".\
format(strategy.name, solution[0], solution[1], solution[2], solution[3])
solutions = sorted(solutions, reverse=True)
best_move = solutions[0]
print "Solutia finala cu scorul {0} si mutarea Start ({1},{2}) End {3}".\
format(best_move[0], best_move[1], best_move[2], best_move[3])
MouseUtils.doMove(best_move, (len(self.board[best_move[3]])))
def run(self):
prev_img = ImageGrab.grab(self.box)
while True:
MouseUtils.random_idle_mouse_movement()
time.sleep(0.5)
MouseUtils.random_idle_mouse_movement()
img = ImageGrab.grab()
img = img.crop(self.box)
self.do_recognition()
if self.can_start_move(img, prev_img):
if self.can_start_move(img, self.im_end):
break
self.do_step()
prev_img = img