def split_characters(image, threshold=255): if type(image) == np.ndarray: arr = image image = np_to_pil(image) else: arr = pil_to_np(image, mode="L") start = 0 boxes = [] space = True unconnected = 0 trns = arr.transpose() # whites = trns[np.min(trns) >= threshold] whites = np.min(trns) >= threshold print(whites) # whites = trns # whites[whites >= threshold] = 1 # whites[whites <= threshold] = 0 # print(np.sum(whites)) # print(arr[arr > 200]) # # for i, col in enumerate(arr.transpose()): # min_val = np.min(col) # for i in range(arr.shape[1]): # min_val = min(arr[:,i]) # if min_val >= threshold and not space: # pass # box = [start, 0, i, arr.shape[0]] # boxes.append(box) # space = True # elif min_val < threshold and space: # start = i # space = False # elif i > 0 and (i - start) > 1 and start != 0 and min_val < threshold: # x, y = arr[:,i], arr[:,i-1] # for j in range(1, arr.shape[0]-1): # if x[j] < threshold and any(np.array([y[j-1], y[j], y[j+1]]) < threshold): # break # else: # box = [start, 0, i, arr.shape[0]] # boxes.append(box) # start = i if not space: box = [start, 0, arr.shape[1], arr.shape[0]] boxes.append(box) return [image.crop(bbox) for bbox in boxes]
def split_characters(image, threshold=255):
if type(image) == np.ndarray:
arr = image
image = np_to_pil(image)
else:
arr = pil_to_np(image, mode="L")
start = 0
boxes = []
space = True
unconnected = 0
trns = arr.transpose()
# whites = trns[np.min(trns) >= threshold]
whites = np.min(trns) >= threshold
print(whites)
# whites = trns
# whites[whites >= threshold] = 1
# whites[whites <= threshold] = 0
# print(np.sum(whites))
# print(arr[arr > 200])
#
# for i, col in enumerate(arr.transpose()):
# min_val = np.min(col)
# for i in range(arr.shape[1]):
# min_val = min(arr[:,i])
# if min_val >= threshold and not space:
# pass
# box = [start, 0, i, arr.shape[0]]
# boxes.append(box)
# space = True
# elif min_val < threshold and space:
# start = i
# space = False
# elif i > 0 and (i - start) > 1 and start != 0 and min_val < threshold:
# x, y = arr[:,i], arr[:,i-1]
# for j in range(1, arr.shape[0]-1):
# if x[j] < threshold and any(np.array([y[j-1], y[j], y[j+1]]) < threshold):
# break
# else:
# box = [start, 0, i, arr.shape[0]]
# boxes.append(box)
# start = i
if not space:
box = [start, 0, arr.shape[1], arr.shape[0]]
boxes.append(box)
return [image.crop(bbox) for bbox in boxes]
def update_training_set(image,
digits=None,
binary_threshold=110,
net=None,
new=False):
if not net:
net = NETWORK
if new:
training_set = SupervisedDataSet(INPUTS, OUTPUTS)
elif not TRAINING_SET:
if os.path.isfile(TRAINING_SET_PATH):
with open(TRAINING_SET_PATH, 'rb') as f:
training_set = pickle.load(f)
else:
training_set = SupervisedDataSet(INPUTS, OUTPUTS)
else:
training_set = TRAINING_SET
if not digits:
image.show()
digits = input("What are the characters in this picture? ")
logger.debug("Image is supposed to have the following string: %s" % digits)
if len(digits) == 1:
chars = [image]
else:
chars = graphics.image_characters(image, binary_threshold)
if len(digits) != len(chars):
logger.error(
("The number of characters is not the same ("
"recognized: %d, supposed to be: %d ('%s'))! Dou suru kana~ "
"Skipping this image.") % (len(chars), len(digits), digits))
input()
return
for i, char in enumerate(chars):
d = digits[i]
if d not in "0123456789":
logger.debug("Not adding char %s to the training set" % d)
continue
inputs = graphics.pil_to_np(char.resize((5, 5), 1)).flatten()
outputs = [0] * 10
outputs[int(d)] = 1
training_set.addSample(inputs, outputs)
logger.debug("Added a sample for digits %s. Training set size: %d" %
(d, len(training_set)))
with open(TRAINING_SET_PATH, 'wb') as f:
pickle.dump(training_set, f)
def recognize_digits(image, binary_threshold=110, *args, **kwargs):
image.show()
chars = graphics.image_characters(image, binary_threshold)
digits = ""
for char in chars:
char = graphics.crop_to_bounding_box(char)
# char.show()
char = char.resize((5,5), 1)
inputs = graphics.pil_to_np(char).flatten()
# print(inputs)
r = NETWORK.activate(inputs)
# print(r)
digits += str(r.argmax())
# input("digits so far: %s" % digits)
# print("Digits recognized: %s" % digits)
return digits
def get_bet(self, window, threshold=None, image=False):
bet = window.crop(self.bet_box)
# if image: return bet
if not threshold: threshold = self.ocr_threshold
bet_np = graphics.pil_to_np(bet)
white = graphics.colour_ratio(bet_np, "White")
if white < 0.01:
return None
else:
## THIS PART IS BUGGED AS F**K, but seems to work for now
## No idea yet how to detect green poker chips to crop the image.
for i in range(bet_np.shape[1] // 2, bet_np.shape[1]):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
max_green_ratio = graphics.max_colour_ratio(col,
"Red") # green?!?!
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in column %d is: %.3f" % (i, max_red_ratio))
bet = bet.crop((0, 0, i, bet.height))
break
if max_green_ratio > 0.334:
# print("Max green ratio in column %d is: %.3f" % (i, max_green_ratio))
bet = bet.crop((0, 0, i - 1, bet.height))
break
for i in range(bet_np.shape[1] // 2, -1, -1):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
max_green_ratio = graphics.max_colour_ratio(col,
"Red") # green?!?!
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in colum %d is : %.3f" % (i, max_red_ratio))
bet = bet.crop((i + 1, 0, bet.width, bet.height))
break
if max_green_ratio > 0.334:
# print("Max green ratio in column %d is: %.3f" % (i, max_green_ratio))
bet = bet.crop((i - 1, 0, bet.width, bet.height))
break
# bet.show()
if image: return bet
bet = graphics.recognize_digits(bet, threshold)
return bet
def update_training_set(image, digit, net=None, new=False):
if not net:
net = NETWORK
if new or not hasattr(net, 'training_set'):
net.training_set = SupervisedDataSet(net.training_set.indim, net.training_set.outdim)
image = graphics.np_to_pil(image)
image = graphics.crop_to_bounding_box(image)
# image.show()
image = image.resize((5,5), 1)
# image.show()
outputs = [0]*10
outputs[int(digit)] = 1
inputs = graphics.pil_to_np(image).flatten()
# print("Digit: %s" % digit)
# print("inputs: ", inputs)
# print("outputs: ", outputs)
net.training_set.addSample(inputs, outputs)
print("Added a sample for digits %s. Training set size: %d" % (digit, len(net.training_set)))
def recognize_digits(image, binary_threshold=110, *args, **kwargs):
image.show()
chars = graphics.image_characters(image, binary_threshold)
digits = ""
for char in chars:
char = graphics.crop_to_bounding_box(char)
# char.show()
char = char.resize((5, 5), 1)
inputs = graphics.pil_to_np(char).flatten()
# print(inputs)
r = NETWORK.activate(inputs)
# print(r)
digits += str(r.argmax())
# input("digits so far: %s" % digits)
# print("Digits recognized: %s" % digits)
return digits
def update_training_set(image, digits=None, binary_threshold=110, net=None, new=False):
if not net:
net = NETWORK
if new:
training_set = SupervisedDataSet(INPUTS, OUTPUTS)
elif not TRAINING_SET:
if os.path.isfile(TRAINING_SET_PATH):
with open(TRAINING_SET_PATH, 'rb') as f:
training_set = pickle.load(f)
else:
training_set = SupervisedDataSet(INPUTS, OUTPUTS)
else:
training_set = TRAINING_SET
if not digits:
image.show()
digits = input("What are the characters in this picture? ")
logger.debug("Image is supposed to have the following string: %s" % digits)
if len(digits) == 1:
chars = [image]
else:
chars = graphics.image_characters(image, binary_threshold)
if len(digits) != len(chars):
logger.error(("The number of characters is not the same ("
"recognized: %d, supposed to be: %d ('%s'))! Dou suru kana~ "
"Skipping this image.") % (len(chars), len(digits), digits))
input()
return
for i, char in enumerate(chars):
d = digits[i]
if d not in "0123456789":
logger.debug("Not adding char %s to the training set" % d)
continue
inputs = graphics.pil_to_np(char.resize((5,5), 1)).flatten()
outputs = [0] * 10
outputs[int(d)] = 1
training_set.addSample(inputs, outputs)
logger.debug("Added a sample for digits %s. Training set size: %d" % (d, len(training_set)))
with open(TRAINING_SET_PATH, 'wb') as f:
pickle.dump(training_set, f)
def update_training_set(image, digit, net=None, new=False):
if not net:
net = NETWORK
if new or not hasattr(net, 'training_set'):
net.training_set = SupervisedDataSet(net.training_set.indim,
net.training_set.outdim)
image = graphics.np_to_pil(image)
image = graphics.crop_to_bounding_box(image)
# image.show()
image = image.resize((5, 5), 1)
# image.show()
outputs = [0] * 10
outputs[int(digit)] = 1
inputs = graphics.pil_to_np(image).flatten()
# print("Digit: %s" % digit)
# print("inputs: ", inputs)
# print("outputs: ", outputs)
net.training_set.addSample(inputs, outputs)
print("Added a sample for digits %s. Training set size: %d" %
(digit, len(net.training_set)))
def recognize_digits(image, binary_threshold=110, show=False, *args, **kwargs):
if show:
image.show()
chars = graphics.image_characters(image, binary_threshold)
digits = ""
for char in chars:
char = graphics.crop_to_bounding_box(char)
if not is_digit(char):
digits += recognize_char(char)
continue
char = char.resize((5, 5), 1)
inputs = graphics.pil_to_np(char).flatten()
output = NETWORK.activate(inputs)
top = output.argmax()
digits += str(top)
second = output.argpartition(-2)[-2:-1][0]
logger.debug("Top two values: %.3f (%s), %.3f (%s). Ratio: %.3f" %
(output[top], str(top), output[second], str(second),
output[second] / output[top]))
return digits
def recognize_digits(image, binary_threshold=110, show=False, *args, **kwargs):
if show:
image.show()
chars = graphics.image_characters(image, binary_threshold)
digits = ""
for char in chars:
char = graphics.crop_to_bounding_box(char)
if not is_digit(char):
digits += recognize_char(char)
continue
char = char.resize((5,5), 1)
inputs = graphics.pil_to_np(char).flatten()
output = NETWORK.activate(inputs)
top = output.argmax()
digits += str(top)
second = output.argpartition(-2)[-2:-1][0]
logger.debug("Top two values: %.3f (%s), %.3f (%s). Ratio: %.3f" % (
output[top], str(top), output[second], str(second),
output[second]/output[top]))
return digits
def _create_image_with_info(window):
threshold = 100
font_size = 17
pot = money.pot_image(window)
if graphics.colour_ratio(pot, "White") >= 0.01:
pot = recognition.recognize_digits(pot, threshold)
else:
pot = "NO POT!"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text((330, 270), "POT SIZE: %s" % pot, (255, 0, 0), font=font)
del draw
own_stack = money.own_stack_image(window)
own_stack = recognition.recognize_digits(own_stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text((330, 435), "MY STACK: %s" % own_stack, (255, 0, 0), font=font)
del draw
seat = dealer.find_dealer_seat(window)
loc = dealer.DEALER_OFFSETS[seat]
loc = (loc[0] - 60, loc[1] + 20)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text(loc,
"DEALER HERE (player %d)!" % (seat + 1), (255, 0, 0),
font=font)
del draw
for j in range(5):
p = players.Player(j)
if p.is_in_game(window):
text = "PLAYER IS ACTIVE"
else:
text = "PLAYER INACTIVE"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.card_box.left - 20, p.card_box.top - 20)
draw.text(loc, text, (255, 0, 0), font=font)
del draw
stack = window.crop(p.stack_box)
stack = recognition.recognize_digits(stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.stack_box.left - 20, p.stack_box.top + 20)
draw.text(loc, "STACK: %s" % stack, (255, 0, 0), font=font)
del draw
bet = window.crop(p.bet_box)
bet_np = graphics.pil_to_np(bet)
white = graphics.colour_ratio(bet_np, "White")
if white < 0.01:
text = "NO BETS HERE!"
else:
for i in range(bet_np.shape[1] // 2, bet_np.shape[1]):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
max_green_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in column %d is: %.3f" % (i, max_red_ratio))
bet = bet.crop((0, 0, i, bet.height))
break
if max_green_ratio > 0.334:
# print("Max green ratio in column %d is: %.3f" % (i, max_green_ratio))
bet = bet.crop((0, 0, i - 1, bet.height))
break
for i in range(bet_np.shape[1] // 2, -1, -1):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in colum %d is : %.3f" % (i, max_red_ratio))
bet = bet.crop((i + 1, 0, bet.width, bet.height))
break
text = "PLACED A BET: %s" % recognition.recognize_digits(
bet, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.bet_box.left - 40, p.bet_box.top - 20 + (int(j == 2) * 40))
draw.text(loc, text, (255, 0, 0), font=font)
cc, val_img = actions.get_cc_images(window)
call = recognition.recognize_digits(val_img)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", int(font_size * 1.5))
loc = (540, 490)
draw.text(loc, call, (0, 255, 0), font=font)
br, val_img, _ = actions.get_br_images(window)
bet = recognition.recognize_digits(val_img)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", int(font_size * 1.5))
loc = (660, 490)
draw.text(loc, bet, (0, 255, 0), font=font)
own_cards_image = cards.own_cards_image(window)
own_cards = cards.own_cards(own_cards_image)
flop = [cards.table_card(window, i) for i in range(3)]
turn = cards.table_card(window, 3)
river = cards.table_card(window, 4)
# my_card = cards.
# window.show()
return window
# for pixel in row:
# if np.sum(pixel[:3]) > w_thres:
# pixel[:3] = np.array([0,0,0])
if w_thres:
slice = slice[np.sum(slice, axis=2) <= w_thres]
colour = np.sum(slice[:,col])
# total = np.sum(np.sum(slice[:,:], axis=2) <= w_thres)
# colour = np.sum(np.sum(slice[:,:], axis=2) <= w_thres)
else:
colour = np.sum(slice[:,:,col])
total = np.sum(slice)
# print(slice[:,:,:3] > w_thres)
# for i in range(height):
# for j in range(width):
# t = sum(array[i+offset[0]][j+offset[1]][:3])
# if w_thres and t > w_thres: continue # ignore "white" pixels
# total += t
# colour += array[i+offset[0]][j+offset[1]][col]
print("Total: %d, colour: %d" % (total, colour))
return colour / max(total, 0.00001)
im = graphics.open_image("images/cards/2d.png")
im.show()
im_np = graphics.pil_to_np(im)
print(colour_ratio_rgb(im_np, "Red", offset=(5,7), size=(4,4), w_thres=None))
print(colour_ratio_rgb_opt(im_np, "Red", offset=(5,7), size=(4,4), w_thres=None))
def _create_image_with_info(window):
threshold = 100
font_size = 17
pot = money.pot_image(window)
if graphics.colour_ratio(pot, "White") >= 0.01:
pot = recognition.recognize_digits(pot, threshold)
else:
pot = "NO POT!"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text((330, 270),"POT SIZE: %s" % pot,(255,0,0), font=font)
del draw
own_stack = money.own_stack_image(window)
own_stack = recognition.recognize_digits(own_stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text((330, 435),"MY STACK: %s" % own_stack,(255,0,0), font=font)
del draw
seat = dealer.find_dealer_seat(window)
loc = dealer.DEALER_OFFSETS[seat]
loc = (loc[0]-60, loc[1] + 20)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
draw.text(loc,"DEALER HERE (player %d)!" % (seat + 1),(255,0,0), font=font)
del draw
for j in range(5):
p = players.Player(j)
if p.is_in_game(window):
text = "PLAYER IS ACTIVE"
else:
text = "PLAYER INACTIVE"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.card_box.left-20, p.card_box.top-20)
draw.text(loc, text, (255, 0, 0), font=font)
del draw
stack = window.crop(p.stack_box)
stack = recognition.recognize_digits(stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.stack_box.left-20, p.stack_box.top+20)
draw.text(loc, "STACK: %s" % stack, (255, 0, 0), font=font)
del draw
bet = window.crop(p.bet_box)
bet_np = graphics.pil_to_np(bet)
white = graphics.colour_ratio(bet_np, "White")
if white < 0.01:
text = "NO BETS HERE!"
else:
for i in range(bet_np.shape[1] // 2, bet_np.shape[1]):
col = bet_np[:,i:i+1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
max_green_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in column %d is: %.3f" % (i, max_red_ratio))
bet = bet.crop((0, 0, i, bet.height))
break
if max_green_ratio > 0.334:
# print("Max green ratio in column %d is: %.3f" % (i, max_green_ratio))
bet = bet.crop((0, 0, i-1, bet.height))
break
for i in range(bet_np.shape[1] // 2, -1, -1):
col = bet_np[:,i:i+1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
# print("Max red ratio in each pixel in colum %d is : %.3f" % (i, max_red_ratio))
bet = bet.crop((i+1, 0, bet.width, bet.height))
break
text = "PLACED A BET: %s" % recognition.recognize_digits(bet, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", font_size)
loc = (p.bet_box.left-40, p.bet_box.top-20 + (int(j == 2) * 40))
draw.text(loc, text, (255, 0, 0), font=font)
cc, val_img = actions.get_cc_images(window)
call = recognition.recognize_digits(val_img)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", int(font_size*1.5))
loc = (540, 490)
draw.text(loc, call, (0, 255, 0), font=font)
br, val_img, _ = actions.get_br_images(window)
bet = recognition.recognize_digits(val_img)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("data/font.ttf", int(font_size*1.5))
loc = (660, 490)
draw.text(loc, bet, (0, 255, 0), font=font)
own_cards_image = cards.own_cards_image(window)
own_cards = cards.own_cards(own_cards_image)
flop = [cards.table_card(window, i) for i in range(3)]
turn = cards.table_card(window, 3)
river = cards.table_card(window, 4)
# my_card = cards.
# window.show()
return window
# box = [start, 0, i, arr.shape[0]]
# boxes.append(box)
# start = i
if not space:
box = [start, 0, arr.shape[1], arr.shape[0]]
boxes.append(box)
return [image.crop(bbox) for bbox in boxes]
w = graphics.open_image('images/game/75814.png')
im = money.pot_image(w)
im = im.convert(mode="L")
im = graphics.invert(im)
im = pil_to_np(im, mode="L")
white = np.max(im)
black = np.min(im)
binary_threshold = black + (white - black) * (120 / 255)
im = graphics.binarize(im, binary_threshold)
# text.show()
def f(im):
# chars = None
for i in range(50):
chars = split_characters(im)
# for char in chars:
# char.show()
import cProfile
# cProfile.run('f(im)', sort='tottime')
def create_image_with_info():
threshold = 115
font_size = 17
# window = game_window()
window = game_window(path="images/game/s7.png")
window.show()
pot = money.pot_image(window)
if graphics.colour_ratio(pot, "White") >= 0.01:
pot = graphics.recognize_digits(pot, threshold)
else:
pot = "NO POT!"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
draw.text((330, 270), "POT SIZE: %s" % pot, (255, 0, 0), font=font)
del draw
own_stack = money.own_stack_image(window)
own_stack = graphics.recognize_digits(own_stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
draw.text((330, 435), "MY STACK: %s" % own_stack, (255, 0, 0), font=font)
del draw
seat = dealer.find_dealer_seat(window)
loc = dealer.DEALER_OFFSETS[seat]
loc = (loc[0] - 60, loc[1] + 20)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
draw.text(loc,
"DEALER HERE (player %d)!" % (seat + 1), (255, 0, 0),
font=font)
del draw
for j in range(5):
p = players.Player(j)
if p.is_in_game(window):
text = "PLAYER IS ACTIVE"
else:
text = "PLAYER INACTIVE"
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
loc = (p.card_box.left - 20, p.card_box.top - 20)
draw.text(loc, text, (255, 0, 0), font=font)
del draw
stack = window.crop(p.stack_box)
stack = graphics.recognize_digits(stack, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
loc = (p.stack_box.left - 20, p.stack_box.top + 20)
draw.text(loc, "STACK: %s" % stack, (255, 0, 0), font=font)
del draw
bet = window.crop(p.bet_box)
bet_np = graphics.pil_to_np(bet)
white = graphics.colour_ratio(bet_np, "White")
if white < 0.01:
text = "NO BETS HERE!"
else:
for i in range(bet_np.shape[1] // 2, bet_np.shape[1]):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
max_green_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
print("Max red ratio in each pixel in column %d is: %.3f" %
(i, max_red_ratio))
bet = bet.crop((0, 0, i, bet.height))
break
if max_green_ratio > 0.334:
print("Max green ratio in column %d is: %.3f" %
(i, max_green_ratio))
bet = bet.crop((0, 0, i - 1, bet.height))
break
for i in range(bet_np.shape[1] // 2, -1, -1):
col = bet_np[:, i:i + 1]
max_red_ratio = graphics.max_colour_ratio(col, "Red")
if max_red_ratio > 0.5:
print("Max red ratio in each pixel in colum %d is : %.3f" %
(i, max_red_ratio))
bet = bet.crop((i + 1, 0, bet.width, bet.height))
break
text = "PLACED A BET: %s" % graphics.recognize_digits(
bet, threshold)
draw = PIL.ImageDraw.Draw(window)
font = PIL.ImageFont.truetype("font.ttf", font_size)
loc = (p.bet_box.left - 40, p.bet_box.top - 20 + (int(j == 2) * 40))
draw.text(loc, text, (255, 0, 0), font=font)
# my_card = cards.
window.show()
# box = [start, 0, i, arr.shape[0]]
# boxes.append(box)
# start = i
if not space:
box = [start, 0, arr.shape[1], arr.shape[0]]
boxes.append(box)
return [image.crop(bbox) for bbox in boxes]
w = graphics.open_image('images/game/75814.png')
im = money.pot_image(w)
im = im.convert(mode="L")
im = graphics.invert(im)
im = pil_to_np(im, mode="L")
white = np.max(im)
black = np.min(im)
binary_threshold = black + (white - black) * (120 / 255)
im = graphics.binarize(im, binary_threshold)
# text.show()
def f(im):
# chars = None
for i in range(50):
chars = split_characters(im)
# for char in chars:
# char.show()