def get_gold(img, knearest):
'''Finds gold value'''
img = crop(img, (629, 752, 35, 9))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gold = ''
for i in range(5):
cropped = crop(img, (i * 7, 0, 7, 9))
gold += str(knearest['gold'].predict(cropped))
try:
return int(gold)
except ValueError:
return -1
def get_game_time(img, models):
'''Finds game minutes'''
img = crop(img, (981, 6, 27, 8))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
minutes = ''
seconds = ''
for i in range(2):
min_crop = crop(img, (i * 6, 0, 6, 8))
min_crop = cv2.resize(min_crop, (7, 9))
sec_crop = crop(img, (15 + i * 6, 0, 6, 8))
sec_crop = cv2.resize(sec_crop, (7, 9))
minutes += str(models['gold'].predict(min_crop, threshold=False))
seconds += str(models['gold'].predict(sec_crop, threshold=False))
return int(minutes) + int(seconds) / 100
def get_minimap_coor(analytics, img):
''' Finds the position of character in the minimap '''
analytics.start_timer('get_minimap_coor', 'Finding minimap coordiantes')
map_ = crop(img, (834, 577, 183, 183))
img = cv2.inRange(map_, (255, 255, 255), (255, 255, 255))
kernel = numpy.ones((2, 2), numpy.uint8)
img = cv2.erode(img, kernel, iterations=1)
img = cv2.dilate(img, kernel, iterations=1)
contours, _ = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if contours == []:
raise NoCharacterInMinimap
box = sorted(contours, key=cv2.contourArea, reverse=True)[0]
rect = list(cv2.boundingRect(box))
h, w = img.shape[:2]
left_gap = rect[0]
top_gap = rect[1]
bot_gap = h - rect[1] - rect[3]
right_gap = w - rect[0] - rect[2]
if rect[2] < 40:
if left_gap > right_gap:
rect[2] = 40
else:
rect[0] -= 40 - rect[2]
rect[2] = 40
if rect[3] < 31:
if top_gap > bot_gap:
rect[3] = 31
else:
rect[1] -= 31 - rect[3]
rect[3] = 31
coor = find_center(rect)
analytics.end_timer('get_minimap_coor')
return coor
def identify_object(img, contour, area):
''' Indentify an object from the coordiate '''
if area != 0:
return None
size = img.shape[:2]
coor = tuple(contour[0][0])
output = {'coor': coor}
colors = (0, 255, 0), (255, 0, 0), (255, 255, 0), (0, 0, 255), (0, 0, 134)
mappings = dict(
zip(colors,
('champion', 'structure', 'monster', 'minion', 'small_monster')))
nearest_color = get_nearest(img[coor_offset(coor, (0, -1), size)], colors,
25)
if nearest_color is None:
return None
if mappings[nearest_color] == 'champion':
color = img[coor_offset(coor, (1, 0), size)]
output['name'] = get_nearest_value(
color, ((0, 255, 0), (255, 0, 0), (0, 0, 255)),
('player_champion', 'enemy_champion', 'ally_champion'))
output['center'] = coor[0] + 40, coor[1] + 100
hp_bar = img[coor[1]:coor[1] + 1, coor[0] + 1:coor[0] + 106]
output['health'] = get_hp_value(hp_bar)
output['level'] = get_summoner_level(
crop(img, (coor[0] - 22, coor[1] - 12, 19, 21)), LEVEL_OCR)
output['is_turret'] = tuple(img[coor_offset(coor, (-2, 0),
size)]) == (0, 36, 21)
elif mappings[nearest_color] == 'structure':
output['name'] = 'structure'
output['center'] = coor[0] + 75, coor[1] + 150
elif mappings[nearest_color] == 'monster':
output['name'] = 'monster'
elif mappings[nearest_color] == 'small_monster':
output['name'] = 'small_monster'
elif mappings[nearest_color] == 'minion':
color = img[coor_offset(coor, (1, 0), size)]
colors = (98, 167, 231), (224, 114, 112), (219, 170, 130)
mappings = dict(zip(colors, ('ally_minion', 'enemy_minion', 'plant')))
output['name'] = mappings[get_nearest(color, colors)]
hp_bar = img[coor[1]:coor[1] + 1, coor[0] + 1:coor[0] + 61]
output['health'] = get_small_hp_value(hp_bar)
output['center'] = coor[0] + 30, coor[1] + 35
if output['name'] == 'enemy_minion':
output['aggro'] = get_color_diff(
img[coor_offset(coor, (28, -16), size)], (255, 0, 0)) < 50
output['is_order_side'] = output['center'][1] / \
output['center'][0] > img.shape[0]/img.shape[1]
try:
output['is_turret'] = (
tuple(img[coor_offset(coor, (-1, 0), size)]) == (0, 36, 21)
or tuple(img[output['center'][::-1]]) == (0, 36, 21))
except IndexError:
output['is_turret'] = False
return output
def get_minimap_coor(analytics, img):
''' Finds the position of character in the minimap '''
analytics.start_timer('get_minimap_coor', 'Finding minimap coordiantes')
map_ = crop(img, (834, 577, 183, 183))
img = cv2.inRange(map_, (200, 200, 200), (255, 255, 255))
contours, _ = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if contours == []:
raise NoCharacterInMinimap
box = sorted(contours, key=cv2.contourArea, reverse=True)[0]
coor = find_center(cv2.boundingRect(box))
analytics.end_timer('get_minimap_coor')
return coor
def export_data(label_input, change_img, img, rect):
''' Asks for label value and writes to file '''
img = crop(img, rect)
img_rgb = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
tk_img = ImageTk.PhotoImage(image=Image.fromarray(img_rgb))
change_img(tk_img)
label = label_input()
if label == 'exit':
raise Exception
if label in ['', None]:
return
cv2.imwrite(name(label), img)
def get_attack_speed(image, models):
'''Finds attack speed'''
img = crop(image, (244, 740, 5, 6))
img = cv2.resize(img, (7, 9))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
hundred = models['gold'].predict(img)
img = crop(image, (251, 740, 5, 6))
img = cv2.resize(img, (7, 9))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
tens = models['gold'].predict(img)
img = crop(image, (256, 740, 5, 6))
img = cv2.resize(img, (7, 9))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ones = models['gold'].predict(img)
try:
return int(str(hundred) + str(tens) + str(ones)) / 100
except ValueError:
return -1
def get_champion(img, models):
'''Finds the selected champion'''
img = crop(img, (330, 720, 20, 20))
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return CHAMPIONS_MAPPING[models['champion'].predict(img)]
def prepare(image, rect):
img = crop(image, rect)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return img
def predict(img, rect, key):
nonlocal spells
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = crop(img, rect)
spell = MAPPING[models['summoner_spell'].predict(img)]
spells[spell] = {'spell': spell, 'key': key}