def get_good_against(query, hero=None, early_update=False):
if hero is None:
query = query.split()
hero = ' '.join(query[1:])
hero = hero.strip()
found_hero, hero_name = find_hero_name(hero)
if not found_hero:
return False, hero_name, ''
image_path = os.path.join(constant.GOOD_HERO_IMAGE_PATH,
hero_name + '.png')
threshold_update_time = constant.GOOD_HERO_UPDATE_TIME_THRESHOLD
if early_update:
threshold_update_time = threshold_update_time - constant.EARLY_BY
if not is_file_old(image_path, threshold_update_time):
return True, hero_name, image_path
hero_info = scrap_heroes_info(hero_name)
good_against_info = hero_info[1]
good_against_heroes = good_against_info['Hero']
good_against_heroes_list = list(good_against_heroes)
good_against_heroes_image_path = get_icon_path(good_against_heroes_list,
icon_size='big')
hero_image_path = get_icon_path([hero_name], icon_size='big')[0]
image = make_hero_images(hero_image_path, good_against_heroes_image_path,
constant.GOOD_BG_IMAGE_PATH)
cv2.imwrite(image_path, image)
return True, hero_name, image_path
async def get_skill_build(query, hero=None, early_update=False):
if hero is None:
query = query.split()
hero = ' '.join(query[1:])
hero = hero.strip()
found_hero, hero_name = find_hero_name(hero)
if not found_hero:
return False, hero_name, ''
guide_image_path = os.path.join(constant.GUIDE_SAVE_PATH,
hero_name + '.jpg')
threshold_update_time = constant.GUIDE_THRESHOLD_IMAGE_UPDATE
if early_update:
threshold_update_time = threshold_update_time - constant.EARLY_BY
if not is_file_old(guide_image_path, threshold_update_time):
return True, hero_name, guide_image_path
url = constant.GUIDE_URL.replace('<hero_name>', hero_name)
talent_filename = hero + '_talent.jpg'
talent_screenshot_path = os.path.join(constant.TEMP_IMAGE_PATH,
talent_filename)
await get_screenshot(constant.TALENT_SELECTOR, url, talent_screenshot_path)
skill_filename = hero + '_skill.jpg'
skill_screenshot_path = os.path.join(constant.TEMP_IMAGE_PATH,
skill_filename)
await get_screenshot(constant.SKILL_SELECTOR, url, skill_screenshot_path)
talent_image = cv2.imread(talent_screenshot_path)
talent_crop = crop_image(talent_image, constant.TALENT_CROP_COORDS)
skill_image = cv2.imread(skill_screenshot_path)
skill_crop = crop_image(skill_image, constant.SKILL_CROP_COORDS)
hero_icon_path = os.path.join(constant.ICON_PATH_BIG, hero_name + '.png')
hero_icon = cv2.imread(hero_icon_path)
background_image = cv2.imread(constant.GUIDE_BACKGROUND_PATH)
background_image = cv2.resize(background_image,
(constant.GUIDE_BACKGROUND_SHAPE[1],
constant.GUIDE_BACKGROUND_SHAPE[0]))
background_image[
constant.GUIDE_HERO_ICON_X_Y[0]:constant.GUIDE_HERO_ICON_X_Y[0] +
hero_icon.shape[0],
constant.GUIDE_HERO_ICON_X_Y[1]:constant.GUIDE_HERO_ICON_X_Y[1] +
hero_icon.shape[1]] = hero_icon
background_image = add_border_to_image(background_image,
bordersize=10,
rgb=[0, 0, 0])
background_image = cv2.resize(background_image,
(constant.GUIDE_BACKGROUND_SHAPE[1],
constant.GUIDE_BACKGROUND_SHAPE[0]))
final_image = np.concatenate([talent_crop, background_image, skill_crop],
axis=0)
cv2.imwrite(guide_image_path, final_image)
return True, hero_name, guide_image_path
def get_current_trend():
if not is_file_old(CT_IMAGE_PATH, CT_IMAGE_UPDATE_TIME_THRESHOLD):
return CT_IMAGE_PATH
current_trend_dataframe = get_current_hero_trends()
current_trend_dataframe = current_trend_dataframe[:10]
hero_name_df = current_trend_dataframe[current_trend_dataframe.columns[0]]
numeric_df = current_trend_dataframe[current_trend_dataframe.columns[1:]]
numeric_df = round_df_digits(numeric_df)
heroes_list = hero_name_df.values.tolist()
icon_path_list = get_icon_path(heroes_list)
current_trend_dataframe = pd.concat([hero_name_df, numeric_df], axis=1)
title = 'Current Heroes Trend \n\nWR: Win Rate PR: Pick Rate'
image_path = render_mpl_table(current_trend_dataframe,
icon_list=icon_path_list,
header_columns=0,
col_width=2.6,
title=title,
font_size=20)
return image_path
def get_item_build(query, hero=None, early_update=False):
if hero is None:
query = query.split()
hero = ' '.join(query[1:])
hero = hero.strip()
found_hero, hero_name = find_hero_name(hero)
if not found_hero:
return False, hero_name, ''
item_build_path = os.path.join(constant.ITEM_IMAGE_PATH,
hero_name + '.jpg')
threshold_update_time = constant.ITEM_THRESHOLD_UPDATE
if early_update:
threshold_update_time = threshold_update_time - constant.EARLY_BY
if not is_file_old(item_build_path, threshold_update_time):
return True, hero_name, item_build_path
item_build_info = scrap_item_info(hero_name)
item_image = make_item_image(item_build_info, hero_name)
cv2.imwrite(item_build_path, item_image,
[int(cv2.IMWRITE_JPEG_QUALITY), 50])
return True, hero_name, item_build_path
def get_top_games(length=10):
"""
Creates a table image of current top live games
:param length:
:return: table image path
"""
# If TLG image already exist and hasnt been modified in given Threshold second return the same image
if not is_file_old(constant.TLG_IMAGE_PATH,
constant.TLG_IMAGE_UPDATE_TIME_THRESHOLD):
return constant.TLG_IMAGE_PATH
game_list = dota_api.get_top_live_games()
game_list = game_list['game_list']
notable_player_dict = get_notable_hero_from_d2pt()
results = []
results.append(constant.TLG_CUSTOM_COLUMNS)
for game in game_list:
avg_mmr = game[constant.KEYWORD_AVERAGE_MMR]
game_mode = GAME_MODE[game[constant.KEYWORD_GAME_MODE]]
game_time = game[constant.KEYWORD_GAME_TIME]
hour, sec = game_time // 60, game_time % 60
game_time = str(hour) + ":" + str(sec)
n_spectators = int(game[constant.KEYWORD_SPECTATORS])
r_team_name = "Unknown"
d_team_name = "Unknown"
gold_lead = f"Radiant:{game[constant.KEYWORD_RADIANT_LEAD]}" if int(game[constant.KEYWORD_RADIANT_LEAD]) > 0\
else f"Dire:{abs(int(game[constant.KEYWORD_RADIANT_LEAD]))}"
r_kills = game[constant.KEYWORD_RADIANT_SCORE]
d_kills = game[constant.KEYWORD_DIRE_SCORE]
if constant.KEYWORD_RADIANT_TEAM in game:
r_team_name = game[constant.KEYWORD_RADIANT_TEAM]
if constant.KEYWORD_DIRE_TEAM in game:
d_team_name = game[constant.KEYWORD_DIRE_TEAM]
match_id = game[constant.KEYWORD_MATCH_ID]
notable_players = '' if match_id not in notable_player_dict else notable_player_dict[
match_id]
results.append([
notable_players, r_team_name, d_team_name, avg_mmr, game_mode,
n_spectators, game_time, r_kills, d_kills, gold_lead
])
results = [results[0]] + sorted(
results[1:], key=lambda x: (x[4]), reverse=True)
results = results[:length]
results = pd.DataFrame(results[1:], columns=results[0])
image_path = render_mpl_table(results,
image_path=constant.TLG_IMAGE_PATH,
header_columns=0,
col_width=2.2,
row_height=0.8,
title='TOP LIVE GAMES',
font_size=14,
header_color='#4B4C51',
row_colors=['#8c98b5', '#9dabcc'],
edge_color='#464c5a')
# image_path = DataFrame_to_image(results)
image = cv2.imread(image_path)
image = image[:, int(image.shape[1] * .09):-(int(image.shape[1] * .07))]
cv2.imwrite(image_path, image)
return image_path