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fgosccnt.py
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fgosccnt.py
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#!/usr/bin/env python3
import os
import sys
import re
import argparse
from pathlib import Path
from collections import Counter
from enum import Enum
import itertools
import json
from operator import itemgetter
import math
import datetime
import logging
import multiprocessing
import time
import signal
from typing import Tuple, Union
import io
import cv2
from numpy import ndarray
import numpy as np
import pytesseract
from PIL import Image
from PIL.ExifTags import TAGS
import pageinfo
PROGNAME = "FGOスクショカウント"
VERSION = "0.4.0"
DEFAULT_ITEM_LANG = "jpn" # "jpn": japanese, "eng": English
logger = logging.getLogger(__name__)
watcher_running = True
class CustomAdapter(logging.LoggerAdapter):
"""
この adapter を通した場合、自動的にログ出力文字列の先頭に [target] が挿入される。
target は adapter インスタンス生成時に確定させること。
"""
def process(self, msg, kwargs):
return f"[{self.extra['target']}] {msg}", kwargs
class Ordering(Enum):
"""
ファイルの処理順序を示す定数
"""
NOTSPECIFIED = 'notspecified' # 指定なし
FILENAME = 'filename' # ファイル名
TIMESTAMP = 'timestamp' # 作成日時
def __str__(self):
return str(self.value)
basedir = Path(__file__).resolve().parent
Item_dir = basedir / Path("item/equip/")
CE_dir = basedir / Path("item/ce/")
Point_dir = basedir / Path("item/point/")
train_item = basedir / Path("item.xml") # item stack & bonus
train_chest = basedir / Path("chest.xml") # drop_coount (Old UI)
train_dcnt = basedir / Path("dcnt.xml") # drop_coount (New UI)
train_card = basedir / Path("card.xml") # card name
drop_file = basedir / Path("fgoscdata/hash_drop.json")
eventquest_dir = basedir / Path("fgoscdata/data/json/")
items_img = basedir / Path("data/misc/items_img.png")
bunyan1_img = basedir / Path("data/misc/bunyan1.png")
hasher = cv2.img_hash.PHash_create()
FONTSIZE_UNDEFINED = -1
FONTSIZE_NORMAL = 0
FONTSIZE_SMALL = 1
FONTSIZE_TINY = 2
FONTSIZE_NEWSTYLE = 99
PRIORITY_CE = 9000
PRIORITY_POINT = 3000
PRIORITY_ITEM = 700
PRIORITY_GEM_MIN = 6094
PRIORITY_MAGIC_GEM_MIN = 6194
PRIORITY_SECRET_GEM_MIN = 6294
PRIORITY_PIECE_MIN = 5194
PRIORITY_REWARD_QP = 9012
ID_START = 9500000
ID_QP = 1
ID_FP = 4
ID_REWARD_QP = 5
ID_GEM_MIN = 6001
ID_GEM_MAX = 6007
ID_MAGIC_GEM_MIN = 6101
ID_MAGIC_GEM_MAX = 6107
ID_SECRET_GEM_MIN = 6201
ID_SECRET_GEM_MAX = 6207
ID_PIECE_MIN = 7001
ID_MONUMENT_MAX = 7107
ID_EXP_MIN = 9700100
ID_EXP_MAX = 9707500
ID_2ZORO_DICE = 94047708
ID_3ZORO_DICE = 94047709
ID_NORTH_AMERICA = 93000500
ID_SYURENJYO = 94006800
ID_SYURENJYO_TMP = 94066100
ID_EVNET = 94000000
ID_GREEN_TEA = 94074504
ID_YELLOW_TEA = 94074505
ID_RED_TEA = 94074506
TIMEOUT = 15
QP_UNKNOWN = -1
DEFAULT_POLL_FREQ = 60
DEFAULT_AMT_PROCESSES = 1
class FgosccntError(Exception):
pass
class GainedQPandDropMissMatchError(FgosccntError):
pass
with open(drop_file, encoding='UTF-8') as f:
drop_item = json.load(f)
# JSONファイルから各辞書を作成
item_name = {item["id"]: item["name"] for item in drop_item}
item_name_eng = {item["id"]: item["name_eng"] for item in drop_item
if "name_eng" in item.keys()}
item_shortname = {item["id"]: item["shortname"] for item in drop_item
if "shortname" in item.keys()}
item_dropPriority = {item["id"]: item["dropPriority"] for item in drop_item}
item_background = {item["id"]: item["background"] for item in drop_item
if "background" in item.keys()}
item_type = {item["id"]: item["type"] for item in drop_item}
dist_item = {item["phash_battle"]: item["id"] for item in drop_item
if item["type"] == "Item" and "phash_battle" in item.keys()}
dist_ce = {item["phash"]: item["id"] for item in drop_item
if item["type"] == "Craft Essence"}
dist_ce_narrow = {item["phash_narrow"]: item["id"] for item in drop_item
if item["type"] == "Craft Essence"}
dist_secret_gem = {item["id"]: item["phash_class"] for item in drop_item
if 6200 < item["id"] < 6208
and "phash_class" in item.keys()}
dist_magic_gem = {item["id"]: item["phash_class"] for item in drop_item
if 6100 < item["id"] < 6108 and "phash_class" in item.keys()}
dist_gem = {item["id"]: item["phash_class"] for item in drop_item
if 6000 < item["id"] < 6008 and "phash_class" in item.keys()}
dist_exp_rarity = {item["phash_rarity"]: item["id"] for item in drop_item
if item["type"] == "Exp. UP"
and "phash_rarity" in item.keys()}
dist_exp_rarity_sold = {item["phash_rarity_sold"]: item["id"] for item
in drop_item if item["type"] == "Exp. UP"
and "phash_rarity_sold" in item.keys()}
dist_exp_rarity.update(dist_exp_rarity_sold)
dist_exp_rarity["1fe03fe0517fa0bf"] = 9701200 # fix #368
dist_exp_class = {item["phash_class"]: item["id"] for item in drop_item
if item["type"] == "Exp. UP"
and "phash_class" in item.keys()}
dist_exp_class_sold = {item["phash_class_sold"]: item["id"]
for item in drop_item
if item["type"] == "Exp. UP" and "phash_class_sold"
in item.keys()}
dist_exp_class.update(dist_exp_class_sold)
dist_point = {item["phash_battle"]: item["id"]
for item in drop_item
if item["type"] == "Point" and "phash_battle" in item.keys()}
with open(drop_file, encoding='UTF-8') as f:
drop_item = json.load(f)
freequest = []
evnetfiles = eventquest_dir.glob('**/*.json')
for evnetfile in evnetfiles:
try:
with open(evnetfile, encoding='UTF-8') as f:
event = json.load(f)
freequest = freequest + event
except (OSError, UnicodeEncodeError) as e:
logger.exception(e)
npz = np.load(basedir / Path('background.npz'))
hist_zero = npz["hist_zero"]
hist_gold = npz["hist_gold"]
hist_silver = npz["hist_silver"]
hist_bronze = npz["hist_bronze"]
def has_intersect(a, b):
"""
二つの矩形の当たり判定
隣接するのはOKとする
"""
return max(a[0], b[0]) < min(a[2], b[2]) \
and max(a[1], b[1]) < min(a[3], b[3])
class State():
def set_screen(self):
self.screen_type = "normal"
def set_char_position(self):
logger.debug("JP Standard Position")
def set_font_size(self):
logger.debug("JP Standard Font Size")
def set_max_qp(self):
self.max_qp = 999999999
logger.debug("999,999,999")
class JpNov2020(State):
def set_screen(self):
self.screen_type = "wide"
class JpAug2021(JpNov2020):
def set_font_size(self):
logger.debug("JP New Font Size")
def set_max_qp(self):
self.max_qp = 2000000000
logger.debug("2,000,000,000")
class NaState(State):
def set_char_position(self):
logger.debug("NA Standard Position")
class NaOct2022(NaState):
def set_screen(self):
self.screen_type = "wide"
def set_max_qp(self):
self.max_qp = 2000000000
logger.debug("2,000,000,000")
class Context:
def __init__(self):
self.jp_aug_2021 = JpAug2021()
self.jp_nov_2020 = JpNov2020()
self.jp = State()
self.na = NaState()
self.na_oct2022 = NaOct2022()
self.state = self.jp_aug_2021
self.set_screen()
self.set_font_size()
self.set_char_position()
self.set_max_qp()
def change_state(self, mode):
if mode == "jp":
self.state = self.jp_aug_2021
elif mode == "na":
self.state = self.na_oct2022
else:
raise ValueError("change_state method must be in {}".format(["jp", "na"]))
self.set_screen()
self.set_font_size()
self.set_char_position()
self.set_max_qp()
def set_screen(self):
self.state.set_screen()
def set_char_position(self):
self.state.set_char_position()
def set_font_size(self):
self.state.set_font_size()
def set_max_qp(self):
self.state.set_max_qp()
def get_coodinates(img: ndarray,
display: bool = False) -> Tuple[Tuple[int, int],
Tuple[int, int]]:
threshold: int = 30
height, width = img.shape[:2]
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if display:
cv2.imshow('image', img_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
_, inv = cv2.threshold(img_gray, threshold, 255, cv2.THRESH_BINARY_INV)
if display:
cv2.imshow('image', inv)
cv2.waitKey(0)
cv2.destroyAllWindows()
contours, _ = cv2.findContours(inv, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
contours2 = []
for cnt in contours:
_, _, w, h = cv2.boundingRect(cnt)
area = cv2.contourArea(cnt)
if 1.81 < w/h < 1.83 and area > height / 2 * width / 2 and height/h > 1080/910:
contours2.append(cnt)
if len(contours2) == 0:
raise ValueError("Game screen not found.")
max_contour = max(contours2, key=lambda x: cv2.contourArea(x))
x, y, width, height = cv2.boundingRect(max_contour)
return ((x, y), (x + width, y + height))
def standardize_size(frame_img: ndarray,
display: bool = False) -> Tuple[ndarray, float]:
TRAINING_WIDTH: int = 1754
height, width = frame_img.shape[:2]
if display:
pass
logger.debug("height: %d", height)
logger.debug("width: %d", width)
_, width, _ = frame_img.shape
resize_scale: float = TRAINING_WIDTH / width
logger.debug("resize_scale: %f", resize_scale)
if resize_scale > 1:
frame_img = cv2.resize(frame_img, (0, 0),
fx=resize_scale, fy=resize_scale,
interpolation=cv2.INTER_CUBIC)
elif resize_scale < 1:
frame_img = cv2.resize(frame_img, (0, 0),
fx=resize_scale, fy=resize_scale,
interpolation=cv2.INTER_AREA)
if display:
cv2.imshow('image', frame_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return frame_img, resize_scale
def area_decision(frame_img: ndarray,
display: bool = False) -> str:
"""
FGOアプリの地域を選択
"na", 'jp'に対応
'items_img.png' とのオブジェクトマッチングで判定
"""
img = frame_img[0:100, 0:500]
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if display:
cv2.imshow('image', img_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
template = imread(items_img, 0)
res = cv2.matchTemplate(
img_gray,
template,
cv2.TM_CCOEFF_NORMED
)
threshold = 0.9
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
return "na"
return 'jp'
def check_page_mismatch(page_items: int, chestnum: int, pagenum: int, pages: int, lines: int) -> bool:
if pages == 1:
if chestnum + 1 != page_items:
return False
return True
if not (pages - 1) * 21 <= chestnum <= pages * 21 - 1:
return False
if pagenum == pages:
item_count = chestnum - ((pages - 1) * 21 - 1) + (pages * 3 - lines) * 7
if item_count != page_items:
return False
return True
class ScreenShot:
"""
戦利品スクリーンショットを表すクラス
"""
def __init__(self, args, img_rgb, svm, svm_chest, svm_dcnt, svm_card,
fileextention, exLogger, reward_only=False):
self.exLogger = exLogger
threshold = 80
self.img_rgb_orig = img_rgb
img_blue, img_green, img_red = cv2.split(img_rgb)
if (img_blue==img_green).all() & (img_green==img_red ).all():
raise ValueError("Input image is grayscale")
self.img_gray_orig = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
self.img_hsv_orig = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2HSV)
_, self.img_th_orig = cv2.threshold(self.img_gray_orig,
threshold, 255, cv2.THRESH_BINARY)
((self.x1, self.y1), (self.x2, self.y2)) = get_coodinates(self.img_rgb_orig)
# Remove the extra notch by centering
center = int((self.x2 - self.x1)/2 + self.x1)
half_width = min(center, img_rgb.shape[1] - center)
img_rgb_tmp = img_rgb[:, center - half_width:center + half_width]
try:
self.pagenum, self.pages, self.lines = pageinfo.guess_pageinfo(img_rgb_tmp)
if self.lines / self.pages > 3:
logger.warning("The maximum number of lines has been exceeded")
self.lines = self.pages * 3
except pageinfo.TooManyAreasDetectedError:
self.pagenum, self.pages, self.lines = (-1, -1, -1)
frame_img: ndarray = self.img_rgb_orig[self.y1: self.y2, self.x1: self.x2]
img_resize, resize_scale = standardize_size(frame_img)
self.img_rgb = img_resize
mode = area_decision(img_resize)
logger.debug("lang: %s", mode)
# UI modeを決める
sc = Context()
sc.change_state(mode)
self.max_qp = sc.state.max_qp
self.screen_type = sc.state.screen_type
dcnt_old, dcnt_new = self.drop_count_area(self.img_rgb_orig, resize_scale, sc)
if logger.isEnabledFor(logging.DEBUG):
cv2.imwrite('frame_img.png', img_resize)
if logger.isEnabledFor(logging.DEBUG):
if self.screen_type == "normal":
cv2.imwrite('dcnt_old.png', dcnt_old)
cv2.imwrite('dcnt_new.png', dcnt_new)
self.img_gray = cv2.cvtColor(self.img_rgb, cv2.COLOR_BGR2GRAY)
_, self.img_th = cv2.threshold(self.img_gray,
threshold, 255, cv2.THRESH_BINARY)
self.svm = svm
self.svm_chest = svm_chest
self.svm_dcnt = svm_dcnt
self.height, self.width = self.img_rgb.shape[:2]
if self.screen_type == "normal":
self.chestnum = self.ocr_tresurechest(dcnt_old)
if self.chestnum == -1:
self.chestnum = self.ocr_dcnt(dcnt_new)
else:
self.chestnum = self.ocr_dcnt(dcnt_new)
self.asr_y, self.actual_height = self.detect_scroll_bar()
logger.debug("Total Drop (OCR): %d", self.chestnum)
item_pts = self.img2points(mode)
logger.debug("item_pts:%s", item_pts)
self.items = []
self.current_dropPriority = PRIORITY_REWARD_QP
if reward_only:
# qpsplit.py で利用
item_pts = item_pts[0:1]
prev_item = None
# まんわか用イベント判定
template1 = cv2.imread(str(bunyan1_img), 0)
item15th = self.img_gray[item_pts[15][1]:item_pts[15][3], item_pts[15][0]:item_pts[15][2]]
res = cv2.matchTemplate(item15th, template1, cv2.TM_CCOEFF_NORMED)
threshold = 0.80
loc = np.where(res >= threshold)
self.Bunyan = False
for pt in zip(*loc[::-1]):
self.Bunyan = True
break
for i, pt in enumerate(item_pts):
if self.Bunyan and i == 14:
break
lx, _ = self.find_edge(self.img_th[pt[1]: pt[3],
pt[0]: pt[2]], reverse=True)
logger.debug("lx: %d", lx)
item_img_th = self.img_th[pt[1] + 37: pt[3] - 60,
pt[0] + lx: pt[2] + lx]
if self.is_empty_box(item_img_th):
break
item_img_rgb = self.img_rgb[pt[1]: pt[3],
pt[0] + lx: pt[2] + lx]
item_img_gray = self.img_gray[pt[1]: pt[3],
pt[0] + lx: pt[2] + lx]
if logger.isEnabledFor(logging.DEBUG):
cv2.imwrite('item' + str(i) + '.png', item_img_rgb)
dropitem = Item(args, i, prev_item, item_img_rgb, item_img_gray,
svm, svm_card, fileextention,
self.current_dropPriority, self.exLogger, mode)
if dropitem.id == -1:
break
self.current_dropPriority = item_dropPriority[dropitem.id]
if dropitem.id in [94069601, 94069602, 94069603]:
# まんわかイベントのバニヤンに隠されているドロップが問題を生じるので補正
dropitem.dropnum = 'x3'
self.items.append(dropitem)
prev_item = dropitem
if self.Bunyan:
lx, _ = self.find_edge(self.img_th[item_pts[14][1]: item_pts[14][3],
item_pts[14][0]: item_pts[14][2]], reverse=True)
item_img_rgb = self.img_rgb[item_pts[14][1]: item_pts[14][3],
item_pts[14][0] + lx: item_pts[14][2] + lx]
item_img_gray = self.img_gray[item_pts[14][1]: item_pts[14][3],
item_pts[14][0] + lx: item_pts[14][2] + lx]
dropitem = Item(args, i, prev_item, item_img_rgb, item_img_gray,
svm, svm_card, fileextention,
self.current_dropPriority, self.exLogger, mode)
self.items.append(dropitem)
self.itemlist = self.makeitemlist()
try:
self.total_qp = self.get_qp(mode)
self.qp_gained = self.get_qp_gained(mode)
asr_y, actual_height = self.detect_scroll_bar()
if asr_y == -1 or actual_height == -1:
self.scroll_position = -1
else:
entire_height = 649 # from correct_pageinfo()
self.scroll_position = asr_y / entire_height
except Exception as e:
self.total_qp = -1
self.qp_gained = -1
self.exLogger.warning("QP detection fails")
logger.exception(e)
if self.qp_gained > 0 and len(self.itemlist) == 0:
raise GainedQPandDropMissMatchError
logger.debug(f'pagenum(pageninfo) pagenum: {self.pagenum}, pages: {self.pages}, lines: {self.lines}')
self.pagenum, self.pages, self.lines = self.correct_pageinfo()
logger.debug(f'pagenum(coreected) pagenum: {self.pagenum}, pages: {self.pages}, lines: {self.lines}')
if not reward_only:
self.check_page_mismatch()
# Determine scrollbar's position. AtlasAcademy processing pipeline uses this to group drop-pages
asr_y, actual_height = self.detect_scroll_bar()
if asr_y == -1 or actual_height == -1:
self.scroll_position = -1
else:
entire_height = 649 # from correct_pageinfo()
self.scroll_position = asr_y / entire_height
def check_page_mismatch(self):
if self.Bunyan:
num_items = len(self.itemlist) -1
else:
num_items = len(self.itemlist)
valid = check_page_mismatch(
num_items,
self.chestnum,
self.pagenum,
self.pages,
self.lines,
)
if not valid:
self.exLogger.warning("drops_count is a mismatch:")
self.exLogger.warning("drops_count = %d", self.chestnum)
self.exLogger.warning("drops_found = %d", len(self.itemlist))
def find_notch(self):
"""
直線検出で検出されなかったフチ幅を検出
"""
edge_width = 200
height, width = self.img_hsv_orig.shape[:2]
target_color = 0
for lx in range(edge_width):
img_hsv_x = self.img_hsv_orig[:, lx: lx + 1]
# ヒストグラムを計算
hist = cv2.calcHist([img_hsv_x], [0], None, [256], [0, 256])
# 最小値・最大値・最小値の位置・最大値の位置を取得
_, maxVal, _, maxLoc = cv2.minMaxLoc(hist)
if not (maxLoc[1] == target_color and maxVal > height * 0.7):
break
for rx in range(edge_width):
img_hsv_x = self.img_hsv_orig[:, width - rx - 1: width - rx]
# ヒストグラムを計算
hist = cv2.calcHist([img_hsv_x], [0], None, [256], [0, 256])
# 最小値・最大値・最小値の位置・最大値の位置を取得
_, maxVal, _, maxLoc = cv2.minMaxLoc(hist)
if not (maxLoc[1] == target_color and maxVal > height * 0.7):
break
return lx, rx
def drop_count_area(self, img: ndarray,
resize_scale,
sc,
display: bool = False) -> Tuple[Union[ndarray, None], ndarray]:
# widescreenかどうかで挙動を変える
if resize_scale > 1:
img = cv2.resize(img, (0, 0),
fx=resize_scale, fy=resize_scale,
interpolation=cv2.INTER_CUBIC)
elif resize_scale < 1:
img = cv2.resize(img, (0, 0),
fx=resize_scale, fy=resize_scale,
interpolation=cv2.INTER_AREA)
# ((x1, y1), (_, _)) = get_coodinates(img)
# 相対座標(旧UI)
dcnt_old = None
if sc.state.screen_type == "normal":
dcnt_old = img[int(self.y1*resize_scale) - 81: int(self.y1*resize_scale) - 44,
int(self.x1*resize_scale) + 1446: int(self.x1*resize_scale) + 1505]
if display:
cv2.imshow('image', dcnt_old)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 相対座標(新UI)
lx, rx = self.find_notch()
height, width = img.shape[:2]
if (width - lx - rx)/height > 16/8.96: # Issue #317
# Widescreen
dcnt_new = img[int(self.y1*resize_scale) - 20: int(self.y1*resize_scale) + 13,
width - 495 - rx: width - 415 - int(rx*resize_scale)]
else:
dcnt_new = img[int(self.y1*resize_scale) - 20: int(self.y1*resize_scale) + 13,
width - 430 - rx : width - 340 - rx]
if display:
cv2.imshow('image', dcnt_new)
cv2.waitKey(0)
cv2.destroyAllWindows()
return dcnt_old, dcnt_new
def detect_scroll_bar(self):
'''
Modified from determine_scroll_position()
'''
width = self.img_rgb.shape[1]
topleft = (width - 90, 81)
bottomright = (width, 2 + 753)
if logger.isEnabledFor(logging.DEBUG):
img_copy = self.img_rgb.copy()
cv2.rectangle(img_copy, topleft, bottomright, (0, 0, 255), 3)
cv2.imwrite("./scroll_bar_selected2.jpg", img_copy)
gray_image = self.img_gray[
topleft[1]: bottomright[1],
topleft[0]: bottomright[0]
]
_, binary = cv2.threshold(gray_image, 200, 255, cv2.THRESH_BINARY)
if logger.isEnabledFor(logging.DEBUG):
cv2.imwrite("scroll_bar_binary2.png", binary)
contours = cv2.findContours(
binary,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE
)[0]
pts = []
for cnt in contours:
ret = cv2.boundingRect(cnt)
pt = [ret[0], ret[1], ret[0] + ret[2], ret[1] + ret[3]]
if ret[3] > 10:
pts.append(pt)
if len(pts) == 0:
logger.debug("Can't find scroll bar")
return -1, -1
elif len(pts) > 1:
self.exLogger.warning("Too many objects.")
return -1, -1
else:
return pt[1], pt[3] - pt[1]
def valid_pageinfo(self):
'''
Checking the content of pageinfo and correcting it when it fails
'''
if self.pagenum == -1 or self.pages == -1 or self.lines == -1:
return False
if (self.pagenum == 1 and self.pages == 1 and self.lines == 0) and self.chestnum > 20:
return False
elif self.itemlist[0]["id"] != ID_REWARD_QP and self.pagenum == 1:
return False
elif self.Bunyan and self.chestnum != -1 and self.pagenum != 1 \
and self.lines != int(self.chestnum/7) + 2:
return False
elif self.Bunyan is False and self.chestnum != -1 and self.pagenum != 1 \
and self.lines != int(self.chestnum/7) + 1:
return False
return True
def correct_pageinfo(self):
if self.valid_pageinfo() is False:
self.exLogger.warning("pageinfo validation failed")
if self.asr_y == -1 or self.actual_height == -1:
return 1, 1, 0
entire_height = 645
esr_y = 17
cap_height = 14 # 正規化後の im.height を 1155 であると仮定して計算した値
pagenum = pageinfo.guess_pagenum(self.asr_y, esr_y, self.actual_height, entire_height, cap_height)
pages = pageinfo.guess_pages(self.actual_height, entire_height, cap_height)
lines = pageinfo.guess_lines(self.actual_height, entire_height, cap_height)
return pagenum, pages, lines
else:
return self.pagenum, self.pages, self.lines
def calc_black_whiteArea(self, bw_image):
image_size = bw_image.size
whitePixels = cv2.countNonZero(bw_image)
whiteAreaRatio = (whitePixels / image_size) * 100 # [%]
return whiteAreaRatio
def is_empty_box(self, img_th):
"""
アイテムボックスにアイテムが無いことを判別する
"""
if self.calc_black_whiteArea(img_th) < 1:
return True
return False
def get_qp_from_text(self, text):
"""
capy-drop-parser から流用
"""
qp = 0
power = 1
# re matches left to right so reverse the list
# to process lower orders of magnitude first.
for match in re.findall("[0-9]+", text)[::-1]:
qp += int(match) * power
power *= 1000
return qp
def extract_text_from_image(self, image):
"""
capy-drop-parser から流用
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, qp_image = cv2.threshold(gray, 65, 255, cv2.THRESH_BINARY_INV)
# '+' is needed to ensure that tesseract doesn't force a recognition on it,
# which results in a '4' most of the time.
return pytesseract.image_to_string(
qp_image,
config="-l eng --oem 1 --psm 7 -c tessedit_char_whitelist=+,0123456789",
)
def get_qp(self, mode):
"""
capy-drop-parser から流用
tesseract-OCR is quite slow and changed to use SVM
"""
use_tesseract = False
pt = pageinfo.detect_qp_region(self.img_rgb_orig, mode)
logger.debug('pt from pageinfo: %s', pt)
if pt is None:
use_tesseract = True
qp_total = -1
if use_tesseract is False: # use SVM
im_th = cv2.bitwise_not(
self.img_th_orig[pt[0][1]: pt[1][1], pt[0][0]: pt[1][0]]
)
qp_total = self.ocr_text(im_th)
if use_tesseract or qp_total == -1:
if self.screen_type == "normal":
pt = ((288, 948), (838, 1024))
else:
pt = ((288, 838), (838, 914))
logger.debug('Use tesseract')
qp_total_text = self.extract_text_from_image(
self.img_rgb[pt[0][1]: pt[1][1], pt[0][0]: pt[1][0]]
)
logger.debug('qp_total_text from text: %s', qp_total_text)
qp_total = self.get_qp_from_text(qp_total_text)
logger.debug('qp_total from text: %s', qp_total)
if qp_total > self.max_qp:
self.exLogger.warning(
"qp_total exceeds the system's maximum: %s", qp_total
)
if qp_total == 0:
return QP_UNKNOWN
return qp_total
def get_qp_gained(self, mode):
use_tesseract = False
bounds = pageinfo.detect_qp_region(self.img_rgb_orig, mode)
if bounds is None:
# fall back on hardcoded bound
if self.screen_type == "normal":
bounds = ((398, 858), (948, 934))
else:
bounds = ((398, 748), (948, 824))
use_tesseract = True
else:
# Detecting the QP box with different shading is "easy", while detecting the absence of it
# for the gain QP amount is hard. However, the 2 values have the same font and thus roughly
# the same height (please NA...). You can consider them to be 2 same-sized boxes on top of
# each other.
(topleft, bottomright) = bounds
height = bottomright[1] - topleft[1]
topleft = (topleft[0], topleft[1] - height + int(height*0.12))
bottomright = (bottomright[0], bottomright[1] - height)
bounds = (topleft, bottomright)
logger.debug('Gained QP bounds: %s', bounds)
if logger.isEnabledFor(logging.DEBUG):
img_copy = self.img_rgb.copy()
cv2.rectangle(img_copy, bounds[0], bounds[1], (0, 0, 255), 3)
cv2.imwrite("./qp_gain_detection.jpg", img_copy)
qp_gain = -1
if use_tesseract is False:
im_th = cv2.bitwise_not(
self.img_th_orig[topleft[1]: bottomright[1],
topleft[0]: bottomright[0]]
)
qp_gain = self.ocr_text(im_th)
if use_tesseract or qp_gain == -1:
logger.debug('Use tesseract')
(topleft, bottomright) = bounds
qp_gain_text = self.extract_text_from_image(
self.img_rgb[topleft[1]: bottomright[1],
topleft[0]: bottomright[0]]
)
qp_gain = self.get_qp_from_text(qp_gain_text)
logger.debug('qp from text: %s', qp_gain)
if qp_gain == 0:
qp_gain = QP_UNKNOWN
return qp_gain
def find_edge(self, img_th, reverse=False):
"""
直線検出で検出されなかったフチ幅を検出
"""
edge_width = 4
_, width = img_th.shape[:2]
target_color = 255 if reverse else 0
for i in range(edge_width):
img_th_x = img_th[:, i:i + 1]
# ヒストグラムを計算
hist = cv2.calcHist([img_th_x], [0], None, [256], [0, 256])
# 最小値・最大値・最小値の位置・最大値の位置を取得
_, _, _, maxLoc = cv2.minMaxLoc(hist)
if maxLoc[1] == target_color:
break
lx = i
for j in range(edge_width):
img_th_x = img_th[:, width - j - 1: width - j]
# ヒストグラムを計算
hist = cv2.calcHist([img_th_x], [0], None, [256], [0, 256])
# 最小値・最大値・最小値の位置・最大値の位置を取得
_, _, _, maxLoc = cv2.minMaxLoc(hist)
if maxLoc[1] == 0:
break
rx = j
return lx, rx
def makeitemlist(self):
"""
アイテムを出力
"""
itemlist = []
for item in self.items:
tmp = {}
tmp['id'] = item.id
tmp['name'] = item.name
tmp['dropPriority'] = item_dropPriority[item.id]
tmp['stack'] = int(item.dropnum[1:])
tmp['bonus'] = item.bonus
tmp['category'] = item.category
tmp['x'] = item.position % 7
tmp['y'] = item.position//7
itemlist.append(tmp)
return itemlist
def ocr_text(self, im_th):
h, w = im_th.shape[:2]
# 物体検出
im_th = cv2.bitwise_not(im_th)
contours = cv2.findContours(im_th,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[0]
item_pts = []
for cnt in contours:
ret = cv2.boundingRect(cnt)
area = cv2.contourArea(cnt)
pt = [ret[0], ret[1], ret[0] + ret[2], ret[1] + ret[3]]
if ret[2] < int(w/2) and area > 80 and ret[1] < h/2 \
and 0.3 < ret[2]/ret[3] < 0.85 and ret[3] > h * 0.45:
flag = False
for p in item_pts:
if has_intersect(p, pt):
# どちらかを消す
p_area = (p[2]-p[0])*(p[3]-p[1])
pt_area = ret[2]*ret[3]
if p_area < pt_area:
item_pts.remove(p)
else:
flag = True
if flag is False:
item_pts.append(pt)
if len(item_pts) == 0:
# Recognizing Failure
return -1
item_pts.sort()
if len(item_pts) > len(str(self.max_qp)):
# QP may be misrecognizing the 10th digit or more, so cut it
item_pts = item_pts[len(item_pts) - len(str(self.max_qp)):]
logger.debug("ocr item_pts: %s", item_pts)
logger.debug("ドロップ桁数(OCR): %d", len(item_pts))
# Hog特徴のパラメータ
win_size = (120, 60)
block_size = (16, 16)
block_stride = (4, 4)
cell_size = (4, 4)
bins = 9
res = ""
for pt in item_pts:
test = []
if pt[0] == 0:
tmpimg = im_th[pt[1]:pt[3], pt[0]:pt[2]+1]
else:
tmpimg = im_th[pt[1]:pt[3], pt[0]-1:pt[2]+1]
tmpimg = cv2.resize(tmpimg, (win_size))
hog = cv2.HOGDescriptor(win_size, block_size,
block_stride, cell_size, bins)
test.append(hog.compute(tmpimg)) # 特徴量の格納
test = np.array(test)
pred = self.svm_chest.predict(test)
res = res + str(int(pred[1][0][0]))
return int(res)
def ocr_tresurechest(self, drop_count_img):
"""
宝箱数をOCRする関数
"""
threshold = 80
img_gray = cv2.cvtColor(drop_count_img, cv2.COLOR_BGR2GRAY)
_, img_num = cv2.threshold(img_gray,
threshold, 255, cv2.THRESH_BINARY)
im_th = cv2.bitwise_not(img_num)
h, w = im_th.shape[:2]
# 情報ウィンドウが数字とかぶった部分を除去する
for y in range(h):
im_th[y, 0] = 255
for x in range(w): # ドロップ数7のときバグる対策 #54
im_th[0, x] = 255
return self.ocr_text(im_th)
def pred_dcnt(self, img):
"""
for JP new UI
"""
# Hog特徴のパラメータ
win_size = (120, 60)
block_size = (16, 16)
block_stride = (4, 4)
cell_size = (4, 4)
bins = 9
char = []
tmpimg = cv2.resize(img, (win_size))
hog = cv2.HOGDescriptor(win_size, block_size,
block_stride, cell_size, bins)
char.append(hog.compute(tmpimg)) # 特徴量の格納
char = np.array(char)
pred = self.svm_dcnt.predict(char)
res = str(int(pred[1][0][0]))
return int(res)
def img2num(self, img, img_th, pts, char_w, end):
"""実際より小さく切り抜かれた数字画像を補正して認識させる
"""
height, width = img.shape[:2]
c_center = int(pts[0] + (pts[2] - pts[0])/2)
# newimg = img[:, item_pts[-1][0]-1:item_pts[-1][2]+1]
newimg = img[:, max(int(c_center - char_w/2), 0):min(int(c_center + char_w/2), width)]
threshold2 = 10
ret, newimg_th = cv2.threshold(newimg,
threshold2,
255,
cv2.THRESH_BINARY)
# 上部はもとのやつを上書き
# for w in range(item_pts[-1][2] - item_pts[-1][0] + 2):
for w in range(min(int(c_center + char_w/2), width) - max(int(c_center - char_w/2), 0)):
for h in range(end):
newimg_th[h, w] = img_th[h, w + int(c_center - char_w/2)]
# newimg_th[h, w] = img_th[h, w + item_pts[-1][0]]
newimg_th[height - 1, w] = 0
newimg_th[height - 2, w] = 0
newimg_th[height - 3, w] = 0