/
utils.py
395 lines (347 loc) · 16.3 KB
/
utils.py
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import numpy as np
from fuzzywuzzy.fuzz import partial_ratio
from skimage.metrics import structural_similarity
import cv2
import sys
import base64
import requests
from math import sqrt
import os
import pandas as pd
def get_create_date(file):
"""
获取圣遗物获取日期
:param file: 图像路径
:return: 日期，如：2021/1/11
"""
import time
# os.stat return properties of a file
tmpTime = time.localtime(os.stat(file).st_ctime)
return time.strftime('%Y/%m/%d', tmpTime)
def cv2_imread(img_path):
"""
中文路径支持
"""
return cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), -1)
def save_processed_file(path):
processed_item = [i for i in os.listdir(path)]
with open('processed_file.txt', 'w') as f:
for i in processed_item:
f.write(i)
f.write('\n')
def add_processed_file(file_name):
with open('processed_file.txt', 'a') as f:
f.write(file_name)
f.write('\n')
def add_ignored_file(file_name):
with open('ignored_file.txt', 'a') as f:
f.write(file_name)
f.write('\n')
def load_processed_file():
processed_item = []
for line in open('processed_file.txt', 'r'):
processed_item.append(line.strip('\n'))
for line in open('ignored_file.txt', 'r'):
processed_item.append(line.strip('\n'))
return processed_item
def img_crop(img):
"""
从图片中裁剪圣遗物面板
*使用边缘检测后的顶点坐标*
:param img: 输入图像向量， 默认(1920, 1080, 3)
:return: 输出图像向量， 大小约为(810, 490, 3)， 状态位，1为有效
"""
# assert img.shape[1] / img.shape[0] == 1920 / 1080
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(img, (3, 3), 0)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
minLineLength = 100
maxLineGap = 50
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength, maxLineGap)
def distance(x1, x2, y1, y2):
return sqrt(abs((abs(x2 - x1) ** 2 - abs(y2 - y1) ** 2)))
x_min = sys.maxsize
x_max = 0
y_min = sys.maxsize
y_max = 0
for line in lines:
for x1, y1, x2, y2 in line:
if distance(x1, x2, y1, y2) > 320:
x_min = min(x_min, x1, x2)
x_max = max(x_max, x1, x2)
y_min = min(y_min, y1, y2)
y_max = max(y_max, y1, y2)
y_len = y_max - y_min
x_len = x_max - x_min
if y_len > 500 > x_len > 480:
return img[y_min:y_max, x_min:x_max], 1
else:
# print(y_len)
# print(x_len)
# cv2.imshow('test', img[y_min:y_max, x_min:x_max])
# cv2.waitKey()
# cv2.destroyAllWindows()
return img, 0
def img_crop_2(img):
"""
从图片中裁剪圣遗物面板
*使用边缘检测宽度，如宽度符合条件而高度不符合则使用固定高度*
:param img: 输入图像向量， 默认(1920, 1080, 3)
:return: 输出图像向量， 大小约为(810, 490, 3)， 状态位，1为有效
"""
# assert img.shape[1] / img.shape[0] == 1920 / 1080
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(img, (3, 3), 0)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
minLineLength = 100
maxLineGap = 50
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength, maxLineGap)
def distance(x1, x2, y1, y2):
return sqrt(abs((abs(x2 - x1) ** 2 - abs(y2 - y1) ** 2)))
x_min = sys.maxsize
x_max = 0
y_min = sys.maxsize
y_max = 0
for line in lines:
for x1, y1, x2, y2 in line:
if distance(x1, x2, y1, y2) > 320:
x_min = min(x_min, x1, x2)
x_max = max(x_max, x1, x2)
y_min = min(y_min, y1, y2)
y_max = max(y_max, y1, y2)
y_len = y_max - y_min
x_len = x_max - x_min
if 500 > x_len > 480:
if y_len > 500:
return img[y_min:y_max, x_min:x_max], 1
else:
# cv2.imshow('test', img[y_min:y_min+800, x_min:x_max])
# cv2.waitKey()
# cv2.destroyAllWindows()
return img[y_min:y_min + 800, x_min:x_max], 1
else:
return img, 0
def img_crop_3(img):
"""
从图片中裁剪圣遗物面板
*16：9屏幕特攻，直接匹配像素值*
:param img: 输入图像向量， 限定为(1920, 1080, 3)
:return: 输出图像向量&状态位，1为有效
"""
feature_img = cv2_imread('FeatureImg.png')
if structural_similarity(img[1010:1030, 1600:1750], feature_img, multichannel=True) >= 0.99:
return img[100:800, 1290:1780], 1
else:
test_area = img[510:620, 1060:1160]
unique = list(np.unique(test_area.reshape(-1, 3), axis=0, return_counts=False)[0])
if unique == [216, 229, 236]:
return img[110:800, 714:1200], 1
else:
return img, 0
def image_to_base64(img_np):
image = cv2.imencode('.jpg', img_np)[1].tostring()
return base64.b64encode(image)
def compute_initial_score(stat, value):
percentage_atk = ['4.1%', '4.7%', '5.3%', '5.8%']
value_atk = ['14', '16', '18', '19']
crit_rate = ['2.7%', '3.1%', '3.5%', '3.9%']
crit_dmg = ['5.4%', '6.2%', '7.0%', '7.8%']
recharge_rate = ['4.5%', '5.2%', '5.8%', '6.5%']
if stat == '暴击率':
if value in crit_rate:
return crit_rate.index(value) * 0.4 + 2
elif stat == '暴击伤害':
if value in crit_dmg:
return crit_dmg.index(value) * 0.4 + 2
elif stat == '攻击力':
if value in percentage_atk:
return percentage_atk.index(value) * 0.2 + 1
if value in value_atk:
return value_atk.index(value) * 0.1 + 0.5
elif stat == '元素充能效率':
if value in recharge_rate:
return recharge_rate.index(value) * 0.2 + 1
return 0
class Artifact:
"""
圣遗物类
"""
set_pieces = ''
set_name = ''
star = 5
lv = 0
main_stat = ''
main_stat_value = ''
vice_stat0 = ''
vice_stat0_value = ''
vice_stat1 = ''
vice_stat1_value = ''
vice_stat2 = ''
vice_stat2_value = ''
vice_stat3 = ''
vice_stat3_value = ''
date = ''
def __init__(self, name):
self.name = name
def __str__(self):
return str(self.__dict__)
def get_initial_score(self):
score = 0
score += compute_initial_score(self.vice_stat0, self.vice_stat0_value)
score += compute_initial_score(self.vice_stat1, self.vice_stat1_value)
score += compute_initial_score(self.vice_stat2, self.vice_stat2_value)
score += compute_initial_score(self.vice_stat3, self.vice_stat3_value)
return round(score, 2)
def add_to_excel(self, path):
sheet = pd.read_excel(path)
insert_column = {'圣遗物名称': self.name,
'圣遗物类型': self.set_pieces,
'主属性': self.main_stat,
'主属性数值': self.main_stat_value,
'星级': self.star,
'等级': self.lv,
'副属性1': self.vice_stat0,
'副属性1数值': self.vice_stat0_value,
'副属性2': self.vice_stat1,
'副属性2数值': self.vice_stat1_value,
'副属性3': self.vice_stat2,
'副属性3数值': self.vice_stat2_value,
'副属性4': self.vice_stat3,
'副属性4数值': self.vice_stat3_value,
'所属套装': self.set_name,
'得分': self.get_initial_score(),
'创建时间': self.date
}
sheet = sheet.append(insert_column, ignore_index=True)
sheet.to_excel(path, index=False)
return self.name
def get_stat(img, access_token, date):
"""
OCR获取数据写入到圣遗物对象
:param img: cv2图像对象
:param access_token: 百度ai的文字识别token
:param date: 圣遗物获取日期
:return: 圣遗物对象
"""
img = image_to_base64(img)
params = {"image": img}
request_url = "https://aip.baidubce.com/rest/2.0/ocr/v1/accurate_basic"
# request_url = "https://aip.baidubce.com/rest/2.0/ocr/v1/general_basic"
request_url = request_url + "?access_token=" + access_token
headers = {'content-type': 'application/x-www-form-urlencoded'}
response = requests.post(request_url, data=params, headers=headers)
def get_set_list_index(result):
"""
判断圣遗物套装所在位置，弃用
:param result: OCR结果列表
:return: int 表示位置
"""
set_list = ['幸运儿', '游医', '冒险家', '学士', '战狂', '祭冰之人', '奇迹', '勇士之心', '教官', '祭火之人', '赌徒',
'祭水之人', '武人', '守护之心', '祭雷之人', '流放者', '行者之心', '炽烈的炎之魔女', '角斗士的终幕礼',
'如雷的盛怒', '冰风迷途的勇士', '染血的骑士道', '昔日宗室之仪', '沉沦之心', '悠古的磐岩',
'翠绿之影', '流浪大地的乐团', '逆飞的流星', '平息鸣雷的尊者', '渡过烈火的贤人', '被怜爱的少女']
# loc = [i in set_list for i in result].index(True)
for i in result:
for j in set_list:
if partial_ratio(i, j) > 80:
return result.index(i)
def get_set_name(result):
set_list = ['幸运儿', '游医', '冒险家', '学士', '战狂', '祭冰之人', '奇迹', '勇士之心', '教官', '祭火之人', '赌徒',
'祭水之人', '武人', '守护之心', '祭雷之人', '流放者', '行者之心', '炽烈的炎之魔女', '角斗士的终幕礼',
'如雷的盛怒', '冰风迷途的勇士', '染血的骑士道', '昔日宗室之仪', '沉沦之心', '悠古的磐岩',
'翠绿之影', '流浪大地的乐团', '逆飞的流星', '平息鸣雷的尊者', '渡过烈火的贤人', '被怜爱的少女']
max = 0
output = ''
for item in result:
for set in set_list:
ratio = partial_ratio(set, item)
if ratio > max:
max = ratio
output = set
return output
def get_index(lst, item):
return [index for (index, value) in enumerate(lst) if value == item]
def get_kind(kind):
kind_list = ["生之花", "死之羽", "时之沙", "空之杯", "理之冠"]
for set_kind in kind_list:
if partial_ratio(set_kind, kind) > 80:
return set_kind
def get_name(kind, name):
kind_list = ["生之花", "死之羽", "时之沙", "空之杯", "理之冠"]
name_list = {
"生之花": ["饰金胸花", "历经风雪的思念", "磐陀裂生之花", "夏祭之花", "游医的银莲", "赌徒的胸花", "学士的书签", "染血的铁之心", "勇士的勋章", "武人的红花",
"角斗士的留恋", "教官的胸花", "宗室之花", "守护之花",
"野花记忆的绿野", "流放者之花", "渡火者的决绝", "乐团的晨光", "远方的少女之心", "魔女的炎之花", "雷鸟的怜悯", "战狂的蔷薇", "平雷之心", "奇迹之花",
"故人之心", "幸运儿绿花", "冒险家之花"],
"死之羽": ["摧冰而行的执望", "追忆之风", "嵯峨群峰之翼", "夏祭终末", "游医的枭羽", "赌徒的羽饰", "学士的羽笔", "染血的黑之羽", "流放者之羽", "琴师的箭羽", "守护徽印",
"勇士的期许", "武人的羽饰", "宗室之翎",
"角斗士的归宿", "猎人青翠的箭羽", "少女飘摇的思念", "魔女常燃之羽", "教官的羽饰", "雷灾的孑遗", "渡火者的解脱", "战狂的翎羽", "平雷之羽", "奇迹之羽",
"归乡之羽", "幸运儿鹰羽", "冒险家尾羽"],
"时之沙": ["坚铜罗盘", "冰雪故园的终期", "星罗圭璧之晷", "夏祭之刻", "游医的怀钟", "赌徒的怀表", "学士的时钟", "骑士染血之时", "流放者怀表", "教官的怀表", "终幕的时计",
"勇士的坚毅", "守护座钟", "武人的水漏",
"角斗士的希冀", "翠绿猎人的笃定", "渡火者的煎熬", "宗室时计", "少女苦短的良辰", "魔女破灭之时", "雷霆的时计", "战狂的时计", "平雷之刻", "奇迹之沙",
"逐光之石", "幸运儿沙漏", "冒险家怀表"],
"空之杯": ["沉波之盏", "遍结寒霜的傲骨", "巉岩琢塑之樽", "夏祭水玉", "游医的药壶", "赌徒的骰盅", "学士的墨杯", "染血骑士之杯", "流放者之杯", "守护之皿", "吟游者之壶",
"勇士的壮行", "角斗士的酣醉", "宗室银瓮",
"翠绿猎人的容器", "战狂的骨杯", "少女片刻的闲暇", "魔女的心之火", "教官的茶杯", "降雷的凶兆", "渡火者的醒悟", "平雷之器", "武人的酒杯", "奇迹之杯",
"异国之盏", "幸运儿之杯", "冒险家金杯"],
"理之冠": ["酒渍船帽", "破冰踏雪的回音", "不动玄石之相", "夏祭之面", "祭雷礼冠", "祭火礼冠", "祭水礼冠", "祭冰礼冠", "游医的方巾", "学士的镜片", "赌徒的耳环",
"染血的铁假面", "渡火者的智慧", "流放者头冠", "宗室面具", "勇士的冠冕",
"守护束带", "武人的头巾", "角斗士的凯旋", "翠绿的猎人之冠", "指挥的礼帽", "焦灼的魔女帽", "教官的帽子", "唤雷的头冠", "战狂的鬼面", "平雷之冠",
"少女易逝的芳颜", "奇迹耳坠", "感别之冠", "幸运儿银冠", "冒险家头带"]
}
out_name = ''
max = 0
for set_name in name_list[kind]:
ratio = partial_ratio(set_name, name)
if ratio > max:
max = ratio
out_name = set_name
return out_name
def get_vice_stat_index(result, set_loc):
"""
判断圣遗物副词条所在位置
:param result: OCR结果列表
:return: int 表示位置
"""
stat_list = ['治疗加成', '攻击力', '生命值', '防御力', '元素精通', '元素充能效率', '暴击率', '暴击伤害', '风元素伤害加成', '火元素伤害加成',
'水元素伤害加成', '雷元素伤害加成', '冰元素伤害加成', '岩元素伤害加成' '物理伤害加成']
# loc = get_index([i.split('+')[0] in stat_list for i in result], True)[1:]
loc = []
ptr = 0
for i in result[:set_loc]:
for j in stat_list:
if partial_ratio(i, j) > 95:
loc.append(ptr)
break
ptr += 1
return loc[1:]
if response:
result = response.json()['words_result']
result = [str(i['words']).replace('·', '').replace(':', '') for i in result]
# print(result)
artifact = Artifact(result[0])
artifact.set_pieces = get_kind(result[1])
artifact.name = get_name(artifact.set_pieces, result[0])
artifact.main_stat = result[2]
artifact.main_stat_value = result[3]
artifact.star = len(result[4])
if result[5][0] == '+':
artifact.lv = int(result[5])
for item in result[5:]:
if '+' in item:
if artifact.vice_stat0 == '':
artifact.vice_stat0 = item.split('+')[0]
artifact.vice_stat0_value = str(item.split('+')[1])
elif artifact.vice_stat1 == '':
artifact.vice_stat1 = item.split('+')[0]
artifact.vice_stat1_value = str(item.split('+')[1])
elif artifact.vice_stat2 == '':
artifact.vice_stat2 = item.split('+')[0]
artifact.vice_stat2_value = str(item.split('+')[1])
elif artifact.vice_stat3 == '':
artifact.vice_stat3 = item.split('+')[0]
artifact.vice_stat3_value = str(item.split('+')[1])
artifact.set_name = get_set_name(result)
artifact.date = date
return artifact