import logging
import os
import random

import cv2
import numpy as np
from image import read_json

# size = np.array([[[500, 501, 502], [600, 601, 602], [100, 101, 102]],
#         [[500, 501, 502], [600, 601, 602], [100, 101, 102]],
#         [[500, 501, 502], [600, 601, 602], [100, 101, 102]]
#         ])
# print(size.shape)
# print(size[..., ::-1])

# img, _, _ = read_json("train/dataset-line/2/train_0.json")
# img.save('train_0.jpg')
# with open('train_463.jpg', 'wb') as f:
#         f.write(img)

# print(int(891.999999))
#
# _list = [[1, 2], [2, 3], [3, 4]]
# delete_list = [[1, 2]]
#
# _list.remove(delete_list[0])
# print(_list)
#
# size = (100, 1024)
# image = np.zeros(size[::-1], dtype='uint8')
# cv2.imshow("image", image)
# cv2.waitKey(0)

# image = cv2.imread("8.png")
# ret, binary = cv2.threshold(image, 180, 255, cv2.THRESH_BINARY)
# print("阈值：", ret)
# cv2.imshow("binary", binary)
# cv2.waitKey(0)

# _image = [[1, 0, 1],
#           [0, 0, 0]
#           ]
# _image = np.array(_image)
# print(np.where(_image >= 1))

# localPath = r"C:\Users\Administrator\Desktop\Test_ODPS\1623857120150.pdf"
# max_file_size_mb = 2
# if os.path.exists(localPath):
#     file_size_mb = int(os.path.getsize(localPath)/1024/1024)
#     print(file_size_mb)
#     if file_size_mb >= max_file_size_mb:
#         print("file size > " + str(max_file_size_mb))

# _str = "*" * 10
# print(_str)

print(np.pi / 2)
print(np.arctan(1))

print(random.sample([1, 1, 2], 2))

# from bs4 import BeautifulSoup
# import pandas as pd
# df = pd.read_excel("C:/Users/Administrator/Desktop/pb_screen_increase.xlsx")
# for index, row in df.iterrows():
#     df.loc[index, "dochtmlcon"] = str(BeautifulSoup(row["dochtmlcon"]).find("div", id="pcontent"))
# df.to_excel("C:/Users/Administrator/Desktop/pb_screen_increase_new.xlsx", index=False)

dict_stage = {"设计阶段":"设计",
              "环评阶段":"环评",
              "施工准备":"监理",
              "施工在建":"施工"}
list_stage_v = []
for k,v in dict_stage.items():
    list_stage_v.append("(?P<%s>%s)"%(k,v))
print("|".join(list_stage_v))