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- 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))
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