import logging import os 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)