Data in the following format:
{"60091.jpg": [[214.1664, 192.99996, 261.00032, 224.83332000000001, 1], [74.83328, 160.49988, 224.0, 267.4998, 1]]}
{"60092.jpg": [[15.166656, 197.49996, 80.16640000000001, 233.49996000000002, 1], [52.250048, 215.49996, 85.49951999999999, 254.5002, 3]}
{"60093.jpg": []}
{"60094.jpg": [[171.33312, 188.49996000000002, 243.8336, 240.66647999999998, 1]]}
{"60095.jpg": []}
{"60096.jpg": []}
{"60097.jpg": []}
Converted to
000002.jpg Dog 44 28 132 121
000002.jpg Cat 46 18 112 123
000003.jpg GGG 54 19 243 178
The utility is then converted to XML format for visualization
import pandas as pd
import csv
label=open(‘label.idl‘,‘r‘)
label_list=label.readlines()
#fo = open("foo.txt", "wb")
output_file =‘./foo.txt‘
output = open(output_file,‘wb‘)
writer = csv.writer(output)
temp=[]#pre define tuple or list is ok
for content in label_list:
obj=pd.read_json(content,orient=‘index‘)
(row,col)=obj.shape
for i in range(col):
Temp=[obj.IX[0].name,obj.IX[0][I][4],obj.IX[0][I][0],obj.IX[0][I][1],obj.IX[0][I][2],obj.IX[0][I][3]]
writer.writerow(temp)
# writer.writerow(obj)
# writer.writerow([obj.index,obj[i][0]])
# writer.writerow(obj[i][0])
# output.writelines(str(obj[i]))
# output.write(str(obj[i]))
# fo.write(str(obj[1][0][0]))
# fo.write(str(obj[1][0][0]);
#
# obj[1][0][0]
# obj[1][0][1]
# obj[1][0][2]
# obj[1][0][3]
# obj[1][0][4]
# i++
#obj[]
#print obj[0][]
#(obj[0].to_str()).split(‘,‘)[4]
output.close()
label.close()
Working with IDL files