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Usage:
# Create train data:
python xml_to_csv.py -i [PATH_TO_IMAGES_FOLDER]/train -o [PATH_TO_ANNOTATIONS_FOLDER]/train_labels.csv
# Create test data:
python xml_to_csv.py -i [PATH_TO_IMAGES_FOLDER]/test -o [PATH_TO_ANNOTATIONS_FOLDER]/test_labels.csv
"""
import os
import glob
import pandas as pd
import argparse
import xml.etree.ElementTree as ET
def xml_to_csv(path):
"""Iterates through all .xml files (generated by labelImg) in a given directory and combines them in a single Pandas datagrame.
Parameters:
----------
path : {str}
The path containing the .xml files
Returns
-------
Pandas DataFrame
The produced dataframe
"""
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall('object'):
value = (root.find('filename').text,
int(root.find('size')[0].text),
int(root.find('size')[1].text),
member[0].text,
int(member[4][0].text),
int(member[4][1].text),
int(member[4][2].text),
int(member[4][3].text)
)
xml_list.append(value)
column_name = ['filename', 'width', 'height',
'class', 'xmin', 'ymin', 'xmax', 'ymax']
xml_df = pd.DataFrame(xml_list, columns=column_name)
return xml_df
def main():
# Initiate argument parser
parser = argparse.ArgumentParser(
description="Sample TensorFlow XML-to-CSV converter")
parser.add_argument("-i",
"--inputDir",
help="Path to the folder where the input .xml files are stored",
type=str)
parser.add_argument("-o",
"--outputFile",
help="Name of output .csv file (including path)", type=str)
args = parser.parse_args()
if(args.inputDir is None):
args.inputDir = os.getcwd()
if(args.outputFile is None):
args.outputFile = args.inputDir + "/labels.csv"
assert(os.path.isdir(args.inputDir))
xml_df = xml_to_csv(args.inputDir)
xml_df.to_csv(
args.outputFile, index=None)
print('Successfully converted xml to csv.')
if __name__ == '__main__':
main()
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