xml_to_coco.py

import os
import glob
import json
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET

save_json_path = 'val_coco.json'

def xml_to_csv(path):
    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

image_path = os.path.join(os.getcwd(), ("validation\\XML"))
data = xml_to_csv(image_path)
# data.to_csv(('XML' + '_labels.csv'), index=None)
print('Successfully converted {} xml to csv.'.format(image_path))

images = []
categories = []
annotations = []

category = {}
category["supercategory"] = 'none'
category["id"] = 0
category["name"] = 'None'
categories.append(category)

data['fileid'] = data['filename'].astype('category').cat.codes
data['categoryid']= pd.Categorical(data['class'],ordered= True).codes
data['categoryid'] = data['categoryid']+1
data['annid'] = data.index

def image(row):
    image = {}
    image["height"] = row.height
    image["width"] = row.width
    image["id"] = row.fileid
    image["file_name"] = row.filename
    return image

def category(row):
    category = {}
    category["supercategory"] = 'None'
    category["id"] = row.categoryid
    category["name"] = row[4]
    return category

def annotation(row):
    annotation = {}
    area = (row.xmax -row.xmin)*(row.ymax - row.ymin)
    annotation["segmentation"] = []
    annotation["iscrowd"] = 0
    annotation["area"] = area
    annotation["image_id"] = row.fileid

    annotation["bbox"] = [row.xmin, row.ymin, row.xmax -row.xmin,row.ymax-row.ymin ]

    annotation["category_id"] = row.categoryid
    annotation["id"] = row.annid
    return annotation

for row in data.itertuples():
    annotations.append(annotation(row))

imagedf = data.drop_duplicates(subset=['fileid']).sort_values(by='fileid')
for row in imagedf.itertuples():
    images.append(image(row))

catdf = data.drop_duplicates(subset=['categoryid']).sort_values(by='categoryid')
for row in catdf.itertuples():
    categories.append(category(row))

data_coco = {}
data_coco["images"] = images
data_coco["categories"] = categories
data_coco["annotations"] = annotations


json.dump(data_coco, open(save_json_path, "w"), indent=4)