Convert Pascal VOC format xml file to COCO format json file

Most of the code is Convert-Pascal-VOC-to-COCO (github) I am diverting it.

For personal reasons, I had to use a COCO format dataset to learn object detection. However, the learning data created by the annotation tool so far cannot be used immediately in the Pascal VOC format xml file. There is little information when converting a Pascal VOC format dataset to a COCO format dataset, isn't it?

Maybe I just haven't been found. ..

Python code

Converts all xml files under the ./annotation/ directory and outputs to one json file (train.json).

The name of the category is racket, player ,,, but please rewrite it according to the data.

`XML2JSON.py`


import os
import glob
import xml.etree.ElementTree as ET
import xmltodict
import json
from xml.dom import minidom
from collections import OrderedDict

def XML2JSON(xmlFiles):
    attrDict = dict()
    attrDict["categories"]=[{"supercategory":"none","id":1,"name":"racket"},
                    {"supercategory":"none","id":2,"name":"player"},
                    {"supercategory":"none","id":3,"name":"tennisball"},
                    {"supercategory":"none","id":4,"name":"umpire"},
                {"supercategory":"none","id":5,"name":"ballperson"},
                {"supercategory":"none","id":6,"name":"camera"},
                {"supercategory":"none","id":7,"name":"player"},
                {"supercategory":"none","id":8,"name":"tv"},
                {"supercategory":"none","id":9,"name":"smartphone"}
                  ]
    images = list()
    annotations = list()
    image_id = 0
    for file in xmlFiles:    
        image_id = image_id + 1      
        annotation_path=file
        image = dict()
        doc = xmltodict.parse(open(annotation_path).read(), force_list=('object'))
        image['file_name'] = str(doc['annotation']['filename'])
        image['height'] = int(doc['annotation']['size']['height'])
        image['width'] = int(doc['annotation']['size']['width'])
        image['id'] = image_id
        print ("File Name: {} and image_id {}".format(file, image_id))
        images.append(image)
        id1 = 1
        if 'object' in doc['annotation']:
            for obj in doc['annotation']['object']:
                for value in attrDict["categories"]:
                    annotation = dict()          
                    if str(obj['name']) == value["name"]:
                        annotation["iscrowd"] = 0
                        annotation["image_id"] = image_id
                        x1 = int(obj["bndbox"]["xmin"])  - 1
                        y1 = int(obj["bndbox"]["ymin"]) - 1
                        x2 = int(obj["bndbox"]["xmax"]) - x1
                        y2 = int(obj["bndbox"]["ymax"]) - y1                         
                        annotation["bbox"] = [x1, y1, x2, y2]
                        annotation["area"] = float(x2 * y2)
                        annotation["category_id"] = value["id"]
                        annotation["ignore"] = 0
                        annotation["id"] = id1
                        annotation["segmentation"] = [[x1,y1,x1,(y1 + y2), (x1 + x2), (y1 + y2), (x1 + x2), y1]]
                        id1 +=1
                        annotations.append(annotation)

            else:
                print("File: {} doesn't have any object".format(file))

        else:
            print("File: {} not found".format(file))
            

    attrDict["images"] = images    
    attrDict["annotations"] = annotations
    attrDict["type"] = "instances"

    jsonString = json.dumps(attrDict)
    with open("train.json", "w") as f:
        f.write(jsonString)


path="./annotations/"
trainXMLFiles=glob.glob(os.path.join(path, '*.xml'))
XML2JSON(trainXMLFiles)