Script for Converting Pascal VOC annotations to Coco Json format

This script shows and example conversion of our table dataset pascal voc

annotations conversion to coco annotations

Usage :

You need to first create a txt file containing names of all pascal voc files

You can use following linux command

ls -1 | sed -e 's/.xml$//' | sort -n > "/path/to/folder/coco.txt"

And then read the comments in this script to understand its working

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

def generateVOC2Json(rootDir,xmlFiles): attrDict = dict()

Add categories according to you Pascal VOC annotations

attrDict["categories"]=[{"supercategory":"none","id":1,"name":"Table"}, {"supercategory":"none","id":2,"name":"cell"}, {"supercategory":"none","id":3,"name":"borderless"}

{"supercategory":"none","id":4,"name":"item_name"},

    # {"supercategory":"none","id":5,"name":"item_desc"},
    # {"supercategory":"none","id":6,"name":"price"},
    # {"supercategory":"none","id":7,"name":"total_price_text"},
    # {"supercategory":"none","id":8,"name":"total_price"},
    # {"supercategory":"none","id":9,"name":"footer"}
        ]

images = list() annotations = list() id1 = 1

Some random variables

cnt_bor = 0 cnt_cell = 0 cnt_bless = 0

Main execution loop

for root, dirs, files in os.walk(rootDir): image_id = 0 for file in xmlFiles: image_id = image_id + 1 if file in files: annotation_path = os.path.abspath(os.path.join(root, file)) image = dict() doc = xmltodict.parse(open(annotation_path,'r',encoding='UTF-8').read()) 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) if 'object' in doc['annotation']: for key,vals in doc['annotation'].items(): if(key=='object'): for value in attrDict["categories"]: if(not isinstance(vals, list)): vals = [vals] for val in vals: if str(val['name']) == value["name"]: annotation = dict() annotation["iscrowd"] = 0 annotation["image_id"] = image_id x1 = int(val["bndbox"]["xmin"]) - 1 y1 = int(val["bndbox"]["ymin"]) - 1 x2 = int(val["bndbox"]["xmax"]) - x1 y2 = int(val["bndbox"]["ymax"]) - y1 annotation["bbox"] = [x1, y1, x2, y2] annotation["area"] = float(x2 * y2) annotation["category_id"] = value["id"]

                # Tracking the count
                if(value["id"] == 1):
                  cnt_bor += 1
                if(value["id"] == 2):
                  cnt_cell += 1
                if(value["id"] == 3):
                  cnt_bless += 1

                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"

Printing out some statisticsf

print(len(images)) print("Bordered : ",cnt_bor," Cell : ",cnt_cell," Bless : ",cnt_bless) print(len(annotations))

Save the final JSON file

jsonString = json.dumps(attrDict)

jsonString = json.dumps(attrDict, indent = 4, sort_keys=True) with open(r'D:\banmian\data\ICDAR2019_cTDaR\ICDAR2019_cTDaR\test\TRACKB2\coco.json', "w",encoding='UTF-8') as f: f.write(jsonString)

Path to the txt file (see at the top of this script)

trainFile = r"D:\banmian\data\ICDAR2019_cTDaR\ICDAR2019_cTDaR\test\TRACKB2\dataset.txt" trainXMLFiles = list() with open(trainFile, "r") as f: for line in f: fileName = line.strip() print(fileName) trainXMLFiles.append(fileName + ".xml")

Path to the pascal voc xml files

rootDir = "D:/banmian/data/ICDAR2019_cTDaR/ICDAR2019_cTDaR/test_ground_truth/TRACKB2"

Start execution

generateVOC2Json(rootDir, trainXMLFiles)

:\anaconda\envs\cacsde\python.exe "D:/banmian/CascadeTabNet-master/CascadeTabNet-master/Data Preparation/generateVOC2JSON.py" Traceback (most recent call last): File "D:/banmian/CascadeTabNet-master/CascadeTabNet-master/Data Preparation/generateVOC2JSON.py", line 120, in generateVOC2Json(rootDir, trainXMLFiles) File "D:/banmian/CascadeTabNet-master/CascadeTabNet-master/Data Preparation/generateVOC2JSON.py", line 49, in generateVOC2Json image['file_name'] = str(doc['annotation']['filename']) KeyError: 'annotation' cTDaR_t10000

DevashishPrasad / CascadeTabNet

run voc2josn ,KeyError: 'annotation' #155