convert labelImg XML rectangles to labelMe JSON polygons with Image data

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I have annotated the images in labelImg tool and got the annotation in XML form. I need to convert this in LabelMe JSON format with imageData encoded in it.

Sample Input:

input image

sample XML:

<annotation>
    <folder>blocks</folder>
    <filename>sample_annotation.jpg</filename>
    <path>/path/sample_annotation.jpg</path>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>720</width>
        <height>540</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>cube</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>90</xmin>
            <ymin>87</ymin>
            <xmax>196</xmax>
            <ymax>194</ymax>
        </bndbox>
    </object>
    <object>
        <name>cube</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>498</xmin>
            <ymin>188</ymin>
            <xmax>607</xmax>
            <ymax>296</ymax>
        </bndbox>
    </object>
</annotation>

Sample output required:


{'imageData': '/9j/2w.........../9k=',
 'imageHeight': 540,
 'imagePath': 'sample_annotation.jpg',
 'imageWidth': 720,
 'shapes': [{'group_id': None,
   'label': 'cube',
   'points': [[90, 87], [196, 194]],
   'shape_type': 'rectangle'},
  {'group_id': None,
   'label': 'cube',
   'points': [[498, 188], [607, 296]],
   'shape_type': 'rectangle'}]}
pandas computer-vision opencv xml python
1 Answers

This is how I solved it.

Step 1: XML to CSV format

### xml to csv
import cv2
import os
import pandas as pd
import xml.etree.ElementTree as ET

def xml2csv(xml_path):
    """Convert XML to CSV

    Args:
        xml_path (str): Location of annotated XML file
    Returns:
        pd.DataFrame: converted csv file

    """
    print("xml to csv {}".format(xml_path))
    xml_list = []
    xml_df=pd.DataFrame()
    try:
        tree = ET.parse(xml_path)
        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)
    except Exception as e:
        print('xml conversion failed:{}'.format(e))
        return pd.DataFrame(columns=['filename,width,height','class','xmin','ymin','xmax','ymax'])
    return xml_df

Calling function to get the converted CSV

xml_path='/path/to/sample_annotation.xml'
xml_csv=xml2csv(xml_path)

Intermediate CSV looks like this:


                filename  width  height class  xmin  ymin  xmax  ymax
0  sample_annotation.jpg    720     540  cube    90    87   196   194
1  sample_annotation.jpg    720     540  cube   498   188   607   296

Step 2: CSV to LabelMe JSON

import cv2
import numpy as np
import os
import json
import pandas as pd
import base64

def df2labelme(symbolDict,image_path,image):
    """ convert annotation in CSV format to labelme JSON

    Args:
        symbolDict (dataframe): annotations in dataframe
        image_path (str): path to image
        image (np.ndarray): image read as numpy array

    Returns:
        JSON: converted labelme JSON

    """
    try:
        symbolDict['min']= symbolDict[['xmin','ymin']].values.tolist()
        symbolDict['max']= symbolDict[['xmax','ymax']].values.tolist()
        symbolDict['points']= symbolDict[['min','max']].values.tolist()
        symbolDict['shape_type']='rectangle'
        symbolDict['group_id']=None
        height,width,_=image.shape
        symbolDict['height']=height
        symbolDict['width']=width
        encoded = base64.b64encode(open(image_path, "rb").read())
        symbolDict.loc[:,'imageData'] = encoded
        symbolDict.rename(columns = {'class':'label','filename':'imagePath','height':'imageHeight','width':'imageWidth'},inplace=True)
        converted_json = (symbolDict.groupby(['imagePath','imageWidth','imageHeight','imageData'], as_index=False)
                     .apply(lambda x: x[['label','points','shape_type','group_id']].to_dict('r'))
                     .reset_index()
                     .rename(columns={0:'shapes'})
                     .to_json(orient='records'))
        converted_json = json.loads(converted_json)[0]
    except Exception as e:
        converted_json={}
        print('error in labelme conversion:{}'.format(e))
    return converted_json

Calling JSON converter

image_path='path/to/sample_annotation.jpg'
image=cv2.imread(image_path)
csv_json=df2labelme(xml_csv,image_path,image)

Final output


{'imageData': '/9j/2w.........../9k=',
 'imageHeight': 540,
 'imagePath': 'sample_annotation.jpg',
 'imageWidth': 720,
 'shapes': [{'group_id': None,
   'label': 'cube',
   'points': [[90, 87], [196, 194]],
   'shape_type': 'rectangle'},
  {'group_id': None,
   'label': 'cube',
   'points': [[498, 188], [607, 296]],
   'shape_type': 'rectangle'}]}
user_96059070
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