Are you tired of dealing with the hassle of converting your custom dataset polygon annotation format from .xml to COCO JSON? Look no further! In this comprehensive guide, we’ll take you by the hand and walk you through the step-by-step process of converting your .xml files to COCO JSON format. Buckle up and get ready to unlock the full potential of your dataset!
Understanding the Importance of COCO JSON Format
Before we dive into the conversion process, it’s essential to understand why COCO JSON format has become the industry standard for object detection and segmentation tasks. COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset that has been widely adopted by the computer vision community.
The COCO JSON format provides a unified and structured way of representing annotations, making it easier to share and compare results across different models and frameworks. By converting your custom dataset to COCO JSON, you’ll be able to tap into the vast ecosystem of COCO-compatible tools and libraries, accelerating your project’s development and accuracy.
The Custom Dataset Polygon Annotation Format
<annotation>
<folder>path_to_folder</folder>
<filename>image_filename</filename>
<path>image_path</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>image_width</width>
<height>image_height</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>object_class</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>xmin</xmin>
<ymin>ymin</ymin>
<xmax>xmax</xmax>
<ymax>ymax</ymax>
</bndbox>
<polygon>
<x>x1</x>
<y>y1</y>
<x>x2</x>
<y>y2</y>
<x>x3</x>
<y>y3</y>
...
<x>xN</x>
<y>yN</y>
</polygon>
</object>
</annotation>
In this example, we have a custom dataset polygon annotation format saved in .xml format. The annotation contains information about the image, such as the folder, filename, and path, as well as the object’s class, bounding box, and polygon coordinates.
Converting .xml to COCO JSON Format
To convert your custom dataset polygon annotation format from .xml to COCO JSON, you’ll need to follow these steps:
-
Install the required libraries: You’ll need to install the `xmltodict` and `json` libraries using pip:
pip install xmltodict json -
Parse the .xml file: Use the `xmltodict` library to parse the .xml file and convert it into a Python dictionary:
import xmltodictdict_data = xmltodict.parse(xml_file.read())
-
Extract the relevant information: Extract the necessary information from the dictionary, such as the image file name, width, height, and object annotations:
image_filename = dict_data['annotation']['filename']
image_width = int(dict_data['annotation']['size']['width'])
image_height = int(dict_data['annotation']['size']['height'])
objects = dict_data['annotation']['object'] -
Create the COCO JSON dictionary: Create a new dictionary to store the COCO JSON format data:
coco_dict = {}
coco_dict['images'] = []
coco_dict['annotations'] = []
coco_dict['categories'] = [] -
Populate the COCO JSON dictionary: Populate the COCO JSON dictionary with the extracted information:
image_id = 1
coco_image = {}
coco_image['id'] = image_id
coco_image['file_name'] = image_filename
coco_image['width'] = image_width
coco_image['height'] = image_height
coco_dict['images'].append(coco_image)for obj in objects:
coco_annotation = {}
coco_annotation['id'] = obj['name']
coco_annotation['image_id'] = image_id
coco_annotation['category_id'] = obj['name']
coco_annotation['bbox'] = [int(obj['bndbox']['xmin']), int(obj['bndbox']['ymin']), int(obj['bndbox']['xmax']) - int(obj['bndbox']['xmin']), int(obj['bndbox']['ymax']) - int(obj['bndbox']['ymin'])]
coco_annotation['segmentation'] = [obj['polygon']]
coco_dict['annotations'].append(coco_annotation)category_id = 1
for obj in objects:
coco_category = {}
coco_category['id'] = category_id
coco_category['name'] = obj['name']
coco_dict['categories'].append(coco_category)
category_id += 1 -
Write the COCO JSON file: Use the `json` library to write the COCO JSON dictionary to a file:
import jsonwith open('output.json', 'w') as f:
json.dump(coco_dict, f)
Example COCO JSON Output
{
"images": [
{
"id": 1,
"file_name": "image_filename.jpg",
"width": 1024,
"height": 768
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [100, 100, 300, 300],
"segmentation": [[100, 100, 200, 100, 200, 200, 100, 200]]
}
],
"categories": [
{
"id": 1,
"name": "object_class"
}
]
}
In this example, we’ve converted the custom dataset polygon annotation format from .xml to COCO JSON. The resulting COCO JSON file contains information about the image, object annotations, and categories.
Conclusion
Converting your custom dataset polygon annotation format from .xml to COCO JSON is a crucial step in unlocking the full potential of your dataset. By following the steps outlined in this guide, you’ll be able to tap into the vast ecosystem of COCO-compatible tools and libraries, accelerating your project’s development and accuracy.
Remember to adjust the script according to your specific needs and requirements. Happy converting!
| Format | Description |
|---|---|
| .xml | Custom dataset polygon annotation format |
| COCON JSON | Common Objects in Context JSON format |
References:
Frequently Asked Question
Converting custom dataset polygon annotation formats from XML to COCO JSON can be a daunting task, but don’t worry, we’ve got you covered! Here are the answers to your burning questions.
What is the general structure of the XML file I need to convert?
The XML file typically contains annotation information for each image, including object labels, bounding boxes, and polygon coordinates. The structure may vary depending on the annotation tool used, but it usually follows this format:
<annotation>
<folder>…</folder>
<filename>…</filename>
<size>…</size>
<object>
<name>…</name>
<bndbox>…</bndbox>
<polygon>…</polygon>
</object>
</annotation>
How do I extract the necessary information from the XML file?
You can use an XML parsing library, such as ElementTree in Python, to extract the information. You’ll need to iterate through each annotation, extracting the image file name, object labels, bounding box coordinates, and polygon coordinates.
What is the COCO JSON format, and how does it differ from the XML format?
The COCO JSON format is a standardized format for object detection and segmentation annotations. It consists of a single JSON file containing all annotation information. The key differences from the XML format are: 1) it’s in JSON format, 2) it uses a more compact and organized structure, and 3) it includes additional information like image heights and widths.
How do I convert the extracted information into the COCO JSON format?
You can create a Python script to convert the extracted information into the COCO JSON format. You’ll need to create a dictionary with the required fields, such as ‘images’, ‘annotations’, and ‘categories’, and then dump it to a JSON file.
Are there any tools or libraries available to simplify the conversion process?
Yes, there are tools and libraries available to simplify the conversion process. For example, the OpenCV library provides functions for XML and JSON handling, and the pycocotools library provides a Python API for working with COCO datasets. You can also find pre-written scripts and converters online.
