How to convert PASCAL VOC to YOLO - training-data

Question

I was trying to develop some way to convert annotations between formats, and it's quit hard to find information but here I have :
This one is PASCAL VOC
<width>800</width>
<height>450</height>
<depth>3</depth>
<bndbox>
<xmin>474</xmin>
<ymin>2</ymin>
<xmax>726</xmax> <!-- shape_width = 252 -->
<ymax>449</ymax> <!-- shape_height = 447 -->
</bndbox>
convert to YOLO darknet
2 0.750000 0.501111 0.315000 0.993333
note initial 2 it's a category

Answer 1

My classmates and I have created a python package called PyLabel to help others with this task and other labelling tasks.
This would be the basic code to convert from voc to coco:
!pip install pylabel
from pylabel import importer
dataset = importer.ImportVOC(path=path_to_annotations)
dataset.exporter.ExportToYoloV5()
You can find sample notebooks and the source code here https://github.com/pylabel-project/pylabel

Answer 2

using some math: (also can be useful to COCO)
categ_index [(xmin + xmax) / 2 / image_width] [(ymin + ymax) / 2 / image_height] [(xmax - xmin) / image_width] [(ymax - ymin) / image_height]
in js code
const categ_index = 2;
const { width: image_width, height: image_height } = {
width: 800,
height: 450,
};
const { xmin, ymin, xmax, ymax } = {
xmin: 474,
ymin: 2,
xmax: 727,
ymax: 449,
};
const x_coord = (xmin + xmax) / 2 / image_width;
const y_coord = (ymin + ymax) / 2 / image_height;
const shape_width = (xmax - xmin) / image_width;
const shape_height = (ymax - ymin) / image_height;
console.log(`${categ_index} ${x_coord.toFixed(7)} ${y_coord.toFixed(7)} ${shape_width.toFixed(7)} ${shape_height.toFixed(7)}`);
// output
// 2 0.7506250 0.5011111 0.3162500 0.9933333

Answer 3

I use the following snippet to convert Pascal_VOC to YOLO. Yolo uses normalized coordinates so it is important to have the height and width of your image. Otherwise you can't calculate it.
Here is my snippet:
# Convert Pascal_Voc bb to Yolo
def pascal_voc_to_yolo(x1, y1, x2, y2, image_w, image_h):
return [((x2 + x1)/(2*image_w)), ((y2 + y1)/(2*image_h)), (x2 - x1)/image_w, (y2 - y1)/image_h]
I wrote an article about object detection format and how to convert them. You can check my blog post on Medium: https://christianbernecker.medium.com/convert-bounding-boxes-from-coco-to-pascal-voc-to-yolo-and-back-660dc6178742
Have fun!