Convert annotations to YoloV8 format?

[fatbringer]

I want to train YoloV8 using this dataset for person detection and get bounding box.
I see the current annotation are just xy coordinates.
Would you know how to convert your annotations to YoloV8 annotation?

[fatbringer]

ok i will answer myself since i fixd it

import os
from pathlib import Path
import yaml

from ultralytics.utils.downloads import download

def visdrone2yolo(dir):
    from PIL import Image
    from tqdm import tqdm

 

    def convert_box(size, box):
        # Convert VisDrone box to YOLO xywh box
        dw = 1. / size[0]
        dh = 1. / size[1]
        return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh

    (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
    pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
    for f in pbar:
        img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
        lines = []
        with open(f, 'r') as file:  # read annotation.txt
            for row in [x.split(',') for x in file.read().strip().splitlines()]:
                if row[4] == '0':  # VisDrone 'ignored regions' class 0
                    continue
                #modifications to include only pedestrian and people
                if row[5] == "1" or "2" or "3" or "7":
                    #cls = int(row[5]) - 1
                    cls = 0 #turns all the people classes into 0, pls make sure consistent across all training
                    box = convert_box(img_size, tuple(map(int, row[:4])))
                    lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
                    with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
                        fl.writelines(lines)  # write label.txt
    
    print("done")


# Download

with open("VisDrone.yaml", "r") as stream:
    try:
        yamlfile = yaml.safe_load(stream)
    except yaml.YAMLError as exc:
        print(exc)

print("cwd is: ", Path.cwd())
base_dir = Path.cwd()
dir = base_dir.joinpath(yamlfile['path'])
#dir = ( Path.cwd() / yamlfile['path'] )  # dataset root dir
print("dataset root dir is: ", dir)
urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
        'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
        'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
        'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']

#download(urls, dir=dir, curl=True, threads=4)

# Convert
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
    visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

thanks!