Converting VisDrone annotations to YOLO format using multiprocessing - multithreading

Question

I tried using VisDrone.yaml for the conversion but it takes very long. I tried modifying the yaml but couldn't make it work.
I tried :
def visdrone2yolo_worker(f):
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
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
cls = int(row[5]) - 1
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(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
fl.writelines(lines) # write label.txt
def visdrone2yolo(dir):
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
with Pool(50) as p: # create a pool of 50 worker processes
p.map(visdrone2yolo_worker, pbar) # apply visdrone2yolo_worker to each file in pbar in parallel
# Convert
dir = Path(yaml['path']) # dataset root dir
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
I get:
File "/usr/lib/python3.9/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/usr/lib/python3.9/multiprocessing/pool.py", line 478, in _map_async
chunksize, extra = divmod(len(iterable), len(self._pool) * 4)
TypeError: 'NoneType' object cannot be interpreted as an integer
I tried also without the pbar but it didn't work..