Annotation conversion from CMU to YOLO about yolo-hand-detection HOT 3 CLOSED

import glob
import os
import json

from scipy.io import loadmat
from PIL import Image

dataset_name = "test"
dataset = "data\\%s" % dataset_name

data = []
annotations = glob.glob("%s\\*.json" % dataset)

# create annotation dict
ann_map = {}
for ann in annotations:
	key = os.path.splitext(os.path.basename(ann))[0][:-2]
	
	if not key in ann_map:
		ann_map[key] = []

	ann_map[key].append(ann)

def load(file):
    with open(file, 'r') as f:
        return json.load(f)

def write(file, content):
	with open(file, 'w') as outfile:
		outfile.write(content)

def bounding_box(xs, ys):
	x_min = min(xs)
	x_max = max(xs)
	y_min = min(ys)
	y_max = max(ys)

	w = x_max - x_min
	h = y_max - y_min

	return (x_min + (w/2), y_min + (h/2), w, h)

for key in ann_map:
	anns = ann_map[key]
	default_file = os.path.basename(ann_map[key][0])
	default_file_name = os.path.splitext(default_file)[0]

	im = Image.open("%s\\%s.jpg" % (dataset, default_file_name))
	width = im.size[0]
	height = im.size[1]

	ann_txt = []

	for annFile in anns:
		filename = os.path.basename(annFile)
		name = os.path.splitext(filename)[0]
		detections = []

		print(annFile)
		desc = load(annFile)
		keypoints = desc["hand_pts"]

		xs = [l[0] for l in keypoints]
		ys = [l[1] for l in keypoints]
		
		x, y, w, h = bounding_box(xs, ys)

		ann_txt.append("%s %s %s %s %s" % (0, x / width, y / height, w / width, h / height))

	write("%s\\%s.txt" % (dataset, default_file_name), '\n'.join(ann_txt))
	data.append("%s\\%s.jpg" % (dataset, default_file_name))


write("%s.txt" % dataset_name, '\n'.join(data))
print("done!")