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# author: wujiahao # last modified: 2022/3/12 # convert darkface to yolo format
and split train val test import os import cv2 import shutil from sklearn.
model_selectionimport train_test_split data_root = '/data1/wjh/darkface' #
anndir imgdir dst_path = '/data1/wjh/yolo/darkface' img_list = os.listdir(os.
path.join(data_root, 'image')) # 6000 trainset, valset = train_test_split(
img_list, test_size=3000) # 3000 train valset, testset = train_test_split(
img_list, test_size=2000) # 2000 test, 1000 val for type, dts in enumerate([
trainset,valset, testset]): if not os.path.exists(os.path.join(dst_path, [
'train', 'val', 'test'][type])): os.mkdir(os.path.join(dst_path, ['train', 'val'
, 'test'][type])) os.mkdir(os.path.join(dst_path, ['train', 'val', 'test'][type]
, 'images')) os.mkdir(os.path.join(dst_path, ['train', 'val', 'test'][type],
'labels')) for imgname in dts: img_file_path = os.path.join(data_root, 'image',
imgname) img = cv2.imread(img_file_path) h_img, w_img, c_img = img.shape shutil.
copy(img_file_path, os.path.join(dst_path, ['train', 'val', 'test'][type],
'images', imgname)) with open(os.path.join(data_root, 'label', imgname.split('.'
)[0]+'.txt'), 'r') as f: ann = [[int(i) for i in line.strip().split()] for line
in f.readlines()[1:]] # abs l t r b, should convert to rel x_center y_center w h
# vis # for line in ann: # cv2.rectangle(img, (line[0], line[1]), (line[2],
line[3]), color=[0,255,0], thickness=2, lineType=4) # cv2.imwrite('try.png',
img) # exit(0) ann = [[(line[0]+line[2])/2/w_img, (line[1]+line[3])/2/h_img, (
line[2]-line[0])/w_img, (line[3]-line[1])/h_img] for line in ann] out_lines = [f
'{0} {line[0]} {line[1]} {line[2]} {line[3]}\n' for line in ann] with open(os.
path.join(dst_path, ['train', 'val', 'test'][type], 'labels', imgname.split('.')
[0]+'.txt'), 'w') as f: f.writelines(out_lines)