yolo转coco格式(txt格式转json格式)
2023-12-23 18:52:09
import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse
classes = ['carpetweed', 'morningglory', 'palmer_amaranth']
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default='/home/test/images',type=str, help="path of images")
parser.add_argument('--label_path', default='/home/test/labels',type=str, help="path of labels .txt")
parser.add_argument('--save_path', type=str,default='/home/rendoudou/datasets/CottonWeedDet3/data.json', help="if not split the dataset, give a path to a json file")
arg = parser.parse_args()
def yolo2coco(arg):
print("Loading data from ", arg.image_path, arg.label_path)
assert os.path.exists(arg.image_path)
assert os.path.exists(arg.label_path)
originImagesDir = arg.image_path
originLabelsDir = arg.label_path
# images dir name
indexes = os.listdir(originImagesDir)
dataset = {'categories': [], 'annotations': [], 'images': []}
for i, cls in enumerate(classes, 0):
dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})
# 标注的id
ann_id_cnt = 0
for k, index in enumerate(tqdm(indexes)):
# 支持 png jpg 格式的图片.
txtFile = f'{index[:index.rfind(".")]}.txt'
stem = index[:index.rfind(".")]
# 读取图像的宽和高
try:
im = cv2.imread(os.path.join(originImagesDir, index))
height, width, _ = im.shape
except Exception as e:
print(f'{os.path.join(originImagesDir, index)} read error.\nerror:{e}')
# 添加图像的信息
if not os.path.exists(os.path.join(originLabelsDir, txtFile)):
# 如没标签,跳过,只保留图片信息.
continue
dataset['images'].append({'file_name': index,
'id': stem,
'width': width,
'height': height})
with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:
labelList = fr.readlines()
for label in labelList:
label = label.strip().split()
x = float(label[1])
y = float(label[2])
w = float(label[3])
h = float(label[4])
# convert x,y,w,h to x1,y1,x2,y2
H, W, _ = im.shape
x1 = (x - w / 2) * W
y1 = (y - h / 2) * H
x2 = (x + w / 2) * W
y2 = (y + h / 2) * H
# 标签序号从0开始计算, coco2017数据集标号混乱,不管它了。
cls_id = int(label[0])
width = max(0, x2 - x1)
height = max(0, y2 - y1)
dataset['annotations'].append({
'area': width * height,
'bbox': [x1, y1, width, height],
'category_id': cls_id,
'id': ann_id_cnt,
'image_id': stem,
'iscrowd': 0,
# mask, 矩形是从左上角点按顺时针的四个顶点
'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]
})
ann_id_cnt += 1
# 保存结果
with open(arg.save_path, 'w') as f:
json.dump(dataset, f)
print('Save annotation to {}'.format(arg.save_path))
if __name__ == "__main__":
yolo2coco(arg)
文章来源:https://blog.csdn.net/weixin_43722052/article/details/135171585
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