coco—>voc，voc—>darknet，coco—>darknet
Because I mostly get json and darknet formats. The coco format data set is not very often downloaded and used, and the voc one has never been used, so this article briefly introduces the code directly, and merges the coco to voc and voc to darknet together. So if you are one of these three needs, this article will be useful to you.
coco—>voc
import os
import time
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
from pycocotools.coco import COCO
def trans_id(category_id):
names = []
namesid = []
for i in range(0, len(cats)):
names.append(cats[i]['name'])
namesid.append(cats[i]['id'])
# print('id:{1}\t {0}'.format(names[i], namesid[i]))
index = namesid.index(category_id)
return index
# root = 'D:\\val\\thermal_8_bit\\' # 你下载的 COCO 数据集所在目录
# dataType = '2019'
anno = '/home/alpha/桌面/22222/Safety/_annotations.coco.json'
xml_dir = '/home/alpha/桌面/22222/Safety/json_xml/'
coco = COCO(anno) # 读文件
cats = coco.loadCats(coco.getCatIds()) # 这里loadCats就是coco提供的接口，获取类别
# Create anno dir
dttm = time.strftime("%Y%m%d%H%M%S", time.localtime())
# if os.path.exists(xml_dir):
# os.rename(xml_dir, xml_dir + dttm)
# os.mkdir(xml_dir)
with open(anno, 'r') as load_f:
f = json.load(load_f)
imgs = f['images'] # json文件的img_id和图片对应关系 imgs列表表示多少张图
cat = f['categories']
df_cate = pd.DataFrame(f['categories']) # json中的类别
df_cate_sort = df_cate.sort_values(["id"], ascending=True) # 按照类别id排序
categories = list(df_cate_sort['name']) # 获取所有类别名称
print('categories = ', categories)
df_anno = pd.DataFrame(f['annotations']) # json中的annotation
for i in tqdm(range(len(imgs))): # 大循环是images所有图片
xml_content = []
file_name = imgs[i]['file_name'] # 通过img_id找到图片的信息
height = imgs[i]['height']
img_id = imgs[i]['id']
width = imgs[i]['width']
# xml文件添加属性
xml_content.append("<annotation>")
xml_content.append(" <folder>VOC2007</folder>")
xml_content.append(" <filename>" + file_name + "</filename>")
xml_content.append(" <size>")
xml_content.append(" <width>" + str(width) + "</width>")
xml_content.append(" <height>" + str(height) + "</height>")
xml_content.append(" </size>")
xml_content.append(" <segmented>0</segmented>")
# 通过img_id找到annotations
annos = df_anno[df_anno["image_id"].isin([img_id])] # (2,8)表示一张图有两个框
for index, row in annos.iterrows(): # 一张图的所有annotation信息
bbox = row["bbox"]
category_id = row["category_id"]
# cate_name = categories[trans_id(category_id)]
cate_name = cat[category_id]['name']
# add new object
xml_content.append("<object>")
xml_content.append("<name>" + cate_name + "</name>")
xml_content.append("<pose>Unspecified</pose>")
xml_content.append("<truncated>0</truncated>")
xml_content.append("<difficult>0</difficult>")
xml_content.append("<bndbox>")
xml_content.append("<xmin>" + str(int(bbox[0])) + "</xmin>")
xml_content.append("<ymin>" + str(int(bbox[1])) + "</ymin>")
xml_content.append("<xmax>" + str(int(bbox[0] + bbox[2])) + "</xmax>")
xml_content.append("<ymax>" + str(int(bbox[1] + bbox[3])) + "</ymax>")
xml_content.append("</bndbox>")
xml_content.append("</object>")
xml_content.append("</annotation>")
x = xml_content
xml_content = [x[i] for i in range(0, len(x)) if x[i] != "\n"]
### list存入文件
xml_path = os.path.join(xml_dir, file_name.replace('.jpg', '.xml'))
with open(xml_path, 'w+', encoding="utf8") as f:
f.write('\n'.join(xml_content))
xml_content[:] = []
voc—>darknet
import argparse
import glob
import os
import xml.etree.ElementTree as ET
import json
from tqdm import tqdm
def parse_args():
"""
参数配置
"""
parser = argparse.ArgumentParser(description='xml2json')
parser.add_argument('--raw_label_dir', help='the path of raw label', default='/home/alpha/桌面/22222/Safety/json_xml') # voc路径
parser.add_argument('--pic_dir', help='the path of picture', default='/home/alpha/桌面/22222/Safety/valid') # 图片路径
parser.add_argument('--save_dir', help='the path of new label', default='/home/alpha/桌面/22222/Safety/json') # 保存路径
args = parser.parse_args()
return args
def read_xml_gtbox_and_label(xml_path):
"""
读取xml内容
"""
tree = ET.parse(xml_path)
root = tree.getroot()
size = root.find('size')
width = int(size.find('width').text)
height = int(size.find('height').text)
# depth = int(size.find('depth').text)
points = []
for obj in root.iter('object'):
cls = obj.find('name').text
pose = obj.find('pose').text
xmlbox = obj.find('bndbox')
xmin = float(xmlbox.find('xmin').text)
xmax = float(xmlbox.find('xmax').text)
ymin = float(xmlbox.find('ymin').text)
ymax = float(xmlbox.find('ymax').text)
box = [xmin, ymin, xmax, ymax]
point = [cls, box]
points.append(point)
return points, width, height
def main():
"""
主函数
"""
args = parse_args()
labels = glob.glob(args.raw_label_dir + '/*.xml')
for i, label_abs in tqdm(enumerate(labels), total=len(labels)):
_, label = os.path.split(label_abs)
label_name = label.rstrip('.xml')
# img_path = os.path.join(args.pic_dir, label_name + '.jpg')
img_path = label_name + '.jpg'
points, width, height = read_xml_gtbox_and_label(label_abs)
json_str = {
}
json_str['version'] = '4.5.6'
json_str['flags'] = {
}
shapes = []
for i in range(len(points)):
# 判断是否是左下角的点为关键点
if points[i][0] == "left head":
shape = {
}
shape['label'] = 'head'
shape['points'] = [[points[i][1][0], points[i][1][3]]]
shape['group_id'] = None
# 类型为点
shape['shape_type'] = 'point'
shape['flags'] = {
}
shapes.append(shape)
# 判断是否是右下角的点是关键点
elif points[i][0] == "right head":
shape = {
}
shape['label'] = 'head'
shape['points'] = [[points[i][1][2], points[i][1][3]]]
shape['group_id'] = None
shape['shape_type'] = 'point'
shape['flags'] = {
}
shapes.append(shape)
# 其余的情况
else:
shape = {
}
shape['label'] = points[i][0]
shape['points'] = [[points[i][1][0], points[i][1][1]],
[points[i][1][2], points[i][1][3]]]
shape['group_id'] = None
# labelIMG的标注类型基本都为长方形
shape['shape_type'] = 'rectangle'
shape['flags'] = {
}
shapes.append(shape)
json_str['shapes'] = shapes
json_str['imagePath'] = img_path
json_str['imageData'] = None
json_str['imageHeight'] = height
json_str['imageWidth'] = width
with open(os.path.join(args.save_dir, label_name + '.json'), 'w') as f:
json.dump(json_str, f, indent=2)
if __name__ == '__main__':
main()