coco—>voc，voc—>darknet，coco—>darknet
由于我较多的都是拿到json和darknet格式。而coco格式的数据集并不是很经常下载用到，voc的就更加从来没用过，所以本篇就简单介绍直接上代码，将coco转voc以及voc转darknet合并在一起。所以如果你是这三个需求中的一个，本篇将都会对你有用。
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()