By converting the COCO dataset into a YOLO format dataset, individual categories can be extracted and separated.

Tips: The COCO original data set format is .json format, which needs to be converted to VOC format first, and then converted to .txt format required by YOLO

1. Dataset download

Baidu Netdisk: https://pan.baidu.com/s/1YcxHxXfyoViTn2GH2xrPRA
Extraction code: 38ts

2. COCO to VOC format

############### (80 categories are converted) ###############

import argparse, json
import cytoolz
from lxml import etree, objectify
import os, re
 
 
def instance2xml_base(anno):
    E = objectify.ElementMaker(annotate=False)
    anno_tree = E.annotation(
        E.folder('VOC2014_instance/{}'.format(anno['category_id'])),
        E.filename(anno['file_name']),
        E.source(
            E.database('MS COCO 2014'),
            E.annotation('MS COCO 2014'),
            E.image('Flickr'),
            E.url(anno['coco_url'])
        ),
        E.size(
            E.width(anno['width']),
            E.height(anno['height']),
            E.depth(3)
        ),
        E.segmented(0),
    )
    return anno_tree
 
 
def instance2xml_bbox(anno, bbox_type='xyxy'):
    """bbox_type: xyxy (xmin, ymin, xmax, ymax); xywh (xmin, ymin, width, height)"""
    assert bbox_type in ['xyxy', 'xywh']
    if bbox_type == 'xyxy':
        xmin, ymin, w, h = anno['bbox']
        xmax = xmin+w
        ymax = ymin+h
    else:
        xmin, ymin, xmax, ymax = anno['bbox']
    E = objectify.ElementMaker(annotate=False)
    anno_tree = E.object(
        E.name(anno['category_id']),
        E.bndbox(
            E.xmin(xmin),
            E.ymin(ymin),
            E.xmax(xmax),
            E.ymax(ymax)
        ),
        E.difficult(anno['iscrowd'])
    )
    return anno_tree
 
 
def parse_instance(content, outdir):
    categories = {
    
    d['id']: d['name'] for d in content['categories']}
    # merge images and annotations: id in images vs image_id in annotations
    merged_info_list = list(map(cytoolz.merge, cytoolz.join('id', content['images'], 'image_id', content['annotations'])))
    # convert category id to name
    for instance in merged_info_list:
        instance['category_id'] = categories[instance['category_id']]
    # group by filename to pool all bbox in same file
    for name, groups in cytoolz.groupby('file_name', merged_info_list).items():
        anno_tree = instance2xml_base(groups[0])
        # if one file have multiple different objects, save it in each category sub-directory
        filenames = []
        for group in groups:
            filenames.append(os.path.join(outdir, re.sub(" ", "_", group['category_id']),
                                    os.path.splitext(name)[0] + ".xml"))
            anno_tree.append(instance2xml_bbox(group, bbox_type='xyxy'))
        for filename in filenames:
            etree.ElementTree(anno_tree).write(filename, pretty_print=True)
        print("Formating instance xml file {} done!".format(name))
 
 
def keypoints2xml_base(anno):
    annotation = etree.Element("annotation")
    etree.SubElement(annotation, "folder").text = "VOC2014_keypoints"
    etree.SubElement(annotation, "filename").text = anno['file_name']
    source = etree.SubElement(annotation, "source")
    etree.SubElement(source, "database").text = "MS COCO 2014"
    etree.SubElement(source, "annotation").text = "MS COCO 2014"
    etree.SubElement(source, "image").text = "Flickr"
    etree.SubElement(source, "url").text = anno['coco_url']
    size = etree.SubElement(annotation, "size")
    etree.SubElement(size, "width").text = str(anno["width"])
    etree.SubElement(size, "height").text = str(anno["height"])
    etree.SubElement(size, "depth").text = '3'
    etree.SubElement(annotation, "segmented").text = '0'
    return annotation
 
 
def keypoints2xml_object(anno, xmltree, keypoints_dict, bbox_type='xyxy'):
    assert bbox_type in ['xyxy', 'xywh']
    if bbox_type == 'xyxy':
        xmin, ymin, w, h = anno['bbox']
        xmax = xmin+w
        ymax = ymin+h
    else:
        xmin, ymin, xmax, ymax = anno['bbox']
    key_object = etree.SubElement(xmltree, "object")
    etree.SubElement(key_object, "name").text = anno['category_id']
    bndbox = etree.SubElement(key_object, "bndbox")
    etree.SubElement(bndbox, "xmin").text = str(xmin)
    etree.SubElement(bndbox, "ymin").text = str(ymin)
    etree.SubElement(bndbox, "xmax").text = str(xmax)
    etree.SubElement(bndbox, "ymax").text = str(ymax)
    etree.SubElement(key_object, "difficult").text = '0'
    keypoints = etree.SubElement(key_object, "keypoints")
    for i in range(0, len(keypoints_dict)):
        keypoint = etree.SubElement(keypoints, keypoints_dict[i+1])
        etree.SubElement(keypoint, "x").text = str(anno['keypoints'][i*3])
        etree.SubElement(keypoint, "y").text = str(anno['keypoints'][i*3+1])
        etree.SubElement(keypoint, "v").text = str(anno['keypoints'][i*3+2])
    return xmltree
 
 
def parse_keypoints(content, outdir):
    keypoints = dict(zip(range(1, len(content['categories'][0]['keypoints'])+1), content['categories'][0]['keypoints']))
    # merge images and annotations: id in images vs image_id in annotations
    merged_info_list = map(cytoolz.merge, cytoolz.join('id', content['images'], 'image_id', content['annotations']))
    # convert category name to person
    for keypoint in merged_info_list:
        keypoint['category_id'] = "person"
    # group by filename to pool all bbox and keypoint in same file
    for name, groups in cytoolz.groupby('file_name', merged_info_list).items():
        filename = os.path.join(outdir, os.path.splitext(name)[0]+".xml")
        anno_tree = keypoints2xml_base(groups[0])
        for group in groups:
            anno_tree = keypoints2xml_object(group, anno_tree, keypoints, bbox_type="xyxy")
        doc = etree.ElementTree(anno_tree)
        doc.write(open(filename, "w"), pretty_print=True)
        print("Formating keypoints xml file {} done!".format(name))
 
 
def main(args):
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    content = json.load(open(args.anno_file, 'r'))
    if args.type == 'instance':
        # make subdirectories
        sub_dirs = [re.sub(" ", "_", cate['name']) for cate in content['categories']]
        for sub_dir in sub_dirs:
            sub_dir = os.path.join(args.output_dir, str(sub_dir))
            if not os.path.exists(sub_dir):
                os.makedirs(sub_dir)
        parse_instance(content, args.output_dir)
    elif args.type == 'keypoint':
        parse_keypoints(content, args.output_dir)
 
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--anno_file", default = '这里填写COCO的标签路径例如：/Path/to/instances_train2014.json', help="annotation file for object instance/keypoint")
    parser.add_argument("--type", default = 'instance',type=str, help="object instance or keypoint", choices=['instance', 'keypoint'])
    parser.add_argument("--output_dir", default = '这里填写生成VOC标签的保存路径', help="output directory for voc annotation xml file")
    args = parser.parse_args()
    main(args)

############## (select a category to convert,Only need to modify 64 lines of code）##############

import argparse, json
import cytoolz
from lxml import etree, objectify
import os, re
 
 
def instance2xml_base(anno):
    E = objectify.ElementMaker(annotate=False)
    anno_tree = E.annotation(
        E.folder('VOC2014_instance/{}'.format(anno['category_id'])),
        E.filename(anno['file_name']),
        E.source(
            E.database('MS COCO 2014'),
            E.annotation('MS COCO 2014'),
            E.image('Flickr'),
            E.url(anno['coco_url'])
        ),
        E.size(
            E.width(anno['width']),
            E.height(anno['height']),
            E.depth(3)
        ),
        E.segmented(0),
    )
    return anno_tree
 
 
def instance2xml_bbox(anno, bbox_type='xyxy'):
    """bbox_type: xyxy (xmin, ymin, xmax, ymax); xywh (xmin, ymin, width, height)"""
    assert bbox_type in ['xyxy', 'xywh']
    if bbox_type == 'xyxy':
        xmin, ymin, w, h = anno['bbox']
        xmax = xmin+w
        ymax = ymin+h
    else:
        xmin, ymin, xmax, ymax = anno['bbox']
    E = objectify.ElementMaker(annotate=False)
    anno_tree = E.object(
        E.name(anno['category_id']),
        E.bndbox(
            E.xmin(xmin),
            E.ymin(ymin),
            E.xmax(xmax),
            E.ymax(ymax)
        ),
        E.difficult(anno['iscrowd'])
    )
    return anno_tree
 
 
def parse_instance(content, outdir):
    categories = {
    
    d['id']: d['name'] for d in content['categories']}
    # merge images and annotations: id in images vs image_id in annotations
    merged_info_list = list(map(cytoolz.merge, cytoolz.join('id', content['images'], 'image_id', content['annotations'])))
    # convert category id to name
    for instance in merged_info_list:
        instance['category_id'] = categories[instance['category_id']]
    # group by filename to pool all bbox in same file
    for name, groups in cytoolz.groupby('file_name', merged_info_list).items():
        anno_tree = instance2xml_base(groups[0])
        # if one file have multiple different objects, save it in each category sub-directory
        filenames = []
        for group in groups:
            if group['category_id']=='person':   #这里可以修改为COCO的其他类别，就可以输出相应的VOC格式文件
                filenames.append(os.path.join(outdir, re.sub(" ", "_", group['category_id']),
                                        os.path.splitext(name)[0] + ".xml"))
                anno_tree.append(instance2xml_bbox(group, bbox_type='xyxy'))
        for filename in filenames:
            etree.ElementTree(anno_tree).write(filename, pretty_print=True)
        print("Formating instance xml file {} done!".format(name))
 
 
def keypoints2xml_base(anno):
    annotation = etree.Element("annotation")
    etree.SubElement(annotation, "folder").text = "VOC2014_keypoints"
    etree.SubElement(annotation, "filename").text = anno['file_name']
    source = etree.SubElement(annotation, "source")
    etree.SubElement(source, "database").text = "MS COCO 2014"
    etree.SubElement(source, "annotation").text = "MS COCO 2014"
    etree.SubElement(source, "image").text = "Flickr"
    etree.SubElement(source, "url").text = anno['coco_url']
    size = etree.SubElement(annotation, "size")
    etree.SubElement(size, "width").text = str(anno["width"])
    etree.SubElement(size, "height").text = str(anno["height"])
    etree.SubElement(size, "depth").text = '3'
    etree.SubElement(annotation, "segmented").text = '0'
    return annotation
 
 
def keypoints2xml_object(anno, xmltree, keypoints_dict, bbox_type='xyxy'):
    assert bbox_type in ['xyxy', 'xywh']
    if bbox_type == 'xyxy':
        xmin, ymin, w, h = anno['bbox']
        xmax = xmin+w
        ymax = ymin+h
    else:
        xmin, ymin, xmax, ymax = anno['bbox']
    key_object = etree.SubElement(xmltree, "object")
    etree.SubElement(key_object, "name").text = anno['category_id']
    bndbox = etree.SubElement(key_object, "bndbox")
    etree.SubElement(bndbox, "xmin").text = str(xmin)
    etree.SubElement(bndbox, "ymin").text = str(ymin)
    etree.SubElement(bndbox, "xmax").text = str(xmax)
    etree.SubElement(bndbox, "ymax").text = str(ymax)
    etree.SubElement(key_object, "difficult").text = '0'
    keypoints = etree.SubElement(key_object, "keypoints")
    for i in range(0, len(keypoints_dict)):
        keypoint = etree.SubElement(keypoints, keypoints_dict[i+1])
        etree.SubElement(keypoint, "x").text = str(anno['keypoints'][i*3])
        etree.SubElement(keypoint, "y").text = str(anno['keypoints'][i*3+1])
        etree.SubElement(keypoint, "v").text = str(anno['keypoints'][i*3+2])
    return xmltree
 
 
def parse_keypoints(content, outdir):
    keypoints = dict(zip(range(1, len(content['categories'][0]['keypoints'])+1), content['categories'][0]['keypoints']))
    # merge images and annotations: id in images vs image_id in annotations
    merged_info_list = map(cytoolz.merge, cytoolz.join('id', content['images'], 'image_id', content['annotations']))
    # convert category name to person
    for keypoint in merged_info_list:
        keypoint['category_id'] = "person"
    # group by filename to pool all bbox and keypoint in same file
    for name, groups in cytoolz.groupby('file_name', merged_info_list).items():
        filename = os.path.join(outdir, os.path.splitext(name)[0]+".xml")
        anno_tree = keypoints2xml_base(groups[0])
        for group in groups:
            anno_tree = keypoints2xml_object(group, anno_tree, keypoints, bbox_type="xyxy")
        doc = etree.ElementTree(anno_tree)
        doc.write(open(filename, "w"), pretty_print=True)
        print("Formating keypoints xml file {} done!".format(name))
 
 
def main(args):
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    content = json.load(open(args.anno_file, 'r'))
    if args.type == 'instance':
        # make subdirectories
        sub_dirs = [re.sub(" ", "_", cate['name']) for cate in content['categories']]
        for sub_dir in sub_dirs:
            sub_dir = os.path.join(args.output_dir, str(sub_dir))
            if not os.path.exists(sub_dir):
                os.makedirs(sub_dir)
        parse_instance(content, args.output_dir)
    elif args.type == 'keypoint':
        parse_keypoints(content, args.output_dir)
 
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--anno_file", default = '这里填写COCO的标签路径例如：/Path/to/instances_train2014.json', help="annotation file for object instance/keypoint")
    parser.add_argument("--type", default = 'instance',type=str, help="object instance or keypoint", choices=['instance', 'keypoint'])
    parser.add_argument("--output_dir", default = '这里填写生成VOC标签的保存路径', help="output directory for voc annotation xml file")
    args = parser.parse_args()
    main(args)

3. VOC to YOLO.TXT

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join


def convert(size, box):
    # size=(width, height)  b=(xmin, xmax, ymin, ymax)
    # x_center = (xmax+xmin)/2        y_center = (ymax+ymin)/2
    # x = x_center / width            y = y_center / height
    # w = (xmax-xmin) / width         h = (ymax-ymin) / height

    x_center = (box[0] + box[1]) / 2.0
    y_center = (box[2] + box[3]) / 2.0
    x = x_center / size[0]
    y = y_center / size[1]

    w = (box[1] - box[0]) / size[0]
    h = (box[3] - box[2]) / size[1]

    # print(x, y, w, h)
    return (x, y, w, h)


def convert_annotation(xml_files_path, save_txt_files_path, classes):
    xml_files = os.listdir(xml_files_path)
    # print(xml_files)
    for xml_name in xml_files:
        print(xml_name)
        xml_file = os.path.join(xml_files_path, xml_name)
        out_txt_path = os.path.join(save_txt_files_path, xml_name.split('.')[0] + '.txt')
        out_txt_f = open(out_txt_path, 'w')
        tree = ET.parse(xml_file)
        root = tree.getroot()

        size = root.find('size')
        w = int(size.find('width').text)
        h = int(size.find('height').text)


        for obj in root.iter('object'):
            difficult = obj.find('difficult').text
            cls = obj.find('name').text
            if cls not in classes or int(difficult) == 1:
                continue
            cls_id = classes.index(cls)
            xmlbox = obj.find('bndbox')
            b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
                 float(xmlbox.find('ymax').text))
            # b=(xmin, xmax, ymin, ymax)
            # print(w, h, b)
            bb = convert((w, h), b)
            out_txt_f.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


if __name__ == "__main__":
    # 把forklift_pallet的voc的xml标签文件转化为yolo的txt标签文件
    # 1、需要转化的类别
    # classes = ['car',  'bus',  'van', 'others']  # 注意：这里根据自己的类别名称及种类自行更改
    classes = ['person']  # 注意：这里根据自己的类别名称及种类自行更改
    # 2、voc格式的xml标签文件路径
    xml_files1 = r'E:\project\py-project\network\dataset\COCO\voc\person'
    # 3、转化为yolo格式的txt标签文件存储路径
    save_txt_files1 = r'E:\project\py-project\network\dataset\COCO\yolo'

    convert_annotation(xml_files1, save_txt_files1, classes)

4. Reference

1. Convert the COCO dataset into a VOC dataset, and separate categories can also be extracted, such as pedestrian detection datasets.
2. https://github.com/CasiaFan/Dataset_to_VOC_converter