已实现数据集转换

一、CoCo1.1 CoCo2VOC1.2 CoCo2YOLO 二、VOC2.1 VOC2CoCo2.2 VOC2YOLO 三、YOLO3.1 YOLO2CoCo3.2 YOLO2VOC 四、TT100K4.1 TT100K2YOLO

一、CoCo

1.1 CoCo2VOC

from pycocotools.coco import COCOimport osfrom lxml import etree, objectifyimport shutilfrom tqdm import tqdmimport sysimport argparse# 将类别名字和id建立索引def catid2name(coco):    classes = dict()    for cat in coco.dataset['categories']:        classes[cat['id']] = cat['name']    return classes# 将标签信息写入xmldef save_anno_to_xml(filename, size, objs, save_path):    E = objectify.ElementMaker(annotate=False)    anno_tree = E.annotation(        E.folder("DATA"),        E.filename(filename),        E.source(            E.database("The VOC Database"),            E.annotation("PASCAL VOC"),            E.image("flickr")        ),        E.size(            E.width(size['width']),            E.height(size['height']),            E.depth(size['depth'])        ),        E.segmented(0)    )    for obj in objs:        E2 = objectify.ElementMaker(annotate=False)        anno_tree2 = E2.object(            E.name(obj[0]),            E.pose("Unspecified"),            E.truncated(0),            E.difficult(0),            E.bndbox(                E.xmin(obj[1]),                E.ymin(obj[2]),                E.xmax(obj[3]),                E.ymax(obj[4])            )        )        anno_tree.append(anno_tree2)    anno_path = os.path.join(save_path, filename[:-3] + "xml")    etree.ElementTree(anno_tree).write(anno_path, pretty_print=True)# 利用cocoAPI从json中加载信息def load_coco(anno_file, xml_save_path):    if os.path.exists(xml_save_path):        shutil.rmtree(xml_save_path)    os.makedirs(xml_save_path)    coco = COCO(anno_file)    classes = catid2name(coco)    imgIds = coco.getImgIds()    classesIds = coco.getCatIds()    for imgId in tqdm(imgIds):        size = {}        img = coco.loadImgs(imgId)[0]        filename = img['file_name']        width = img['width']        height = img['height']        size['width'] = width        size['height'] = height        size['depth'] = 3        annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)        anns = coco.loadAnns(annIds)        objs = []        for ann in anns:            object_name = classes[ann['category_id']]            # bbox:[x,y,w,h]            bbox = list(map(int, ann['bbox']))            xmin = bbox[0]            ymin = bbox[1]            xmax = bbox[0] + bbox[2]            ymax = bbox[1] + bbox[3]            obj = [object_name, xmin, ymin, xmax, ymax]            objs.append(obj)        save_anno_to_xml(filename, size, objs, xml_save_path)def parseJsonFile(data_dir, xmls_save_path):    assert os.path.exists(data_dir), "data dir:{} does not exits".format(data_dir)    if os.path.isdir(data_dir):        data_types = ['train2017', 'val2017']        for data_type in data_types:            ann_file = 'instances_{}.json'.format(data_type)            xmls_save_path = os.path.join(xmls_save_path, data_type)            load_coco(ann_file, xmls_save_path)    elif os.path.isfile(data_dir):        anno_file = data_dir        load_coco(anno_file, xmls_save_path)if __name__ == '__main__':    """    脚本说明：        该脚本用于将coco格式的json文件转换为voc格式的xml文件    参数说明：        data_dir:json文件的路径        xml_save_path:xml输出路径    """    parser = argparse.ArgumentParser()    parser.add_argument('-d', '--data-dir', type=str, default='./data/labels/coco/train.json', help='json path')    parser.add_argument('-s', '--save-path', type=str, default='./data/convert/voc', help='xml save path')    opt = parser.parse_args()    print(opt)    if len(sys.argv) > 1:        parseJsonFile(opt.data_dir, opt.save_path)    else:        data_dir = './data/labels/coco/train.json'        xml_save_path = './data/convert/voc'        parseJsonFile(data_dir=data_dir, xmls_save_path=xml_save_path)

1.2 CoCo2YOLO

from pycocotools.coco import COCOimport osimport shutilfrom tqdm import tqdmimport sysimport argparseimages_nums = 0category_nums = 0bbox_nums = 0# 将类别名字和id建立索引def catid2name(coco):    classes = dict()    for cat in coco.dataset['categories']:        classes[cat['id']] = cat['name']    return classes# 将[xmin,ymin,xmax,ymax]转换为yolo格式[x_center, y_center, w, h](做归一化)def xyxy2xywhn(object, width, height):    cat_id = object[0]    xn = object[1] / width    yn = object[2] / height    wn = object[3] / width    hn = object[4] / height    out = "{} {:.5f} {:.5f} {:.5f} {:.5f}".format(cat_id, xn, yn, wn, hn)    return outdef save_anno_to_txt(images_info, save_path):    filename = images_info['filename']    txt_name = filename[:-3] + "txt"    with open(os.path.join(save_path, txt_name), "w") as f:        for obj in images_info['objects']:            line = xyxy2xywhn(obj, images_info['width'], images_info['height'])            f.write("{}\n".format(line))# 利用cocoAPI从json中加载信息def load_coco(anno_file, xml_save_path):    if os.path.exists(xml_save_path):        shutil.rmtree(xml_save_path)    os.makedirs(xml_save_path)    coco = COCO(anno_file)    classes = catid2name(coco)    imgIds = coco.getImgIds()    classesIds = coco.getCatIds()    with open(os.path.join(xml_save_path, "classes.txt"), 'w') as f:        for id in classesIds:            f.write("{}\n".format(classes[id]))    for imgId in tqdm(imgIds):        info = {}        img = coco.loadImgs(imgId)[0]        filename = img['file_name']        width = img['width']        height = img['height']        info['filename'] = filename        info['width'] = width        info['height'] = height        annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)        anns = coco.loadAnns(annIds)        objs = []        for ann in anns:            object_name = classes[ann['category_id']]            # bbox:[x,y,w,h]            bbox = list(map(float, ann['bbox']))            xc = bbox[0] + bbox[2] / 2.            yc = bbox[1] + bbox[3] / 2.            w = bbox[2]            h = bbox[3]            obj = [ann['category_id'], xc, yc, w, h]            objs.append(obj)        info['objects'] = objs        save_anno_to_txt(info, xml_save_path)def parseJsonFile(json_path, txt_save_path):    assert os.path.exists(json_path), "json path:{} does not exists".format(json_path)    if os.path.exists(txt_save_path):        shutil.rmtree(txt_save_path)    os.makedirs(txt_save_path)    assert json_path.endswith('json'), "json file:{} It is not json file!".format(json_path)    load_coco(json_path, txt_save_path)if __name__ == '__main__':    """    脚本说明：        该脚本用于将coco格式的json文件转换为yolo格式的txt文件    参数说明：        json_path:json文件的路径        txt_save_path:txt保存的路径    """    parser = argparse.ArgumentParser()    parser.add_argument('-jp', '--json-path', type=str, default='./data/labels/coco/train.json', help='json path')    parser.add_argument('-s', '--save-path', type=str, default='./data/convert/yolo', help='txt save path')    opt = parser.parse_args()    if len(sys.argv) > 1:        print(opt)        parseJsonFile(opt.json_path, opt.save_path)        # print("image nums: {}".format(images_nums))        # print("category nums: {}".format(category_nums))        # print("bbox nums: {}".format(bbox_nums))    else:        json_path = './data/labels/coco/train.json'  # r'D:\practice\compete\goodsDec\data\train\train.json'        txt_save_path = './data/convert/yolo'        parseJsonFile(json_path, txt_save_path)        # print("image nums: {}".format(images_nums))        # print("category nums: {}".format(category_nums))        # print("bbox nums: {}".format(bbox_nums))

二、VOC

2.1 VOC2CoCo

import xml.etree.ElementTree as ETimport osimport jsonfrom datetime import datetimeimport sysimport argparsecoco = dict()coco['images'] = []coco['type'] = 'instances'coco['annotations'] = []coco['categories'] = []category_set = dict()image_set = set()category_item_id = -1image_id = 000000annotation_id = 0def addCatItem(name):    global category_item_id    category_item = dict()    category_item['supercategory'] = 'none'    category_item_id += 1    category_item['id'] = category_item_id    category_item['name'] = name    coco['categories'].append(category_item)    category_set[name] = category_item_id    return category_item_iddef addImgItem(file_name, size):    global image_id    if file_name is None:        raise Exception('Could not find filename tag in xml file.')    if size['width'] is None:        raise Exception('Could not find width tag in xml file.')    if size['height'] is None:        raise Exception('Could not find height tag in xml file.')    image_id += 1    image_item = dict()    image_item['id'] = image_id    image_item['file_name'] = file_name    image_item['width'] = size['width']    image_item['height'] = size['height']    image_item['license'] = None    image_item['flickr_url'] = None    image_item['coco_url'] = None    image_item['date_captured'] = str(datetime.today())    coco['images'].append(image_item)    image_set.add(file_name)    return image_iddef addAnnoItem(object_name, image_id, category_id, bbox):    global annotation_id    annotation_item = dict()    annotation_item['segmentation'] = []    seg = []    # bbox[] is x,y,w,h    # left_top    seg.append(bbox[0])    seg.append(bbox[1])    # left_bottom    seg.append(bbox[0])    seg.append(bbox[1] + bbox[3])    # right_bottom    seg.append(bbox[0] + bbox[2])    seg.append(bbox[1] + bbox[3])    # right_top    seg.append(bbox[0] + bbox[2])    seg.append(bbox[1])    annotation_item['segmentation'].append(seg)    annotation_item['area'] = bbox[2] * bbox[3]    annotation_item['iscrowd'] = 0    annotation_item['ignore'] = 0    annotation_item['image_id'] = image_id    annotation_item['bbox'] = bbox    annotation_item['category_id'] = category_id    annotation_id += 1    annotation_item['id'] = annotation_id    coco['annotations'].append(annotation_item)def read_image_ids(image_sets_file):    ids = []    with open(image_sets_file, 'r') as f:        for line in f.readlines():            ids.append(line.strip())    return idsdef parseXmlFilse(data_dir, json_save_path, split='train'):    assert os.path.exists(data_dir), "data path:{} does not exist".format(data_dir)    labelfile = split + ".txt"    image_sets_file = os.path.join(data_dir, "ImageSets", "Main", labelfile)    xml_files_list = []    if os.path.isfile(image_sets_file):        ids = read_image_ids(image_sets_file)        xml_files_list = [os.path.join(data_dir, "Annotations", f"{i}.xml") for i in ids]    elif os.path.isdir(data_dir):        # 修改此处xml的路径即可        # xml_dir = os.path.join(data_dir,"labels/voc")        xml_dir = data_dir        xml_list = os.listdir(xml_dir)        xml_files_list = [os.path.join(xml_dir, i) for i in xml_list]    for xml_file in xml_files_list:        if not xml_file.endswith('.xml'):            continue        tree = ET.parse(xml_file)        root = tree.getroot()        # 初始化        size = dict()        size['width'] = None        size['height'] = None        if root.tag != 'annotation':            raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))        # 提取图片名字        file_name = root.findtext('filename')        assert file_name is not None, "filename is not in the file"        # 提取图片 size {width,height,depth}        size_info = root.findall('size')        assert size_info is not None, "size is not in the file"        for subelem in size_info[0]:            size[subelem.tag] = int(subelem.text)        if file_name is not None and size['width'] is not None and file_name not in image_set:            # 添加coco['image'],返回当前图片ID            current_image_id = addImgItem(file_name, size)            print('add image with name: {}\tand\tsize: {}'.format(file_name, size))        elif file_name in image_set:            raise Exception('file_name duplicated')        else:            raise Exception("file name:{}\t size:{}".format(file_name, size))        # 提取一张图片内所有目标object标注信息        object_info = root.findall('object')        if len(object_info) == 0:            continue        # 遍历每个目标的标注信息        for object in object_info:            # 提取目标名字            object_name = object.findtext('name')            if object_name not in category_set:                # 创建类别索引                current_category_id = addCatItem(object_name)            else:                current_category_id = category_set[object_name]            # 初始化标签列表            bndbox = dict()            bndbox['xmin'] = None            bndbox['xmax'] = None            bndbox['ymin'] = None            bndbox['ymax'] = None            # 提取box:[xmin,ymin,xmax,ymax]            bndbox_info = object.findall('bndbox')            for box in bndbox_info[0]:                bndbox[box.tag] = int(box.text)            if bndbox['xmin'] is not None:                if object_name is None:                    raise Exception('xml structure broken at bndbox tag')                if current_image_id is None:                    raise Exception('xml structure broken at bndbox tag')                if current_category_id is None:                    raise Exception('xml structure broken at bndbox tag')                bbox = []                # x                bbox.append(bndbox['xmin'])                # y                bbox.append(bndbox['ymin'])                # w                bbox.append(bndbox['xmax'] - bndbox['xmin'])                # h                bbox.append(bndbox['ymax'] - bndbox['ymin'])                print('add annotation with object_name:{}\timage_id:{}\tcat_id:{}\tbbox:{}'.format(object_name,                                                                                                   current_image_id,                                                                                                   current_category_id,                                                                                                   bbox))                addAnnoItem(object_name, current_image_id, current_category_id, bbox)    json_parent_dir = os.path.dirname(json_save_path)    if not os.path.exists(json_parent_dir):        os.makedirs(json_parent_dir)    json.dump(coco, open(json_save_path, 'w'))    print("class nums:{}".format(len(coco['categories'])))    print("image nums:{}".format(len(coco['images'])))    print("bbox nums:{}".format(len(coco['annotations'])))if __name__ == '__main__':    """    脚本说明：        本脚本用于将VOC格式的标注文件.xml转换为coco格式的标注文件.json    参数说明：        voc_data_dir:两种格式            1.voc2012文件夹的路径，会自动找到voc2012/imageSets/Main/xx.txt            2.xml标签文件存放的文件夹        json_save_path:json文件输出的文件夹        split:主要用于voc2012查找xx.txt,如train.txt.如果用格式2，则不会用到该参数    """    voc_data_dir = 'D:/jinxData/voctest/Annotations'    json_save_path = 'D:/jinxData/voc/voc2coco/train.json'    split = 'train'    parseXmlFilse(data_dir=voc_data_dir, json_save_path=json_save_path, split=split)

将annotations目录下的所有xml标注文件按coco格式写入了json文件中。

2.2 VOC2YOLO

import osimport jsonimport shutilfrom lxml import etreefrom tqdm import tqdmcategory_set = set()image_set = set()bbox_nums = 0class VOC2YOLO:    def __init__(self):        self.original_datasets = 'voc'        self.to_datasets = 'yolo'    def parse_xml_to_dict(self, xml):        """        将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dict        Args:            xml: xml tree obtained by parsing XML file contents using lxml.etree        Returns:            Python dictionary holding XML contents.        """        if len(xml) == 0:  # 遍历到底层，直接返回tag对应的信息            return {xml.tag: xml.text}        result = {}        for child in xml:            child_result = self.parse_xml_to_dict(child)  # 递归遍历标签信息            if child.tag != 'object':                result[child.tag] = child_result[child.tag]            else:                if child.tag not in result:  # 因为object可能有多个，所以需要放入列表里                    result[child.tag] = []                result[child.tag].append(child_result[child.tag])        return {xml.tag: result}    def write_classIndices(self, category_set):        class_indices = dict((k, v) for v, k in enumerate(category_set))        json_str = json.dumps(dict((val, key) for key, val in class_indices.items()), indent=4)        with open('class_indices.json', 'w') as json_file:            json_file.write(json_str)    def xyxy2xywhn(self, bbox, size):        bbox = list(map(float, bbox))        size = list(map(float, size))        xc = (bbox[0] + (bbox[2] - bbox[0]) / 2.) / size[0]        yc = (bbox[1] + (bbox[3] - bbox[1]) / 2.) / size[1]        wn = (bbox[2] - bbox[0]) / size[0]        hn = (bbox[3] - bbox[1]) / size[1]        return (xc, yc, wn, hn)    def parser_info(self, info: dict, only_cat=True, class_indices=None):        filename = info['annotation']['filename']        image_set.add(filename)        objects = []        width = int(info['annotation']['size']['width'])        height = int(info['annotation']['size']['height'])        for obj in info['annotation']['object']:            obj_name = obj['name']            category_set.add(obj_name)            if only_cat:                continue            xmin = round(float(obj['bndbox']['xmin']))            ymin = round(float(obj['bndbox']['ymin']))            xmax = round(float(obj['bndbox']['xmax']))            ymax = round(float(obj['bndbox']['ymax']))            bbox = self.xyxy2xywhn((xmin, ymin, xmax, ymax), (width, height))            if class_indices is not None:                obj_category = class_indices[obj_name]                object = [obj_category, bbox]                objects.append(object)        return filename, objects    def parseXmlFilse(self, voc_dir, save_dir):        assert os.path.exists(voc_dir), "ERROR {} does not exists".format(voc_dir)        if os.path.exists(save_dir):            shutil.rmtree(save_dir)        os.makedirs(save_dir)        xml_files = [os.path.join(voc_dir, i) for i in os.listdir(voc_dir) if os.path.splitext(i)[-1] == '.xml']        for xml_file in xml_files:            with open(xml_file) as fid:                xml_str = fid.read()            xml = etree.fromstring(xml_str)            info_dict = self.parse_xml_to_dict(xml)            self.parser_info(info_dict, only_cat=True)        with open(save_dir + "/classes.txt", 'w') as classes_file:            for cat in sorted(category_set):                classes_file.write("{}\n".format(cat))        class_indices = dict((v, k) for k, v in enumerate(sorted(category_set)))        xml_files = tqdm(xml_files)        for xml_file in xml_files:            with open(xml_file) as fid:                xml_str = fid.read()            xml = etree.fromstring(xml_str)            info_dict = self.parse_xml_to_dict(xml)            filename, objects = self.parser_info(info_dict, only_cat=False, class_indices=class_indices)            if len(objects) != 0:                global bbox_nums                bbox_nums += len(objects)                with open(save_dir + "/" + filename.split(".")[0] + ".txt", 'w') as f:                    for obj in objects:                        f.write(                            "{} {:.5f} {:.5f} {:.5f} {:.5f}\n".format(obj[0], obj[1][0], obj[1][1], obj[1][2],                                                                      obj[1][3]))if __name__ == '__main__':    voc2yolo = VOC2YOLO()    voc_dir = 'D:/jinxData/voctest/Annotations'    save_dir = 'D:/jinxData/voctest/convert'    voc2yolo.parseXmlFilse(voc_dir, save_dir)    print("image nums: {}".format(len(image_set)))    print("category nums: {}".format(len(category_set)))    print("bbox nums: {}".format(bbox_nums))

此处得到的是全部的标签信息，可根据如下代码进行train、val和test的比例划分：

import osimport randomdef voc_proportion_divide(xmlfilepath, txtsavepath, trainval_percent, train_percent):    '''    vod数据集比例自定义划分    Args:        xmlfilepath: xml文件的地址， xml一般存放在Annotations下，如'D:\jinx\Annatations'        txtsavepath:地址选择自己数据下的ImageSets/Main，如'D:\jinx\ImageSets\Main'        trainval_percent: 训练和验证集比例        train_percent: 训练集比例(如trainval_percent=0.8，train_percent=0.7表示0.7train、 0.1val、0.2test)    '''    total_xml = os.listdir(xmlfilepath)    if not os.path.exists(txtsavepath):        os.makedirs(txtsavepath)    num = len(total_xml)    list_index = range(num)    tv = int(num * trainval_percent)    tr = int(tv * train_percent)    trainval = random.sample(list_index, tv)    train = random.sample(trainval, tr)    file_trainval = open(txtsavepath + '/trainval.txt', 'w')    file_test = open(txtsavepath + '/test.txt', 'w')    file_train = open(txtsavepath + '/train.txt', 'w')    file_val = open(txtsavepath + '/val.txt', 'w')    for i in list_index:        name = total_xml[i][:-4] + '\n'        if i in trainval:            file_trainval.write(name)            if i in train:                file_train.write(name)            else:                file_val.write(name)        else:            file_test.write(name)    file_trainval.close()    file_train.close()    file_val.close()    file_test.close()

三、YOLO

3.1 YOLO2CoCo

import argparseimport jsonimport osimport sysimport shutilfrom datetime import datetimeimport cv2coco = dict()coco['images'] = []coco['type'] = 'instances'coco['annotations'] = []coco['categories'] = []category_set = dict()image_set = set()image_id = 000000annotation_id = 0def addCatItem(category_dict):    for k, v in category_dict.items():        category_item = dict()        category_item['supercategory'] = 'none'        category_item['id'] = int(k)        category_item['name'] = v        coco['categories'].append(category_item)def addImgItem(file_name, size):    global image_id    image_id += 1    image_item = dict()    image_item['id'] = image_id    image_item['file_name'] = file_name    image_item['width'] = size[1]    image_item['height'] = size[0]    image_item['license'] = None    image_item['flickr_url'] = None    image_item['coco_url'] = None    image_item['date_captured'] = str(datetime.today())    coco['images'].append(image_item)    image_set.add(file_name)    return image_iddef addAnnoItem(object_name, image_id, category_id, bbox):    global annotation_id    annotation_item = dict()    annotation_item['segmentation'] = []    seg = []    # bbox[] is x,y,w,h    # left_top    seg.append(bbox[0])    seg.append(bbox[1])    # left_bottom    seg.append(bbox[0])    seg.append(bbox[1] + bbox[3])    # right_bottom    seg.append(bbox[0] + bbox[2])    seg.append(bbox[1] + bbox[3])    # right_top    seg.append(bbox[0] + bbox[2])    seg.append(bbox[1])    annotation_item['segmentation'].append(seg)    annotation_item['area'] = bbox[2] * bbox[3]    annotation_item['iscrowd'] = 0    annotation_item['ignore'] = 0    annotation_item['image_id'] = image_id    annotation_item['bbox'] = bbox    annotation_item['category_id'] = category_id    annotation_id += 1    annotation_item['id'] = annotation_id    coco['annotations'].append(annotation_item)def xywhn2xywh(bbox, size):    bbox = list(map(float, bbox))    size = list(map(float, size))    xmin = (bbox[0] - bbox[2] / 2.) * size[1]    ymin = (bbox[1] - bbox[3] / 2.) * size[0]    w = bbox[2] * size[1]    h = bbox[3] * size[0]    box = (xmin, ymin, w, h)    return list(map(int, box))def parseXmlFilse(image_path, anno_path, save_path, json_name='train.json'):    assert os.path.exists(image_path), "ERROR {} dose not exists".format(image_path)    assert os.path.exists(anno_path), "ERROR {} dose not exists".format(anno_path)    if os.path.exists(save_path):        shutil.rmtree(save_path)    os.makedirs(save_path)    json_path = os.path.join(save_path, json_name)    category_set = []    with open(anno_path + '/classes.txt', 'r') as f:        for i in f.readlines():            category_set.append(i.strip())    category_id = dict((k, v) for k, v in enumerate(category_set))    addCatItem(category_id)    images = [os.path.join(image_path, i) for i in os.listdir(image_path)]    files = [os.path.join(anno_path, i) for i in os.listdir(anno_path)]    images_index = dict((v.split(os.sep)[-1][:-4], k) for k, v in enumerate(images))    for file in files:        if os.path.splitext(file)[-1] != '.txt' or 'classes' in file.split(os.sep)[-1]:            continue        if file.split(os.sep)[-1][:-4] in images_index:            index = images_index[file.split(os.sep)[-1][:-4]]            img = cv2.imread(images[index])            shape = img.shape            filename = images[index].split(os.sep)[-1]            current_image_id = addImgItem(filename, shape)        else:            continue        with open(file, 'r') as fid:            for i in fid.readlines():                i = i.strip().split()                category = int(i[0])                category_name = category_id[category]                bbox = xywhn2xywh((i[1], i[2], i[3], i[4]), shape)                addAnnoItem(category_name, current_image_id, category, bbox)    json.dump(coco, open(json_path, 'w'))    print("class nums:{}".format(len(coco['categories'])))    print("image nums:{}".format(len(coco['images'])))    print("bbox nums:{}".format(len(coco['annotations'])))if __name__ == '__main__':    """    脚本说明：        本脚本用于将yolo格式的标注文件.txt转换为coco格式的标注文件.json    参数说明：        anno_path:标注文件txt存储路径        save_path:json文件输出的文件夹        image_path:图片路径        json_name:json文件名字    """    anno_path = 'D:/jinxData/TT100K45/labels/test'    save_path = 'D:/jinxData/YOLO/yolo2coco/test'    image_path = 'D:/jinxData/TT100K45/images/test'    json_name = 'train.json'    parseXmlFilse(image_path, anno_path, save_path, json_name)

train和val同理。

3.2 YOLO2VOC

import argparseimport osimport sysimport shutilimport cv2from lxml import etree, objectify# 将标签信息写入xmlfrom tqdm import tqdmimages_nums = 0category_nums = 0bbox_nums = 0def save_anno_to_xml(filename, size, objs, save_path):    E = objectify.ElementMaker(annotate=False)    anno_tree = E.annotation(        E.folder("DATA"),        E.filename(filename),        E.source(            E.database("The VOC Database"),            E.annotation("PASCAL VOC"),            E.image("flickr")        ),        E.size(            E.width(size[1]),            E.height(size[0]),            E.depth(size[2])        ),        E.segmented(0)    )    for obj in objs:        E2 = objectify.ElementMaker(annotate=False)        anno_tree2 = E2.object(            E.name(obj[0]),            E.pose("Unspecified"),            E.truncated(0),            E.difficult(0),            E.bndbox(                E.xmin(obj[1][0]),                E.ymin(obj[1][1]),                E.xmax(obj[1][2]),                E.ymax(obj[1][3])            )        )        anno_tree.append(anno_tree2)    anno_path = os.path.join(save_path, filename[:-3] + "xml")    etree.ElementTree(anno_tree).write(anno_path, pretty_print=True)def xywhn2xyxy(bbox, size):    bbox = list(map(float, bbox))    size = list(map(float, size))    xmin = (bbox[0] - bbox[2] / 2.) * size[1]    ymin = (bbox[1] - bbox[3] / 2.) * size[0]    xmax = (bbox[0] + bbox[2] / 2.) * size[1]    ymax = (bbox[1] + bbox[3] / 2.) * size[0]    box = [xmin, ymin, xmax, ymax]    return list(map(int, box))def parseXmlFilse(image_path, anno_path, save_path):    global images_nums, category_nums, bbox_nums    assert os.path.exists(image_path), "ERROR {} dose not exists".format(image_path)    assert os.path.exists(anno_path), "ERROR {} dose not exists".format(anno_path)    if os.path.exists(save_path):        shutil.rmtree(save_path)    os.makedirs(save_path)    category_set = []    with open(anno_path + '/classes.txt', 'r') as f:        for i in f.readlines():            category_set.append(i.strip())    category_nums = len(category_set)    category_id = dict((k, v) for k, v in enumerate(category_set))    images = [os.path.join(image_path, i) for i in os.listdir(image_path)]    files = [os.path.join(anno_path, i) for i in os.listdir(anno_path)]    images_index = dict((v.split(os.sep)[-1][:-4], k) for k, v in enumerate(images))    images_nums = len(images)    for file in tqdm(files):        if os.path.splitext(file)[-1] != '.txt' or 'classes' in file.split(os.sep)[-1]:            continue        if file.split(os.sep)[-1][:-4] in images_index:            index = images_index[file.split(os.sep)[-1][:-4]]            img = cv2.imread(images[index])            shape = img.shape            filename = images[index].split(os.sep)[-1]        else:            continue        objects = []        with open(file, 'r') as fid:            for i in fid.readlines():                i = i.strip().split()                category = int(i[0])                category_name = category_id[category]                bbox = xywhn2xyxy((i[1], i[2], i[3], i[4]), shape)                obj = [category_name, bbox]                objects.append(obj)        bbox_nums += len(objects)        save_anno_to_xml(filename, shape, objects, save_path)if __name__ == '__main__':    """    脚本说明：        本脚本用于将yolo格式的标注文件.txt转换为voc格式的标注文件.xml    参数说明：        anno_path:标注文件txt存储路径        save_path:json文件输出的文件夹        image_path:图片路径    """    anno_path = 'D:/jinxData/TT100K45/labels/test'    save_path = 'D:/jinxData/YOLO/yolo2voc/test'    image_path = 'D:/jinxData/TT100K45/images/test'    parseXmlFilse(image_path, anno_path, save_path)    print("image nums: {}".format(images_nums))    print("category nums: {}".format(category_nums))    print("bbox nums: {}".format(bbox_nums))

train、val和test分别执行一次即可。

以上代码参考自博文数据转换。

四、TT100K

4.1 TT100K2YOLO

import osimport jsonfrom random import randomimport cv2import shutilimport jsonimport xml.dom.minidomfrom tqdm import tqdmimport argparseclass TT100K2COCO:    def __init__(self):        self.original_datasets = 'tt100k'        self.to_datasets = 'coco'    def class_statistics(self):        # os.makedirs('annotations', exist_ok=True)        # 存放数据的父路径        parent_path = 'D:/jinxData/TT100K/data'        # 读TT100K原始数据集标注文件        with open(os.path.join(parent_path, 'annotations.json')) as origin_json:            origin_dict = json.load(origin_json)            classes = origin_dict['types']        # 建立统计每个类别包含的图片的字典        sta = {}        for i in classes:            sta[i] = []        images_dic = origin_dict['imgs']        # 记录所有保留的图片        saved_images = []        # 遍历TT100K的imgs        for image_id in images_dic:            image_element = images_dic[image_id]            image_path = image_element['path']            # 添加图像的信息到dataset中            image_path = image_path.split('/')[-1]            obj_list = image_element['objects']            # 遍历每张图片的标注信息            for anno_dic in obj_list:                label_key = anno_dic['category']                # 防止一个图片多次加入一个标签类别                if image_path not in sta[label_key]:                    sta[label_key].append(image_path)        # 只保留包含图片数超过100的类别        result = {k: v for k, v in sta.items() if len(v) >= 100}        for i in result:            print("the type of {} includes {} images".format(i, len(result[i])))            saved_images.extend(result[i])        saved_images = list(set(saved_images))        print("total types is {}".format(len(result)))        type_list = list(result.keys())        result = {"type": type_list, "details": result, "images": saved_images}        print(type_list)        # 保存结果        json_name = os.path.join(parent_path, 'statistics.json')        with open(json_name, 'w', encoding="utf-8") as f:            json.dump(result, f, ensure_ascii=False, indent=1)    def original_datasets2object_datasets_re(self):        '''        重新划分数据集        :return:        '''        # os.makedirs('annotations2', exist_ok=True)        # 存放数据的父路径        parent_path = 'D:/jinxData/TT100K/data'        # 读TT100K原始数据集标注文件        with open(os.path.join(parent_path, 'annotations.json')) as origin_json:            origin_dict = json.load(origin_json)        with open(os.path.join(parent_path, 'statistics.json')) as select_json:            select_dict = json.load(select_json)            classes = select_dict['type']        train_dataset = {'info': {}, 'licenses': [], 'categories': [], 'images': [], 'annotations': []}        val_dataset = {'info': {}, 'licenses': [], 'categories': [], 'images': [], 'annotations': []}        test_dataset = {'info': {}, 'licenses': [], 'categories': [], 'images': [], 'annotations': []}        label = {}  # 记录每个标志类别的id        count = {}  # 记录每个类别的图片数        owntype_sum = {}        info = {            "year": 2021,  # 年份            "version": '1.0',  # 版本            "description": "TT100k_to_coco",  # 数据集描述            "contributor": "Tecent&Tsinghua",  # 提供者            "url": 'https://cg.cs.tsinghua.edu.cn/traffic-sign/',  # 下载地址            "date_created": 2021 - 1 - 15        }        licenses = {            "id": 1,            "name": "null",            "url": "null",        }        train_dataset['info'] = info        val_dataset['info'] = info        test_dataset['info'] = info        train_dataset['licenses'] = licenses        val_dataset['licenses'] = licenses        test_dataset['licenses'] = licenses        # 建立类别和id的关系        for i, cls in enumerate(classes):            train_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'traffic_sign'})            val_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'traffic_sign'})            test_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'traffic_sign'})            label[cls] = i            count[cls] = 0            owntype_sum[cls] = 0        images_dic = origin_dict['imgs']        obj_id = 1        # 计算出每个类别共‘包含’的图片数        for image_id in images_dic:            image_element = images_dic[image_id]            image_path = image_element['path']            image_name = image_path.split('/')[-1]            # 在所选的类别图片中            if image_name not in select_dict['images']:                continue            # 处理TT100K中的标注信息            obj_list = image_element['objects']            # 记录图片中包含最多的实例所属的type            includes_type = {}            for anno_dic in obj_list:                if anno_dic["category"] not in select_dict["type"]:                    continue                # print(anno_dic["category"])                if anno_dic["category"] in includes_type:                    includes_type[anno_dic["category"]] += 1                else:                    includes_type[anno_dic["category"]] = 1            # print(includes_type)            own_type = max(includes_type, key=includes_type.get)            owntype_sum[own_type] += 1        # TT100K的annotation转换成coco的        for image_id in images_dic:            image_element = images_dic[image_id]            image_path = image_element['path']            image_name = image_path.split('/')[-1]            # 在所选的类别图片中            if image_name not in select_dict['images']:                continue            print("dealing with {} image".format(image_path))            # shutil.copy(os.path.join(parent_path,image_path),os.path.join(parent_path,"dataset/JPEGImages"))            # 处理TT100K中的标注信息            obj_list = image_element['objects']            # 记录图片中包含最多的实例所属的type            includes_type = {}            for anno_dic in obj_list:                if anno_dic["category"] not in select_dict["type"]:                    continue                # print(anno_dic["category"])                if anno_dic["category"] in includes_type:                    includes_type[anno_dic["category"]] += 1                else:                    includes_type[anno_dic["category"]] = 1            # print(includes_type)            own_type = max(includes_type, key=includes_type.get)            count[own_type] += 1            num_rate = count[own_type] / owntype_sum[own_type]            # 切换dataset的引用对象，从而划分数据集根据每个类别类别的总数量按7：2：1分为了train_set,val_set,test_set。            # 其中每个图片所属类别根据该图片包含的类别的数量决定（归属为含有类别最多的类别）            if num_rate < 0.7:                dataset = train_dataset            elif num_rate < 0.9:                dataset = val_dataset            else:                print("dataset=test_dataset")                dataset = test_dataset            for anno_dic in obj_list:                if anno_dic["category"] not in select_dict["type"]:                    continue                x = anno_dic['bbox']['xmin']                y = anno_dic['bbox']['ymin']                width = anno_dic['bbox']['xmax'] - anno_dic['bbox']['xmin']                height = anno_dic['bbox']['ymax'] - anno_dic['bbox']['ymin']                label_key = anno_dic['category']                dataset['annotations'].append({                    'area': width * height,                    'bbox': [x, y, width, height],                    'category_id': label[label_key],                    'id': obj_id,                    'image_id': image_id,                    'iscrowd': 0,                    # mask, 矩形是从左上角点按顺时针的四个顶点                    'segmentation': [[x, y, x + width, y, x + width, y + height, x, y + height]]                })                # 每个标注的对象id唯一                obj_id += 1            # 用opencv读取图片，得到图像的宽和高            im = cv2.imread(os.path.join(parent_path, image_path))            # print(image_path)            H, W, _ = im.shape            # 添加图像的信息到dataset中            dataset['images'].append({'file_name': image_name,                                      'id': image_id,                                      'width': W,                                      'height': H})        # 保存结果        for phase in ['train', 'val', 'test']:            json_name = os.path.join(parent_path, 'dataset/annotations/{}.json'.format(phase))            json_name = json_name.replace('\\', '/')            with open(json_name, 'w', encoding="utf-8") as f:                if phase == 'train':                    json.dump(train_dataset, f, ensure_ascii=False, indent=1)                if phase == 'val':                    json.dump(val_dataset, f, ensure_ascii=False, indent=1)                if phase == 'test':                    json.dump(test_dataset, f, ensure_ascii=False, indent=1)     def divide_TrainValTest(self, source, target):        '''        创建文件路径        :param source: 源文件位置        :param target: 目标文件位置        '''        # for i in ['train', 'val', 'test']:        #     path = target + '/' + i        #     if not os.path.exists(path):        #         os.makedirs(path)        # 遍历目录下的文件名，复制对应的图片到指定目录        for root, dirs, files in os.walk(source):            for file in files:                file_name = os.path.splitext(file)[0]                if file_name == 'train' or file_name == 'val' or file_name =='test' or file_name =='classes':                    continue                image_path = os.path.join(file_name + '.jpg')                # print(image_path)                if 'train' in source:                    shutil.copyfile('D:/jinxData/TT100K/data/image_reparation/'                                    + image_path, target + '/train/' + image_path)                elif 'val' in source:                    shutil.copyfile('D:/jinxData/TT100K/data/image_reparation/'                                    + image_path, target + '/val/' + image_path)                elif 'test' in source:                    shutil.copyfile('D:/jinxData/TT100K/data/image_reparation/'                                    + image_path, target + '/test/' + image_path)if __name__ == '__main__':    tt100k = TT100K2COCO()    # tt100k.class_statistics()    # tt100k.original_datasets2object_datasets_re()    # tt100k.coco_json2yolo_txt('train')    # tt100k.coco_json2yolo_txt('val')    # tt100k.coco_json2yolo_txt('test')    tt100k.divide_TrainValTest('D:/jinxData/TT100K/data/dataset/annotations/train', 'D:/jinxData/TT100K/data')    tt100k.divide_TrainValTest('D:/jinxData/TT100K/data/dataset/annotations/val', 'D:/jinxData/TT100K/data')    tt100k.divide_TrainValTest('D:/jinxData/TT100K/data/dataset/annotations/test', 'D:/jinxData/TT100K/data')