本文共 4868 字，大约阅读时间需要 16 分钟。

直接贴源码：

# @TIME  :2019/7/21 15:34# @File  :coco2xml.py"""从coco instance.json生成voc--xml文件""""""首先得下载编译cocoapipip install cythongit clone https://github.com/cocodataset/cocoapi.gitcd coco/PythonAPImake"""import sysbag_path = "../cocoapi-master/PythonAPI/"if not bag_path in sys.path:    sys.path.append(bag_path)from pycocotools.coco import COCOimport osimport shutilfrom tqdm import tqdmimport skimage.io as ioimport matplotlib.pyplot as pltimport cv2from PIL import Image, ImageDraw# the path you want to save your results for coco to vocsavepath = "../result/"#保存生成文件路径img_dir = savepath + 'images/'#保存生成jpg文件路径anno_dir = savepath + 'Annotations/'#保存生成xml文件路径# datasets_list=['train2014', 'val2014']datasets_list = ['train2017']#coco数据集里面图片# Store annotations and train2014/val2014/... in this folderdataDir = '../../coco/coco2017/'#coco数据集整体文件，里面包含annotations和图片文件夹#path = os.path.abspath(dataDir)#print(path)headstr = """\
       
    
     VOC
        
    
     %s
        
            
    
     My Database
            
    
     COCO
            
    flickr        
    
     NULL
            
            
     
      NULL
             
     
      company
         
        
            
     
      %d
             
     
      %d
             
     
      %d
         
        
    
     0
    """objstr = """\    
    """tailstr = '''\
   '''# if the dir is not exists,make it,else delete itdef mkr(path):    if os.path.exists(path):        shutil.rmtree(path)        os.mkdir(path)    else:        os.mkdir(path)mkr(img_dir)mkr(anno_dir)def id2name(coco):    classes = dict()    for cls in coco.dataset['categories']:        classes[cls['id']] = cls['name']    return classesdef write_xml(anno_path, head, objs, tail):    f = open(anno_path, "w")    f.write(head)    for obj in objs:        f.write(objstr % (obj[0], obj[1], obj[2], obj[3], obj[4]))    f.write(tail)def save_annotations_and_imgs(coco, dataset, filename, objs):    # eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml    anno_path = anno_dir + filename[:-3] + 'xml'    img_path = dataDir + dataset + '/' + filename    #print(img_path)    dst_imgpath = img_dir + filename    img = cv2.imread(img_path)    if (img.shape[2] == 1):        #print(filename + " not a RGB image")        return    shutil.copy(img_path, dst_imgpath)    head = headstr % (filename, img.shape[1], img.shape[0], img.shape[2])    tail = tailstr    write_xml(anno_path, head, objs, tail)def showimg(coco, dataset, img, classes, cls_id, show=True):    global dataDir    I = Image.open('%s/%s/%s' % (dataDir, dataset, img['file_name']))    # 通过id，得到注释的信息    annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)    # print(annIds)    anns = coco.loadAnns(annIds)    # print(anns)    # coco.showAnns(anns)    objs = []    for ann in anns:        class_name = classes[ann['category_id']]        if 'bbox' in ann:            bbox = ann['bbox']            xmin = int(bbox[0])            ymin = int(bbox[1])            xmax = int(bbox[2] + bbox[0])            ymax = int(bbox[3] + bbox[1])            obj = [class_name, xmin, ymin, xmax, ymax]            objs.append(obj)            draw = ImageDraw.Draw(I)            draw.rectangle([xmin, ymin, xmax, ymax])    if show:        plt.figure()        plt.axis('off')        plt.imshow(I)        plt.show()    return objsfor dataset in datasets_list:    # ./COCO/annotations/instances_train2014.json    annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataset)    # COCO API for initializing annotated data    coco = COCO(annFile)    '''    COCO 对象创建完毕后会输出如下信息:    loading annotations into memory...    Done (t=0.81s)    creating index...    index created!    至此, json 脚本解析完毕, 并且将图片和对应的标注数据关联起来.    '''    # show all classes in coco    classes = id2name(coco)    classes_names = []    for key, value in classes.items():        classes_names.append(value)    classes_ids = coco.getCatIds(catNms=classes_names)    img_ids_totoal =[]    for cls in classes_names:        # Get ID number of this class        cls_id = coco.getCatIds(catNms=[cls])        img_ids = coco.getImgIds(catIds=cls_id)        for img_id in img_ids:            img_ids_totoal.append(img_id)    print(len(img_ids_totoal))    tem=set(img_ids_totoal)    temp = list(tem)    temp.sort()    print(len(tem))    print(len(temp))    for imgId in tqdm(temp):        img = coco.loadImgs(imgId)[0]        filename = img['file_name']        # print(filename)        objs = showimg(coco, dataset, img, classes, classes_ids, show=False)        # print(objs)        save_annotations_and_imgs(coco, dataset, filename, objs)

参考：

转载地址：http://wrzlf.baihongyu.com/