将COCO格式数据转为VOC格式

男娘i 2023-07-18 12:48 33阅读 0赞

参考：

[https://blog.csdn.net/weixin\_39881922/article/details/85120379][https_blog.csdn.net_weixin_39881922_article_details_85120379]

将COCO格式数据转为VOC格式

'''
    把coco数据集合的所有标注转换到voc格式，不改变图片命名方式，
    注意，原来有一些图片是黑白照片，检测出不是 RGB 图像，这样的图像不会被放到新的文件夹中
    更新日期：2019-11-19
    '''
    from pycocotools.coco import \
        COCO  # 这个包可以从git上下载https://github.com/cocodataset/cocoapi/tree/master/PythonAPI，也可以直接用修改后的coco.py
    import os, cv2, shutil
    from lxml import etree, objectify
    from tqdm import tqdm
    from PIL import Image
    
    CKimg_dir = '/media/zlee/Elements/mmdetection/coco2VOC/images'
    CKanno_dir = '/media/zlee/Elements/mmdetection/coco2VOC/annotations'
    
    
    # 若模型保存文件夹不存在，创建模型保存文件夹，若存在，删除重建
    def mkr(path):
        if os.path.exists(path):
            shutil.rmtree(path)
            os.mkdir(path)
        else:
            os.mkdir(path)
    
    
    def save_annotations(filename, objs, filepath):
        annopath = CKanno_dir + "/" + filename[:-3] + "xml"  # 生成的xml文件保存路径
        dst_path = CKimg_dir + "/" + filename
        img_path = filepath
        img = cv2.imread(img_path)
        im = Image.open(img_path)
        if im.mode != "RGB":
            print(filename + " not a RGB image")
            im.close()
            return
        im.close()
        shutil.copy(img_path, dst_path)  # 把原始图像复制到目标文件夹
        E = objectify.ElementMaker(annotate=False)
        anno_tree = E.annotation(
            E.folder('1'),
            E.filename(filename),
            E.source(
                E.database('CKdemo'),
                E.annotation('VOC'),
                E.image('CK')
            ),
            E.size(
                E.width(img.shape[1]),
                E.height(img.shape[0]),
                E.depth(img.shape[2])
            ),
            E.segmented(0)
        )
        for obj in objs:
            E2 = objectify.ElementMaker(annotate=False)
            anno_tree2 = E2.object(
                E.name(obj[0]),
                E.pose(),
                E.truncated("0"),
                E.difficult(0),
                E.bndbox(
                    E.xmin(obj[2]),
                    E.ymin(obj[3]),
                    E.xmax(obj[4]),
                    E.ymax(obj[5])
                )
            )
            anno_tree.append(anno_tree2)
        etree.ElementTree(anno_tree).write(annopath, pretty_print=True)
    
    
    def showbycv(coco, dataType, img, classes, origin_image_dir, verbose=False):
        filename = img['file_name']
        filepath = os.path.join(origin_image_dir, dataType, filename)
        I = cv2.imread(filepath)
        annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
        anns = coco.loadAnns(annIds)
        objs = []
        for ann in anns:
            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 = [name, 1.0, xmin, ymin, xmax, ymax]
                objs.append(obj)
                if verbose:
                    cv2.rectangle(I, (xmin, ymin), (xmax, ymax), (255, 0, 0))
                    cv2.putText(I, name, (xmin, ymin), 3, 1, (0, 0, 255))
        save_annotations(filename, objs, filepath)
        if verbose:
            cv2.imshow("img", I)
            cv2.waitKey(0)
    
    
    def catid2name(coco):  # 将名字和id号建立一个字典
        classes = dict()
        for cat in coco.dataset['categories']:
            classes[cat['id']] = cat['name']
            # print(str(cat['id'])+":"+cat['name'])
        return classes
    
    
    def get_CK5(origin_anno_dir, origin_image_dir, verbose=False):
        dataTypes = ['train2017', 'val2017']
        for dataType in dataTypes:
            annFile = 'instances_{}.json'.format(dataType)
            annpath = os.path.join(origin_anno_dir, annFile)
            coco = COCO(annpath)
            classes = catid2name(coco)
            imgIds = coco.getImgIds()
            # imgIds=imgIds[0:1000]#测试用，抽取10张图片，看下存储效果
            for imgId in tqdm(imgIds):
                img = coco.loadImgs(imgId)[0]
                showbycv(coco, dataType, img, classes, origin_image_dir, verbose=False)
    
    
    def main():
        base_dir = '/media/zlee/Elements/mmdetection/coco2VOC'  # step1 这里是一个新的文件夹，存放转换后的图片和标注
        image_dir = os.path.join(base_dir, 'images')  # 在上述文件夹中生成images，annotations两个子文件夹
        anno_dir = os.path.join(base_dir, 'annotations')
        mkr(image_dir)
        mkr(anno_dir)
        origin_image_dir = '/media/zlee/Elements/mmdetection/data/coco/images'  # step 2原始的coco的图像存放位置
        origin_anno_dir = '/media/zlee/Elements/mmdetection/data/coco/annotations'  # step 3 原始的coco的标注存放位置
        verbose = True  # 是否需要看下标记是否正确的开关标记，若是true,就会把标记展示到图片上
        get_CK5(origin_anno_dir, origin_image_dir, verbose)
    
    
    if __name__ == "__main__":
        main()

[https_blog.csdn.net_weixin_39881922_article_details_85120379]: https://blog.csdn.net/weixin_39881922/article/details/85120379