Yolopose关键点检测：自己标注数据集，制作数据集(一)

最近实习需要搞的工程涉及到姿态估计，对这2天的学习做一下总结归纳，第一步就是准备数据集，查资料后发现window环境下使用的标注软件就是labelme（coco-annotator好像就可以直接输出coco格式的JSON文件，后期继续学习），labelme产出的JSON文件需要转换到coco格式的JSON文件，然后将JSON文件转成Yolo需要的txt文件，才能送工程进行训练，转来转去的个人感觉非常麻烦。
本博文主要内容是分享关键点的labelme2coco——JSON文件转换，cocoJSON2cocotxt的转换，以及coco_kpts文件夹的内容布局。

用labelme标注关键点与后续的labelme2coco转换代码息息相关，假设标注对象是人体，关键点有17个：“nose”,“left_eye”, “right_eye”,“left_ear”, “right_ear”,“left_shoulder”, “right_shoulder”,“left_elbow”, “right_elbow”,“left_wrist”, “right_wrist”,“left_hip”, “right_hip”,“left_knee”, “right_knee”,“left_ankle”, “right_ankle”，label编号从1依次排到17，然后再把人用rectangle框出来label编号为bbox(Yolopose是需要画框的，并且numclass已经固定好了为person)，所有的Group id都不用写，JSON文件会自动补上null但不影响我们后续操作，填写label及Group id的界面如下图所示：

选取一张图片，把17个关键点都标注出来，再把人框出来，如果对象显示半个身体，不满17个关键点，也无所谓，没有哪个部位就把那个部位标注数值空出来就可以了，一张图片操作结束后，就如下图所示：

coco图片的格式是12位数字，标注完后的JSON文件用相同的名称命名即可。

CSDN上的其他转换代码，我都没法直接跑，也没有相关的讲解，于是我就拿了现有的代码修改了一些，并记录了我的学习过程。先介绍一下哪些部分需要修改的，然后在附上整个代码，以17个人体关键点标注好的JSON文件为例。

"keypoints":#这是固定的，要是其他的关键点标注，则需要修改内容并且与前面的label编号一一对应。                    [                        "nose",                        "left_eye", "right_eye",                        "left_ear", "right_ear",                        "left_shoulder", "right_shoulder",                        "left_elbow", "right_elbow",                        "left_wrist", "right_wrist",                        "left_hip", "right_hip",                        "left_knee", "right_knee",                        "left_ankle", "right_ankle"                    ],                "skeleton": [                    [16, 14],                    [14, 12],                    [17, 15],                    [15, 13],                    [12, 13],                    [6, 12],                    [7, 13],                    [6, 7],                    [6, 8],                    [7, 9],                    [8, 10],                    [9, 11],                    [2, 3],                    [1, 2],                    [1, 3],                    [2, 4],                    [3, 5],                    [4, 6],                    [5, 7]                ]

json_file[-17:-5]#这是为了取文件的名称，比如000000000001，如果文件名称变了，则需要修改json_file[-17:-4]#这是为了取文件的名称，比如000000000001.(多一个.)

keypoints 里面放的是（x,y,v）：x，y是坐标值，v一般取2，0代表没有该点，1代表该点存在但是被遮挡了，2代表该点存在且没有被遮挡。(具体可以参考coco数据集的格式，本文主要介绍如何应用)

keypoints = [0] * 3 * 17  #这里是我们标注的关节点个数  如有改动，需要修改

json_path = r'E:\val2017\Json'   #存放需要转换的json文件夹，变动需要修改

c = tococo(json_path, save_path=r'E:\val2017\annotations\val.json', a=1)  #输出的地址及名称，变动需要修改

整个代码如下，可以做到一个文件夹下所有的JSON文件一起转换并输出一个coco格式的JSON文件

import numpy as npimport jsonimport globimport codecsimport osclass MyEncoder(json.JSONEncoder):    def default(self, obj):        if isinstance(obj, np.integer):            return int(obj)        elif isinstance(obj, np.floating):            return float(obj)        elif isinstance(obj, np.ndarray):            return obj.tolist()        else:            return super(MyEncoder, self).default(obj)class tococo(object):    def __init__(self, jsonfile, save_path, a):        self.images = []        self.categories = [            {                "supercategory": "person",                "id": 1,                "name": "person",                "keypoints":                    [                        "nose",                        "left_eye", "right_eye",                        "left_ear", "right_ear",                        "left_shoulder", "right_shoulder",                        "left_elbow", "right_elbow",                        "left_wrist", "right_wrist",                        "left_hip", "right_hip",                        "left_knee", "right_knee",                        "left_ankle", "right_ankle"                    ],                "skeleton": [                    [16, 14],                    [14, 12],                    [17, 15],                    [15, 13],                    [12, 13],                    [6, 12],                    [7, 13],                    [6, 7],                    [6, 8],                    [7, 9],                    [8, 10],                    [9, 11],                    [2, 3],                    [1, 2],                    [1, 3],                    [2, 4],                    [3, 5],                    [4, 6],                    [5, 7]                ]            }        ]        self.annotations = []        self.jsonfile = os.listdir(jsonfile)        self.save_path = save_path  # 保存json的路径        self.class_id = a  # class  我们的类别只有一个 person        self.coco = {}        self.path = jsonfile    def labelme_to_coco(self):        for num, json_file in enumerate(self.jsonfile):            json_file = os.path.join(self.path, json_file)            data = codecs.open(json_file, 'r')            data = json.load(data)            self.images.append(self.get_images(json_file[-17:-4] + 'jpg', data["imageHeight"], data["imageWidth"]))            shapes = data["shapes"]            annotation = {}  # 一个annotation代表一张图片中的所有samples            num_keypoints = 0            keypoints = [0] * 3 * 17  #这里是我们标注的关节点个数  如有改动，需要修改            flag = 0            for shape in shapes:                if shape['shape_type'] == 'rectangle' or shape["label"] == 'bbox':                    bbox = []                    temp = shape["points"]                    try:                        x_min = min(temp[0][0], temp[1][0])                    except IndexError as e:                        print('class: {}, image: {}'.format(self.class_id, int(json_file[-17:-5])))                    x_max = max(temp[0][0], temp[1][0])                    y_min = min(temp[0][1], temp[1][1])                    y_max = max(temp[0][1], temp[1][1])                    bbox.append(x_min)                    bbox.append(y_min)                    w = x_max - x_min + 1                    h = y_max - y_min + 1                    bbox.append(w)                    bbox.append(h)                    annotation['bbox'] = bbox                    flag = flag + 1                else:                    idx = int(shape['label'])                    try:                        keypoints[(idx - 1) * 3 + 0] = shape['points'][0][0]                        keypoints[(idx - 1) * 3 + 1] = shape['points'][0][1]                        keypoints[(idx - 1) * 3 + 2] = 2                        num_keypoints = num_keypoints + 1                    except IndexError as e:                        print('class: {}, image: {}'.format(self.class_id, int(json_file[-17:-5])))            if flag == 0:                print('{}\\{} does not contain bbox\n'.format(self.class_id, json_file))            annotation['segmentation'] = [[]]            annotation['num_keypoints'] = num_keypoints            annotation['iscrowd'] = 0            annotation['keypoints'] = keypoints            annotation['image_id'] = int(json_file[-17:-5])  # 对应的图片ID            if 'bbox' not in annotation:                annotation['bbox'] = [0, 0, data['imageWidth'], data['imageHeight']]                annotation['area'] = 0            else:                annotation['area'] = int(bbox[2] * bbox[3])            annotation['category_id'] = 1            annotation['id'] = int(json_file[-17:-5])  # 对象id            self.annotations.append(annotation)            self.image_id = int(json_file[-17:-5])        self.coco["images"] = self.images        self.coco["categories"] = self.categories        self.coco["annotations"] = self.annotations    def get_images(self, filename, height, width):        image = {}        image["height"] = height        image['width'] = width        image["id"] = int(filename[-16:-4])        image["file_name"] = filename        return image    def get_categories(self, name, class_id):        category = {}        category["supercategory"] = "person"        category['id'] = class_id        category['name'] = name        return category    def save_json(self):        self.labelme_to_coco()        coco_data = self.coco        # 保存json文件        json.dump(coco_data, open(self.save_path, 'w'), indent=4, cls=MyEncoder)  # indent=4 更加美观显示        return self.image_idjson_path = r'E:\val2017\Json_old'   #保存json的文件夹路径c = tococo(json_path, save_path=r'E:\val2017\annotations_old\val.json', a=1)  #我们将我们的左右json文件合成为一个json文件，这是最后json文件的名称image_id = c.save_json()

本人将5个JSON文件放在同一个文件夹下运行转换代码，最后val.json通过VS2022(其他软件也可以)打开后内容如下所示：

# !/usr/bin python3# encoding    : utf-8 -*-# @author     :   liangjian# @software   : PyCharm# @file       :   0408.py# @Time       :   2021/4/8 21:51import skimage.io as ioimport pylabimport time as timeimport jsonimport numpy as npfrom collections import defaultdictimport itertoolsimport matplotlib.pyplot as pltfrom matplotlib.collections import PatchCollectiondef _isArrayLike(obj):    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')class COCO:    def __init__(self, annotation_file=None):        """        Constructor of Microsoft COCO helper class for reading and visualizing annotations.        :param annotation_file (str): location of annotation file        :param image_folder (str): location to the folder that hosts images.        :return:        """        # load dataset        self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()        self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)        if not annotation_file == None:            print('loading annotations into memory...')            tic = time.time()            dataset = json.load(open(annotation_file, 'r'))            assert type(dataset) == dict, 'annotation file format {} not supported'.format(type(dataset))            print('Done (t={:0.2f}s)'.format(time.time() - tic))            self.dataset = dataset            self.createIndex()    def createIndex(self):        # create index        print('creating index...')        anns, cats, imgs = {}, {}, {}        imgToAnns, catToImgs = defaultdict(list), defaultdict(list)        if 'annotations' in self.dataset:            for ann in self.dataset['annotations']:                imgToAnns[ann['image_id']].append(ann)                anns[ann['id']] = ann        if 'images' in self.dataset:            for img in self.dataset['images']:                imgs[img['id']] = img        if 'categories' in self.dataset:            for cat in self.dataset['categories']:                cats[cat['id']] = cat        if 'annotations' in self.dataset and 'categories' in self.dataset:            for ann in self.dataset['annotations']:                catToImgs[ann['category_id']].append(ann['image_id'])        print('index created!')        # create class members        self.anns = anns        self.imgToAnns = imgToAnns        self.catToImgs = catToImgs        self.imgs = imgs        self.cats = cats    def getCatIds(self, catNms=[], supNms=[], catIds=[]):        """        filtering parameters. default skips that filter.        :param catNms (str array)  : get cats for given cat names        :param supNms (str array)  : get cats for given supercategory names        :param catIds (int array)  : get cats for given cat ids        :return: ids (int array)   : integer array of cat ids        """        catNms = catNms if _isArrayLike(catNms) else [catNms]        supNms = supNms if _isArrayLike(supNms) else [supNms]        catIds = catIds if _isArrayLike(catIds) else [catIds]        if len(catNms) == len(supNms) == len(catIds) == 0:            cats = self.dataset['categories']        else:            cats = self.dataset['categories']            # print(' ')            # print('keypoints的cat就只有人1种')            # print(cats)            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms]            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds]            # print(cats)        ids = [cat['id'] for cat in cats]        return ids    def loadCats(self, ids=[]):        """        Load cats with the specified ids.        :param ids (int array)       : integer ids specifying cats        :return: cats (object array) : loaded cat objects        """        if _isArrayLike(ids):            return [self.cats[id] for id in ids]        elif type(ids) == int:            return [self.cats[ids]]    def getImgIds(self, imgIds=[], catIds=[]):        '''        Get img ids that satisfy given filter conditions.        :param imgIds (int array) : get imgs for given ids        :param catIds (int array) : get imgs with all given cats        :return: ids (int array)  : integer array of img ids        '''        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]        catIds = catIds if _isArrayLike(catIds) else [catIds]        if len(imgIds) == len(catIds) == 0:            ids = self.imgs.keys()        else:            ids = set(imgIds)            for i, catId in enumerate(catIds):                if i == 0 and len(ids) == 0:                    ids = set(self.catToImgs[catId])                else:                    ids &= set(self.catToImgs[catId])        return list(ids)    def loadImgs(self, ids=[]):        """        Load anns with the specified ids.        :param ids (int array)       : integer ids specifying img        :return: imgs (object array) : loaded img objects        """        if _isArrayLike(ids):            return [self.imgs[id] for id in ids]        elif type(ids) == int:            return [self.imgs[ids]]    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):        """        Get ann ids that satisfy given filter conditions. default skips that filter        :param imgIds  (int array)     : get anns for given imgs               catIds  (int array)     : get anns for given cats               areaRng (float array)   : get anns for given area range (e.g. [0 inf])               iscrowd (boolean)       : get anns for given crowd label (False or True)        :return: ids (int array)       : integer array of ann ids        """        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]        catIds = catIds if _isArrayLike(catIds) else [catIds]        if len(imgIds) == len(catIds) == len(areaRng) == 0:            anns = self.dataset['annotations']        else:            # 根据imgIds找到所有的ann            if not len(imgIds) == 0:                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]                anns = list(itertools.chain.from_iterable(lists))            else:                anns = self.dataset['annotations']            # 通过各类条件如catIds对anns进行筛选            anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds]            anns = anns if len(areaRng) == 0 else [ann for ann in anns if                       ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]        if not iscrowd == None:            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]        else:            ids = [ann['id'] for ann in anns]        return ids    def loadAnns(self, ids=[]):        """        Load anns with the specified ids.        :param ids (int array)       : integer ids specifying anns        :return: anns (object array) : loaded ann objects        """        if _isArrayLike(ids):            return [self.anns[id] for id in ids]        elif type(ids) == int:            return [self.anns[ids]]    def showAnns(self, anns):        """        Display the specified annotations.        :param anns (array of object): annotations to display        :return: None        """        if len(anns) == 0:            return 0        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:            datasetType = 'instances'        elif 'caption' in anns[0]:            datasetType = 'captions'        else:            raise Exception('datasetType not supported')        if datasetType == 'instances':            ax = plt.gca()            ax.set_autoscale_on(False)            polygons = []            color = []            for ann in anns:                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]                # if 'segmentation' in ann:                #     if type(ann['segmentation']) == list:                #         # polygon                #         for seg in ann['segmentation']:                #             poly = np.array(seg).reshape((int(len(seg)/2), 2))                #             polygons.append(Polygon(poly))                #             color.append(c)                #     else:                #         # mask                #         t = self.imgs[ann['image_id']]                #         if type(ann['segmentation']['counts']) == list:                #             rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])                #         else:                #             rle = [ann['segmentation']]                #         m = maskUtils.decode(rle)                #         img = np.ones( (m.shape[0], m.shape[1(余下全部）], 3（from 1， 10张取一张）) )                #         if ann['iscrowd'] == 1(余下全部）:                #             color_mask = np.array([2.0,166.0,101.0])/255                #         if ann['iscrowd'] == 0:                #             color_mask = np.random.random((1(余下全部）, 3（from 1， 10张取一张）)).tolist()[0]                #         for i in range(3（from 1， 10张取一张）):                #             img[:,:,i] = color_mask[i]                #         ax.imshow(np.dstack( (img, m*0.5) ))                if 'keypoints' in ann and type(ann['keypoints']) == list:                    # turn skeleton into zero-based index                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton']) - 1                    kp = np.array(ann['keypoints'])                    x = kp[0::3]                    y = kp[1::3]                    v = kp[2::3]                    for sk in sks:                        if np.all(v[sk] > 0):                        #     # 画点之间的连接线plt.plot(x[sk], y[sk], linewidth=1, color=c)                    # 画点                    plt.plot(x[v > 0], y[v > 0], 'o', markersize=4, markerfacecolor=c, markeredgecolor='k', markeredgewidth=1)                    plt.plot(x[v > 1], y[v > 1], 'o', markersize=4, markerfacecolor=c, markeredgecolor=c, markeredgewidth=1)            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)            ax.add_collection(p)            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)            ax.add_collection(p)        elif datasetType == 'captions':            for ann in anns:                print(ann['caption'])pylab.rcParams['figure.figsize'] = (8.0, 10.0)annFile = r'E:\val2017\annotations_old\val.json'     #转换之后的json文件路径img_prefix = r'E:\val2017'#图片文件夹路径# initialize COCO api for instance annotationscoco = COCO(annFile)# getCatIds(catNms=[], supNms=[], catIds=[])# 通过输入类别的名字、大类的名字或是种类的id，来筛选得到图片所属类别的idcatIds = coco.getCatIds(catNms=['person'])# getImgIds(imgIds=[], catIds=[])# 通过图片的id或是所属种类的id得到图片的idimgIds = coco.getImgIds(catIds=catIds)# imgIds = coco.getImgIds(imgIds=[1407])# loadImgs(ids=[])# 得到图片的id信息后，就可以用loadImgs得到图片的信息了# 在这里我们随机选取之前list中的一张图片img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]I = io.imread('%s/%s' % (img_prefix, img['file_name']))plt.imshow(I)plt.axis('off')ax = plt.gca()# getAnnIds(imgIds=[], catIds=[], areaRng=[], iscrowd=None)# 通过输入图片的id、类别的id、实例的面积、是否是人群来得到图片的注释idannIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)# loadAnns(ids=[])# 通过注释的id，得到注释的信息anns = coco.loadAnns(annIds)print('anns:', anns)coco.showAnns(anns)plt.imshow(I)plt.axis('off')plt.show()

需要修改的地方为

annFile = r'E:\val2017\annotations\val.json'     #转换之后的coco格式json文件路径img_prefix = r'E:\val2017'#图片文件夹路径

如果不出意外的话，就不会出现意外了，可以看到你标注的效果如下图所示：

至此，labelme下的JSON转coco格式的JSON就完成了，由于篇幅问题，coco格式的JSON转yolo需要的txt相关介绍，在Yolopose关键点检测：自己标注数据集，制作数据集(二)中有相关讲解。

来源地址：https://blog.csdn.net/m0_57458432/article/details/128220346