【数据集】Yolo人体关键点数据集处理

鲸可落2024-08-09 8:20

文章目录

1、介绍

人体关键点检测（Human Keypoints Detection）又称为人体姿态估计2D Pose，是计算机视觉中一个相对基础的任务，是人体动作识别、行为分析、人机交互等的前置任务。一般情况下可以将人体关键点检测细分为单人/多人关键点检测、2D/3D关键点检测，同时有算法在完成关键点检测之后还会进行关键点的跟踪，也被称为人体姿态跟踪。

本次要介绍的数据集是2D关键点检测数据集，数据集主要来自COCO2017，经过对COCO数据集JSON文件进行预处理提取人体关键点信息，一共提取10000 张人体姿态数据集，以及对应的必要信息，已经转化为Yolo格式存储。

2、数据集格式

复制代码 
keypoint_dataset
	|
	|_______images
	|		  |_____000000000049.jpg
	|		  |_____ ......
	|_______labels
	|		  |_____000000000049.txt
	|		  |_____ ......

3 、COCO人体关键点示意图

下图中，共17个关节点（鼻子x1、眼睛x2、耳朵x2、肩部x2、肘部x2、手腕x2、髋部x2、膝关节x2、脚腕x2）：

4、数据集预处理

这里数据集预处理主要包括5个内容，分别是read_json、show、json2yolo、split_dataset、validation。

4.1、读取JSON文件

python 复制代码 
def read_json(json_path):
    d = defaultdict(list)
    for json_name in tqdm(os.listdir(json_path)):
        json_full_path = json_path + '/' + json_name
        with open(json_full_path, 'r', encoding='utf-8') as file:  
            data = json.load(file)
        image_name = data['image_name']
        # category = data['category']
        # width = data['width']
        # height = data['height']
        # keypoints = data['key_points']
        # bbox = data['bbox']
        image_id = image_name.split('.')[0]
        d[int(image_id)]= data 
    print(f'json 文件读取完成,一共{len(d.keys())}个数据')
    return d

4.2、可视化

python 复制代码 
def visualization(ax,keypoints,bbox):
    #随机颜色
    c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
    #关键点之间的连线
    ls = [[15,13],[13,11],[16,14],[14,12],[11,12],[5,11],[6,12],[5,6],[5,7],
          [6,8],[7,9],[8,10],[1,2],[0,1],[0,2],[1,3],[2,4],[3,5],[4,6]]
    sks = np.array(ls)
    #获取关键点坐标x,y,v
    kp = np.array(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
    p = plt.plot(x[v>0], y[v>0],'o',markersize=4, markerfacecolor=c, markeredgecolor='k',markeredgewidth=1)
    p = plt.plot(x[v>1], y[v>1],'o',markersize=4, markerfacecolor=c, markeredgecolor=c, markeredgewidth=1)
    # 画矩形边界,多边形填充+矩形边界:
    x, y, w, h = bbox[0],bbox[1],bbox[2],bbox[3]
    ax.add_patch(Polygon(xy=[[x, y], [x, y+h], [x+w, y+h], [x+w, y]], color='k', alpha=0.3))
    ax.add_patch(Rectangle(xy=(x, y), width=w, height=h, fill=False, color=c, alpha=1))
    plt.plot(x + w/2,y+h/2,'*',markersize=5, markerfacecolor=c, markeredgecolor=c, markeredgewidth=1)

def show(data,image_root):
    for image_id in data.keys():

        img = io.imread('%s/%s' % (image_root, data[image_id]['image_name']))
        plt.axis('off')
        ax = plt.gca()
        for i in range(len(data[image_id]['key_points'])):
            visualization(ax, data[image_id]['key_points'][i], data[image_id]['bbox'][i])
        plt.imshow(img)
        plt.axis('off')
        plt.show()

4.3、JSON格式转Yolo格式

yolo标注格式为：类别 、标注框中心点(x,y)、长和宽(w,h)，关键点坐标以及可见度(kx1,ky1,kv1,kx2,ky2,kv2...)，然后并根据图片长宽进行归一化处理

python 复制代码 
def json2yolo(data,txt_path):
    if not os.path.exists(txt_path):
        os.mkdir(txt_path)
    for image_id in tqdm(data.keys()):
        txt_name = data[image_id]['image_name'].split('.')[0]
        category = data[image_id]['category']
        width = data[image_id]['width']
        height = data[image_id]['height']
        keypoints = data[image_id]['key_points']
        bbox = data[image_id]['bbox']
        '''
        yolo标注格式
        类别 、标注框中心点、长和宽，关键点坐标以及可见度
        根据图片长高进行归一化处理
        '''
        for i in range(len(keypoints)):
            #对标注框进行预处理
            box = bbox[i]
            keypoint = keypoints[i]
            # print(keypoint)
            #x,y为左上角坐标，w,h为宽高
            x,y,w,h = box[0],box[1],box[2],box[3]
            xx = x + w/2
            yy = y + h/2
            #归一化
            xx,yy,ww,hh = xx/width,yy/height,w/width,h/height
            #关键点归一化
            proc_keypoint = []
            for p in range(0,len(keypoint),3):
                x,y,v = keypoint[p],keypoint[p+1],keypoint[p+2]
                kx,ky = x/width, y/height
                proc_keypoint.extend([kx,ky,v])
            #写入txt文件
            yolo_str = f'{category} {xx:.6f} {yy:.6f} {ww:.6f} {hh:.6f} '
            yolo_str = yolo_str + ' '.join([f'{i:.6f}' for i in proc_keypoint])
            
            with open(f'{txt_path}/{txt_name}.txt','a+') as f:
                f.write(yolo_str + '\n')
    print('转化为yolo txt格式完成!')

4.4、划分数据集

划分数据集格式为yolo需要的格式

python 复制代码 
def split_dataset(image_root,txt_root,img_targe_file,label_target_file,split_ratio=0.6):
    imgs = os.listdir(image_root)
    import random
    random.seed(2024)
    random.shuffle(imgs)
    #这里仅仅取了1万张图片进行测试，由于显存限制，
    ims = imgs[:10000]
    random.shuffle(ims)
    train_num = int(len(ims)*split_ratio)
    val_num = int(len(ims)*(1-split_ratio)/2)

    train_set = ims[:train_num]
    val_set = ims[train_num:train_num+val_num]
    test_set = ims[-val_num:]

    move_file(train_set,image_root,txt_root,img_targe_file,label_target_file,mode='train')
    move_file(val_set,image_root,txt_root,img_targe_file,label_target_file,mode='val')
    move_file(test_set,image_root,txt_root,img_targe_file,label_target_file,mode='test')

def move_file(dataset,image_root,txt_root,img_targe_file,label_target_file,mode='train'):
    for img in tqdm(dataset):
        img_path = image_root + '/' + img
        txt_path = txt_root + '/' + img.replace('jpg','txt')
        img_targe_file_full = img_targe_file + '/' + mode
        label_target_file_full = label_target_file + '/' + mode


        shutil.copy(img_path, img_targe_file_full)
        shutil.copy(txt_path, label_target_file_full)

4.5、验证

python 复制代码 
def validation():
    images = r'E:\datasets\keypoint_dataset\datasets\images'
    labels = r'E:\datasets\keypoint_dataset\datasets\labels'
    for mode in ['train','val','test']:
        img_path = images + '/' + mode
        label_path = labels + '/' + mode
        assert len(os.listdir(img_path)) == len(os.listdir(label_path)),f'{mode}数据划分验证失败'
        for img,lb in zip(os.listdir(img_path),os.listdir(label_path)):
            img_name = img.split('.')[0]
            lb_name = lb.split('.')[0]
            if img_name != lb_name:
                 assert '数据划分验证失败'
    print('数据划分验证成功！')
    pass

5、完整代码

python 复制代码 
import json
from collections import defaultdict
import numpy as np
import os
from tqdm import tqdm
import shutil
from matplotlib import pyplot as plt
import skimage.io as io

from matplotlib.patches import Polygon,Rectangle
def visualization(ax,keypoints,bbox):
    #随机颜色
    c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
    #关键点之间的连线
    ls = [[15,13],[13,11],[16,14],[14,12],[11,12],[5,11],[6,12],[5,6],[5,7],
          [6,8],[7,9],[8,10],[1,2],[0,1],[0,2],[1,3],[2,4],[3,5],[4,6]]
    sks = np.array(ls)
    #获取关键点坐标x,y,v
    kp = np.array(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
    p = plt.plot(x[v>0], y[v>0],'o',markersize=4, markerfacecolor=c, markeredgecolor='k',markeredgewidth=1)
    p = plt.plot(x[v>1], y[v>1],'o',markersize=4, markerfacecolor=c, markeredgecolor=c, markeredgewidth=1)
    # 画矩形边界,多边形填充+矩形边界:
    x, y, w, h = bbox[0],bbox[1],bbox[2],bbox[3]
    ax.add_patch(Polygon(xy=[[x, y], [x, y+h], [x+w, y+h], [x+w, y]], color='k', alpha=0.3))
    ax.add_patch(Rectangle(xy=(x, y), width=w, height=h, fill=False, color=c, alpha=1))
    plt.plot(x + w/2,y+h/2,'*',markersize=5, markerfacecolor=c, markeredgecolor=c, markeredgewidth=1)

def read_json(json_path):
    d = defaultdict(list)
    
    for json_name in tqdm(os.listdir(json_path)):
        json_full_path = json_path + '/' + json_name
        with open(json_full_path, 'r', encoding='utf-8') as file:  
            data = json.load(file)
        image_name = data['image_name']
        # category = data['category']
        # width = data['width']
        # height = data['height']
        # keypoints = data['key_points']
        # bbox = data['bbox']
        image_id = image_name.split('.')[0]
        
        d[int(image_id)]= data 
    
    print(f'json 文件读取完成,一共{len(d.keys())}个数据')
    return d
def show(data,image_root):
    for image_id in data.keys():

        img = io.imread('%s/%s' % (image_root, data[image_id]['image_name']))
        plt.axis('off')
        ax = plt.gca()
        for i in range(len(data[image_id]['key_points'])):
            visualization(ax, data[image_id]['key_points'][i], data[image_id]['bbox'][i])
        plt.imshow(img)
        plt.axis('off')
        plt.show()
def json2yolo(data,txt_path):
    if not os.path.exists(txt_path):
        os.mkdir(txt_path)
    for image_id in tqdm(data.keys()):
        txt_name = data[image_id]['image_name'].split('.')[0]
        category = data[image_id]['category']
        width = data[image_id]['width']
        height = data[image_id]['height']
        keypoints = data[image_id]['key_points']
        bbox = data[image_id]['bbox']
        '''
        yolo标注格式
        类别 、标注框中心点、长和宽，关键点坐标以及可见度
        根据图片长高进行归一化处理
        '''
        for i in range(len(keypoints)):
            #对标注框进行预处理
            box = bbox[i]
            keypoint = keypoints[i]
            # print(keypoint)
            #x,y为左上角坐标，w,h为宽高
            x,y,w,h = box[0],box[1],box[2],box[3]
            xx = x + w/2
            yy = y + h/2
            #归一化
            xx,yy,ww,hh = xx/width,yy/height,w/width,h/height
            #关键点归一化
            proc_keypoint = []
            for p in range(0,len(keypoint),3):
                x,y,v = keypoint[p],keypoint[p+1],keypoint[p+2]
                kx,ky = x/width, y/height
                proc_keypoint.extend([kx,ky,v])
            #写入txt文件
            yolo_str = f'{category} {xx:.6f} {yy:.6f} {ww:.6f} {hh:.6f} '
            yolo_str = yolo_str + ' '.join([f'{i:.6f}' for i in proc_keypoint])
            
            with open(f'{txt_path}/{txt_name}.txt','a+') as f:
                f.write(yolo_str + '\n')
    print('转化为yolo txt格式完成!')

def split_dataset(image_root,txt_root,img_targe_file,label_target_file,split_ratio=0.6):
    imgs = os.listdir(image_root)
    import random
    random.seed(2024)
    random.shuffle(imgs)
    #这里仅仅取了1万张图片进行测试，由于显存限制，
    ims = imgs[:10000]
    random.shuffle(ims)
    train_num = int(len(ims)*split_ratio)
    val_num = int(len(ims)*(1-split_ratio)/2)

    train_set = ims[:train_num]
    val_set = ims[train_num:train_num+val_num]
    test_set = ims[-val_num:]

    move_file(train_set,image_root,txt_root,img_targe_file,label_target_file,mode='train')
    move_file(val_set,image_root,txt_root,img_targe_file,label_target_file,mode='val')
    move_file(test_set,image_root,txt_root,img_targe_file,label_target_file,mode='test')

def move_file(dataset,image_root,txt_root,img_targe_file,label_target_file,mode='train'):
    for img in tqdm(dataset):
        img_path = image_root + '/' + img
        txt_path = txt_root + '/' + img.replace('jpg','txt')
        img_targe_file_full = img_targe_file + '/' + mode
        label_target_file_full = label_target_file + '/' + mode


        shutil.copy(img_path, img_targe_file_full)
        shutil.copy(txt_path, label_target_file_full)
def validation():
    images = r'E:\datasets\keypoint_dataset\datasets\images'
    labels = r'E:\datasets\keypoint_dataset\datasets\labels'
    for mode in ['train','val','test']:
        img_path = images + '/' + mode
        label_path = labels + '/' + mode
        assert len(os.listdir(img_path)) == len(os.listdir(label_path)),f'{mode}数据划分验证失败'
        for img,lb in zip(os.listdir(img_path),os.listdir(label_path)):
            img_name = img.split('.')[0]
            lb_name = lb.split('.')[0]
            if img_name != lb_name:
                 assert '数据划分验证失败'
    print('数据划分验证成功！')
    pass

if __name__ == '__main__':
    root = 'E:/datasets/keypoint_dataset'
    image_root = 'E:/datasets/keypoint_dataset/images'
    json_root = 'E:/datasets/keypoint_dataset/labels'
    txt_root = root + '/'+ 'txt'
    #读取json文件
    data = read_json(json_root)
    #可视化
    # show(data,image_root)
    #json2yolo
    # json2yolo(data,txt_root)
    #split dataset
    # img_target_file = root + '/'+ 'datasets' + '/images'
    # label_target_file = root + '/'+ 'datasets' + '/labels'
    # split_dataset(image_root,txt_root,img_target_file,label_target_file,split_ratio=0.7)
    #validation验证
    validation()

6、数据集下载链接

数据集下载链接

相关推荐
Coovally AI模型快速验证3 小时前
无人机小目标检测新SOTA：MASF-YOLO重磅开源，多模块协同助力精度飞跃
人工智能·yolo·目标检测·机器学习·计算机视觉·无人机
飞翔的佩奇5 小时前
【完整源码+数据集+部署教程】骰子点数识别图像实例分割系统源码和数据集：改进yolo11-DCNV2
python·yolo·计算机视觉·数据集·yolo11·骰子点数识别图像实例分割
HUIMU_1 天前
DAY20-新世纪DL（DeepLearning/深度学习）战士：终（目标检测/YOLO）3
深度学习·yolo·目标检测·滑动窗口·非极大值抑制·交并比·bouding box
max5006001 天前
YOLOv8主干网络替换为UniConvNet的详细指南
运维·开发语言·人工智能·python·算法·yolo
Python图像识别2 天前
57_基于深度学习的农作物虫害检测识别系统（yolo11、yolov8、yolov5+UI界面+Python项目源码+模型+标注好的数据集）
python·深度学习·yolo
a1111111111ss2 天前
基于 YOLOv11n 的无人机航拍小目标检测算法学习
yolo·目标检测·无人机
Coovally AI模型快速验证2 天前
轻量级注意力模型HOTSPOT-YOLO：无人机光伏热异常检测新SOTA，mAP高达90.8%
人工智能·学习·yolo·计算机视觉·目标跟踪·无人机
Coovally AI模型快速验证2 天前
突破闭集限制：3D-MOOD 实现开集单目 3D 检测新 SOTA
人工智能·yolo·计算机视觉·3d·目标跟踪·无人机
Virgil1393 天前
【YOLO学习笔记】数据增强mosaic、Mixup、透视放射变换
笔记·学习·yolo
2202_756749693 天前
YOLO 目标检测：YOLOv5网络结构、Focus、CSP、自适应Anchor、激活函数SiLU、SPPF、C3
人工智能·yolo·目标检测·php
热门推荐
012025 年高教社杯全国大学生数学建模竞赛C 题 NIPT 的时点选择与胎儿的异常判定 完整成品思路模型代码分享，全网首发高质量！！！022025年数学建模国赛C题超详细解题思路032025全国大学生数学建模C题保姆级思路模型（持续更新）：NIPT 的时点选择与胎儿的异常判定042025高教社杯国赛数学建模选题建议+初步分析052025国赛B题保姆级教程思路分析 碳化硅外延层厚度的确定06UV安装并设置国内源07奈飞工厂官网，国内Netflix影视在线看|中文网页电脑版入口08（E题｜AI 辅助智能体测）2025年高教杯全国大学生数学建模国赛解题思路|完整代码论文集合09KGG转MP3工具|非KGM文件|解密音频10不再让Windows更新！&Edge游戏助手卸载及关闭自动更新