瑞芯微RV1126B开发板（EASY-EAI-PI2） 人体关键点识别

1. 人体关键点识别简介人体关键点识别是一种基于深度学习的对人进行检测定位与姿势估计的模型广泛应用于体育分析、动物行为监测和机器人等领域帮助机器实时解读物理动作。本算法具有运行效率高、实时性强的特点。本人员检测算法在数据集表现如下所示基于EASY-EAI-PI2(RV1126B)硬件主板的运行效率17个人体关键点索引定义2. 快速上手2.1 开发环境准备如果您初次阅读此文档请阅读《入门指南/开发环境准备/Easy-Eai编译环境准备与更新》并按照其相关的操作进行编译环境的部署。在PC端Ubuntu系统中执行run脚本进入EASY-EAI编译环境具体如下所示。cd ~/develop_environment ./run.sh 22042.2 源码下载在EASY-EAI编译环境下创建存放源码仓库的管理目录cd /opt mkdir EASY-EAI-Toolkit cd EASY-EAI-Toolkit通过git工具在管理目录内克隆远程仓库git clone https://github.com/EASY-EAI/EASY-EAI-Toolkit-1126B.git注* 此处可能会因网络原因造成卡顿请耐心等待。* 如果实在要在gitHub网页上下载也要把整个仓库下载下来不能单独下载本实例对应的目录。2.3 模型部署要完成算法Demo的执行需要先下载人体关键点检测算法模型。百度网盘链接为https://pan.baidu.com/s/13BsL5MZ4NQ8jDDe-5WLXDw?pwd1234提取码1234 。同时需要把下载的人体关键点检测算法模型复制粘贴到Release/目录2.4 例程编译进入到对应的例程目录执行编译操作具体命令如下所示cd EASY-EAI-Toolkit-1126B/Demos/algorithm-person_pose/ ./build.sh cpres注* 由于依赖库部署在板卡上因此交叉编译过程中必须保持/mnt挂载。* 若build.sh脚本带有cpres参数则会把Release/目录下的所有资源都拷贝到开发板上。2.5 例程运行及效果通过串口调试或ssh调试进入板卡后台定位到例程部署的位置如下所示cd /userdata/Demo/algorithm-person_pose/运行例程命令如下所示sudo ./test-person_pose person_pose_m.model test.jpg在EASY-EAI编译环境可以取回测试图片:cp /mnt/userdata/Demo/algorithm-person_pose/result.jpg .结果图片如下所示API的详细说明以及API的调用本例程源码详细信息见下方说明。3. 人体关键点检测API说明3.1 引用方式为方便客户在本地工程中直接调用我们的EASY EAI api库此处列出工程中需要链接的库以及头文件等方便用户直接添加。3.2 人体关键点识别初始化函数人体关键点识别初始化函数原型如下所示。int person_pose_init(const char *c, person_pose_context_t *p_person_pose, int cls_num)具体介绍如下所示。3.3 人体关键点识别运行函数人体关键点识别运行函数person_pose_run原型如下所示。std::vectorperson_pose_result_t person_pose_run(cv::Mat image, person_pose_context_t *p_person_pose, float nms_threshold, float conf_threshold);具体介绍如下所示。3.4 人体关键点识别释放函数人体关键点识别释放函数原型如下所示。int person_pose_release(person_pose_context_t* p_person_pose)具体介绍如下所示。4. 人体关键识别算法例程例程目录为Demos/algorithm-person_pose/test-person_pose.cpp操作流程如下。参考例程如下所示。#include stdint.h #include stdio.h #include stdlib.h #include string.h #include person_pose.h #include opencv2/opencv.hpp // 画线 cv::Mat draw_line(cv::Mat image, float *key1, float *key2, cv::Scalar color) { if (key1[2] 0.1 key2[2] 0.1) { cv::Point pt1(key1[0], key1[1]); cv::Point pt2(key2[0], key2[1]); cv::circle(image, pt1, 2, color, 2); cv::circle(image, pt2, 2, color, 2); cv::line(image, pt1, pt2, color, 2); } return image; } // 绘制结果: // 0鼻子, 1左眼, 2右眼3左耳4右耳5左肩6右肩7左肘8右肘9左腕10右腕11左髋关节12右髋关节13左膝14右膝15左脚踝16右脚踝 cv::Mat draw_image(cv::Mat image, std::vectorperson_pose_result_t results) { long unsigned int i 0; for (i 0; i results.size(); i) { // 绘制脸部 image draw_line(image, results[i].keypoints[0], results[i].keypoints[1], CV_RGB(0, 255, 0)); image draw_line(image, results[i].keypoints[0], results[i].keypoints[2], CV_RGB(0, 255, 0)); image draw_line(image, results[i].keypoints[1], results[i].keypoints[3], CV_RGB(0, 255, 0)); image draw_line(image, results[i].keypoints[2], results[i].keypoints[4], CV_RGB(0, 255, 0)); image draw_line(image, results[i].keypoints[3], results[i].keypoints[5], CV_RGB(0, 255, 0)); image draw_line(image, results[i].keypoints[4], results[i].keypoints[6], CV_RGB(0, 255, 0)); // 绘制上半身 image draw_line(image, results[i].keypoints[5], results[i].keypoints[6], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[5], results[i].keypoints[7], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[7], results[i].keypoints[9], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[6], results[i].keypoints[8], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[8], results[i].keypoints[10], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[5], results[i].keypoints[11], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[6], results[i].keypoints[12], CV_RGB(0, 0, 255)); image draw_line(image, results[i].keypoints[11], results[i].keypoints[12], CV_RGB(0, 0, 255)); // 绘制下半身 image draw_line(image, results[i].keypoints[11], results[i].keypoints[13], CV_RGB(255, 255, 0)); image draw_line(image, results[i].keypoints[13], results[i].keypoints[15], CV_RGB(255, 255, 0)); image draw_line(image, results[i].keypoints[12], results[i].keypoints[14], CV_RGB(255, 255, 0)); image draw_line(image, results[i].keypoints[14], results[i].keypoints[16], CV_RGB(255, 255, 0)); cv::Rect rect(results[i].left, results[i].top, (results[i].right - results[i].left), (results[i].bottom - results[i].top)); cv::rectangle(image, rect, CV_RGB(255, 0, 0), 2); } return image; } /// 主函数 int main(int argc, char **argv) { if (argc ! 3) { printf(%s model_path image_path\n, argv[0]); return -1; } const char *p_model_path argv[1]; const char *p_img_path argv[2]; printf(Model path %s, image path %s\n\n, p_model_path, p_img_path); cv::Mat image cv::imread(p_img_path); printf(Image size (%d, %d)\n, image.rows, image.cols); int ret; person_pose_context_t yolo11_pose; memset(yolo11_pose, 0, sizeof(yolo11_pose)); person_pose_init(p_model_path, yolo11_pose, 1); double start_time static_castdouble(cv::getTickCount()); std::vectorperson_pose_result_t results person_pose_run(image, yolo11_pose, 0.35, 0.35); double end_time static_castdouble(cv::getTickCount()); double time_elapsed (end_time - start_time) / cv::getTickFrequency() * 1000; std::cout person pose run time: time_elapsed ms std::endl; // 绘制结果 image draw_image(image, results); cv::imwrite(result.jpg, image); printf(Detect size %ld\n, results.size()); ret person_pose_release(yolo11_pose); return ret; }