OpenCV实现人体姿态估计(人体关键点检测）OpenPose

人体姿态估计(人体关键点检测）OpenPose"/>

OpenCV实现人体姿态估计(人体关键点检测）OpenPose

OpenCV实现人体姿态估计(人体关键点检测）OpenPose

OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以Caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用，基于它的实例如雨后春笋般涌现。

其理论基础来自Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields ，是CVPR 2017的一篇论文，作者是来自CMU感知计算实验室的曹哲（）,Tomas Simon,Shih-En Wei,Yaser Sheikh 。

人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景，人们更加熟悉的应用就是抖音尬舞机。

OpenPose的效果并不怎么好，强烈推荐《2D Pose人体关键点检测(Python/Android /C++ Demo) 》2D Pose人体关键点实时检测(Python/Android /C++ Demo)_pan_jinquan的博客-CSDN博客 ，提供了C++推理代码和Android Demo

人体关键点检测需要用到人体检测，请查看鄙人另一篇博客：2D Pose人体关键点实时检测(Python/Android /C++ Demo)_pan_jinquan的博客-CSDN博客

OpenPose项目Github链接：

OpenCV实现的Demo链接:.py）

1、实现原理

输入一幅图像，经过卷积网络提取特征，得到一组特征图，然后分成两个岔路，分别使用 CNN网络提取Part Confidence Maps 和 Part Affinity Fields；

得到这两个信息后，我们使用图论中的 Bipartite Matching（偶匹配） 求出Part Association，将同一个人的关节点连接起来，由于PAF自身的矢量性，使得生成的偶匹配很正确，最终合并为一个人的整体骨架；
最后基于PAFs求Multi-Person Parsing—>把Multi-person parsing问题转换成graphs问题—>Hungarian Algorithm(匈牙利算法)
（匈牙利算法是部图匹配最常见的算法，该算法的核心就是寻找增广路径，它是一种用增广路径求二分图最大匹配的算法。)

2、实现神经网络

阶段一：VGGNet的前10层用于为输入图像创建特征映射。

阶段二：使用2分支多阶段CNN，其中第一分支预测身体部位位置（例如肘部，膝部等）的一组2D置信度图（S）。 如下图所示，给出关键点的置信度图和亲和力图 - 左肩。

第二分支预测一组部分亲和度的2D矢量场（L），其编码部分之间的关联度。 如下图所示，显示颈部和左肩之间的部分亲和力。

阶段三： 通过贪心推理解析置信度和亲和力图，对图像中的所有人生成2D关键点。

3.OpenCV-OpenPose实现推理代码

# -*-coding: utf-8 -*-
"""@Project: python-learning-notes@File   : openpose_for_image_test.py@Author : panjq@E-mail : pan_jinquan@163@Date   : 2019-07-29 21:50:17
"""import cv2 as cv
import os
import globBODY_PARTS = {"Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,"LEye": 15, "REar": 16, "LEar": 17, "Background": 18}POSE_PAIRS = [["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"]]def detect_key_point(model_path, image_path, out_dir, inWidth=368, inHeight=368, threshhold=0.2):net = cv.dnn.readNetFromTensorflow(model_path)frame = cv.imread(image_path)frameWidth = frame.shape[1]frameHeight = frame.shape[0]scalefactor = 2.0net.setInput(cv.dnn.blobFromImage(frame, scalefactor, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False))out = net.forward()out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elementsassert (len(BODY_PARTS) == out.shape[1])points = []for i in range(len(BODY_PARTS)):# Slice heatmap of corresponging body's part.heatMap = out[0, i, :, :]# Originally, we try to find all the local maximums. To simplify a sample# we just find a global one. However only a single pose at the same time# could be detected this way._, conf, _, point = cv.minMaxLoc(heatMap)x = (frameWidth * point[0]) / out.shape[3]y = (frameHeight * point[1]) / out.shape[2]# Add a point if it's confidence is higher than threshold.points.append((int(x), int(y)) if conf > threshhold else None)for pair in POSE_PAIRS:partFrom = pair[0]partTo = pair[1]assert (partFrom in BODY_PARTS)assert (partTo in BODY_PARTS)idFrom = BODY_PARTS[partFrom]idTo = BODY_PARTS[partTo]if points[idFrom] and points[idTo]:cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 3)cv.ellipse(frame, points[idFrom], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)cv.ellipse(frame, points[idTo], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)t, _ = net.getPerfProfile()freq = cv.getTickFrequency() / 1000cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))cv.imwrite(os.path.join(out_dir, os.path.basename(image_path)), frame)cv.imshow('OpenPose using OpenCV', frame)cv.waitKey(0)def detect_image_list_key_point(image_dir, out_dir, inWidth=480, inHeight=480, threshhold=0.3):image_list = glob.glob(image_dir)for image_path in image_list:detect_key_point(image_path, out_dir, inWidth, inHeight, threshhold)if __name__ == "__main__":model_path = "pb/graph_opt.pb"# image_path = "body/*.jpg"out_dir = "result"# detect_image_list_key_point(image_path,out_dir)image_path = "./test.jpg"detect_key_point(model_path, image_path, out_dir, inWidth=368, inHeight=368, threshhold=0.05)

参考资料:

[1].Python+OpenCV+OpenPose实现人体姿态估计（人体关键点检测）_不脱发的程序猿-CSDN博客