再发超火抖音单人多人尬舞pythoncv2实现完整版

　　1 说明
　　=====
　　1.1 再发：是因为之前发过单人图和视频的骨架检测，多人报错。
　　《OpenPose：实现抖音很火的人体骨架和视频动态舞》
　　《人体骨架舞：OpenPose用python-opencv实现》
　　1.2 本次代码是完整的全套均可，本次代码讲解清楚，逐步分析，注释清楚，适合收藏。
　　即：
　　单人图片和单人视频检测，单人摄像头检测
　　多人图片和多人视频检测，多人摄像头检测
　　1.3 python的编程思维和编程要点复习，opencv相关知识。
　　2 图片
　　2.1 原图：来自今日头条免费正版图库
　　2.2 效果图
　　3 视频
　　=====
　　3.1 视频来源：something视频节选you-get https://y.qq.com/n/yqq/mv/v/s0023dwf6xi.html
　　3.2 多人视频操作效果图
　　4 代码讲解
　　========
　　4.1 # 第1步：代码头部注释 # 代码名：person-openpose-all.py # 本代码说明，是在源代码的基础上进行修改 # 原代码来自：https://github.com/spmallick/learnopencv # 下的OpenPose-Multi-Person代码 # 可执行图片和视频的人体骨架测试 # 单人和多人骨架均可以 # 视频来自摄像头和mp4均可以
　　4.2 # 第2步：模块导入 import cv2 import numpy as np import os  #路径管理
　　4.3 附注环境：python3.8+opencv4.4.0+linux系统+微软编辑器vscode，个人喜欢点击运行按钮。# 第3步：路径管理 curpath = os.path.realpath(__file__)        # 获取当前代码的绝对路径 dirpath = os.path.dirname(curpath)          # 获取当前文件的文件夹路径
　　4.4 模型加载和参数设置#第4步：模型加载 protoFile = dirpath+＂/pose/coco/pose_deploy_linevec.prototxt＂ #需要自己提前下载，第一篇文章已经告诉如何下载 weightsFile = dirpath+＂/pose/coco/pose_iter_440000.caffemodel＂  #参数设置 nPoints = 18 # COCO Output Format，名称列表，可注释掉 #keypointsMapping = [＂Nose＂, ＂Neck＂, ＂R-Sho＂, ＂R-Elb＂, ＂R-Wr＂, ＂L-Sho＂, ＂L-Elb＂, ＂L-Wr＂, ＂R-Hip＂, ＂R-Knee＂, ＂R-Ank＂, ＂L-Hip＂, ＂L-Knee＂, ＂L-Ank＂, ＂R-Eye＂, ＂L-Eye＂, ＂R-Ear＂, ＂L-Ear＂]  POSE_PAIRS = [[1,2], [1,5], [2,3], [3,4], [5,6], [6,7],               [1,8], [8,9], [9,10], [1,11], [11,12], [12,13],               [1,0], [0,14], [14,16], [0,15], [15,17],               [2,17], [5,16] ]  mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44],           [19,20], [21,22], [23,24], [25,26], [27,28], [29,30],           [47,48], [49,50], [53,54], [51,52], [55,56],           [37,38], [45,46]]  #颜色列表 colors = [ [0,100,255], [0,100,255], [0,255,255], [0,100,255], [0,255,255], [0,100,255],          [0,255,0], [255,200,100], [255,0,255], [0,255,0], [255,200,100], [255,0,255],          [0,0,255], [255,0,0], [200,200,0], [255,0,0], [200,200,0], [0,0,0]]
　　4.5 这块是关键，搞透了就是大牛，一般不需要调整。# 第5步：函数定义 # 获取关节点函数 def getKeypoints(probMap, threshold=0.1):          mapSmooth = cv2.GaussianBlur(probMap,(3,3),0,0)     mapMask = np.uint8(mapSmooth>threshold)          keypoints = []     #find the blobs     contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)     #for each blob find the maxima     for cnt in contours:         blobMask = np.zeros(mapMask.shape)         blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)         maskedProbMap = mapSmooth * blobMask         _, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)         keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))      return keypoints   # Find valid connections between the different joints of a all persons present def getValidPairs(output):     valid_pairs = []     invalid_pairs = []     n_interp_samples = 10     paf_score_th = 0.1     conf_th = 0.7     # loop for every POSE_PAIR     for k in range(len(mapIdx)):         # A->B constitute a limb         pafA = output[0, mapIdx[k][0], :, :]         pafB = output[0, mapIdx[k][1], :, :]         pafA = cv2.resize(pafA, (frameWidth, frameHeight))         pafB = cv2.resize(pafB, (frameWidth, frameHeight))          # Find the keypoints for the first and second limb         candA = detected_keypoints[POSE_PAIRS[k][0]]         candB = detected_keypoints[POSE_PAIRS[k][1]]         nA = len(candA)         nB = len(candB)          if( nA != 0 and nB != 0):             valid_pair = np.zeros((0,3))             for i in range(nA):                 max_j=-1                 maxScore = -1                 found = 0                 for j in range(nB):                     # Find d_ij                     d_ij = np.subtract(candB[j][:2], candA[i][:2])                     norm = np.linalg.norm(d_ij)                     if norm:                         d_ij = d_ij / norm                     else:                         continue                     # Find p(u)                     interp_coord = list(zip(np.linspace(candA[i][0], candB[j][0], num=n_interp_samples),                                             np.linspace(candA[i][1], candB[j][1], num=n_interp_samples)))                     # Find L(p(u))                     paf_interp = []                     for k in range(len(interp_coord)):                         paf_interp.append([pafA[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))],                                            pafB[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))] ])                     # Find E                     paf_scores = np.dot(paf_interp, d_ij)                     avg_paf_score = sum(paf_scores)/len(paf_scores)                      # Check if the connection is valid                     # If the fraction of interpolated vectors aligned with PAF is higher then threshold -> Valid Pair                     if ( len(np.where(paf_scores > paf_score_th)[0]) / n_interp_samples ) > conf_th :                         if avg_paf_score > maxScore:                             max_j = j                             maxScore = avg_paf_score                             found = 1                 # Append the connection to the list                 if found:                     valid_pair = np.append(valid_pair, [[candA[i][3], candB[max_j][3], maxScore]], axis=0)              # Append the detected connections to the global list             valid_pairs.append(valid_pair)         else: # If no keypoints are detected             #print(＂No Connection : k = {}＂.format(k))             invalid_pairs.append(k)             valid_pairs.append([])      return valid_pairs, invalid_pairs   #分配到人的关节点和关节线函数 # This function creates a list of keypoints belonging to each person # For each detected valid pair, it assigns the joint(s) to a person def getPersonwiseKeypoints(valid_pairs, invalid_pairs):     # the last number in each row is the overall score     personwiseKeypoints = -1 * np.ones((0, 19))      for k in range(len(mapIdx)):         if k not in invalid_pairs:             partAs = valid_pairs[k][:,0]             partBs = valid_pairs[k][:,1]             indexA, indexB = np.array(POSE_PAIRS[k])              for i in range(len(valid_pairs[k])):                 found = 0                 person_idx = -1                 for j in range(len(personwiseKeypoints)):                     if personwiseKeypoints[j][indexA] == partAs[i]:                         person_idx = j                         found = 1                         break                  if found:                     personwiseKeypoints[person_idx][indexB] = partBs[i]                     personwiseKeypoints[person_idx][-1] += keypoints_list[partBs[i].astype(int), 2] + valid_pairs[k][i][2]                  # if find no partA in the subset, create a new subset                 elif not found and k < 17:                     row = -1 * np.ones(19)                     row[indexA] = partAs[i]                     row[indexB] = partBs[i]                     # add the keypoint_scores for the two keypoints and the paf_score                     row[-1] = sum(keypoints_list[valid_pairs[k][i,:2].astype(int), 2]) + valid_pairs[k][i][2]                     personwiseKeypoints = np.vstack([personwiseKeypoints, row])     return personwiseKeypoints
　　4.6 #第6步：导入类型 #图片类 #常规是image1或者image，为了与视频代码兼容 #这里采用frame代替原来的image1 #frame = cv2.imread(dirpath+＂/11.jpeg＂)  #---视频类--- cap = cv2.VideoCapture(dirpath+＂/s.mp4＂) #mp4视频，cpu生成有一点慢 #cap = cv2.VideoCapture(0)  #摄像头 hasFrame, frame = cap.read() #生成本目录下的视频 vid_writer = cv2.VideoWriter(dirpath+＂/outputs.avi＂,cv2.VideoWriter_fourcc(＂M＂,＂J＂,＂P＂,＂G＂), 10, (frame.shape[1],frame.shape[0])) #---视频类---
　　4.7 #第7步：启动cpu训练和调动模型 net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) net.setPreferableBackend(cv2.dnn.DNN_TARGET_CPU) print(＂Using CPU device＂)
　　4.8 #第8步：循环内设置 while cv2.waitKey(1) < 0:     #增加一张输出的黑色图片，用于显示骨架和数字     out = np.zeros(frame.shape,np.uint8)  #add      #---视频类，图片不需要，可以注释掉---     hasFrame, frame = cap.read()     frameCopy = np.copy(frame)     #退出设置     if not hasFrame:         cv2.waitKey()         break     #---视频类---      frameWidth = frame.shape[1]     frameHeight = frame.shape[0]      # Fix the input Height and get the width according to the Aspect Ratio     inHeight = 368     inWidth = int((inHeight/frameHeight)*frameWidth)      inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),                             (0, 0, 0), swapRB=False, crop=False)      net.setInput(inpBlob)     output = net.forward()      detected_keypoints = []     keypoints_list = np.zeros((0,3))     keypoint_id = 0     threshold = 0.1      for part in range(nPoints):         probMap = output[0,part,:,:]         probMap = cv2.resize(probMap, (frame.shape[1], frame.shape[0]))         keypoints = getKeypoints(probMap, threshold)          keypoints_with_id = []         for i in range(len(keypoints)):             keypoints_with_id.append(keypoints[i] + (keypoint_id,))             keypoints_list = np.vstack([keypoints_list, keypoints[i]])             keypoint_id += 1          detected_keypoints.append(keypoints_with_id)      frameClone = frame.copy()      for i in range(nPoints):         for j in range(len(detected_keypoints[i])):             cv2.circle(frameClone, detected_keypoints[i][j][0:2], 5, colors[i], -1, cv2.LINE_AA)     #展示窗口1：关节点     cv2.imshow(＂1-Keypoints＂,frameClone)      valid_pairs, invalid_pairs = getValidPairs(output)     personwiseKeypoints = getPersonwiseKeypoints(valid_pairs, invalid_pairs)      for i in range(17):         for n in range(len(personwiseKeypoints)):             index = personwiseKeypoints[n][np.array(POSE_PAIRS[i])]             if -1 in index:                 continue             B = np.int32(keypoints_list[index.astype(int), 0])             A = np.int32(keypoints_list[index.astype(int), 1])             cv2.line(frameClone, (B[0], A[0]), (B[1], A[1]), colors[i], 3, cv2.LINE_AA)              cv2.line(out, (B[0], A[0]), (B[1], A[1]), colors[i], 3, cv2.LINE_AA)  #add      #展示窗口2：骨架==关节线     cv2.imshow(＂2-Detected Pose＂ , frameClone)     #展示窗口3：纯骨架==关节线     cv2.imshow(＂3-Pure Detected Pose＂ , out)  #add  #---视频类，需要下面的代码，图片检测，可以注释掉---     vid_writer.write(frameClone)  vid_writer.release() #---视频类---
　　结束，如果实时，可能需要GPU或者高性能计算机。
　　基本完整！！