OpenPose实现抖音很火的人体骨架和视频动态舞

　　1 说明
　　=====
　　1.1 OpenPose 是基于卷积神经网络和监督学习并以 caffe 为框架写成的开源库。
　　1.2 可以实现人的面部表情、躯干和四肢甚至手指的跟踪，适用多人且具有较好的鲁棒性。
　　1.3 是世界上第一个基于深度学习的实时多人二维姿态估计，为机器理解人类提供了一个高质量的信息维度。
　　1.4 代码来源：https://github.com/spmallick/learnopencv
　　2 效果图
　　======
　　2.1 图片
　　2.2 视频
　　视频节选
　　3 准备
　　=====
　　3.1 对源代码进行修改、注释、运行，提高可读性和可操作性，适合小白，入门秒懂。
　　3.2 环境：python3.8+opencv4.4.0+深度deepin-linux操作系统。
　　3.3 模型下载地址，官网也有太慢了。网友提供，谢谢 https://blog.csdn.net/GL_a_/article/details/81661821
　　3.4 文档结构
　　说明是自己单独的，output.avi是视频生成的效果图
　　4 图片人体骨架
　　============
　　4.1 代码：OpenPoseImage.py#打开终端输入：本机 #python3.8 OpenPoseImage.py --device cpu --image_file single.jpeg  #第1步：导入模块 import cv2 import time import numpy as np import argparse  #终端参数设置 parser = argparse.ArgumentParser(description=＂Run keypoint detection＂) #启动cpu运行 parser.add_argument(＂--device＂, default=＂cpu＂, help=＂Device to inference on＂) parser.add_argument(＂--image_file＂, default=＂single.jpeg＂, help=＂Input image＂) args = parser.parse_args()  #第3步：模型设置 #模型下载地址：https://blog.csdn.net/GL_a_/article/details/81661821 #国内的，快  #模型判定 MODE = ＂COCO＂ #if MODE is ＂COCO＂: if MODE==＂COCO＂:     #当前目录下的文件夹     protoFile = ＂pose/coco/pose_deploy_linevec.prototxt＂     #或者直接下载，太慢了，文件很大     #http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel     weightsFile = ＂pose/coco/pose_iter_440000.caffemodel＂  #缺少     nPoints = 18     POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]]  #附加 #elif MODE is ＂MPI＂ : elif MODE==＂MPI＂ :     protoFile = ＂pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt＂     #或者直接下载，太慢了，文件很大     #http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel     weightsFile = ＂pose/mpi/pose_iter_160000.caffemodel＂  #缺少     nPoints = 15     POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ]   #第4步：opencv读取图片 frame = cv2.imread(args.image_file) frameCopy = np.copy(frame) frameWidth = frame.shape[1] frameHeight = frame.shape[0] threshold = 0.1  #第5步：模型读取和深度学习设置骨架和骨架点 #读取模型 net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)  #默认启动cpu if args.device == ＂cpu＂:     net.setPreferableBackend(cv2.dnn.DNN_TARGET_CPU)     print(＂Using CPU device＂)  #附加设置，启动GPU elif args.device == ＂gpu＂:     net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)     net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)     print(＂Using GPU device＂)  #时间设置 t = time.time() # input image dimensions for the network inWidth = 368 inHeight = 368 inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),                           (0, 0, 0), swapRB=False, crop=False)  net.setInput(inpBlob)  output = net.forward() print(＂time taken by network : {:.3f}＂.format(time.time() - t))  H = output.shape[2] W = output.shape[3]  #骨架点 # Empty list to store the detected keypoints points = []  for i in range(nPoints):     # confidence map of corresponding body＂s part.     probMap = output[0, i, :, :]      # Find global maxima of the probMap.     minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)          # Scale the point to fit on the original image     x = (frameWidth * point[0]) / W     y = (frameHeight * point[1]) / H      if prob > threshold :          cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)         cv2.putText(frameCopy, ＂{}＂.format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)          # Add the point to the list if the probability is greater than the threshold         points.append((int(x), int(y)))     else :         points.append(None)  #骨架 # Draw Skeleton，画骨架 for pair in POSE_PAIRS:     partA = pair[0]     partB = pair[1]      if points[partA] and points[partB]:         cv2.line(frame, points[partA], points[partB], (0, 255, 255), 2)         cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)  #显示生成图片 cv2.imshow(＂Output-Keypoints＂, frameCopy) cv2.imshow(＂Output-Skeleton＂, frame)  #输出生成图片 #cv2.imwrite(＂Output-Keypoints.jpg＂, frameCopy) #cv2.imwrite(＂Output-Skeleton.jpg＂, frame)  print(＂Total time taken : {:.3f}＂.format(time.time() - t))  cv2.waitKey(0)
　　4.2 操作和效果图
　　5 视频骨架测试
　　============
　　5.1 代码OpenPoseVideo.py：#打开终端，需要一定的时间 #python3.8 OpenPoseVideo.py --device cpu --video_file sample_video.mp4  import cv2 import time import numpy as np import argparse  parser = argparse.ArgumentParser(description=＂Run keypoint detection＂) parser.add_argument(＂--device＂, default=＂cpu＂, help=＂Device to inference on＂) parser.add_argument(＂--video_file＂, default=＂sample_video.mp4＂, help=＂Input Video＂) args = parser.parse_args()  MODE = ＂MPI＂ #注意源代码是is，python3改为== #if MODE is ＂COCO＂: if MODE==＂COCO＂:     protoFile = ＂pose/coco/pose_deploy_linevec.prototxt＂     weightsFile = ＂pose/coco/pose_iter_440000.caffemodel＂     nPoints = 18     POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]]  #elif MODE is ＂MPI＂ : elif MODE==＂MPI＂ :     protoFile = ＂pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt＂     weightsFile = ＂pose/mpi/pose_iter_160000.caffemodel＂     nPoints = 15     POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ]  inWidth = 368 inHeight = 368 threshold = 0.1  input_source = args.video_file cap = cv2.VideoCapture(input_source) hasFrame, frame = cap.read()  #生成本目录下的视频 vid_writer = cv2.VideoWriter(＂output.avi＂,cv2.VideoWriter_fourcc(＂M＂,＂J＂,＂P＂,＂G＂), 10, (frame.shape[1],frame.shape[0]))  net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) if args.device == ＂cpu＂:     net.setPreferableBackend(cv2.dnn.DNN_TARGET_CPU)     print(＂Using CPU device＂) elif args.device == ＂gpu＂:     net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)     net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)     print(＂Using GPU device＂)  while cv2.waitKey(1) < 0:     t = time.time()     hasFrame, frame = cap.read()     frameCopy = np.copy(frame)     if not hasFrame:         cv2.waitKey()         break      frameWidth = frame.shape[1]     frameHeight = frame.shape[0]      inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),                               (0, 0, 0), swapRB=False, crop=False)     net.setInput(inpBlob)     output = net.forward()      H = output.shape[2]     W = output.shape[3]     # Empty list to store the detected keypoints     points = []      for i in range(nPoints):         # confidence map of corresponding body＂s part.         probMap = output[0, i, :, :]          # Find global maxima of the probMap.         minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)                  # Scale the point to fit on the original image         x = (frameWidth * point[0]) / W         y = (frameHeight * point[1]) / H          if prob > threshold :              cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)             cv2.putText(frameCopy, ＂{}＂.format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)              # Add the point to the list if the probability is greater than the threshold             points.append((int(x), int(y)))         else :             points.append(None)      # Draw Skeleton     for pair in POSE_PAIRS:         partA = pair[0]         partB = pair[1]          if points[partA] and points[partB]:             cv2.line(frame, points[partA], points[partB], (0, 255, 255), 3, lineType=cv2.LINE_AA)             cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)             cv2.circle(frame, points[partB], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)      cv2.putText(frame, ＂time taken = {:.2f} sec＂.format(time.time() - t), (50, 50), cv2.FONT_HERSHEY_COMPLEX, .8, (255, 50, 0), 2, lineType=cv2.LINE_AA)     cv2.imshow(＂Output-Skeleton＂, frame)      vid_writer.write(frame)  vid_writer.release()
　　5.2 因为需要一定的时间，过程省略，效果图如文章开头。