抖音魔性舞蹈左右摆：实现抖音很火的人体骨架和动态舞

抖音魔性舞蹈左右摆：实现抖音很火的人体骨架和动态舞2.1 图片======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/learnopencv2 效果图

1 说明

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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

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2.1 图片

2.2 视频

视频节选

3 准备

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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

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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

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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。