抖音全黑骨架怎么拍（实现抖音很火的人体骨架和视频动态舞）

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。