opencv openpose

cpu比较卡，躺着好像不能检测 

  

   

    

     

    
    

     

      # To use Inference Engine backend, specify location of plugins:
     
    
   

    

     

    
    

     

      # export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH
     
    
   

    

     

    
    

     

      import cv2 as cv
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      parser = argparse.ArgumentParser(
     
    
   

    

     

    
    

     

       description='This script is used to demonstrate OpenPose human pose estimation network '
     
    
   

    

     

    
    

     
 'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '
     
    
   

    

     

    
    

     
 'The sample and model are simplified and could be used for a single person on the frame.')
     
    
   

    

     

    
    

     

      parser.add_argument('--input', default='pbug3_450x420.avi', help='Path to video. Skip to capture frames from camera')
     
    
   

    

     

    
    

     

      parser.add_argument('--ouput', default='outpose.avi', help='Path to output video')
     
    
   

    

     

    
    

     

      parser.add_argument('--proto', default='pose_deploy_linevec.prototxt', help='Path to .prototxt')
     
    
   

    

     

    
    

     

      parser.add_argument('--model', default='pose_iter_440000.caffemodel', help='Path to .caffemodel')
     
    
   

    

     

    
    

     

      parser.add_argument('--dataset', default='COCO', help='Specify what kind of model was trained. '
     
    
   

    

     

    
    

     
 'It could be (COCO, MPI) depends on dataset.')
     
    
   

    

     

    
    

     

      parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
     
    
   

    

     

    
    

     

      parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
     
    
   

    

     

    
    

     

      parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      args = parser.parse_args()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      if args.dataset == 'COCO':
     
    
   

    

     

    
    

     

       BODY_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"]]
     
    
   

    

     

    
    

     

      else:
     
    
   

    

     

    
    

     
 assert (args.dataset == 'MPI')
     
    
   

    

     

    
    

     

       BODY_PARTS = {"Head": 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, "Chest": 14,
     
    
   

    

     

    
    

     
 "Background": 15}
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       POSE_PAIRS = [["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
     
    
   

    

     

    
    

     

       ["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
     
    
   

    

     

    
    

     

       ["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
     
    
   

    

     

    
    

     

       ["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"]]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # visualize
     
    
   

    

     

    
    

     

      colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0],
     
    
   

    

     

    
    

     

       [0, 255, 0], \
     
    
   

    

     

    
    

     

       [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255],
     
    
   

    

     

    
    

     

       [85, 0, 255], \
     
    
   

    

     

    
    

     

       [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      inWidth = args.width
     
    
   

    

     

    
    

     

      inHeight = args.height
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      net = cv.dnn.readNetFromCaffe(args.proto, args.model)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      cap = cv.VideoCapture(0)
     
    
   

    

     

    
    

     

      # 视频总帧数
     
    
   

    

     

    
    

     

      frameNum = cap.get(cv.CAP_PROP_FRAME_COUNT)
     
    
   

    

     

    
    

     

      # vedio writer
     
    
   

    

     

    
    

     

      # fourcc = cv.VideoWriter_fourcc('m', 'p', '4', 'v')
     
    
   

    

     

    
    

     

      fourcc = cv.VideoWriter_fourcc(*'XVID')
     
    
   

    

     

    
    

     

      # 保存size必须和输出size设定为一致，否则无法写入保存文件
     
    
   

    

     

    
    

     

      w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
     
    
   

    

     

    
    

     

      h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
     
    
   

    

     

    
    

     

      size = (w, h)
     
    
   

    

     

    
    

     

      poseout = cv.VideoWriter(args.ouput, fourcc, 20.0, size)
     
    
   

    

     

    
    

     

      # create a black use numpy,size is:512*512
     
    
   

    

     

    
    

     

      poseFrame = np.zeros((h, w, 3), np.uint8)
     
    
   

    

     

    
    

     

      # fill the image with white
     
    
   

    

     

    
    

     

      poseFrame.fill(0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # cv.namedWindow('OpenPose')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print('Start...')
     
    
   

    

     

    
    

     

      print('共计{}帧图像.'.format(frameNum))
     
    
   

    

     

    
    

     

      # 计时开始
     
    
   

    

     

    
    

     

      start_time = time.time()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      j = 0
     
    
   

    

     

    
    

     

      while True:
     
    
   

    

     

    
    

     

       hasFrame, frame = cap.read()
     
    
   

    

     

    
    

     
 if not hasFrame:
     
    
   

    

     

    
    

     
 # cv.waitKey()
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       frameWidth = frame.shape[1]
     
    
   

    

     

    
    

     

       frameHeight = frame.shape[0]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       inp = cv.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
     
    
   

    

     

    
    

     

       (0, 0, 0), swapRB=False, crop=False)
     
    
   

    

     

    
    

     

       net.setInput(inp)
     
    
   

    

     

    
    

     

       out = net.forward()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # assert(len(BODY_PARTS) == out.shape[1])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # reset
     
    
   

    

     

    
    

     

       points = []
     
    
   

    

     

    
    

     

       poseFrame.fill(0)
     
    
   

    

     

    
    

     
 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 > args.thr else None)
     
    
   

    

     

    
    

     
 # *************** points ***********
     
    
   

    

     

    
    

     
 # print(points)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       i = 0
     
    
   

    

     

    
    

     
 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], colors[i], 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)
     
    
   

    

     

    
    

     

       i += 1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # t, _ = net.getPerfProfile()
     
    
   

    

     

    
    

     
 # freq = cv.getTickFrequency() / 1000
     
    
   

    

     

    
    

     
 # cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
     
    
   

    

     

    
    

     
 # neck = points[BODY_PARTS['Neck']]
     
    
   

    

     

    
    

     
 # left_wrist = points[BODY_PARTS['LWrist']]
     
    
   

    

     

    
    

     
 # right_wrist = points[BODY_PARTS['RWrist']]
     
    
   

    

     

    
    

     
 # print(neck, left_wrist, right_wrist)
     
    
   

    

     

    
    

     
 # if neck and left_wrist and right_wrist and left_wrist[1] < neck[1] and right_wrist[1] < neck[1]:
     
    
   

    

     

    
    

     
 # cv.putText(frame, 'HANDS UP!', (10, 100), cv.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       cv.imshow('OpenPose', frame)
     
    
   

    

     

    
    

     

       cv.waitKey(1)
     
    
   

    

     

    
    

     
 # poseout.write(poseFrame)
     
    
   

    

     

    
    

     
 # cv.imwrite('outpose/pose_%d.jpg'%(j), frame)
     
    
   

    

     

    
    

     
 # j += 1
     
    
   

    

     

    
    

     
 # if j % 20 == 0:
     
    
   

    

     

    
    

     
 # # 记录时间
     
    
   

    

     

    
    

     
 # end_time = time.time()
     
    
   

    

     

    
    

     
 # print('已处理{}/{}帧图像， 用时{:.4f}s， 平均每帧用时{:.4f}s'.format(j, int(frameNum), end_time - start_time,
     
    
   

    

     

    
    

     
 # (end_time - start_time) / j))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 计时结束
     
    
   

    

     

    
    

     

      end_time = time.time()
     
    
   

    

     

    
    

     

      print('共处理{}帧图像，用时{:.4f}s'.format(j, end_time - start_time))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      cap.release()
     
    
   

    

     

    
    

     

      poseout.release()
     
    
   

    

     

    
    

     

      cv.destroyAllWindows()
     
    
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/115035831