Python OpenCV实现姿态识别的详细代码_Python

前言

想要使用摄像头实现一个多人姿态识别

环境安装

下载并安装 Anaconda

官网连接 https://anaconda.cloud/installers

Python OpenCV实现姿态识别的详细代码

安装 Jupyter Notebook

检查Jupyter Notebook是否安装

Python OpenCV实现姿态识别的详细代码

Tip：这里涉及到一个切换Jupyter Notebook内核的问题，在我这篇文章中有提到
AnacondaNavigator Jupyter Notebook更换Python内核http://www.tuohang.net/article/238496.htm

生成Jupyter Notebook项目目录

打开Anaconda Prompt切换到项目目录

Python OpenCV实现姿态识别的详细代码

输入Jupyter notebook在浏览器中打开 Jupyter Notebook

Python OpenCV实现姿态识别的详细代码

并创建新的记事本

Python OpenCV实现姿态识别的详细代码

下载训练库

图片以及训练库都在下方链接
https://github.com/quanhua92/human-pose-estimation-opencv
将图片和训练好的模型放到项目路径中
graph_opt.pb为训练好的模型

单张图片识别

导入库

import cv2 as cv
import os
import matplotlib.pyplot as plt

加载训练模型

net=cv.dnn.readNetFromTensorflow("graph_opt.pb")

初始化

inWidth=368
inHeight=368
thr=0.2

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"] ]

载入图片

img = cv.imread("image.jpg")

显示图片

plt.imshow(img)

Python OpenCV实现姿态识别的详细代码

调整图片颜色

plt.imshow(cv.cvtColor(img,cv.COLOR_BGR2RGB))

Python OpenCV实现姿态识别的详细代码

姿态识别

def pose_estimation(frame):
  frameWidth=frame.shape[1]
  frameHeight=frame.shape[0]
  net.setInput(cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False))
  out = net.forward()
  out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elements
  
  assert(len(BODY_PARTS) == out.shape[1])
  points = []
  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 > thr else None)
      
  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]
		# 绘制线条
      if points[idFrom] and points[idTo]:
          cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 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)
          
  t, _ = net.getPerfProfile()
  freq = cv.getTickFrequency() / 1000
  cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
  return frame
# 处理图片
estimated_image=pose_estimation(img)
# 显示图片
plt.imshow(cv.cvtColor(estimated_image,cv.COLOR_BGR2RGB))

Python OpenCV实现姿态识别的详细代码

视频识别

Tip：与上面图片识别代码是衔接的

Python OpenCV实现姿态识别的详细代码

视频来自互联网，侵删

cap = cv.VideoCapture('testvideo.mp4')
cap.set(3,800)
cap.set(4,800)
if not cap.isOpened():
  cap=cv.VideoCapture(0)
  raise IOError("Cannot open vide")
  
while cv.waitKey(1) < 0:
  hasFrame,frame=cap.read()
  if not hasFrame:
      cv.waitKey()
      break
      
  frameWidth=frame.shape[1]
  frameHeight=frame.shape[0]
  net.setInput(cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False))
  out = net.forward()
  out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elements
  assert(len(BODY_PARTS) == out.shape[1])
  points = []
  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 > thr else None)
  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], (0, 255, 0), 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)
          
  t, _ = net.getPerfProfile()
  freq = cv.getTickFrequency() / 1000
  cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
  cv.imshow('Video Tutorial',frame)

实时摄像头识别

Tip：与上面图片识别代码是衔接的

Python OpenCV实现姿态识别的详细代码

cap = cv.VideoCapture(0)
cap.set(cv.CAP_PROP_FPS,10)
cap.set(3,800)
cap.set(4,800)
if not cap.isOpened():
  cap=cv.VideoCapture(0)
  raise IOError("Cannot open vide")
  
while cv.waitKey(1) < 0:
  hasFrame,frame=cap.read()
  if not hasFrame:
      cv.waitKey()
      break
      
  frameWidth=frame.shape[1]
  frameHeight=frame.shape[0]
  net.setInput(cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False))
  out = net.forward()
  out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elements
  assert(len(BODY_PARTS) == out.shape[1])
  points = []
  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 > thr else None)
  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], (0, 255, 0), 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)
          
  t, _ = net.getPerfProfile()
  freq = cv.getTickFrequency() / 1000
  cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
  cv.imshow('Video Tutorial',frame)

参考

DeepLearning_by_PhDScholar
Human Pose Estimation using opencv | python | OpenPose | stepwise implementation for beginners
https://www.youtube.com/watch?v=9jQGsUidKHs

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原文链接：https://blog.csdn.net/a71468293a/article/details/123011891