Python OpenCV实现姿态识别的详细代码

目录

• 前言
• 环境安装
• 下载并安装Anaconda
• 安装JupyterNotebook
• 生成JupyterNotebook项目目录
• 下载训练库
• 单张图片识别
• 导入库
• 加载训练模型
• 初始化
• 载入图片
• 显示图片
• 调整图片颜色
• 姿态识别
• 视频识别
• 实时摄像头识别
• 参考

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

安装 Jupyter Notebook

检查Jupyter Notebook是否安装

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https://www.jb51.net/article/238496.htm

生成Jupyter Notebook项目目录

打开Anaconda Prompt切换到项目目录

输入Jupyter notebook在浏览器中打开 Jupyter Notebook

并创建新的记事本

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)

调整图片颜色

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

姿态识别

def pose_estimation(frame):
    frameWidth=frame.shape[1]
    frameHeight=frame.shape[0]
www.cppcns.com    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))

视频识别

Tip：与上面图片识别代码是衔接的

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 simpli编程客栈fy 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.
   编程客栈     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_www.cppcns.comPARTS)
        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

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:
    http://www.cppcns.com    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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