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Python OpenCV实现姿态识别的详细代码

2024-04-02 19:04:59 243人浏览薄情痞子

Python 官方文档：入门教程 => 点击学习

摘要

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

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]
    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 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


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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