开发者

Python OpenCV实现姿态识别的详细代码

开发者 https://www.devze.com 2022-12-11 12:22 出处:网络 作者: SlowFeather
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
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

        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

        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]
        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))

        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 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

        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:
            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

        到此这篇关于Python OpenCV实现姿态识别的文章就介绍到这了,更多相关Python姿态识别内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们!

        0

        精彩评论

        暂无评论...
        验证码 换一张
        取 消

        关注公众号