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 beginnershttps://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!
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