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matploylib常用的一些方法1.标签和图例二级目录三级目录1.标签和图例import matplotlib.pyplot as pltimport numpy as npx = np.random.randn(30)y = np.random.randn(30)fig = plt.figure() #定义一个实例#通过此方式向实例中添加我们需要的子图ax1 = fig.add_subplot(2,2,1) # 前2个数字表示将整块图划分成了两行两列，一共四张子图ax2 = f

import cv2#cap = cv2.VideoCapture('action.mp4') #读取指定视频cap = cv2.VideoCapture(0)fourcc = cv2.VideoWriter_fourcc(*'XVID')# fps = cap.get(cv2.CAP_PROP_FPS)size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) #保证摄像头的

import cv2videoCapture = cv2.VideoCapture(‘express1.mp4’)fps = 25 # 保存视频的帧率size = (400, 520) # 保存视频的大小videoWriter = cv2.VideoWriter(‘resizex1.mp4’, cv2.VideoWriter_fourcc(‘X’, ‘V’, ‘I’, ‘D’), fps, size)i = 0while True:success, frame = videoCapture

图像、视频或调用摄像头均能实现OpenCV实现人体姿态估计(人体关键点检测）OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以Caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用。其理论基础来自Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields ，是CVPR 2017的

python opencv调用摄像头并保存到当地文件import cv2#cap = cv2.VideoCapture('action.mp4') #读取指定视频cap = cv2.VideoCapture(0)fourcc = cv2.VideoWriter_fourcc(*'XVID')# fps = cap.get(cv2.CAP_PROP_FPS)size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PR

Python cv指定间隔截取视频帧并保存图像到指定路径import cv2import globimport osfrom datetime import datetimedef video_to_frames(path): """ 输入：path(视频文件的路径) """ # VideoCapture视频读取类 videoCapture = cv2.VideoCapture() videoCapture.open(path) # 帧率