1.检测人脸，画人脸中心的运动轨迹
import cv2 import numpy as np #import argparse from collections import deque #ap = argparse.ArgumentParser() #args = vars(ap.parse_args()) face_cascade=cv2.CascadeClassifier("F:/software/anaconda/installdocument/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml") cap=cv2.VideoCapture(0) pts = deque(maxlen=124) while True: ret,frame=cap.read() frame=cv2.flip(frame,1) # print i.shape gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces=face_cascade.detectMultiScale(gray,1.3,5) l=len(faces) print (l) for (x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(0,200,200),2) cv2.putText(frame,'face',(int(w/2+x),int(y-h/5)),cv2.FONT_HERSHEY_PLAIN,2.0,(255,255,255),2,1) center=(int(x+w/2),int(y+h/2)) print (center) pts.appendleft(center) for i in range(1,len(pts)): if pts[i-1]is None or pts[i]is None: continue thickness = int(np.sqrt(64 / float(i + 1)) * 2) cv2.line(frame, pts[i - 1], pts[i], (0, 225, 225), thickness) cv2.imshow("rstp",frame) if cv2.waitKey(1) & 0xFF == ord('q'): break #摄像头释放 cap.release() #销毁所有窗口 cv2.destroyAllWindows()
2.获取视频中特定区域的颜色点运动轨迹
from collections import deque import numpy as np #import imutils import cv2 import time #设定红色阈值，HSV空间 redLower = np.array([130, 51, 51]) redUpper = np.array([255, 255, 255]) #初始化追踪点的列表 mybuffer = 64 pts = deque(maxlen=mybuffer) #打开摄像头 camera = cv2.VideoCapture(0) #等待两秒 time.sleep(2) #遍历每一帧，检测红色瓶盖 while True: #读取帧 (ret, frame) = camera.read() #判断是否成功打开摄像头 if not ret: print ('No Camera' ) break #frame = imutils.resize(frame, width=600) #转到HSV空间 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #根据阈值构建掩膜 mask = cv2.inRange(hsv, redLower, redUpper) #腐蚀操作 mask = cv2.erode(mask, None, iterations=2) #膨胀操作，其实先腐蚀再膨胀的效果是开运算，去除噪点 mask = cv2.dilate(mask, None, iterations=2) #轮廓检测 cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] #初始化瓶盖圆形轮廓质心 center = None #如果存在轮廓 if len(cnts) > 0: #找到面积最大的轮廓 c = max(cnts, key = cv2.contourArea) #确定面积最大的轮廓的外接圆 ((x, y), radius) = cv2.minEnclosingCircle(c) #计算轮廓的矩 M = cv2.moments(c) #计算质心 center = (int(M["m10"]/M["m00"]), int(M["m01"]/M["m00"])) #只有当半径大于10时，才执行画图 if radius > 10: cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2) cv2.circle(frame, center, 5, (0, 0, 255), -1) #把质心添加到pts中，并且是添加到列表左侧 pts.appendleft(center) #遍历追踪点，分段画出轨迹 for i in range(1, len(pts)): if pts[i - 1] is None or pts[i] is None: continue #计算所画小线段的粗细 thickness = int(np.sqrt(mybuffer / float(i + 1)) * 2.5) #画出小线段 cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness) #res = cv2.bitwise_and(frame, frame, mask=mask) cv2.imshow('Frame', frame) #键盘检测，检测到esc键退出 k = cv2.waitKey(5)&0xFF if k == 27: break #摄像头释放 camera.release() #销毁所有窗口 cv2.destroyAllWindows()
参考：https://blog.csdn.net/xiao__run/article/details/80572523