opencv画轨迹

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