自学教程：通过OpenCV实现对指定颜色的物体追踪

import argparsefrom collections import dequeimport cv2import numpy as np

def grab_contours(cnts):    # 如果 cv2.findContours 返回的轮廓元组的长度为“2”，那么我们使用的是 OpenCV v2.4、v4-beta 或 v4-official    if len(cnts) == 2:        cnts = cnts[0]    # 如果轮廓元组的长度为“3”，那么我们使用的是 OpenCV v3、v4-pre 或 v4-alpha    elif len(cnts) == 3:        cnts = cnts[1]    else:        raise Exception(("Contours tuple must have length 2 or 3, "            "otherwise OpenCV changed their cv2.findContours return "            "signature yet again. Refer to OpenCV's documentation "            "in that case"))    return cntsdef resize(image, width=None, height=None, inter=cv2.INTER_AREA):    dim = None    (h, w) = image.shape[:2]    # 如果高和宽为None则直接返回    if width is None and height is None:        return image    # 检查宽是否是None    if width is None:        # 计算高度的比例并并按照比例计算宽度        r = height / float(h)        dim = (int(w * r), height)    # 高为None    else:        # 计算宽度比例，并计算高度        r = width / float(w)        dim = (width, int(h * r))    resized = cv2.resize(image, dim, interpolation=inter)    # return the resized image    return resized

 命令行参数ap = argparse.ArgumentParser()ap.add_argument("-v", "--video", help="path to video")ap.add_argument("-b", "--buffer", type=int, default=64, help="max buffer size")args = vars(ap.parse_args())# 绿色树叶的HSV色域空间范围greenLower = (29, 86, 6)greenUpper = (64, 255, 255)pts = deque(maxlen=args["buffer"])vs = cv2.VideoCapture(0)fps = 30    #保存视频的FPS，可以适当调整size=(600,450)fourcc=cv2.VideoWriter_fourcc(*'XVID')videowrite=cv2.VideoWriter('output.avi',fourcc,fps,size)