步骤：

采用OpenCV滚动条来确定阈值
设置高低阈值，利用inRange函数，将图像转为二值图，为方便之后的轮廓提取
使用findContours函数，提取二值图中所有的轮廓，并采用cv2.RETR_TREE，建立轮廓等级树
等级树初始是升序，我们要获取最大的那个轮廓，那么就进行sort降序排序
最后，第一个轮廓的最小外边框的参数就可以用boundingRect获取到了

（一）滚动条获取阈值

视频中截图的一张带有橘子的图

代码

import cv2
import numpy as npdef nothing(x):passcv2.namedWindow('image')
cv2.createTrackbar('a','image',0,255,nothing)
cv2.createTrackbar('b','image',0,255,nothing)
cv2.createTrackbar('c','image',0,255,nothing)
cv2.createTrackbar('d','image',0,255,nothing)
cv2.createTrackbar('e','image',0,255,nothing)
cv2.createTrackbar('f','image',0,255,nothing)frame = cv2.imread('orange.jpg')
frame = cv2.resize(frame,(700,400))while True:a = cv2.getTrackbarPos('a', 'image')b = cv2.getTrackbarPos('b', 'image')c = cv2.getTrackbarPos('c', 'image')d = cv2.getTrackbarPos('d', 'image')e = cv2.getTrackbarPos('e', 'image')f = cv2.getTrackbarPos('f', 'image')hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)low_orange = np.array([a, b, c])high_orange = np.array([d, e, f])mask = cv2.inRange(hsv_img, low_orange, high_orange)cv2.imshow('image',mask)k = cv2.waitKey(1)&0xffif k==27:break

（二）获取到图像中的包围橘子对应的白色图形的最小矩形框的信息

检测橘子轮廓最小外边框代码

import cv2
import numpy as npclass OrangeDetector:def __init__(self):self.low_orange = np.array([10, 152, 89])self.high_orange = np.array([180, 255, 255])def detect(self, frame):hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)mask = cv2.inRange(hsv_img, self.low_orange, self.high_orange)contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)box = (0, 0, 0, 0)for cnt in contours:(x, y, w, h) = cv2.boundingRect(cnt)box = (x, y, x + w, y + h)breakreturn box

二、获取橘子检测框的质心

od = OrangeDetector()
orange_bbox = od.detect(frame)
x, y, x2, y2 = orange_bbox
cx = int((x + x2) / 2)
cy = int((y + y2) / 2)

三、将质心送入卡尔曼滤波器，获取下一次的质心位置

predicted = kf.predict(cx, cy)

四、绘图质心中心的圆圈，让效果直观显示出来

卡尔曼滤波预测代码

import cv2
from orange_detector import OrangeDetector
from kalmanfilter import KalmanFiltercap = cv2.VideoCapture("orange.mp4")
od = OrangeDetector()
kf = KalmanFilter()while True:ret, frame = cap.read()if ret is False:breakorange_bbox = od.detect(frame)x, y, x2, y2 = orange_bboxcx = int((x + x2) / 2)cy = int((y + y2) / 2)predicted = kf.predict(cx, cy)cv2.circle(frame, (cx, cy), 20, (0, 0, 255), 4)cv2.circle(frame, (predicted[0], predicted[1]), 20, (255, 0, 0), 4)cv2.imshow("Frame", frame)key = cv2.waitKey(10)if key == 27:break