Python轮廓追踪【OpenCV形态学操作】

概要

一些理论知识

OpenCV形态学操作理论1
OpenCV形态学操作理论2
OpenCV轮廓操作|轮廓类似详解

代码

代码如下，可以直接运行

import cv2 as cv# 定义结构元素
kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
# print kernelcapture = cv.VideoCapture(0)
print (capture.isOpened())
ok, frame = capture.read()
lower_b = (65, 43, 46)
upper_b = (110, 255, 255)height, width = frame.shape[0:2]
screen_center = width / 2
offset = 50while ok:# 将图像转成HSV颜色空间hsv_frame = cv.cvtColor(frame, cv.COLOR_BGR2HSV)# 基于颜色的物体提取mask = cv.inRange(hsv_frame, lower_b, upper_b)mask2 = cv.morphologyEx(mask, cv.MORPH_OPEN, kernel)mask3 = cv.morphologyEx(mask2, cv.MORPH_CLOSE, kernel)# 找出面积最大的区域contours,_ = cv.findContours(mask3, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)maxArea = 0maxIndex = 0for i, c in enumerate(contours):area = cv.contourArea(c)if area > maxArea:maxArea = areamaxIndex = i# 绘制cv.drawContours(frame, contours, maxIndex, (255, 255, 0), 2)# 获取外切矩形x, y, w, h = cv.boundingRect(contours[maxIndex])cv.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)# 获取中心像素点center_x = int(x + w / 2)center_y = int(y + h / 2)cv.circle(frame, (center_x, center_y), 5, (0, 0, 255), -1)# 简单的打印反馈数据，之后补充运动控制if center_x < screen_center - offset:print ("turn left")elif screen_center - offset <= center_x <= screen_center + offset:print ("keep")elif center_x > screen_center + offset:print ("turn right")cv.imshow("mask4", mask3)cv.imshow("frame", frame)cv.waitKey(1)ok, frame = capture.read()

运行结果