实验环境

（1）硬件环境

（2）软件环境

实验步骤

1. 启动虚拟机，在Windows电脑中，使用Vmware运行Ubuntu系统。
2. 找到路径 /Home/dofbot_ws/src/dofbot_color_follow/色块定位.ipynb，右击选择用Jupyter Notebook启动，输入密码 shujia。
3. 在Jupyter Notebook界面中运行代码，通过交互控件选择需要识别的颜色。
4. 观察摄像头采集的图像中是否成功标记出目标色块，并获取其中心点坐标。

实验结果

红色方块

蓝色方块

绿色方块

黄色方块

主要代码

代码路径：/Home/dofbot_ws/src/dofbot_color_follow/色块定位.ipynb

导入头文件：

import cv2 as cv 
import threading 
import random 
from time import sleep 
import ipywidgets as widgets 
from IPython.display import display 
from positioning import color_follow

主要的识别函数（获取色块目标中心点）：

def follow_function(self, img, HSV_config): 
    (color_lower, color_upper) = HSV_config 
    self.img = cv.resize(img, (640, 480), ) 
    self.img = cv.GaussianBlur(self.img, (5, 5), 0) 
    hsv = cv.cvtColor(self.img, cv.COLOR_BGR2HSV) 
    mask = cv.inRange(hsv, color_lower, color_upper) 
    mask = cv.erode(mask, None, iterations=2) 
    mask = cv.dilate(mask, None, iterations=2) 
    mask = cv.GaussianBlur(mask, (5, 5), 0) 
    cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL, 
                          cv.CHAIN_APPROX_SIMPLE)[-2] 
    if len(cnts) > 0: 
        cnt = max(cnts, key=cv.contourArea) 
        (color_x, color_y), color_radius = cv.minEnclosingCircle(cnt) 
        if color_radius > 10: 
            # 将检测到的颜色用圆形线圈标记出来 
            cv.circle(self.img, (int(color_x), int(color_y)), int(color_radius), 
                     (255, 0, 255), 3) 
            print(color_x,color_y) 
    return self.img

创建控件与主进程：

# 创建交互控件
button_layout = widgets.Layout(width='200px', height='100px', align_self='center') 
output = widgets.Output() 
color_follow = widgets.Button(description='color_follow', button_style='success', 
                             layout=button_layout) 
choose_color = widgets.ToggleButtons(options=['red', 'green', 'blue', 'yellow'], 
                                    button_style='success', 
                                    tooltips=['红色', '绿色', '蓝色', '黄色']) 
follow_cancel = widgets.Button(description='follow_cancel', button_style='danger', 
                              layout=button_layout) 
exit_button = widgets.Button(description='Exit', button_style='danger', 
                            layout=button_layout) 
imgbox = widgets.Image(format='jpg', height=480, width=640, 
                      layout=widgets.Layout(align_self='auto')) 

# 布局设置
img_box = widgets.VBox([imgbox, choose_color], layout=widgets.Layout(align_self='auto')) 
Slider_box = widgets.VBox([color_follow,follow_cancel,exit_button], 
                         layout=widgets.Layout(align_self='auto')) 
controls_box = widgets.HBox([img_box, Slider_box], layout=widgets.Layout(align_self='auto')) 

# 主进程
def camera(): 
    global HSV_learning,model 
    # 打开摄像头
    capture = cv.VideoCapture(0) 
    capture.set(3, 640) 
    capture.set(4, 480) 
    capture.set(5, 30) 
    # 循环读取摄像头画面
    while capture.isOpened(): 
        try: 
            _, img = capture.read() 
            img = cv.resize(img, (640, 480)) 
            if model == 'color_follow': 
                img = follow.follow_function(img, color_hsv[choose_color.value])
                cv.putText(img, choose_color.value, (int(img.shape[0] / 2), 50), 
                          cv.FONT_HERSHEY_SIMPLEX, 2, color[random.randint(0, 254)], 2) 
            if model == 'learning_color': 
                img,HSV_learning = follow.get_hsv(img) 
            if model == 'Exit': 
                cv.destroyAllWindows() 
                capture.release() 
                break 
            imgbox.value = cv.imencode('.jpg', img)[1].tobytes() 
        except KeyboardInterrupt:
            capture.release()