树莓派视觉小车 — OpenCV巡线(HSL色彩空间、PID)

admin • 2022-01-30 08:14 • 人工智能

试错

试错1：形态学处理

一开始用的形态学处理，自行改变阈值，调试之后，进行处理，发现效果不是太好，于是改成了HSV色彩空间。

试错2：HSV色彩空间

之前没注意到，HSV色彩空间很难识别白色：

HSV：

不难看出，如果寻白色线的话，HSV色彩空间不是一个很好的选择，下面引入HSL色彩空间：

HSL：

所以，如果是巡白色的话，建议用HSL色彩空间。

注意：巡线小车的摄像头不能太低，如果太低了，可能让小车自己的影子会阻碍光线。

hsv中的效果：

hsl中的效果：

可以看出，已经能大致找到白线了。

基础理论

1、HSV与HSL色彩空间

HSV：

不难看出，如果寻白色线的话，HSV色彩空间不是一个很好的选择，下面引入HSL色彩空间：

HSL：

所以，如果是巡白色的话，建议用HSL色彩空间。

2、PID调节

个人理解：

P：拉力

I：推动力

D：阻力

一、OpenCV图像处理

1、在HSL色彩空间下得到二值图

# 在HSV色彩空间下得到二值图
def Get_HSV(image):
    # 1 get trackbar's value
    hmin = cv2.getTrackbarPos('hmin', 'h_binary')
    hmax = cv2.getTrackbarPos('hmax', 'h_binary')
    smin = cv2.getTrackbarPos('smin', 's_binary')
    smax = cv2.getTrackbarPos('smax', 's_binary')
    lmin = cv2.getTrackbarPos('lmin', 'l_binary')
    lmax = cv2.getTrackbarPos('lmax', 'l_binary')

    # 2 to HSV
    hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
    cv2.imshow('hls', hls)
    h, l, s = cv2.split(hls)

    # 3 set threshold (binary image)
    # if value in (min, max):white; otherwise:black
    h_binary = cv2.inRange(np.array(h), np.array(hmin), np.array(hmax))
    s_binary = cv2.inRange(np.array(s), np.array(smin), np.array(smax))
    l_binary = cv2.inRange(np.array(l), np.array(lmin), np.array(lmax))

    # 4 get binary（对H、S、V三个通道分别与操作）
    binary = 255 - cv2.bitwise_and(h_binary, cv2.bitwise_and(s_binary, l_binary))

    # 5 Show
    cv2.imshow('h_binary', h_binary)
    cv2.imshow('s_binary', s_binary)
    cv2.imshow('l_binary', l_binary)
    cv2.imshow('binary', binary)

    return binary

2、对二值图形态学处理

# 图像处理
def Image_Processing():
    global frame, binary
    # Capture the frames
    ret, frame = camera.read()

    # to binary
    binary = Get_HSV(frame)

    blur = cv2.GaussianBlur(binary, (5, 5), 0)
    cv2.imshow('blur', blur)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (35, 35))
    Open = cv2.morphologyEx(blur, cv2.MORPH_OPEN, kernel)
    cv2.imshow('Open', Open)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    Erode = cv2.morphologyEx(Open, cv2.MORPH_ERODE, kernel)
    cv2.imshow('Erode', Erode)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    Dilate = cv2.morphologyEx(Erode, cv2.MORPH_DILATE, kernel)
    cv2.imshow('Dilate', Dilate)

    binary = Erode#Dilate

3、找出线的轮廓和中心点坐标

# 找线
def Find_Line():
    global x, y, image
    # 1 找出所有轮廓
    bin2, contours, hierarchy = cv2.findContours(binary, 1, cv2.CHAIN_APPROX_NONE)
    
    # 2 找出最大轮廓
    if len(contours) > 0:
        # 最大轮廓
        c = max(contours, key=cv2.contourArea)
        M = cv2.moments(c)

        # 中心点坐标
        x = int(M['m10'] / M['m00'])
        y = int(M['m01'] / M['m00'])
        #print(x, y)

        # 显示
        image = frame.copy()
        # 标出中心位置
        cv2.line(image, (x, 0), (x, 720), (0, 0, 255), 1)
        cv2.line(image, (0, y), (1280, y), (0, 0, 255), 1)
        # 画出轮廓
        cv2.drawContours(image, contours, -1, (128, 0, 128), 2)
        cv2.imshow("image", image)

    else:
        print("not found the line")

        (x,y) = (0, 0)

二、PID

比例：获取当前时刻白线中心点与图像中点的误差，作为当前误差。

积分：获取上一时刻的误差。

def Pid():
    global turn_speed, x, y, speed
    global error, last_error, pre_error, out_pid

    error = abs(x - width / 2)

    out_pid = int(proportion * error - integral * last_error + derivative * pre_error)
    turn_speed = out_pid

    # 保存本次误差，以便下一次运算
    pre_error = last_error
    last_error = error

    # 限值
    if (turn_speed < 30):
        turn_speed = 30
    elif (turn_speed > 100):
        turn_speed = 100
    if (speed < 0):
        speed = 0
    elif (speed > 100):
        speed = 100

    print(error, out_pid, turn_speed, (x, y))

三、运动控制

# 巡线
def Follow_Line():
    global turn_speed, x, y,speed, back_speed
        
    '''if(x < width / 2 and y>2*height/3):
        Left(turn_speed)
    elif(x>3*width/2 and y>2*height/3):
        Right(turn_speed)'''
    if(0<x<width/4):
        Left(turn_speed)
        print("turn left")
    elif(3*width/4<x<width):
        Right(turn_speed)
        print("turn right")
    #直角拐弯
    elif(y>3*height/4):
        if(x<width/2):
            Left(turn_speed*2)
            print("turn left")
        elif(x>=width/2):
            Right(turn_speed*2)
            print("turn right")
    elif(x>=width/4 and x<=3*width/4):
        Forward(speed)
    
    elif(x==0 and y==0):
        Back(back_speed)

总代码

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

import numpy as np
import cv2
import Adafruit_PCA9685
import RPi.GPIO as GPIO
import time

l_motor = 18
left_Forward = 22
left_back = 27

r_motor = 23
right_Forward = 25
right_back = 24

pwm_servo = Adafruit_PCA9685.PCA9685()

width, height = 160, 120
camera = cv2.VideoCapture(0)
camera.set(3, width)
camera.set(4, height)

# pid
error = 0  # 当前误差e[k]
last_error = 0  # 上一次误差e[k-1]
pre_error = 0  # 上上次误差e[k-2]
proportion = 1  # 比例系数3 0.2
integral = 0.5  # 积分系数1.2
derivative = 0  # 微分系数1.2

stop_flag = 1
control_flag = 1
turn_speed = 30
speed = 30
back_speed = 30


def Motor_Init():
    global L_Motor, R_Motor
    L_Motor = GPIO.PWM(l_motor, 100)
    R_Motor = GPIO.PWM(r_motor, 100)
    L_Motor.start(0)
    R_Motor.start(0)


def Direction_Init():
    GPIO.setup(left_back, GPIO.OUT)
    GPIO.setup(left_Forward, GPIO.OUT)
    GPIO.setup(l_motor, GPIO.OUT)

    GPIO.setup(right_Forward, GPIO.OUT)
    GPIO.setup(right_back, GPIO.OUT)
    GPIO.setup(r_motor, GPIO.OUT)


def set_servo_angle(channel, angle):
    angle = 4096 * ((angle * 11) + 500) / 20000
    pwm_servo.set_pwm_freq(50)  # frequency==50Hz (servo)
    pwm_servo.set_pwm(channel, 0, int(angle))


def TrackBar_Init():
    # 1 create windows
    cv2.namedWindow('h_binary')
    cv2.namedWindow('s_binary')
    cv2.namedWindow('l_binary')
    # 2 Create Trackbar
    cv2.createTrackbar('hmin', 'h_binary', 0, 179, call_back)
    cv2.createTrackbar('hmax', 'h_binary', 110, 179, call_back)
    cv2.createTrackbar('smin', 's_binary', 0, 255, call_back)
    cv2.createTrackbar('smax', 's_binary', 51, 255, call_back)  # 51
    cv2.createTrackbar('lmin', 'l_binary', 0, 255, call_back)
    cv2.createTrackbar('lmax', 'l_binary', 255, 255, call_back)
    '''cv2.namedWindow('binary')
    cv2.createTrackbar('thresh', 'binary', 154, 255, call_back)  '''
    #   创建滑动条     滑动条值名称 窗口名称   滑动条值 滑动条阈值 回调函数


def Init():
    GPIO.setwarnings(False)
    GPIO.setmode(GPIO.BCM)
    Direction_Init()
    Motor_Init()
    TrackBar_Init()


def Forward(turn_speed):
    L_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(left_Forward, 1)  # left_Forward
    GPIO.output(left_back, 0)  # left_back

    R_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(right_Forward, 1)  # right_Forward
    GPIO.output(right_back, 0)  # right_back


def Back(turn_speed):
    L_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(left_Forward, 0)  # left_Forward
    GPIO.output(left_back, 1)  # left_back

    R_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(right_Forward, 0)  # right_Forward
    GPIO.output(right_back, 1)  # right_back


def Left(turn_speed):
    L_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(left_Forward, 0)  # left_Forward
    GPIO.output(left_back, 1)  # left_back

    R_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(right_Forward, 1)  # right_Forward
    GPIO.output(right_back, 0)  # right_back


def Right(turn_speed):
    L_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(left_Forward, 1)  # left_Forward
    GPIO.output(left_back, 0)  # left_back

    R_Motor.ChangeDutyCycle(turn_speed)
    GPIO.output(right_Forward, 0)  # right_Forward
    GPIO.output(right_back, 1)  # right_back


def Stop():
    L_Motor.ChangeDutyCycle(0)
    GPIO.output(left_Forward, 0)  # left_Forward
    GPIO.output(left_back, 0)  # left_back

    R_Motor.ChangeDutyCycle(0)
    GPIO.output(right_Forward, 0)  # right_Forward
    GPIO.output(right_back, 0)  # right_back


# 回调函数
def call_back(*arg):
    pass


# 在HSV色彩空间下得到二值图
def Get_HSV(image):
    # 1 get trackbar's value
    hmin = cv2.getTrackbarPos('hmin', 'h_binary')
    hmax = cv2.getTrackbarPos('hmax', 'h_binary')
    smin = cv2.getTrackbarPos('smin', 's_binary')
    smax = cv2.getTrackbarPos('smax', 's_binary')
    lmin = cv2.getTrackbarPos('lmin', 'l_binary')
    lmax = cv2.getTrackbarPos('lmax', 'l_binary')

    # 2 to HSV
    hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
    cv2.imshow('hls', hls)
    h, l, s = cv2.split(hls)

    # 3 set threshold (binary image)
    # if value in (min, max):white; otherwise:black
    h_binary = cv2.inRange(np.array(h), np.array(hmin), np.array(hmax))
    s_binary = cv2.inRange(np.array(s), np.array(smin), np.array(smax))
    l_binary = cv2.inRange(np.array(l), np.array(lmin), np.array(lmax))

    # 4 get binary（对H、S、V三个通道分别与操作）
    binary = 255 - cv2.bitwise_and(h_binary, cv2.bitwise_and(s_binary, l_binary))

    # 5 Show
    cv2.imshow('h_binary', h_binary)
    cv2.imshow('s_binary', s_binary)
    cv2.imshow('l_binary', l_binary)
    cv2.imshow('binary', binary)

    return binary


# 手动控制小车（上下左右，案件事件判断）
# 控制方式：w、s、a、d分别表示：上、下、左、右
def Key_Control(keyboard):
    global stop_flag, control_flag
    if keyboard == ord("w"):
        Forward(50)
        time.sleep(0.1)
        Stop()
    elif keyboard == ord("s"):
        Back(50)
        time.sleep(0.1)
        Stop()
    elif keyboard == ord("a"):
        Left(50)
        time.sleep(0.1)
        Stop()
    elif keyboard == ord("d"):
        Right(50)
        time.sleep(0.1)
        Stop()


# 图像处理
def Image_Processing():
    global frame, binary
    # Capture the frames
    ret, frame = camera.read()

    # to binary
    binary = Get_HSV(frame)

    blur = cv2.GaussianBlur(binary, (5, 5), 0)
    cv2.imshow('blur', blur)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (35, 35))
    Open = cv2.morphologyEx(blur, cv2.MORPH_OPEN, kernel)
    cv2.imshow('Open', Open)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    Erode = cv2.morphologyEx(Open, cv2.MORPH_ERODE, kernel)
    cv2.imshow('Erode', Erode)

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    Dilate = cv2.morphologyEx(Erode, cv2.MORPH_DILATE, kernel)
    cv2.imshow('Dilate', Dilate)

    binary = Erode  # Dilate


# 找线
def Find_Line():
    global x, y, image
    # 1 找出所有轮廓
    bin2, contours, hierarchy = cv2.findContours(binary, 1, cv2.CHAIN_APPROX_NONE)

    # 2 找出最大轮廓
    if len(contours) > 0:
        # 最大轮廓
        c = max(contours, key=cv2.contourArea)
        M = cv2.moments(c)

        # 中心点坐标
        x = int(M['m10'] / M['m00'])
        y = int(M['m01'] / M['m00'])
        # print(x, y)

        # 显示
        image = frame.copy()
        # 标出中心位置
        cv2.line(image, (x, 0), (x, 720), (0, 0, 255), 1)
        cv2.line(image, (0, y), (1280, y), (0, 0, 255), 1)
        # 画出轮廓
        cv2.drawContours(image, contours, -1, (128, 0, 128), 2)
        cv2.imshow("image", image)

    else:
        print("not found the line")

        (x, y) = (0, 0)


def Pid():
    global turn_speed, x, y, speed
    global error, last_error, pre_error, out_pid

    error = abs(x - width / 2)

    out_pid = int(proportion * error - integral * last_error + derivative * pre_error)
    turn_speed = out_pid

    # 保存本次误差，以便下一次运算
    pre_error = last_error
    last_error = error

    # 限值
    if (turn_speed < 30):
        turn_speed = 30
    elif (turn_speed > 100):
        turn_speed = 100
    if (speed < 0):
        speed = 0
    elif (speed > 100):
        speed = 100

    print(error, out_pid, turn_speed, (x, y))


# 巡线
def Follow_Line():
    global turn_speed, x, y, speed, back_speed

    '''if(x < width / 2 and y>2*height/3):
        Left(turn_speed)
    elif(x>3*width/2 and y>2*height/3):
        Right(turn_speed)'''
    if (0 < x < width / 4):
        Left(turn_speed)
        print("turn left")
    elif (3 * width / 4 < x < width):
        Right(turn_speed)
        print("turn right")
    # 直角拐弯
    elif (y > 3 * height / 4):
        if (x < width / 2):
            Left(turn_speed * 2)
            print("turn left")
        elif (x >= width / 2):
            Right(turn_speed * 2)
            print("turn right")
    elif (x >= width / 4 and x <= 3 * width / 4):
        Forward(speed)

    elif (x == 0 and y == 0):
        Back(back_speed)


def Control():
    global control_flag, speed, proportion, integral
    keyboard = cv2.waitKey(1)
    # 加速减速
    if (keyboard == ord('k')):
        speed += 5
    elif (keyboard == ord('l')):
        speed -= 5
    print(speed)

    if keyboard == ord("n"):
        integral += 0.01
    elif keyboard == ord("m"):
        integral -= 0.01
    print(integral)

    if (control_flag == -1):
        Follow_Line()
        if keyboard == 32:
            control_flag *= -1
            Stop()

    else:
        Key_Control(keyboard)
        if keyboard == 32:
            control_flag *= -1
            Stop()

    print(control_flag)


if __name__ == '__main__':
    Init()
    set_servo_angle(4, 140)  # top servo     lengthwise
    # 0:back    180:front
    set_servo_angle(5, 90)  # bottom servo  crosswise
    # 0:left    180:right

    while True:
        Image_Processing()
        Find_Line()
        Pid()
        Control()
        if cv2.waitKey(1) == ord('q'):
            cv2.destroyAllWindows()
            break

其实一开始主要是想玩机器视觉，小车的运动控制研究的不算精细，PID研究的也不深。

有很多是自己的想法，有错误欢迎指正，有建议也欢迎交流，谢谢。

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树莓派视觉小车 — OpenCV巡线(HSL色彩空间、PID)

试错

试错1：形态学处理

试错2：HSV色彩空间

基础理论

1、HSV与HSL色彩空间

2、PID调节

一、OpenCV图像处理

1、在HSL色彩空间下得到二值图

2、对二值图形态学处理

3、找出线的轮廓和中心点坐标

二、PID

三、运动控制

总代码

最新文章

分类

标签云

树莓派视觉小车 — OpenCV巡线(HSL色彩空间、PID)

试错

试错1：形态学处理

试错2：HSV色彩空间

基础理论

1、HSV与HSL色彩空间

2、PID调节

一、OpenCV图像处理

1、在HSL色彩空间下得到二值图

2、 对二值图形态学处理

3、找出线的轮廓和中心点坐标

二、PID

三、运动控制

总代码

最新文章

分类

标签云

2、对二值图形态学处理