这篇文章主要讲解了“怎么实现OpenCV物体跟踪树莓派视觉小车效果”,文中的讲解内容简单清晰,易于学习与理解,下面请大家跟着小编的思路慢慢深入,一起来研究和学习“怎么实现OpenCV物体跟踪树莓派视觉小车效果”吧!
一、初始化
二、运动控制函数
三、舵机角度控制
四、摄像头&&图像处理
1、打开摄像头
2、把图像转换为灰度图
3、 高斯滤波(去噪)
4、亮度增强
5、转换为二进制
6、闭运算处理
7、获取轮廓
代码
五、获取最大轮廓坐标
六、运动
1、没有识别到轮廓(静止)
2、向前走
3、向左转
4、向右转
代码
总代码
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_front,GPIO.OUT) GPIO.setup(l_motor,GPIO.OUT) GPIO.setup(right_front,GPIO.OUT) GPIO.setup(right_back,GPIO.OUT) GPIO.setup(r_motor,GPIO.OUT) def Servo_Init(): global pwm_servo pwm_servo=Adafruit_PCA9685.PCA9685() def Init(): GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) Direction_Init() Servo_Init() Motor_Init()
def Front(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,1) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,1) #right_front GPIO.output(right_back,0) #right_back def Back(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,0) #left_front GPIO.output(left_back,1) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,0) #right_front GPIO.output(right_back,1) #right_back def Left(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,0) #left_front GPIO.output(left_back,1) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,1) #right_front GPIO.output(right_back,0) #right_back def Right(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,1) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,0) #right_front GPIO.output(right_back,1) #right_back def Stop(): L_Motor.ChangeDutyCycle(0) GPIO.output(left_front,0) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(0) GPIO.output(right_front,0) #right_front GPIO.output(right_back,0) #right_back
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))
set_servo_angle(4, 110) #top servo lengthwise #0:back 180:front set_servo_angle(5, 90) #bottom servo crosswise #0:left 180:right
上面的(4):是顶部的舵机(摄像头上下摆动的那个舵机)
下面的(5):是底部的舵机(摄像头左右摆动的那个舵机)
# 1 Image Process img, contours = Image_Processing()
width, height = 160, 120 camera = cv2.VideoCapture(0) camera.set(3,width) camera.set(4,height)
打开摄像头,并设置窗口大小。
设置小窗口的原因: 小窗口实时性比较好。
# Capture the frames ret, frame = camera.read()
# to gray gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('gray',gray)
# Gausi blur blur = cv2.GaussianBlur(gray,(5,5),0)
#brighten blur = cv2.convertScaleAbs(blur, None, 1.5, 30)
#to binary ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV) cv2.imshow('binary',binary)
#Close kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17,17)) close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) cv2.imshow('close',close)
#get contours binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE) cv2.drawContours(image, contours, -1, (255,0,255), 2) cv2.imshow('image', image)
def Image_Processing(): # Capture the frames ret, frame = camera.read() # Crop the image image = frame cv2.imshow('frame',frame) # to gray gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('gray',gray) # Gausi blur blur = cv2.GaussianBlur(gray,(5,5),0) #brighten blur = cv2.convertScaleAbs(blur, None, 1.5, 30) #to binary ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV) cv2.imshow('binary',binary) #Close kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17,17)) close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) cv2.imshow('close',close) #get contours binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE) cv2.drawContours(image, contours, -1, (255,0,255), 2) cv2.imshow('image', image) return frame, contours
由于有可能出现多个物体,我们这里只识别最大的物体(深度学习可以搞分类,还没学到这,学到了再做),得到它的坐标。
# 2 get coordinates x, y = Get_Coord(img, contours)
def Get_Coord(img, contours): image = img.copy() try: contour = max(contours, key=cv2.contourArea) cv2.drawContours(image, contour, -1, (255,0,255), 2) cv2.imshow('new_frame', image) # get coord M = cv2.moments(contour) x = int(M['m10']/M['m00']) y = int(M['m01']/M['m00']) print(x, y) return x,y except: print 'no objects' return 0,0
返回最大轮廓的坐标:
根据反馈回来的坐标,判断它的位置,进行运动。
# 3 Move Move(x,y)
if x==0 and y==0: Stop()
识别到物体,且在正中央(中间1/2区域),让物体向前走。
#go ahead elif width/4 <x and x<(width-width/4): Front(70)
物体在左边1/4区域。
#left elif x < width/4: Left(50)
物体在右边1/4区域。
#Right elif x > (width-width/4): Right(50)
def Move(x,y): global second #stop if x==0 and y==0: Stop() #go ahead elif width/4 <x and x<(width-width/4): Front(70) #left elif x < width/4: Left(50) #Right elif x > (width-width/4): Right(50)
#Object Tracking import RPi.GPIO as GPIO import time import Adafruit_PCA9685 import numpy as np import cv2 second = 0 width, height = 160, 120 camera = cv2.VideoCapture(0) camera.set(3,width) camera.set(4,height) l_motor = 18 left_front = 22 left_back = 27 r_motor = 23 right_front = 25 right_back = 24 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_front,GPIO.OUT) GPIO.setup(l_motor,GPIO.OUT) GPIO.setup(right_front,GPIO.OUT) GPIO.setup(right_back,GPIO.OUT) GPIO.setup(r_motor,GPIO.OUT) def Servo_Init(): global pwm_servo pwm_servo=Adafruit_PCA9685.PCA9685() def Init(): GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) Direction_Init() Servo_Init() Motor_Init() def Front(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,1) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,1) #right_front GPIO.output(right_back,0) #right_back def Back(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,0) #left_front GPIO.output(left_back,1) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,0) #right_front GPIO.output(right_back,1) #right_back def Left(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,0) #left_front GPIO.output(left_back,1) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,1) #right_front GPIO.output(right_back,0) #right_back def Right(speed): L_Motor.ChangeDutyCycle(speed) GPIO.output(left_front,1) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(speed) GPIO.output(right_front,0) #right_front GPIO.output(right_back,1) #right_back def Stop(): L_Motor.ChangeDutyCycle(0) GPIO.output(left_front,0) #left_front GPIO.output(left_back,0) #left_back R_Motor.ChangeDutyCycle(0) GPIO.output(right_front,0) #right_front GPIO.output(right_back,0) #right_back 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 Image_Processing(): # Capture the frames ret, frame = camera.read() # Crop the image image = frame cv2.imshow('frame',frame) # to gray gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('gray',gray) # Gausi blur blur = cv2.GaussianBlur(gray,(5,5),0) #brighten blur = cv2.convertScaleAbs(blur, None, 1.5, 30) #to binary ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV) cv2.imshow('binary',binary) #Close kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17,17)) close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) cv2.imshow('close',close) #get contours binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE) cv2.drawContours(image, contours, -1, (255,0,255), 2) cv2.imshow('image', image) return frame, contours def Get_Coord(img, contours): image = img.copy() try: contour = max(contours, key=cv2.contourArea) cv2.drawContours(image, contour, -1, (255,0,255), 2) cv2.imshow('new_frame', image) # get coord M = cv2.moments(contour) x = int(M['m10']/M['m00']) y = int(M['m01']/M['m00']) print(x, y) return x,y except: print 'no objects' return 0,0 def Move(x,y): global second #stop if x==0 and y==0: Stop() #go ahead elif width/4 <x and x<(width-width/4): Front(70) #left elif x < width/4: Left(50) #Right elif x > (width-width/4): Right(50) if __name__ == '__main__': Init() set_servo_angle(4, 110) #top servo lengthwise #0:back 180:front set_servo_angle(5, 90) #bottom servo crosswise #0:left 180:right while 1: # 1 Image Process img, contours = Image_Processing() # 2 get coordinates x, y = Get_Coord(img, contours) # 3 Move Move(x,y) # must include this codes(otherwise you can't open camera successfully) if cv2.waitKey(1) & 0xFF == ord('q'): Stop() GPIO.cleanup() break #Front(50) #Back(50) #$Left(50) #Right(50) #time.sleep(1) #Stop()
检测原理是基于最大轮廓的检测,没有用深度学习的分类,所以容易受到干扰,后期学完深度学习会继续优化。有意见或者想法的朋友欢迎交流。
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