树莓派摄像头运用python颜色识别

运用摄像头拍照

import cv2
import os
 
output_dir ='/home/pi/Desktop/py'
i = 1
cap = cv2.VideoCapture(0)
while 1:
    ret, frame = cap.read()
    cv2.imshow('cap', frame)
    flag = cv2.waitKey(1)
    if flag == 13:#按下回车键
        output_path = os.path.join(output_dir,  "%04d.jpg" % i)
        cv2.imwrite(output_path, frame)
        i += 1
    if flag == 27:#按下ESC键
        break

对颜色的hsv值进行选择

import cv2
import numpy as np

def nothing(x):
pass

use track bar to perfectly define (1/2)

the lower and upper values for HSV color space(2/2)

cv2.namedWindow("Tracking")
#参数：1 Lower/Upper HSV 3 startValue 4 endValue
cv2.createTrackbar("LH","Tracking",35,255,nothing)
cv2.createTrackbar("LS","Tracking",43,255,nothing)
cv2.createTrackbar("LV","Tracking",46,255,nothing)
cv2.createTrackbar("UH","Tracking",77,255,nothing)
cv2.createTrackbar("US","Tracking",255,255,nothing)
cv2.createTrackbar("UV","Tracking",255,255,nothing)


while True:
    frame = cv2.imread('/home/pi/Desktop/py/0002.jpg')#这里是你照片存放的路径
    hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
    
    l_h = cv2.getTrackbarPos("LH","Tracking")
    l_s = cv2.getTrackbarPos("LS","Tracking")
    l_v = cv2.getTrackbarPos("LV","Tracking")
    
    u_h = cv2.getTrackbarPos("UH","Tracking")
    u_s = cv2.getTrackbarPos("US","Tracking")
    u_v = cv2.getTrackbarPos("UV","Tracking")
    
    
    l_g = np.array([l_h, l_s, l_v]) # lower green value
    u_g = np.array([u_h,u_s,u_v])

    mask = cv2.inRange(hsv,l_g,u_g)
    
    res=cv2.bitwise_and(frame,frame,mask=mask) # src1,src2
     
    cv2.imshow("frame", frame)
    cv2.imshow("mask", mask)
    cv2.imshow("res", res)
    key = cv2.waitKey(1)
    if key == 27: # Esc
        break

cv2.destroyAllWindows()

对颜色的画框捕抓

import cv2
import numpy as np
import imutils
from imutils import contours
#import RPi.GPIO as GPIO

#PIN5=5#引脚选用，选用GPIO5
#PIN6=6#选用GPIO6
#auto=0
#straight_line_speed=9 #电机最大的旋转速度，百分比值为#5.8。不转时的百分比值为10.5
#circle_speed=9
#servo_straight=8.2
#GPIO.setmode(GPIO.BCM)
#GPIO.setup(PIN5,GPIO.OUT)
#GPIO.setup(PIN6,GPIO.OUT)
#pwm_servo=GPIO.PWM(PIN5,58.8)#pin5用于舵机
#pwm_motor=GPIO.PWM(PIN6,58.8)#pin6用于电机
#pwm_servo.start(0)#初始占空比
#pwm_motor.start(0)

# 颜色阈值
lower = np.array([158,139,49])
upper = np.array([255,255,212])
# 内核
kernel = np.ones((5, 5), np.uint8)

    # 打开摄像头
vc = cv2.VideoCapture(0)
if vc.isOpened():
    flag, frame = vc.read()
    # 翻转图像
    # 这一步可以忽略，博主的摄像头是反着的
    # 所以加上这句话可以让摄像头的图像正过来
    #frame = imutils.rotate(frame, 180)
    cv2.imshow("frame", frame)
else:
    flag = False

''''def servo(a):
    if auto:
        pwm_servo.ChangeDutyCycle(a)
    else:
        GPIO.setup(PIN5,GPIO.IN,pull_up_down=GPIO.PUD_UP)#设置引脚5为输入引脚，并开启上拉电阻
        
def motor(a):
    if auto:
        pwm_motor.ChangeDutyCycle(a)
    else:
        GPIO.setup(PIN6,GPIO.IN,pull_up_down=GPIO.PUD_UP)#设置引脚6为输入引脚，并开启上拉电阻
 ''''
    

def find_color():
    flag, frame = vc.read()
    # 翻转图像
    #frame = imutils.rotate(frame, 180)
    draw_frame = frame.copy()
    #if frame is None:
      #  break
    if flag is True:
        '''下面对摄像头读取到的图像进行处理，这个步骤是比较重要的'''
        # 转换颜色空间HSV
        frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        # 颜色识别
        img = cv2.inRange(frame_hsv, lower, upper)
        # 膨胀操作
        dilation = cv2.dilate(img, kernel, iterations=2)
        # 闭操作
        closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)
        # 高斯滤波
        closing = cv2.GaussianBlur(closing, (5, 5), 0)
        # 边缘检测
        edges = cv2.Canny(closing, 10, 20)
        '''上面进行那么多操作就是为了得到更好的目标图形，具体效果因环境而异'''
        # 寻找轮廓
        cnts, _ = cv2.findContours(
            edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        # 判断轮廓数量也就是判断是否寻找到轮廓，如果没有找到轮廓就不继续进行操作
        if len(cnts) > 0:
            # 存放轮廓面积的列表
            s = []
            # 存放最大轮廓的索引
            max_index = 0
            # 获得排序后的轮廓列表以及每个轮廓对应的外接矩形
            (cnts, boundingRects) = contours.sort_contours(cnts)
            # 寻找面积最大的轮廓的索引
            for cnt in cnts:
                s.append(cv2.contourArea(cnt))
            max_index = s.index(max(s))
            # 根据面积最大轮廓的索引找到它的外接矩形的信息
            (x, y, w, h) = boundingRects[max_index]
            # 画矩形
            frame_out = cv2.rectangle(
                         draw_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
           '''' if 140=300:              
                #print("left")
                servo(servo_straight+1.5)
                motor(circle_speed)
        else:
            #print("find")
            servo(servo_straight-1.5)
            motor(circle_speed)
    ''''        
        cv2.imshow("frame", draw_frame)
        
while(True):
    find_color()
    if cv2.waitKey(10) == 27:
        break
            
vc.release()
cv2.destroyAllWindows()
GPIO.cleanup()#清空引脚定义，设置为输入状态，防止接短路