使用树莓派opencv-python实现色块追踪_python色块定位

代码如下，该代码利用hsv空间进行颜色识别，要提前给出阈值范围才可以匹配到

识别到对应色块能以色块为中心画圈，返回值为色块的中心坐标，配合舵机云台可以实现追踪

#!usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import serial
import time
#cap = cv2.VideoCapture('D:/Open CV/py/ziliao/zhuizong/VID_20220724_202819.mp4')
cap = cv2.VideoCapture(0)
ser = serial.Serial('/dev/ttyAMA0', 9600)
 
def fenge(x):
    #分割得到的数据，将坐标除以4，得到小于255的数，便于发送，将小数和整数分割
    x_yuchu=x/4
    x_intpart=x//4
    x_fracpart=(x_yuchu-x_intpart)*100
    return (x_intpart,x_fracpart)#返回（整数，小数）
while (cap.isOpened()):
    start_time = time.time()
    ret, frame1 = cap.read()
    if ser.isOpen == False:
        ser.open()          # 打开串口
    if ret == True:
        #frame=cv2.resize(frame1,None,fx=0.5,fy=0.5)
        # 转换BGR2HSV
        hsv = cv2.cvtColor(frame1, cv2.COLOR_BGR2HSV)
        # 设定物体HSV阈值
        upper = np.array([156, 230, 255])
        lower = np.array([[0,116, 237]])
        mask = cv2.inRange(hsv, lower, upper)
        mask = cv2.erode(mask, None, iterations=2)
        mask = cv2.dilate(mask, None, iterations=2)
        # 找最大轮廓，提取质心
        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
        center = None
        if len(cnts) > 0:
            c = max(cnts, key=cv2.contourArea)
            M = cv2.moments(c)
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
            print("cent_x=",center[0])
            print("cent_y=",640-center[1])
        '''  这里是查看中心图标
        size = mask.shape
        y = size[0]//2
        x = size[1] // 2
        print("img_x=", x)
        print("img_y=",y)
        '''
        size = ser.inWaiting()
        if size != 0:
            res = ser.read(size)
            zuobiao_x=fenge(center[0])
            zuobiao_y=fenge(center[1])
                        #发送坐标  0x5b   0xb3  x坐标/4整数   x坐标/4小数  y坐标/4整数    y坐标/4小数
            writebuf = bytearray([0x5b, 0xb3,zuobiao_x[0],zuobiao_x[1],zuobiao_y[0],zuobiao_y[1], 0x5b])
            ser.write(writebuf)# 读取内容并显示
            print(res) #调试用，查看收到的数据
            ser.flushInput()		# 情况接收缓存区
            time.sleep(0.5)			# 软件延时
        cv2.circle(mask, center, 7, 128, -1)#画点
        cv2.circle(mask,center,30,(0,255,255))#画圆
        # 可以根据质心坐标判断小车左右方向
        # cv2.imshow('frame',frame)
        cv2.imshow('mask', mask)
        cv2.imshow('image', frame1)
        # cv2.imshow('res',res)
        k = cv2.waitKey(5) & 0xFF
        if k == 27:  # esc退出
            break
        #print("FPS:", 1.0 / (time.time() - start_time))  # 计算一下处理的帧速
cap.release()
cv2.destroyAllWindows()