#encoding:utf-8
'''
程序修改思路2018.6.14
问题：程序运行速度过慢，分割不准
解决放案：改成颜色分割，像素降低
拐弯方向的解决放案:固定一个方向转，如果那侧没有盲道，则再转180度
改进：如果一张图里面有两条直线以上则认为有盲道emm。。。
'''
import numpy as np
import ConfigParser
import cv2
import PIL.Image as Image
from sklearn.cluster import KMeans
import math
from time import time
'''上车之后这里修改成视频流'''
#from xunfei_voice.conversation import Conversation as conv
count = 0
#Speak = conv()
cf = ConfigParser.ConfigParser()
cf.read('/home/lixuan/camera.conf')
choose = int(cf.get('db','db_mangdao'))
flag1 = 0
flag2 = 0
count1 = 0
from socket import socket, AF_INET, SOCK_DGRAM
ADDR = 'localhost'
PORT = 20000
class socket_sender(object):
def __init__(self, addr, port):
self.sock = socket(AF_INET, SOCK_DGRAM)
self.addr = addr
self.port = port
def send_data(self, data):
self.sock.sendto(data, (self.addr, self.port))
sender = socket_sender('192.168.20.136',20000)
sender_to_voice = socket_sender('localhost',20000)
def cal_angle(x1,y1,x2,y2):
if (y2-y1) == 0:
return 90.0#90度认为平行于x轴
else:
angle = abs(math.degrees(math.atan((x2-x1)/(y2-y1))))#这里我是做的对y的夹角
#print angle
return angle
cap = cv2.VideoCapture(choose)
#设置腐蚀
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
while (True):
start = time()
ret,image = cap.read()
image = cv2.pyrDown(image)
#image = cv2.erode(image,kernel)
#image = cv2.imread("318.jpg")
#cv2.imshow("h",image)
#转换lab空间,并且对图像进行预处理:灰度腐蚀
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
#cv2.imshow("L*a*b*", lab)
#lab1 = eroded = cv2.erode(lab,kernel)
#L,A,B = cv2.split(lab1,mv=None)
#cv2.imwrite("C.jpeg",lab1)
# 像素的提取,特征提取
img = Image.fromarray(lab)
data = []
#img = image.open(f)
row,col = img.size
for i in range(row):
for j in range(col):
x,y,z = img.getpixel((i,j))
data.append([x/256.0,y/256.0,z/256.0])
#imgData,row,col = loadData('C.jpeg')
imgData = np.mat(data)
#kmeans聚类
'''这个地方通过修改该聚类中心cluster来改变环境影响,而且可以更改kmeans的迭代次数这里我选用默认的'''
label = KMeans(n_clusters=2).fit_predict(imgData)
label = label.reshape([row,col])
pic_new = Image.new("L", (row, col))
for i in range(row):
for j in range(col):
pic_new.putpixel((i,j), int(256/(label[i][j]+1)))
pic_new.save("result-bull-4.jpg", "JPEG")
#print pic_new
#hough变换求直线
img = cv2.imread("result-bull-4.jpg")
img = cv2.GaussianBlur(img,(3,3),0)
edges = cv2.Canny(img, 50, 150, apertureSize = 3)
#第一个参数是一个二值化图像 所以在进行霍夫变换之前 先进行 二值化 或者 Canny 缘检测。第二和第三个值分别代 ρ 和 θ 的精确度。第四个参数是阈值， 只有累加其中的值高于阈值时才被认为是一条直线
#lines = cv2.HoughLines(edges,1,np.pi/180,118) #这里对最后一个参数使用了经验型的值
lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
try:
lines1 = lines[:,0,:]#提取为二维
#print lines1
result = img.copy()
#flag1 = 1
for x1,y1,x2,y2 in lines1[:]:
cv2.line(result,(x1,y1),(x2,y2),(255,0,0),3)
angle_temple = cal_angle(x1,y1,x2,y2)
if angle_temple > 60 and flag2==0:#这个是阈值是否拐弯的
count += 1
flag1 = 1
if count >=2:
if flag2 ==0:
#Speak.tts_play("拐弯") #认为两条以上的边界是拐弯的地方 其他的可能是条纹
sender_to_voice.send_data("拐弯")
#ss, addr = s.accept()
#print 'got connected from',addr
#ss.send('turn')
sender.send_data('turn')
flag2 =1
count=0
break
else:
count1+=1
if count1 >=8:
flag2 = 0
count1 = 0
break
'''
这里面还得该因为还得等车转弯完成防止转过头,这里面的参数可以通过上车调试之后才能确定
用延时或者再判断一遍较角度来实现
'''
#这里要转90
#break#防止一直拐弯
print angle_temple
'''
一些改进想法这个地方要做角度的计算来确定是否拐弯
'''
if flag1 == 0:
count = 0
else:flag1 = 0
#cv2.imshow('Canny', edges)
cv2.imshow('Result', result)
sender_to_voice.send_data('发现盲道')
except:
print "没有盲道"
stop = time()
print(str(stop-start) + "秒")
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
