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from web 2.py
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"""
作者：杨一
此版代码为使用socket进行网络图像传输，并在算力较高的计算机平台上进行霍夫圆判断的代码。
目前处于只能勉强跑的屎山状态，但是其中的大部分函数和代码都可以直接应用到比赛中。
此代码（from web 2.py）为先运行的代码（接收端（高算力电脑）），运行完此代码后运行from web 1.py
from web 1.py是运行在机器人上的代码。
注意：ip地址接收端就是空的！！！
买一个好一点的路由器！！！！
"""
import socket
import time
import cv2
import numpy as np
width = 480
height = 180
MinRadius = 1
MaxRadius = 300
minyuanxinyuzhi = 180
maxyuanxinyuzhi = 300
def gaussian_mle(data):
mu = data.mean(axis=0)
var = (data-mu).T @ (data-mu) / data.shape[0] # this is slightly suboptimal, but instructive
return mu, var
def tf (x1,y1,r1):
seita = 0
l = 0
tf_out = np.zeros(2)
seita = x1/8
seita = seita - 30
print("seita = %f" % seita)
l = 800000*((1/50)/15)/r1
print("distence = %f" % l)
tf_out[0] = seita
tf_out[1] = l
return tf_out
def ReceiveVideo():
# IP地址'0.0.0.0'为等待客户端连接
x_q = np.zeros(25)
y_q = np.zeros(25)
r_q = np.zeros(25)
x_q_new = np.zeros(25)
y_q_new = np.zeros(25)
r_q_new = np.zeros(25)
address = ('', 8002)
# 建立socket对象，参数意义见https://blog.csdn.net/rebelqsp/article/details/22109925
# socket.AF_INET：服务器之间网络通信
# socket.SOCK_STREAM：流式socket , for TCP
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# 将套接字绑定到地址, 在AF_INET下,以元组（host,port）的形式表示地址.
s.bind(address)
# 开始监听TCP传入连接。参数指定在拒绝连接之前，操作系统可以挂起的最大连接数量。该值至少为1，大部分应用程序设为5就可以了。
s.listen(1)
def recvall(sock, count):
buf = b'' # buf是一个byte类型
while count:
# 接受TCP套接字的数据。数据以字符串形式返回，count指定要接收的最大数据量.
newbuf = sock.recv(count)
if not newbuf: return None
buf += newbuf
count -= len(newbuf)
return buf
# 接受TCP连接并返回（conn,address）,其中conn是新的套接字对象，可以用来接收和发送数据。addr是连接客户端的地址。
# 没有连接则等待有连接
conn, addr = s.accept()
print('connect from:' + str(addr))
while 1:
start = time.time() # 用于计算帧率信息
length = recvall(conn, 16) # 获得图片文件的长度,16代表获取长度
stringData = recvall(conn, int(length)) # 根据获得的文件长度，获取图片文件
data = np.frombuffer(stringData, np.uint8) # 将获取到的字符流数据转换成1维数组
decimg = cv2.imdecode(data, cv2.IMREAD_COLOR) # 将数组解码成图像
# cv2.imwrite("./test.jpg", decimg)
# print(decimg)
cv2.waitKey(1)
# cv2.imshow('SERVER',decimg)#显示图像
resized_height1 = int(width * 0.75)
frame1 = cv2.resize(decimg, (width, resized_height1))
hsv = cv2.cvtColor(frame1, cv2.COLOR_BGR2HSV) # HSV空间
lower_red = np.array([0, 40, 45]) # 设定红色的阈值
upper_red = np.array([30, 255, 255])
mask = cv2.inRange(hsv, lower_red, upper_red) # 设定取值范围
res1 = cv2.bitwise_and(frame1, frame1, mask=mask) # 对原图像处理
cv2.imshow('res', hsv)
# circles1 = cv2.HoughCircles(res1, cv2.HOUGH_GRADIENT, 1, 1000, param1=100, param2=30, minRadius=MinRadius,
# maxRadius=MaxRadius)
grey = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
circles1 = cv2.HoughCircles(grey, cv2.HOUGH_GRADIENT, 1, 1000, param1=100, param2=30, minRadius=MinRadius,maxRadius=MaxRadius)
if np.all(circles1) == 1:
Radius = circles1[0, 0, 2]
r_q_new = np.roll(r_q, 1)
r_q_new[0] = Radius
r_q = r_q_new
r_final = gaussian_mle(r_q)
print("radius = %f" % r_final[0])
x = circles1[0, 0, 0]
x_q_new = np.roll(x_q, 1)
x_q_new[0] = x
x_q = x_q_new
x_final = gaussian_mle(x_q)
print("x = %f" % x_final[0])
y = circles1[0, 0, 1]
y_q_new = np.roll(y_q, 1)
y_q_new[0] = y
y_q = y_q_new
y_final = gaussian_mle(y_q)
print("y = %f" % y_final[0])
circles = circles1[0, :, :] # 提取为2维
circles = np.uint16(np.around(circles)) # 四舍五入，取整
for i in circles[:]:
cv2.circle(grey, (i[0], i[1]), i[2], (255, 100, 100), 5) # 画圆
cv2.circle(grey, (i[0], i[1]), 2, (255, 100, 100), 10) # 画圆心
for j in circles[:]:
yuanxinx = j[0]
cv2.line(grey, (minyuanxinyuzhi, 0), (minyuanxinyuzhi, 480), (255, 100, 100), 2, 4)
cv2.line(grey, (maxyuanxinyuzhi, 0), (maxyuanxinyuzhi, 480), (255, 100, 100), 2, 4)
cv2.imshow('res', grey)
if yuanxinx <= minyuanxinyuzhi:
print("右")
elif yuanxinx >= maxyuanxinyuzhi:
print("左")
else:
tf(x_final[0], y_final[0], r_final[0])
# continue
else:
print("no circle")
# continue
# circles = circles1[0, :, :] # 提取为2维
# circles = np.unit16(np.around(circles)) # 四舍五入，取整
# for i in circles[:]:
# cv2.circle(res1, (i[0], i[1]), i[2], (255, 100, 100), 5) # 画圆
# cv2.circle(res1, (i[0], i[1]), 2, (255, 100, 100), 10) # 画圆心
# for j in circles[:]:
# yuanxinx = j[0]
# cv2.line(res1, (minyuanxinyuzhi, 0), (minyuanxinyuzhi, 180), (0, 255, 0), 2, 4)
# cv2.line(res1, (maxyuanxinyuzhi, 0), (maxyuanxinyuzhi, 180), (0, 255, 0), 2, 4)
# if yuanxinx <= minyuanxinyuzhi:
# print("→")
# # robot.movement.right_ward()
# elif yuanxinx >= maxyuanxinyuzhi:
# # robot.movement.left_ward()
# print("←")
# else:
# break
end = time.time()
seconds = end - start
fps = 1 / seconds;
conn.send(bytes(str(int(fps)), encoding='utf-8'))
# k = cv2.waitKey(10)&0xff
# if k == 27:
# break
s.close()
# cv2.destroyAllWindows()
if __name__ == '__main__':
ReceiveVideo()