8403_host.py

import numpy as np
import tensorflow as tf
from robotPi import robotPi
from rev_cam import rev_cam  # 摄像头倒转添加
import os
import socket
import cv2
import numpy
import time
import sys



# tf模型目录
inference_path = tf.Graph()
filepath = os.getcwd() + '/model/472/-472'  # 472
# /number is model name

temp_image = np.zeros(width * height * channel, 'uint8')
cap = cv2.VideoCapture(0)

def model_prediction:



def ReceiveVideo():
    # IP地址'0.0.0.0'为等待客户端连接
    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 = numpy.frombuffer(stringData, numpy.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(0.1)#1
        cv2.imshow('SERVER',decimg)#显示图像

        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()
    return decimal
    # cv2.destroyAllWindows()




def auto_pilot_host():  # 自主前进程序
    with tf.Session(graph=inference_path) as sess:
        init = tf.global_variables_initializer()
        sess.run(init)
        saver = tf.train.import_meta_graph(filepath + '.meta')  # 调用训练的模型
        saver.restore(sess, filepath)

        tf_X = sess.graph.get_tensor_by_name('input:0')  # 调用所需要的参数
        pred = sess.graph.get_operation_by_name('pred')
        number = pred.outputs[0]

    while True:
        frame = ReceiveVideo()




if __name__ == '__main__':