ROS机器人Diego 1#整合Tensorflow MNIST，玩数字识别

机器学习中最经典的例子就是MNIST通过图片来识别0~9的数字，这篇文章将介绍如何将基于Tensorflow MNIST整合到Diego1#机器人中作为一个节点，此节点将订阅Image消息，通过MNIST识别后将结果发布消息给讯飞语音节点，讯飞语音节点会告诉我们识别的数字是几。

相关源代码已经上传到本人的github。

1. 安装Tensorflow

只需一句命令即可安装

pip install tensorflow

官方有4中安装方法，在这里选择直接安装的方式

2. 创建diego_tensorflow_mnist 包

catkin_create_pkg diego_tensorflow_mnist std_msgs rospy roscpp cv_bridge

在diego_tensorflow_mnist目录下创建scripts和launch目录

scripts目录用于存放python的源代码
launch目录用于存放ROS launch文件

下载相关代码到scripts目录

3.ROS节点

有关nnist的算法都已经写好，我们只需要调用其中的功能封装成ROS节点即可，有关封装的代码请见tensorflow_in_ros_mnist.py

#!/usr/bin/env pythonimport rospyfrom sensor_msgs.msg import Imagefrom std_msgs.msg import Int16from std_msgs.msg import Stringfrom cv_bridge import CvBridgeimport cv2import numpy as npimport tensorflow as tfdef weight_variable(shape):  initial = tf.truncated_normal(shape, stddev=0.1)  return tf.Variable(initial)def bias_variable(shape):  initial = tf.constant(0.1, shape=shape)  return tf.Variable(initial)def conv2d(x, W):  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1],                       padding='SAME')def max_pool_2x2(x):  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],                        strides=[1, 2, 2, 1], padding='SAME')def makeCNN(x,keep_prob):    # --- define CNN model    W_conv1 = weight_variable([5, 5, 1, 32])    b_conv1 = bias_variable([32])    h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)    h_pool1 = max_pool_2x2(h_conv1)    W_conv2 = weight_variable([3, 3, 32, 64])    b_conv2 = bias_variable([64])    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)    h_pool2 = max_pool_2x2(h_conv2)    W_fc1 = weight_variable([7 * 7 * 64, 1024])    b_fc1 = bias_variable([1024])    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)    W_fc2 = weight_variable([1024, 10])    b_fc2 = bias_variable([10])    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)    return y_convclass RosTensorFlow():    def __init__(self):    rospy.init_node('rostensorflow')    # Set the shutdown function (stop the robot)        rospy.on_shutdown(self.shutdown)        model_path = rospy.get_param("~model_path", "")        image_topic = rospy.get_param("~image_topic", "")        self._cv_bridge = CvBridge()        self.x = tf.placeholder(tf.float32, [None,28,28,1], name="x")        self.keep_prob = tf.placeholder("float")        self.y_conv = makeCNN(self.x,self.keep_prob)        self._saver = tf.train.Saver()        self._session = tf.InteractiveSession()        init_op = tf.global_variables_initializer()        self._session.run(init_op)        self._saver.restore(self._session, model_path+"/model.ckpt")        self._sub = rospy.Subscriber(image_topic, Image, self.callback, queue_size=1)        #self._pub = rospy.Publisher('result', Int16, queue_size=1)        self._pub = rospy.Publisher('xfwords', String, queue_size=1)    def callback(self, image_msg):        cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")        cv_image_gray = cv2.cvtColor(cv_image, cv2.COLOR_RGB2GRAY)        ret,cv_image_binary = cv2.threshold(cv_image_gray,128,255,cv2.THRESH_BINARY_INV)        cv_image_28 = cv2.resize(cv_image_binary,(28,28))        np_image = np.reshape(cv_image_28,(1,28,28,1))        predict_num = self._session.run(self.y_conv, feed_dict={self.x:np_image,self.keep_prob:1.0})        answer = np.argmax(predict_num,1)        rospy.loginfo('%d' % answer)        self._pub.publish(str(answer))        rospy.sleep(3)     def shutdown(self):        rospy.loginfo("Stopping the tensorflow nnist...")        rospy.sleep(1)   if __name__ == '__main__':    try:        RosTensorFlow()        rospy.spin()    except rospy.ROSInterruptException:        rospy.loginfo("RosTensorFlow has started.")

有关MNIST具体算法实现部分网上有很多教程，这里只说明与ROS整合部分

class RosTensorFlow():    def __init__(self):    rospy.init_node('rostensorflow')    # Set the shutdown function (stop the robot)        rospy.on_shutdown(self.shutdown)        model_path = rospy.get_param("~model_path", "")        image_topic = rospy.get_param("~image_topic", "")

在RosTensorFlow类的开始部分，是标准的节点定义方法，model_path变量用于获取launch文件中定义的model的路径，image_topic变量用于获取launch文件中定义image主题

        self._sub = rospy.Subscriber(image_topic, Image, self.callback, queue_size=1)

以上这段代码是让该节点订阅image topic，并且知道回调函数

        self._pub = rospy.Publisher('xfwords', String, queue_size=1)

以上这段代码定义将发布讯飞语音主题，xfwords

    def callback(self, image_msg):        cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")        cv_image_gray = cv2.cvtColor(cv_image, cv2.COLOR_RGB2GRAY)        ret,cv_image_binary = cv2.threshold(cv_image_gray,128,255,cv2.THRESH_BINARY_INV)        cv_image_28 = cv2.resize(cv_image_binary,(28,28))        np_image = np.reshape(cv_image_28,(1,28,28,1))        predict_num = self._session.run(self.y_conv, feed_dict={self.x:np_image,self.keep_prob:1.0})        answer = np.argmax(predict_num,1)        rospy.loginfo('%d' % answer)        self._pub.publish(str(answer))        rospy.sleep(3) 

主要的处理都在callback回调函数中，首先将从image主题中经过一系列的处理转换成numpy数组，然后调用tensorflow进行识别，将可能的结果过放在predict_num数组中，取其中最有可能的值，就是结果，将结果作为讯飞语音topic发送出去

4.launch文件

在launch文件夹下创建一个名为nnist.launch的文件，文件内容如下：

<launch>    <node pkg="diego_tensorflow_nnist" name="tensorflow_in_ros_mnist" type="tensorflow_in_ros_mnist.py" output="screen">        <param name="image_topic" value="/usb_cam/image_raw" />        <param name="model_path" value="$(find diego_tensorflow_nnist)/scripts/model" />    </node> </launch>

相关的主题，和路径可以在这里修改

5.启动节点

roscorerosrun xfei_asr tts_subscribe_speakroslaunch usb_cam usb_cam-test.launchroslaunch diego_tensorflow_nnist nest.launch

这里我们用到了usb_cam，有关此包的使用请见http://wiki.ros.org/usb_cam

启动后我们就可以在纸上面写几个数字，放在摄像头前，diego1#会告诉你数字是多少。