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robot_learning/tensorflow_mnist/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8.3)
+project(tensorflow_mnist)
+
+## Compile as C++11, supported in ROS Kinetic and newer
+# add_compile_options(-std=c++11)
+
+## Find catkin macros and libraries
+## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
+## is used, also find other catkin packages
+find_package(catkin REQUIRED COMPONENTS
+  cv_bridge
+  rospy
+  std_msgs
+)
+
+## System dependencies are found with CMake's conventions
+# find_package(Boost REQUIRED COMPONENTS system)
+
+
+## Uncomment this if the package has a setup.py. This macro ensures
+## modules and global scripts declared therein get installed
+## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
+# catkin_python_setup()
+
+################################################
+## Declare ROS messages, services and actions ##
+################################################
+
+## To declare and build messages, services or actions from within this
+## package, follow these steps:
+## * Let MSG_DEP_SET be the set of packages whose message types you use in
+##   your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
+## * In the file package.xml:
+##   * add a build_depend tag for "message_generation"
+##   * add a build_depend and a run_depend tag for each package in MSG_DEP_SET
+##   * If MSG_DEP_SET isn't empty the following dependency has been pulled in
+##     but can be declared for certainty nonetheless:
+##     * add a run_depend tag for "message_runtime"
+## * In this file (CMakeLists.txt):
+##   * add "message_generation" and every package in MSG_DEP_SET to
+##     find_package(catkin REQUIRED COMPONENTS ...)
+##   * add "message_runtime" and every package in MSG_DEP_SET to
+##     catkin_package(CATKIN_DEPENDS ...)
+##   * uncomment the add_*_files sections below as needed
+##     and list every .msg/.srv/.action file to be processed
+##   * uncomment the generate_messages entry below
+##   * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
+
+## Generate messages in the 'msg' folder
+# add_message_files(
+#   FILES
+#   Message1.msg
+#   Message2.msg
+# )
+
+## Generate services in the 'srv' folder
+# add_service_files(
+#   FILES
+#   Service1.srv
+#   Service2.srv
+# )
+
+## Generate actions in the 'action' folder
+# add_action_files(
+#   FILES
+#   Action1.action
+#   Action2.action
+# )
+
+## Generate added messages and services with any dependencies listed here
+# generate_messages(
+#   DEPENDENCIES
+#   std_msgs
+# )
+
+################################################
+## Declare ROS dynamic reconfigure parameters ##
+################################################
+
+## To declare and build dynamic reconfigure parameters within this
+## package, follow these steps:
+## * In the file package.xml:
+##   * add a build_depend and a run_depend tag for "dynamic_reconfigure"
+## * In this file (CMakeLists.txt):
+##   * add "dynamic_reconfigure" to
+##     find_package(catkin REQUIRED COMPONENTS ...)
+##   * uncomment the "generate_dynamic_reconfigure_options" section below
+##     and list every .cfg file to be processed
+
+## Generate dynamic reconfigure parameters in the 'cfg' folder
+# generate_dynamic_reconfigure_options(
+#   cfg/DynReconf1.cfg
+#   cfg/DynReconf2.cfg
+# )
+
+###################################
+## catkin specific configuration ##
+###################################
+## The catkin_package macro generates cmake config files for your package
+## Declare things to be passed to dependent projects
+## INCLUDE_DIRS: uncomment this if you package contains header files
+## LIBRARIES: libraries you create in this project that dependent projects also need
+## CATKIN_DEPENDS: catkin_packages dependent projects also need
+## DEPENDS: system dependencies of this project that dependent projects also need
+catkin_package(
+#  INCLUDE_DIRS include
+#  LIBRARIES tensorflow_mnist
+#  CATKIN_DEPENDS cv_bridge rospy std_msgs
+#  DEPENDS system_lib
+)
+
+###########
+## Build ##
+###########
+
+## Specify additional locations of header files
+## Your package locations should be listed before other locations
+include_directories(
+# include
+  ${catkin_INCLUDE_DIRS}
+)
+
+## Declare a C++ library
+# add_library(${PROJECT_NAME}
+#   src/${PROJECT_NAME}/tensorflow_mnist.cpp
+# )
+
+## Add cmake target dependencies of the library
+## as an example, code may need to be generated before libraries
+## either from message generation or dynamic reconfigure
+# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
+
+## Declare a C++ executable
+## With catkin_make all packages are built within a single CMake context
+## The recommended prefix ensures that target names across packages don't collide
+# add_executable(${PROJECT_NAME}_node src/tensorflow_mnist_node.cpp)
+
+## Rename C++ executable without prefix
+## The above recommended prefix causes long target names, the following renames the
+## target back to the shorter version for ease of user use
+## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node"
+# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "")
+
+## Add cmake target dependencies of the executable
+## same as for the library above
+# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
+
+## Specify libraries to link a library or executable target against
+# target_link_libraries(${PROJECT_NAME}_node
+#   ${catkin_LIBRARIES}
+# )
+
+#############
+## Install ##
+#############
+
+# all install targets should use catkin DESTINATION variables
+# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html
+
+## Mark executable scripts (Python etc.) for installation
+## in contrast to setup.py, you can choose the destination
+# install(PROGRAMS
+#   scripts/my_python_script
+#   DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
+# )
+
+## Mark executables and/or libraries for installation
+# install(TARGETS ${PROJECT_NAME} ${PROJECT_NAME}_node
+#   ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+#   LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+#   RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
+# )
+
+## Mark cpp header files for installation
+# install(DIRECTORY include/${PROJECT_NAME}/
+#   DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
+#   FILES_MATCHING PATTERN "*.h"
+#   PATTERN ".svn" EXCLUDE
+# )
+
+## Mark other files for installation (e.g. launch and bag files, etc.)
+# install(FILES
+#   # myfile1
+#   # myfile2
+#   DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
+# )
+
+#############
+## Testing ##
+#############
+
+## Add gtest based cpp test target and link libraries
+# catkin_add_gtest(${PROJECT_NAME}-test test/test_tensorflow_mnist.cpp)
+# if(TARGET ${PROJECT_NAME}-test)
+#   target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
+# endif()
+
+## Add folders to be run by python nosetests
+# catkin_add_nosetests(test)

robot_learning/tensorflow_mnist/launch/ros_tensorflow_mnist.launch

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

robot_learning/tensorflow_mnist/model/checkpoint

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+model_checkpoint_path: "model.ckpt"
+all_model_checkpoint_paths: "model.ckpt"

robot_learning/tensorflow_mnist/note

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+
+sudo apt-get install ros-indigo-cv-bridge ros-indigo-cv-camera
+
+//rosrun xfei_asr tts_subscribe_speak
+
+roslaunch usb_cam usb_cam-test.launch
+roslaunch tensorflow_mnist ros_tensorflow_mnist.launch
+rostopic echo /result

robot_learning/tensorflow_mnist/package.xml

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+<?xml version="1.0"?>
+<package>
+  <name>tensorflow_mnist</name>
+  <version>0.0.0</version>
+  <description>The tensorflow_mnist package</description>
+
+  <!-- One maintainer tag required, multiple allowed, one person per tag -->
+  <!-- Example:  -->
+  <!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
+  <maintainer email="hcx@todo.todo">hcx</maintainer>
+
+
+  <!-- One license tag required, multiple allowed, one license per tag -->
+  <!-- Commonly used license strings: -->
+  <!--   BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
+  <license>TODO</license>
+
+
+  <!-- Url tags are optional, but multiple are allowed, one per tag -->
+  <!-- Optional attribute type can be: website, bugtracker, or repository -->
+  <!-- Example: -->
+  <!-- <url type="website">http://wiki.ros.org/tensorflow_mnist</url> -->
+
+
+  <!-- Author tags are optional, multiple are allowed, one per tag -->
+  <!-- Authors do not have to be maintainers, but could be -->
+  <!-- Example: -->
+  <!-- <author email="jane.doe@example.com">Jane Doe</author> -->
+
+
+  <!-- The *_depend tags are used to specify dependencies -->
+  <!-- Dependencies can be catkin packages or system dependencies -->
+  <!-- Examples: -->
+  <!-- Use build_depend for packages you need at compile time: -->
+  <!--   <build_depend>message_generation</build_depend> -->
+  <!-- Use buildtool_depend for build tool packages: -->
+  <!--   <buildtool_depend>catkin</buildtool_depend> -->
+  <!-- Use run_depend for packages you need at runtime: -->
+  <!--   <run_depend>message_runtime</run_depend> -->
+  <!-- Use test_depend for packages you need only for testing: -->
+  <!--   <test_depend>gtest</test_depend> -->
+  <buildtool_depend>catkin</buildtool_depend>
+  <build_depend>cv_bridge</build_depend>
+  <build_depend>rospy</build_depend>
+  <build_depend>std_msgs</build_depend>
+  <run_depend>cv_bridge</run_depend>
+  <run_depend>rospy</run_depend>
+  <run_depend>std_msgs</run_depend>
+
+
+  <!-- The export tag contains other, unspecified, tags -->
+  <export>
+    <!-- Other tools can request additional information be placed here -->
+
+  </export>
+</package>

robot_learning/tensorflow_mnist/scripts/input_data.py

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+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Functions for downloading and reading MNIST data."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gzip
+import os
+import tempfile
+
+import numpy
+from six.moves import urllib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

robot_learning/tensorflow_mnist/scripts/ros_tensorflow_mnist.py

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+#!/usr/bin/env python 
+# -*- coding: utf-8 -*-
+
+import rospy
+from sensor_msgs.msg import Image
+from std_msgs.msg import Int16
+from cv_bridge import CvBridge
+import cv2
+import numpy as np
+import input_data  
+import tensorflow as tf
+
+class MNIST():
+    def __init__(self):
+        image_topic = rospy.get_param("~image_topic", "")
+
+        self._cv_bridge = CvBridge()
+
+        #MNIST数据输入  
+        self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  
+
+        self.x = tf.placeholder(tf.float32,[None, 784]) #图像输入向量  
+        self.W = tf.Variable(tf.zeros([784,10]))        #权重，初始化值为全零  
+        self.b = tf.Variable(tf.zeros([10]))            #偏置，初始化值为全零  
+
+        #进行模型计算，y是预测，y_ 是实际  
+        self.y = tf.nn.softmax(tf.matmul(self.x, self.W) + self.b)  
+
+        self.y_ = tf.placeholder("float", [None,10])  
+
+        #计算交叉熵  
+        self.cross_entropy = -tf.reduce_sum( self.y_*tf.log(self.y))  
+        #接下来使用BP算法来进行微调,以0.01的学习速率  
+        self.train_step = tf.train.GradientDescentOptimizer(0.01).minimize(self.cross_entropy)  
+
+        #上面设置好了模型，添加初始化创建变量的操作  
+        self.init = tf.global_variables_initializer()  
+        #启动创建的模型，并初始化变量  
+        self.sess = tf.Session()  
+        self.sess.run(self.init)  
+
+        #开始训练模型，循环训练1000次  
+        for i in range(1000):  
+            #随机抓取训练数据中的100个批处理数据点  
+            batch_xs, batch_ys = self.mnist.train.next_batch(100)  
+            self.sess.run(self.train_step, feed_dict={self.x:batch_xs, self.y_:batch_ys})  
+
+        ''''' 进行模型评估 '''  
+        #判断预测标签和实际标签是否匹配  
+        correct_prediction = tf.equal(tf.argmax(self.y,1),tf.argmax(self.y_,1))   
+        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  
+
+        #计算所学习到的模型在测试数据集上面的正确率  
+        print( "The predict accuracy with test data set: \n")
+        print( self.sess.run(self.accuracy, feed_dict={self.x:self.mnist.test.images, self.y_:self.mnist.test.labels}) )  
+
+        self._sub = rospy.Subscriber(image_topic, Image, self.callback, queue_size=1)
+        self._pub = rospy.Publisher('result', Int16, queue_size=1)
+
+    def callback(self, image_msg):
+        #预处理接收到的图像数据
+        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))
+
+        #转换输入数据shape,以便于用于网络中
+        np_image = np.reshape(cv_image_28, (1, 784))
+
+        predict_num = self.sess.run(self.y, feed_dict={self.x:np_image, self.y_:self.mnist.test.labels})
+
+        #找到概率最大值
+        answer = np.argmax(predict_num, 1)
+
+        #发布识别结果
+        rospy.loginfo('%d' % answer)
+        self._pub.publish(answer)
+        #rospy.sleep(1) 
+
+    def main(self):
+        rospy.spin()
+
+if __name__ == '__main__':
+    rospy.init_node('ros_tensorflow_mnist')
+    tensor = MNIST()
+    rospy.loginfo("ros_tensorflow_mnist has started.")
+    tensor.main()