"""
tensorflow_in_ros_mnist.py
Copyright 2016 Shunya Seiya
This software is released under the Apache License, Version 2.0
https://opensource.org/licenses/Apache-2.0
"""
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 tensorflow as tf
def 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_conv
class RosTensorFlow():
def __init__(self):
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/model.ckpt")
self._sub = rospy.Subscriber('image', Image, self.callback, queue_size=1)
self._pub = rospy.Publisher('result', Int16, 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(answer)
def main(self):
rospy.spin()
if __name__ == '__main__':
rospy.init_node('rostensorflow')
tensor = RosTensorFlow()
tensor.main()
