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ABO_mix.py
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# encoding: utf-8
import os
import numpy as np
from PIL import Image
import tensorflow as tf
import matplotlib.pyplot as plt
#导入必要的包
N_CLASSES = 3
#要分类的类别数，这里是5分类
IMG_W = 28
IMG_H = 28
#设置图片的size
BATCH_SIZE = 8
CAPACITY = 64
MAX_STEP = 1000
#迭代一千次，如果机器配置好的话，建议至少10000次以上
learning_rate = 0.0001
#学习率
def inference(images, batch_size, n_classes):#ABO卷积神经网络结构
# conv1, shape = [kernel_size, kernel_size, channels, kernel_numbers]
with tf.variable_scope("conv1") as scope:
weights = tf.get_variable("weights",
shape=[3, 3, 3, 16],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
biases = tf.get_variable("biases",
shape=[16],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding="SAME")
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name="conv1")
# pool1 && norm1
with tf.variable_scope("pooling1_lrn") as scope:
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding="SAME", name="pooling1")
norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0,
beta=0.75, name='norm1')
# conv2
with tf.variable_scope("conv2") as scope:
weights = tf.get_variable("weights",
shape=[3, 3, 16, 16],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
biases = tf.get_variable("biases",
shape=[16],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding="SAME")
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name="conv2")
# pool2 && norm2
with tf.variable_scope("pooling2_lrn") as scope:
pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding="SAME", name="pooling2")
norm2 = tf.nn.lrn(pool2, depth_radius=4, bias=1.0, alpha=0.001/9.0,
beta=0.75, name='norm2')
# full-connect1
with tf.variable_scope("fc1") as scope:
reshape = tf.reshape(norm2, shape=[batch_size, -1])
dim = reshape.get_shape()[1].value
weights = tf.get_variable("weights",
shape=[dim, 128],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable("biases",
shape=[128],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
fc1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name="fc1")
# full_connect2
with tf.variable_scope("fc2") as scope:
weights = tf.get_variable("weights",
shape=[128, 128],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable("biases",
shape=[128],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
fc2 = tf.nn.relu(tf.matmul(fc1, weights) + biases, name="fc2")
# softmax
with tf.variable_scope("softmax_linear") as scope:
weights = tf.get_variable("weights",
shape=[128, n_classes],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable("biases",
shape=[n_classes],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
softmax_linear = tf.add(tf.matmul(fc2, weights), biases, name="softmax_linear")
return softmax_linear
def get_one_image(image_input):#对单字图片进行预处理
#image = Image.open(img_dir).convert('RGB')
image = image_input.convert('RGB')
plt.imshow(image)
image = image.resize([28, 28])
image_arr = np.array(image)
return image_arr
def test(image_input):#将分割后的单字输入卷积神经网络进行ABO识别
tf.reset_default_graph()
log_dir = 'mnist/log'#训练好的模型储存在该目录下
image_arr = get_one_image(image_input)#图像预处理
with tf.Graph().as_default():
image = tf.cast(image_arr, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1,28, 28, 3])
print(image.shape)
p = inference(image,1,3)
logits = tf.nn.softmax(p)
x = tf.placeholder(tf.float32,shape = [28,28,3])
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(log_dir)#调用先前训练好的ABO识别模型
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
#调用saver.restore()函数，加载训练好的网络模型
print('Loading success')
else:
print('No checkpoint')
prediction = sess.run(logits, feed_dict={x: image_arr})#识别
max_index = np.argmax(prediction)
print('预测的标签为：')
print(max_index)
print('预测的结果为：')
print(prediction)
#print('This is a A with possibility %.6f' %prediction[:, 0])
#print('This is a B with possibility %.6f' %prediction[:, 1])
#print('This is a O with possibility %.6f' %prediction[:, 2])
#返回识别结果
if max_index == 0:
print('This is a A with possibility %.6f' %prediction[:, 0])
return 'A'
elif max_index == 1:
print('This is a B with possibility %.6f' %prediction[:, 1])
return 'B'
else :
print('This is a O with possibility %.6f' %prediction[:, 2])
return 'O'
#run_training()
def ABO_detection(image_input):#对chinese_out.py的接口
result = test(image_input)
return result