def train():
inputs = tf.placeholder(tf.float32, shape=[None, 128, 128, 3])
outputs = tf.placeholder(tf.float32, shape=[None, class_num])
tf.summary.image('inputs', inputs, 16)
lr = tf.placeholder(tf.float32)
keep_prob = tf.placeholder(tf.float32)
pred = vgg(inputs, class_num, keep_prob)
with tf.name_scope('cross_entropy'):
cross_entropy = tf.reduce_mean(-tf.reduce_sum(outputs * tf.log(tf.clip_by_value(pred, 1e-5, 1.0)), reduction_indices=[1]))
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('accuracy'):
correct = tf.equal(tf.argmax(pred, 1), tf.argmax(outputs, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
tf.summary.scalar('accuracy', accuracy)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
merged = tf.summary.merge_all()
saver = tf.train.Saver()
with tf.Session() as sess:
writer = tf.summary.FileWriter('./log/', sess.graph)
sess.run(tf.global_variables_initializer())
i, stop_count = 0, 0
st = time.time()
while True:
i += 1
if stop_count == producer_num:
break
msg = message.get()
if msg is None:
stop_count += 1
continue
image, label = msg
learning_rate = 1e-5 if i < 500 else 1e-5
sess.run(optimizer, feed_dict={inputs:image, outputs:label, lr:learning_rate, keep_prob:0.5})
if i % 50 == 0:
summary, acc, l = sess.run([merged, accuracy, cross_entropy], feed_dict={inputs:image, outputs:label ,keep_prob:1.0})
print ('iter:{}, acc:{}, loss:{}'.format(i, acc, l))
writer.add_summary(summary, i)
print('run time: ', time.time() - st)
saver.save(sess, './models/vgg.ckpt')
return
def predict(class_num,path,data):
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # 不使用GPU
inputs = tf.placeholder(tf.float32, shape=[None, None, 3])
example = tf.cast(tf.image.resize_images(inputs, [128, 128]), tf.uint8)
example = tf.image.per_image_standardization(example)
example = tf.expand_dims(example, 0)
output = vgg(example, class_num, 1.0)
sess = tf.Session()
tf.train.Saver().restore(sess, path)
pred = sess.run(output, feed_dict={inputs: data})
sess.close()
return pred
def run():
file_name = u'test.txt'
# file_name = u'dataset/中国汉字大全.txt'
texts = read_text(file_name)
fonts_dir = os.path.join('dataset', 'fonts')
fonts = [
os.path.join(os.getcwd(), fonts_dir, path)
for path in os.listdir(fonts_dir)
]
images_gen = generator_images(texts, fonts)
inputs = tf.placeholder(tf.float32, shape=[None, None, 3])
example = tf.cast(tf.image.resize_images(inputs, [128, 128]), tf.uint8)
example = tf.image.per_image_standardization(example)
example = tf.expand_dims(example, 0)
outputs = vgg(example, 2, 1.0)
sess = tf.Session()
restorer = tf.train.Saver()
restorer.restore(sess, 'models/vgg.ckpt')
error = 0
error_texts = []
for index, info in enumerate(images_gen):
image, text = info
image = np.asarray(image)
pred = sess.run(outputs, feed_dict={inputs: image})
pred = np.squeeze(pred)
label = np.squeeze(np.where(pred == np.max(pred)))
if index % 2 != label:
error_texts.append((text, pred.tolist()))
error += 1
print 'test num: {}, error num: {}, acc: {}'.format(
index + 1, error, 1 - float(error) / index)
show_errors(error_texts, fonts)
import tensorflow as tf
from nnets.vgg import vgg
import numpy as np
import tkinter
from tkinter.filedialog import askopenfilename
from tkinter import *
from PIL import Image, ImageFont, ImageDraw
inputs = tf.placeholder(tf.float32, shape=[None, None, 3])
example = tf.cast(tf.image.resize_images(inputs, [128, 128]), tf.uint8)
example = tf.image.per_image_standardization(example)
example = tf.expand_dims(example, 0)
output = vgg(example, 9, 1.0)
sess = tf.Session()
tf.train.Saver().restore(sess, 'models/vgg.ckpt')
print("Model restored.")
def selectPath():
path_ = askopenfilename()
path.set(path_)
def end():
root.destroy()
root = Tk()
path = StringVar()
Label(root, text="目标路径:").grid(row=0, column=0)
from flask import Flask, render_template, request, redirect, url_for
from werkzeug.utils import secure_filename
import os
import tensorflow as tf
from nnets.vgg import vgg
import numpy as np
from PIL import Image, ImageFont, ImageDraw
app = Flask(__name__)
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # 不使用GPU
inputs = tf.placeholder(tf.float32, shape=[None, None, 3])
example = tf.cast(tf.image.resize_images(inputs, [128, 128]), tf.uint8)
example = tf.image.per_image_standardization(example)
example = tf.expand_dims(example, 0)
output = vgg(example, 4, 1.0)
sess = tf.Session()
tf.train.Saver().restore(sess, 'models/vgg.ckpt')
'''导入模型'''
@app.route('/')
def about():
return redirect(url_for('upload'))
@app.route('/upload', methods=['POST', 'GET'])
def upload():
if request.method == 'POST':
f = request.files['file']
basepath = os.path.dirname(__file__) # 当前文件所在路径
def selectPath():
path_ = askopenfilename()
path.set(path_)
'''将选择的路径结果赋值到path中'''
def end():
root.destroy()
'''结束进程'''
inputs = tf.placeholder(tf.float32, shape=[None, None, 3])
example = tf.cast(tf.image.resize_images(inputs, [128, 128]), tf.uint8)
example = tf.image.per_image_standardization(example)
example = tf.expand_dims(example, 0)
output = vgg(example, 5, 1.0)
sess = tf.Session()
tf.train.Saver().restore(sess, 'models/vgg.ckpt')
'''导入模型'''
root = Tk()
root.geometry('570x185+500+400')
'''设置框体的大小'''
path = StringVar()
image_frame = Frame(root)
image_file = im = image_label = None
def create_image_label():
'''创建图片lable和字体识别lable'''
data = Image.open(path.get())