def get_data(gray_image):
tf.reset_default_graph()
captcha = captcha_model.captchaModel()
width, height, char_num, characters, classes = captcha.get_parameter()
img = np.array(gray_image.getdata())
test_x = np.reshape(img, [height, width, 1]) / 255.0
x = tf.placeholder(tf.float32, [None, height, width, 1])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
predict = tf.argmax(tf.reshape(y_conv, [-1, char_num, classes]), 2)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
config = tf.ConfigProto()
#config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
saver.restore(sess, "./capcha_model.ckpt")
pre_list = sess.run(predict, feed_dict={x: [test_x], keep_prob: 1})
for i in pre_list:
s = ''
for j in i:
s += characters[j]
return s
def __init__(
self,
width=82, #验证码图片的宽
height=32, #验证码图片的高
char_num=4, #验证码字符个数
characters=string.digits + string.ascii_uppercase +
string.ascii_lowercase,
modeldir="./capcha_model-5.ckpt"):
self.width = width
self.height = height
self.char_num = char_num
self.characters = characters
self.classes = len(characters)
self.x = tf.placeholder(tf.float32, [None, self.height, self.width, 1])
self.keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(self.width, self.height,
self.char_num, self.classes)
y_conv = model.create_model(self.x, self.keep_prob)
self.predict = tf.argmax(
tf.reshape(y_conv, [-1, self.char_num, self.classes]), 2)
self.init_op = tf.global_variables_initializer()
self.saver = tf.train.Saver()
self.sess = tf.Session()
self.sess.run(self.init_op)
# 使用的模型
self.modeldir = modeldir
self.saver.restore(self.sess, self.modeldir)
def test_captcha():
captcha = generate_captcha.generateCaptcha(width=100,
height=30,
characters=string.digits)
width, height, char_num, characters, classes = captcha.get_parameter()
x = tf.placeholder(tf.float32, [None, height, width, 1])
y_ = tf.placeholder(tf.float32, [None, char_num * classes])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
saver = tf.train.Saver()
with tf.Session() as sess:
# sess.run(tf.global_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint("./ckpt"))
# batch_x, batch_y = captcha.gen_test_captcha()
batch_x, batch_y = captcha.gen_api_captcha()
loss = sess.run([y_conv],
feed_dict={
x: batch_x,
y_: batch_y,
keep_prob: 0.75
})
print("real == %s, predict = %s, result = %s" %
(captcha.decode_captcha(batch_y), captcha.decode_captcha(loss),
"Match" if captcha.decode_captcha(batch_y)
== captcha.decode_captcha(loss) else "Not Match"))
return True if captcha.decode_captcha(batch_y) == captcha.decode_captcha(
loss) else False
def extrain(self,
filename='./model/model-1.ckpt',
restore_file='./capcha_model-5.ckpt'):
x = tf.placeholder(tf.float32, [None, self.height, self.width, 1])
y_ = tf.placeholder(tf.float32, [None, self.char_num * self.classes])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(self.width, self.height,
self.char_num, self.classes)
y_conv = model.create_model(x, keep_prob)
cross_entropy = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
predict = tf.reshape(y_conv, [-1, self.char_num, self.classes])
real = tf.reshape(y_, [-1, self.char_num, self.classes])
correct_prediction = tf.equal(tf.argmax(predict, 2),
tf.argmax(real, 2))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, restore_file)
step = 0
while True:
batch_x, batch_y = next(self.get_img(128))
_, loss = sess.run([train_step, cross_entropy],
feed_dict={
x: batch_x,
y_: batch_y,
keep_prob: 0.75
})
print('step:%d,loss:%f' % (step, loss))
if step % 100 == 0:
batch_x_test, batch_y_test = next(self.get_img(512))
acc = sess.run(accuracy,
feed_dict={
x: batch_x_test,
y_: batch_y_test,
keep_prob: 1.
})
print(
'###############################################step:%d,accuracy:%f'
% (step, acc))
if acc > 0.99:
# 保存模型的名字，名字同样会进行覆盖
saver.save(sess, filename)
break
step += 1
print('train success')
def test_captcha(file_path):
captcha = generate_captcha.generateCaptcha(width=100, height=30, characters=string.digits)
width, height, char_num, characters, classes = captcha.get_parameter()
x = tf.placeholder(tf.float32, [None, height, width, 1])
y_ = tf.placeholder(tf.float32, [None, char_num * classes])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint("./ckpt"))
batch_x, batch_y = captcha.gen_local_captcha(file_path)
loss = sess.run([y_conv], feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
return captcha.decode_captcha(loss)
import string
import sys
import generate_captcha
import captcha_model
if __name__ == '__main__':
captcha = generate_captcha.generateCaptcha()
width, height, char_num, characters, classes = captcha.get_parameter()
gray_image = Image.open(sys.argv[1]).convert('L')
img = np.array(gray_image.getdata())
test_x = np.reshape(img, [height, width, 1]) / 255.0
x = tf.placeholder(tf.float32, [None, height, width, 1])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
predict = tf.argmax(tf.reshape(y_conv, [-1, char_num, classes]), 2)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
gpu_options=gpu_options)) as sess:
sess.run(init_op)
saver.restore(sess, "capcha_model.ckpt")
pre_list = sess.run(predict, feed_dict={x: [test_x], keep_prob: 1})
for i in pre_list:
s = ''
for j in i:
s += characters[j]
print s
import string
import sys
import os
import captcha_model
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
if __name__ == '__main__':
characters = string.digits + string.ascii_uppercase
gray_image = Image.open(sys.argv[1]).convert('L')
img = np.array(gray_image.getdata())
test_x = np.reshape(img, [25, 80, 1]) / 255.0
x = tf.placeholder(tf.float32, [None, 25, 80, 1])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(80, 25, 6, 36)
y_conv = model.create_model(x, keep_prob)
predict = tf.argmax(tf.reshape(y_conv, [-1, 6, 36]), 2)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
gpu_options=gpu_options)) as sess:
sess.run(init_op)
saver.restore(sess, BASE_DIR + os.sep + "capcha_model.ckpt")
pre_list = sess.run(predict, feed_dict={x: [test_x], keep_prob: 1})
for i in pre_list:
s = ''
for j in i:
s += characters[j]
print(s)
def test_model():
# width = 160
# height = 120
width = 120
height = 32
char_num = 5
characters = range(10)
classes = 10
# dirpath = "D:\\support\\watermeter\\image_enhance"
dirpath = "D:\\support\\watermeter\\image3"
dirpath = "G:\\0"
x = tf.placeholder(tf.float32, [None, height, width, 1], name="x")
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
predict = tf.argmax(tf.reshape(y_conv, [-1, char_num, classes]), 2, name="out")
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
# with tf.Session(config=tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)) as sess:
with tf.Session() as sess:
sess.run(init_op)
# ckpt = tf.train.get_checkpoint_state('./model-watermeter/')
ckpt = tf.train.get_checkpoint_state('./model-watermeter/')
save_path = str(ckpt.model_checkpoint_path)
saver.restore(sess, save_path)
tf.train.write_graph(sess.graph_def, 'output_model/pb_model', 'model.pb')
print(save_path)
freeze_graph.freeze_graph('output_model/pb_model/model.pb', '', False, save_path, 'out', 'save/restore_all',
'save/Const:0', 'output_model/pb_model/frozen_model.pb', False, "")
for (root, dirs, files) in os.walk(dirpath, False):
for filename in files:
# if not filename.endswith(".jpg"):
if not filename.endswith(".bmp"):
continue
file_path = os.path.join(root, filename)
image = cv2.imread(file_path)
image_src = np.copy(image)
image = pre_precess_img(image, width, height)
if image is None:
continue
# image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# image = cv2.resize(image, (160, 120))
# cv2.imshow("image", image)
test_x = np.reshape(image, [height, width, 1]) / 255.0
pre_list = sess.run(predict, feed_dict={x: [test_x], keep_prob: 1})
for i in pre_list:
s = ''
for j in i:
s += str(characters[j])
print(filename, s)
# image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
cv2.putText(image_src, s, (0, 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1)
cv2.imshow("image", image_src)
cv2.waitKey(-1)
def train(train_batch_size=64):
captcha = generate_watermeter_label.WaterWaterData(dirpath="/home/test/WMImages")
width = 120 #160
height = 32 # 120
char_num = 5
characters = range(10)
classes = 10
# width,height,char_num,characters,classes = captcha.get_parameter()
x = tf.placeholder(tf.float32, [None, height, width, 1])
y_ = tf.placeholder(tf.float32, [None, char_num * classes])
keep_prob = tf.placeholder(tf.float32)
model = captcha_model.captchaModel(width, height, char_num, classes)
y_conv = model.create_model(x, keep_prob)
cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# tf.summary.histogram('train_step', train_step)
predict = tf.reshape(y_conv, [-1, char_num, classes])
real = tf.reshape(y_, [-1, char_num, classes])
correct_prediction = tf.equal(tf.argmax(predict, 2), tf.argmax(real, 2))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# tf.summary.histogram('accuracy', accuracy)
saver = tf.train.Saver()
with tf.Session() as sess:
step = 1
sess.run(tf.global_variables_initializer())
# ckpt = tf.train.get_checkpoint_state('./model/')
ckpt = tf.train.get_checkpoint_state('./model-watermeter/')
if ckpt and ckpt.model_checkpoint_path:
save_path = str(ckpt.model_checkpoint_path)
saver.restore(sess, save_path)
step = int(save_path[save_path.rfind("-") + 1:]) + 1
print(save_path, str(step))
# log_dir = 'summary/graph2'
# train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)
# test_writer = tf.summary.FileWriter(log_dir + '/test', sess.graph)
while True:
# batch_x, batch_y = next(generate_random_number.keras_label_generator(64))
batch_x, batch_y = next(captcha.keras_label_generator(train_batch_size))
_, loss = sess.run([train_step, cross_entropy], feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
print('step:%d,loss:%f' % (step, loss))
if step % 10 == 0:
# saver.save(sess, "./model/capcha_model.ckpt-" + str(step))
saver.save(sess, "./model-watermeter/capcha_model.ckpt-" + str(step))
# batch_x_test, batch_y_test = next(generate_random_number.keras_label_generator(64))
batch_x_test, batch_y_test = next(captcha.keras_label_generator(train_batch_size*8))
acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.})
print('###############################################step:%d,accuracy:%f' % (step, acc))
if acc > 0.999:
saver.save(sess, "./model-watermeter/capcha_model.ckpt-" + str(step))
break
step += 1
# save freeze graph file
tf.train.write_graph(sess.graph_def, 'output_model/pb_model', 'model.pb')
print(save_path)
freeze_graph.freeze_graph('output_model/pb_model/model.pb', '', False, save_path, 'out', 'save/restore_all',
'save/Const:0', 'output_model/pb_model/frozen_model.pb', False, "")
