def get_data(batch_count=10000,
test_count=1000,
height=CAPTCHA_HEIGHT,
width=CAPTCHA_WIDTH):
"""
获取训练图片组
:param batch_count: default 60
:param width: 验证码宽度
:param height: 验证码高度
:return: batch_x, batch_yc
"""
batch_x = np.zeros([batch_count, height, width, CAPTCHA_CHANNEL])
batch_y = np.zeros([batch_count, CAPTCHA_LEN * LEN]) #60*40
test_x = np.zeros([test_count, height, width, CAPTCHA_CHANNEL])
test_y = np.zeros([test_count, CAPTCHA_LEN * LEN]) #60*40
for i in range(batch_count): # 生成对应的训练集
image, text = gen_captcha_text_and_image()
batch_x[i, :] = image
batch_y[i, :] = text2vec(text) # 验证码文本的向量形式
for i in range(test_count): # 生成对应的训练集
image, text = gen_captcha_text_and_image()
test_x[i, :] = image
test_y[i, :] = text2vec(text) # 验证码文本的向量形式
return batch_x, batch_y, test_x, test_y
def wrap_gen_captcha_text_and_image(shape=(40, 100, 3)): # 修改
'''
返回特定shape图片
:param shape:
:return:
'''
while True:
t, im = gen_captcha_text_and_image()
if im.shape == shape: return t, im
def wrap_gen_captcha_text_and_image(shape=(60, 160, 3)):
"""
返回特定shape图片
:param shape:
:return:
"""
while True:
t, im = gen_captcha_text_and_image()
if im.shape == shape:
return t, im
def __init__(self):
height, width = CAPTCHA_HEIGHT, CAPTCHA_WIDTH
batch_x = np.zeros([10000, height, width, CAPTCHA_CHANNEL])
batch_y = np.zeros([10000, CAPTCHA_LEN * LEN]) # 60*40
test_x = np.zeros([1000, height, width, CAPTCHA_CHANNEL])
test_y = np.zeros([1000, CAPTCHA_LEN * LEN]) # 60*40
for i in range(10000): # 生成对应的训练集
image, text = gen_captcha_text_and_image()
batch_x[i, :] = image
batch_y[i, :] = text2vec(text) # 验证码文本的向量形式
for i in range(1000): # 生成对应的训练集
image, text = gen_captcha_text_and_image()
test_x[i, :] = image
test_y[i, :] = text2vec(text) # 验证码文本的向量形式
self.validation_data = test_x
self.validation_labels = test_y
self.train_data = batch_x
self.train_labels = batch_y
:param width:
:return:
"""
x = tf.placeholder(tf.float32, [None, height * width])
keep_prob = tf.placeholder(tf.float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('model/'))
predict = tf.argmax(
tf.reshape(y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
return text_list
if __name__ == '__main__':
text, image = gen_captcha_text_and_image()
img = Image.fromarray(image)
image = convert2gray(image)
image = image.flatten() / 255
pre_text = captcha2text([image])
print("验证码正确值:", text, ' 模型预测值:', pre_text)
img.show()