原文链接: tf 单个文字识别 data 数据输入

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识别结果相当好

show 9800 [0.96875, 0.16407159]
time 15.302184343338013
(32,) (32,)
['知' '粪' '源' '秆' '吐' '济' '佛' '溢' '埃' '凶' '破' '砸' '功' '佰' '秀' '爬' '惮' '尝''辅' '掣' '潘' '匀' '黍' '旬' '统' '狠' '涛' '鄂' '橙' '缅' '庶' '究']
['知' '粪' '源' '杆' '吐' '济' '佛' '溢' '埃' '凶' '破' '砸' '功' '佰' '秀' '爬' '惮' '尝''辅' '掣' '潘' '匀' '黍' '旬' '统' '狠' '涛' '鄂' '橙' '堰' '庶' '究']

必须加BN, 不然无法收敛!!!!!!!!!!!!!

使用sparse_softmax_cross_entropy_with_logits 作为损失函数时, 会自动进行softmax处理, 并且label比logits少一个维度

需要显示声明utf8, 不然有些汉字会报编码错误

为了能够绘制中文字体 , 需要中文字体库

注意字体的大小和验证码图片大小要合适

# encoding:utf-8
import tensorflow as tf
import numpy as np
from tensorflow.contrib import slim
from captcha.image import ImageCaptcha
from PIL import Image
import matplotlib.pyplot as plt
import timeTRAIN_STEP = 1000000
SHOW_STEP = 100
SAVE_STEP = 200
LR = 0.001
SAVE_PB_PATH = './pb'
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IMAGE_WIDTH = 64 * TEXT_LENGTH
IMAGE_HEIGHT = 64
IMAGE_SHAPE = (IMAGE_WIDTH, IMAGE_HEIGHT, 3)print("N_OUTPUTS", N_OUTPUTS)def random_captcha_text(text_length=1):index = np.random.choice(TEXT_INDEX, text_length)text = TEXT[index]return text, indexdef gen_single(random=True):while True:image = ImageCaptcha(width=IMAGE_WIDTH, height=IMAGE_HEIGHT, fonts=["ios10.ttf"])captcha_text, index = random_captcha_text(text_length=TEXT_LENGTH)if random:#  获得随机生成的验证码#  把验证码列表转为字符串captcha_text = ''.join(captcha_text)#  生成验证码captcha = image.generate(captcha_text)img = Image.open(captcha)img = np.array(img)else:captcha = image.create_captcha_image(captcha_text, (0, 0, 255), (255, 255, 255))img = np.array(captcha)yield img, indexdef gen_batch(batch_size=BATCH_SIZE):images = []labels = []for _ in range(batch_size):image, label = gen_single()images.append(image)labels.append(label)return np.stack(images), np.stack(labels)def parse(img, label):img = tf.cast(img, tf.float32)return pre(img), labeldef pre(img):return img / 127.5 - 1def post(img):img = (img + 1.) * 127.5return np.clip(img, 0, 255).astype(np.uint8)# label (32,)
def label2text(label):labels = TEXT[label]return labelsdef get_iterator():# 需要使用元组,使用列表表示shapes时会报无法hash化的错误data = tf.data.Dataset.from_generator(gen_single, (tf.uint8, tf.int64), (IMAGE_SHAPE, (TEXT_LENGTH,)))data = data.map(parse, num_parallel_calls=6).batch(BATCH_SIZE)# data = data.batch(BATCH_SIZE)data = data.prefetch(BATCH_SIZE)return data.make_initializable_iterator()def get_block(in_x, num_outputs=32, kernel_size=1, stride=1):with slim.arg_scope([slim.conv2d,slim.separable_conv2d],activation_fn=tf.nn.leaky_relu# activation_fn=tf.nn.relu6):#  inceptionnet1 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=1, stride=stride)net1 = slim.batch_norm(net1)net3 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=3, stride=stride)net3 = slim.batch_norm(net3)net5 = slim.conv2d(in_x, num_outputs=num_outputs, kernel_size=5, stride=stride)net5 = slim.batch_norm(net5)net = tf.concat([net1, net3, net5], axis=3)net = slim.conv2d(net, num_outputs=num_outputs, kernel_size=1, stride=2)net = slim.batch_norm(net)# # net = slim.max_pool2d(net, 3)# net = slim.conv2d(net, num_outputs, kernel_size, 2)# res# net1 = slim.conv2d(in_x, num_outputs, kernel_size=3, stride=stride)# net2 = slim.conv2d(net1, num_outputs, kernel_size=3, stride=stride)# net3 = slim.conv2d(net2, num_outputs, kernel_size=3, stride=stride)# net = net3 + net1# net = slim.conv2d(net, num_outputs, kernel_size, 2)return netdef build_net(in_x):net = get_block(in_x, 128)print(net.shape)net = get_block(net, 128)print(net.shape)net = get_block(net, 256)print(net.shape)net = get_block(net, 256)print(net.shape)net = slim.flatten(net)net = slim.batch_norm(net)print(net.shape)with slim.arg_scope([slim.conv2d,slim.fully_connected],activation_fn=tf.nn.leaky_relu# activation_fn=tf.nn.relu6):net = slim.fully_connected(net, 1024)net = slim.batch_norm(net)net = slim.fully_connected(net, 512)net = slim.batch_norm(net)net = slim.fully_connected(net, num_outputs=N_OUTPUTS)print('net', net.shape)return netdef train():iterator = get_iterator()images, labels = iterator.get_next()labels = tf.squeeze(labels, 1)net = build_net(images)print('labels,net', labels.shape, net.shape)loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=net))train_op = tf.train.AdamOptimizer(LR).minimize(loss)# train_op = tf.train.RMSPropOptimizer(LR).minimize(loss)output = tf.argmax(net, 1)prediction = tf.equal(output, labels)accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))with tf.Session() as sess:sess.run(tf.global_variables_initializer())sess.run(iterator.initializer)st = time.time()for step in range(1, 1 + TRAIN_STEP):sess.run(train_op)if not step % SHOW_STEP:print("show", step, sess.run([accuracy, loss]))ed = time.time()print('time', ed - st)  # 25sst = edimages_val, labels_val, prediction_val, output_val = sess.run([images, labels, prediction, output])images_val = post(images_val)rows = [np.concatenate(line, 1)for line in np.array_split(images_val, 8)]images_val = np.concatenate(rows, 0)print(labels_val.shape, prediction_val.shape)plt.imshow(images_val)plt.show()print(label2text(labels_val).flatten())print(label2text(output_val.flatten()))if not step % SAVE_STEP:print('save', step)def show_image():iterator = get_iterator()batch = iterator.get_next()with tf.Session() as sess:sess.run(iterator.initializer)for _ in range(10):image, label = sess.run(batch)print(image.shape, label.shape)print(label2text(label))image = np.concatenate(image, 1)print(image.shape)image = post(image)plt.imshow(image)plt.show()def main():train()if __name__ == '__main__':main()