tf22: ocr识别——不定长数字串识别

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
tf CNN+LSTM+CTC 训练识别不定长数字字符图片
@author: liupeng
"""
from genIDCard  import *

import numpy as np
import time 
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf


#定义一些常量
#图片大小，32 x 256
OUTPUT_SHAPE = (32,256)

#训练最大轮次
num_epochs = 10000

num_hidden = 64
num_layers = 1

obj = gen_id_card()

num_classes = obj.len + 1 + 1  # 10位数字 + blank + ctc blank

#初始化学习速率
INITIAL_LEARNING_RATE = 1e-3
DECAY_STEPS = 5000
REPORT_STEPS = 100
LEARNING_RATE_DECAY_FACTOR = 0.9  # The learning rate decay factor
MOMENTUM = 0.9

DIGITS='0123456789'
BATCHES = 10
BATCH_SIZE = 64
TRAIN_SIZE = BATCHES * BATCH_SIZE

def decode_sparse_tensor(sparse_tensor):
    #print("sparse_tensor = ", sparse_tensor)
    decoded_indexes = list()
    current_i = 0
    current_seq = []
    for offset, i_and_index in enumerate(sparse_tensor[0]):
        i = i_and_index[0]
        if i != current_i:
            decoded_indexes.append(current_seq)
            current_i = i
            current_seq = list()
        current_seq.append(offset)
    decoded_indexes.append(current_seq)
    #print("decoded_indexes = ", decoded_indexes)
    result = []
    for index in decoded_indexes:
        #print("index = ", index)
        result.append(decode_a_seq(index, sparse_tensor))
        #print(result)
    return result
    
def decode_a_seq(indexes, spars_tensor):
    decoded = []
    for m in indexes:
        str = DIGITS[spars_tensor[1][m]]
        decoded.append(str)
    # Replacing blank label to none
    #str_decoded = str_decoded.replace(chr(ord('9') + 1), '')
    # Replacing space label to space
    #str_decoded = str_decoded.replace(chr(ord('0') - 1), ' ')
    # print("ffffffff", str_decoded)
    return decoded

def report_accuracy(decoded_list, test_targets):
    original_list = decode_sparse_tensor(test_targets)
    detected_list = decode_sparse_tensor(decoded_list)
    true_numer = 0
    
    if len(original_list) != len(detected_list):
        print("len(original_list)", len(original_list), "len(detected_list)", len(detected_list),
              " test and detect length desn't match")
        return
    print("T/F: original(length) <-------> detectcted(length)")
    for idx, number in enumerate(original_list):
        detect_number = detected_list[idx]
        hit = (number == detect_number)
        print(hit, number, "(", len(number), ") <-------> ", detect_number, "(", len(detect_number), ")")
        if hit:
            true_numer = true_numer + 1
    print("Test Accuracy:", true_numer * 1.0 / len(original_list))

#转化一个序列列表为稀疏矩阵    
def sparse_tuple_from(sequences, dtype=np.int32):
    """
    Create a sparse representention of x.
    Args:
        sequences: a list of lists of type dtype where each element is a sequence
    Returns:
        A tuple with (indices, values, shape)
    """
    indices = []
    values = []
    
    for n, seq in enumerate(sequences):
        indices.extend(zip([n] * len(seq), xrange(len(seq))))
        values.extend(seq)
 
    indices = np.asarray(indices, dtype=np.int64)
    values = np.asarray(values, dtype=dtype)
    shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
    
    
    return indices, values, shape
    

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.5)
    return tf.Variable(initial) 
 
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
 
def conv2d(x, W, stride=(1, 1), padding='SAME'):
    return tf.nn.conv2d(x, W, strides=[1, stride[0], stride[1], 1],padding=padding) 
 
def max_pool(x, ksize=(2, 2), stride=(2, 2)):
    return tf.nn.max_pool(x, ksize=[1, ksize[0], ksize[1], 1],strides=[1, stride[0], stride[1], 1], padding='SAME')
 
def avg_pool(x, ksize=(2, 2), stride=(2, 2)):
    return tf.nn.avg_pool(x, ksize=[1, ksize[0], ksize[1], 1],strides=[1, stride[0], stride[1], 1], padding='SAME')


# 生成一个训练batch
def get_next_batch(batch_size=128):
    obj = gen_id_card()
    #(batch_size,256,32)
    inputs = np.zeros([batch_size, OUTPUT_SHAPE[1],OUTPUT_SHAPE[0]])
    codes = []

    for i in range(batch_size):
        #生成不定长度的字串
        image, text, vec = obj.gen_image()
        #np.transpose 矩阵转置 (32*256,) => (32,256) => (256,32)
        inputs[i,:] = np.transpose(image.reshape((OUTPUT_SHAPE[0],OUTPUT_SHAPE[1])))
        codes.append(list(text))
    targets = [np.asarray(i) for i in codes]
    print targets
    sparse_targets = sparse_tuple_from(targets)
    #(batch_size,) 值都是256
    seq_len = np.ones(inputs.shape[0]) * OUTPUT_SHAPE[1]

    return inputs, sparse_targets, seq_len
    
#定义CNN网络，处理图片，
def convolutional_layers():
    #输入数据，shape [batch_size, max_stepsize, num_features]
    inputs = tf.placeholder(tf.float32, [None, None, OUTPUT_SHAPE[0]])
    
    #第一层卷积层, 32*256*1 => 16*128*48
    W_conv1 = weight_variable([5, 5, 1, 48])
    b_conv1 = bias_variable([48])
    x_expanded = tf.expand_dims(inputs, 3)
    h_conv1 = tf.nn.relu(conv2d(x_expanded, W_conv1) + b_conv1)
    h_pool1 = max_pool(h_conv1, ksize=(2, 2), stride=(2, 2))
    
    #第二层, 16*128*48 => 16*64*64
    W_conv2 = weight_variable([5, 5, 48, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool(h_conv2, ksize=(2, 1), stride=(2, 1))
    
    #第三层, 16*64*64 => 8*32*128
    W_conv3 = weight_variable([5, 5, 64, 128])
    b_conv3 = bias_variable([128])
    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
    h_pool3 = max_pool(h_conv3, ksize=(2, 2), stride=(2, 2))
    
    #全连接
    W_fc1 = weight_variable([16 * 8 * OUTPUT_SHAPE[1], OUTPUT_SHAPE[1]])
    b_fc1 = bias_variable([OUTPUT_SHAPE[1]])
    
    conv_layer_flat = tf.reshape(h_pool3, [-1, 16 * 8 * OUTPUT_SHAPE[1]])
    
    features = tf.nn.relu(tf.matmul(conv_layer_flat, W_fc1) + b_fc1)
    #（batchsize,256）
    shape = tf.shape(features)
    features = tf.reshape(features, [shape[0], OUTPUT_SHAPE[1], 1])  # batchsize * outputshape * 1
    return inputs,features

def get_train_model():
    #features = convolutional_layers()
    #print features.get_shape()
    
    inputs = tf.placeholder(tf.float32, [None, None, OUTPUT_SHAPE[0]])
    
    #定义ctc_loss需要的稀疏矩阵
    targets = tf.sparse_placeholder(tf.int32)
    
    #1维向量 序列长度 [batch_size,]
    seq_len = tf.placeholder(tf.int32, [None])
    
    #定义LSTM网络
    cell = tf.contrib.rnn.LSTMCell(num_hidden, state_is_tuple=True)
    stack = tf.contrib.rnn.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
    outputs, _ = tf.nn.dynamic_rnn(cell, inputs, seq_len, dtype=tf.float32)
    
    shape = tf.shape(inputs)
    batch_s, max_timesteps = shape[0], shape[1]
    
    outputs = tf.reshape(outputs, [-1, num_hidden])
    W = tf.Variable(tf.truncated_normal([num_hidden,
                                          num_classes],
                                         stddev=0.1), name="W")
    b = tf.Variable(tf.constant(0., shape=[num_classes]), name="b")
    
    logits = tf.matmul(outputs, W) + b

    logits = tf.reshape(logits, [batch_s, -1, num_classes])
    
    logits = tf.transpose(logits, (1, 0, 2))
    
    return logits, inputs, targets, seq_len, W, b
    
def train():
    global_step = tf.Variable(0, trainable=False)
    learning_rate = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
                                                global_step,
                                                DECAY_STEPS,
                                                LEARNING_RATE_DECAY_FACTOR,
                                                staircase=True)
    logits, inputs, targets, seq_len, W, b = get_train_model()
    
    loss = tf.nn.ctc_loss(labels=targets,inputs=logits, sequence_length=seq_len)
    cost = tf.reduce_mean(loss)
    
    #optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=MOMENTUM).minimize(cost, global_step=global_step)
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss,global_step=global_step)
    decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, seq_len, merge_repeated=False)
    
    acc = tf.reduce_mean(tf.edit_distance(tf.cast(decoded[0], tf.int32), targets))
    
    init = tf.global_variables_initializer()
    
    def do_report():
        test_inputs,test_targets,test_seq_len = get_next_batch(BATCH_SIZE)
        test_feed = {inputs: test_inputs,
                     targets: test_targets,
                     seq_len: test_seq_len}
        dd, log_probs, accuracy = session.run([decoded[0], log_prob, acc], test_feed)
        report_accuracy(dd, test_targets)
        # decoded_list = decode_sparse_tensor(dd)
 
    def do_batch():
        train_inputs, train_targets, train_seq_len = get_next_batch(BATCH_SIZE)
        
        feed = {inputs: train_inputs, targets: train_targets, seq_len: train_seq_len}
        
        b_loss,b_targets, b_logits, b_seq_len,b_cost, steps, _ = session.run([loss, targets, logits, seq_len, cost, global_step, optimizer], feed)
        
        #print b_loss
        #print b_targets, b_logits, b_seq_len
        print b_cost, steps
        if steps > 0 and steps % REPORT_STEPS == 0:
            do_report()
            #save_path = saver.save(session, "ocr.model", global_step=steps)
            # print(save_path)
        return b_cost, steps
    
    with tf.Session() as session:
        session.run(init)
        saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
        for curr_epoch in xrange(num_epochs):
            print("Epoch.......", curr_epoch)
            train_cost = train_ler = 0
            for batch in xrange(BATCHES):
                start = time.time()
                c, steps = do_batch()
                train_cost += c * BATCH_SIZE
                seconds = time.time() - start
                print("Step:", steps, ", batch seconds:", seconds)
            
            train_cost /= TRAIN_SIZE
            
            train_inputs, train_targets, train_seq_len = get_next_batch(BATCH_SIZE)
            val_feed = {inputs: train_inputs,
                        targets: train_targets,
                        seq_len: train_seq_len}
 
            val_cost, val_ler, lr, steps = session.run([cost, acc, learning_rate, global_step], feed_dict=val_feed)
 
            log = "Epoch {}/{}, steps = {}, train_cost = {:.3f}, train_ler = {:.3f}, val_cost = {:.3f}, val_ler = {:.3f}, time = {:.3f}s, learning_rate = {}"
            print(log.format(curr_epoch + 1, num_epochs, steps, train_cost, train_ler, val_cost, val_ler, time.time() - start, lr))

if __name__ == '__main__':
    # inputs, sparse_targets,seq_len = get_next_batch(2)
    # decode_sparse_tensor(sparse_targets);
    train()

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
tf CNN+LSTM+CTC 训练识别不定长数字字符图片

@author: pengyuanjie
"""
from genIDCard  import *

import numpy as np
import time 
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import cv2


#定义一些常量
#图片大小，32 x 256
OUTPUT_SHAPE = (32,256)

#训练最大轮次
num_epochs = 10000

num_hidden = 64
num_layers = 1

obj = gen_id_card()

num_classes = obj.len + 1 + 1  # 10位数字 + blank + ctc blank

#初始化学习速率
INITIAL_LEARNING_RATE = 1e-3
DECAY_STEPS = 5000
REPORT_STEPS = 100
LEARNING_RATE_DECAY_FACTOR = 0.9  # The learning rate decay factor
MOMENTUM = 0.9

DIGITS='0123456789'
BATCHES = 10
BATCH_SIZE = 64
TRAIN_SIZE = BATCHES * BATCH_SIZE

def decode_sparse_tensor(sparse_tensor):
    #print("sparse_tensor = ", sparse_tensor)
    decoded_indexes = list()
    current_i = 0
    current_seq = []
    for offset, i_and_index in enumerate(sparse_tensor[0]):
        i = i_and_index[0]
        if i != current_i:
            decoded_indexes.append(current_seq)
            current_i = i
            current_seq = list()
        current_seq.append(offset)
    decoded_indexes.append(current_seq)
    #print("decoded_indexes = ", decoded_indexes)
    result = []
    for index in decoded_indexes:
        #print("index = ", index)
        result.append(decode_a_seq(index, sparse_tensor))
        #print(result)
    return result
    
def decode_a_seq(indexes, spars_tensor):
    decoded = []
    for m in indexes:
        str = DIGITS[spars_tensor[1][m]]
        decoded.append(str)
    # Replacing blank label to none
    #str_decoded = str_decoded.replace(chr(ord('9') + 1), '')
    # Replacing space label to space
    #str_decoded = str_decoded.replace(chr(ord('0') - 1), ' ')
    # print("ffffffff", str_decoded)
    return decoded

def report_accuracy(decoded_list, test_targets):
    original_list = decode_sparse_tensor(test_targets)
    detected_list = decode_sparse_tensor(decoded_list)
    true_numer = 0
    
    if len(original_list) != len(detected_list):
        print("len(original_list)", len(original_list), "len(detected_list)", len(detected_list),
              " test and detect length desn't match")
        return
    print("T/F: original(length) <-------> detectcted(length)")
    for idx, number in enumerate(original_list):
        detect_number = detected_list[idx]
        hit = (number == detect_number)
        print(hit, number, "(", len(number), ") <-------> ", detect_number, "(", len(detect_number), ")")
        if hit:
            true_numer = true_numer + 1
    print("Test Accuracy:", true_numer * 1.0 / len(original_list))

#转化一个序列列表为稀疏矩阵    
def sparse_tuple_from(sequences, dtype=np.int32):
    """
    Create a sparse representention of x.
    Args:
        sequences: a list of lists of type dtype where each element is a sequence
    Returns:
        A tuple with (indices, values, shape)
    """
    indices = []
    values = []
    
    for n, seq in enumerate(sequences):
        indices.extend(zip([n] * len(seq), xrange(len(seq))))
        values.extend(seq)
 
    indices = np.asarray(indices, dtype=np.int64)
    values = np.asarray(values, dtype=dtype)
    shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
    
    
    return indices, values, shape
    

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.5)
    return tf.Variable(initial) 
 
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
 
def conv2d(x, W, stride=(1, 1), padding='SAME'):
    return tf.nn.conv2d(x, W, strides=[1, stride[0], stride[1], 1],padding=padding) 
 
def max_pool(x, ksize=(2, 2), stride=(2, 2)):
    return tf.nn.max_pool(x, ksize=[1, ksize[0], ksize[1], 1],strides=[1, stride[0], stride[1], 1], padding='SAME')
 
def avg_pool(x, ksize=(2, 2), stride=(2, 2)):
    return tf.nn.avg_pool(x, ksize=[1, ksize[0], ksize[1], 1],strides=[1, stride[0], stride[1], 1], padding='SAME')


# 生成一个训练batch
def get_next_batch(batch_size=128, kk=0):
    obj = gen_id_card()
    #(batch_size,256,32)
    inputs = np.zeros([batch_size, OUTPUT_SHAPE[1]+kk,OUTPUT_SHAPE[0]])
    codes = []

    for i in range(batch_size):
        #生成不定长度的字串
        image, text, vec = obj.gen_image()
        image = cv2.resize(image,(OUTPUT_SHAPE[1]+kk, OUTPUT_SHAPE[0]))
        #np.transpose 矩阵转置 (32*256,) => (32,256) => (256,32)
        inputs[i,:] = np.transpose(image.reshape((OUTPUT_SHAPE[0],OUTPUT_SHAPE[1]+kk)))
        codes.append(list(text))
    targets = [np.asarray(i) for i in codes]
    #print targets
    sparse_targets = sparse_tuple_from(targets)
    print (sparse_targets)
    #(batch_size,) 值都是256
    seq_len = np.ones(inputs.shape[0]) * (OUTPUT_SHAPE[1]+kk)
    print ('seq_len:', seq_len)
    return inputs, sparse_targets, seq_len
    
#定义CNN网络，处理图片，
def convolutional_layers():
    #输入数据，shape [batch_size, max_stepsize, num_features]
    inputs = tf.placeholder(tf.float32, [None, None, OUTPUT_SHAPE[0]])
    
    #第一层卷积层, 32*256*1 => 16*128*48
    W_conv1 = weight_variable([5, 5, 1, 48])
    b_conv1 = bias_variable([48])
    x_expanded = tf.expand_dims(inputs, 3)
    h_conv1 = tf.nn.relu(conv2d(x_expanded, W_conv1) + b_conv1)
    h_pool1 = max_pool(h_conv1, ksize=(2, 2), stride=(2, 2))
    
    #第二层, 16*128*48 => 16*64*64
    W_conv2 = weight_variable([5, 5, 48, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool(h_conv2, ksize=(2, 1), stride=(2, 1))
    
    #第三层, 16*64*64 => 8*32*128
    W_conv3 = weight_variable([5, 5, 64, 128])
    b_conv3 = bias_variable([128])
    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
    h_pool3 = max_pool(h_conv3, ksize=(2, 2), stride=(2, 2))
    
    #全连接
    W_fc1 = weight_variable([16 * 8 * OUTPUT_SHAPE[1], OUTPUT_SHAPE[1]])
    b_fc1 = bias_variable([OUTPUT_SHAPE[1]])
    
    conv_layer_flat = tf.reshape(h_pool3, [-1, 16 * 8 * OUTPUT_SHAPE[1]])
    
    features = tf.nn.relu(tf.matmul(conv_layer_flat, W_fc1) + b_fc1)
    #（batchsize,256）
    shape = tf.shape(features)
    features = tf.reshape(features, [shape[0], OUTPUT_SHAPE[1], 1])  # batchsize * outputshape * 1
    return inputs,features

def get_train_model():
    #features = convolutional_layers()
    #print features.get_shape()
    
    inputs = tf.placeholder(tf.float32, [None, None, OUTPUT_SHAPE[0]])
    
    #定义ctc_loss需要的稀疏矩阵
    targets = tf.sparse_placeholder(tf.int32)
    
    #1维向量 序列长度 [batch_size,]
    seq_len = tf.placeholder(tf.int32, [None])
    
    #定义LSTM网络
    cell = tf.contrib.rnn.LSTMCell(num_hidden, state_is_tuple=True)
    stack = tf.contrib.rnn.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
    outputs, _ = tf.nn.dynamic_rnn(cell, inputs, seq_len, dtype=tf.float32)
    
    shape = tf.shape(inputs)
    batch_s, max_timesteps = shape[0], shape[1]
    
    outputs = tf.reshape(outputs, [-1, num_hidden])
    W = tf.Variable(tf.truncated_normal([num_hidden,
                                          num_classes],
                                         stddev=0.1), name="W")
    b = tf.Variable(tf.constant(0., shape=[num_classes]), name="b")
    
    logits = tf.matmul(outputs, W) + b

    logits = tf.reshape(logits, [batch_s, -1, num_classes])
    
    logits = tf.transpose(logits, (1, 0, 2))
    
    return logits, inputs, targets, seq_len, W, b
    
def train():
    global_step = tf.Variable(0, trainable=False)
    learning_rate = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
                                                global_step,
                                                DECAY_STEPS,
                                                LEARNING_RATE_DECAY_FACTOR,
                                                staircase=True)
    logits, inputs, targets, seq_len, W, b = get_train_model()
    
    loss = tf.nn.ctc_loss(labels=targets,inputs=logits, sequence_length=seq_len)
    cost = tf.reduce_mean(loss)
    
    #optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=MOMENTUM).minimize(cost, global_step=global_step)
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss,global_step=global_step)
    decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, seq_len, merge_repeated=False)
    
    acc = tf.reduce_mean(tf.edit_distance(tf.cast(decoded[0], tf.int32), targets))
    
    init = tf.global_variables_initializer()
    
    def do_report(kk):
        test_inputs,test_targets,test_seq_len = get_next_batch(BATCH_SIZE, kk)
        test_feed = {inputs: test_inputs,
                     targets: test_targets,
                     seq_len: test_seq_len}
        dd, log_probs, accuracy = session.run([decoded[0], log_prob, acc], test_feed)
        report_accuracy(dd, test_targets)
        # decoded_list = decode_sparse_tensor(dd)
 
    def do_batch(kk):
        train_inputs, train_targets, train_seq_len = get_next_batch(BATCH_SIZE, kk)
        
        feed = {inputs: train_inputs, targets: train_targets, seq_len: train_seq_len}
        
        b_loss,b_targets, b_logits, b_seq_len,b_cost, steps, _ = session.run([loss, targets, logits, seq_len, cost, global_step, optimizer], feed)
        
        #print b_loss
        #print b_targets, b_logits, b_seq_len
        print b_cost, steps
        if steps > 0 and steps % REPORT_STEPS == 0:
            do_report(kk)
            #save_path = saver.save(session, "ocr.model", global_step=steps)
            # print(save_path)
        return b_cost, steps
    
    with tf.Session() as session:
        session.run(init)
        saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
        for curr_epoch in xrange(num_epochs):
            print("Epoch.......", curr_epoch)
            train_cost = train_ler = 0
            for batch in xrange(BATCHES):
                start = time.time()
                c, steps = do_batch(batch*20)
                train_cost += c * BATCH_SIZE
                seconds = time.time() - start
                print("Step:", steps, ", batch seconds:", seconds)
            
            train_cost /= TRAIN_SIZE
            
            train_inputs, train_targets, train_seq_len = get_next_batch(BATCH_SIZE, curr_epoch*50)
            val_feed = {inputs: train_inputs,
                        targets: train_targets,
                        seq_len: train_seq_len}
 
            val_cost, val_ler, lr, steps = session.run([cost, acc, learning_rate, global_step], feed_dict=val_feed)
 
            log = "Epoch {}/{}, steps = {}, train_cost = {:.3f}, train_ler = {:.3f}, val_cost = {:.3f}, val_ler = {:.3f}, time = {:.3f}s, learning_rate = {}"
            print(log.format(curr_epoch + 1, num_epochs, steps, train_cost, train_ler, val_cost, val_ler, time.time() - start, lr))

if __name__ == '__main__':
    #inputs, sparse_targets,seq_len = get_next_batch(2)
    #decode_sparse_tensor(sparse_targets);
    train()