# -*- coding: utf-8 -*-

import cv2
import numpy as np

from captcha.image import ImageCaptcha


def generate_captcha(text=‘1‘):
    """Generate a digit image."""
    capt = ImageCaptcha(width=28, height=28, font_sizes=[24])
    image = capt.generate_image(text)
    image = np.array(image, dtype=np.uint8)
    return image
    
    
if __name__ == ‘__main__‘:
    output_dir = ‘./datasets/images/‘
    for i in range(50000):
        label = np.random.randint(0, 10)
        image = generate_captcha(str(label))
        image_name = ‘image{}_{}.jpg‘.format(i+1, label)
        output_path = output_dir + image_name
        cv2.imwrite(output_path, image)

inputs = tf.placeholder(tf.float32, shape=[None, 28, 28, 3], name=‘inputs‘)
.......
.......
prob_ = tf.identity(prob, name=‘prob‘)

        constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, [‘inputs‘,‘prob‘]) #训练完毕直接把模型保存为PB格式
        with tf.gfile.FastGFile(‘model3.pb‘, mode=‘wb‘) as f: #模型的名字是model.pb
            f.write(constant_graph.SerializeToString())

# -*- coding: utf-8 -*-

"""Train a CNN model to classifying 10 digits.

Example Usage:
---------------
python3 train.py     --images_path: Path to the training images (directory).
    --model_output_path: Path to model.ckpt.
"""

import cv2
import glob
import numpy as np
import os
import tensorflow as tf

import model
from tensorflow.python.framework import graph_util

flags = tf.app.flags

flags.DEFINE_string(‘images_path‘, None, ‘Path to training images.‘)
flags.DEFINE_string(‘model_output_path‘, None, ‘Path to model checkpoint.‘)
FLAGS = flags.FLAGS


def get_train_data(images_path):
    """Get the training images from images_path.
    
    Args: 
        images_path: Path to trianing images.
        
    Returns:
        images: A list of images.
        lables: A list of integers representing the classes of images.
        
    Raises:
        ValueError: If images_path is not exist.
    """
    if not os.path.exists(images_path):
        raise ValueError(‘images_path is not exist.‘)
        
    images = []
    labels = []
    images_path = os.path.join(images_path, ‘*.jpg‘)
    count = 0
    for image_file in glob.glob(images_path):
        count += 1
        if count % 100 == 0:
            print(‘Load {} images.‘.format(count))
        image = cv2.imread(image_file)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        # Assume the name of each image is imagexxx_label.jpg
        label = float(image_file.split(‘_‘)[-1].split(‘.‘)[0])
        images.append(image)
        labels.append(label)
    images = np.array(images)
    labels = np.array(labels)
    return images, labels


def next_batch_set(images, labels, batch_size=128):
    """Generate a batch training data.
    
    Args:
        images: A 4-D array representing the training images.
        labels: A 1-D array representing the classes of images.
        batch_size: An integer.
        
    Return:
        batch_images: A batch of images.
        batch_labels: A batch of labels.
    """
    indices = np.random.choice(len(images), batch_size)
    batch_images = images[indices]
    batch_labels = labels[indices]
    return batch_images, batch_labels


def main(_):
    inputs = tf.placeholder(tf.float32, shape=[None, 28, 28, 3], name=‘inputs‘)
    labels = tf.placeholder(tf.int32, shape=[None], name=‘labels‘)
    
    cls_model = model.Model(is_training=True, num_classes=10)
    preprocessed_inputs = cls_model.preprocess(inputs)#预处理
    prediction_dict = cls_model.predict(preprocessed_inputs)
    loss_dict = cls_model.loss(prediction_dict, labels)
    loss = loss_dict[‘loss‘]
    postprocessed_dict = cls_model.postprocess(prediction_dict)
    classes = postprocessed_dict[‘classes‘]
    prob = postprocessed_dict[‘prob‘]
    classes_ = tf.identity(classes, name=‘classes‘)
    prob_ = tf.identity(prob, name=‘prob‘)
    acc = tf.reduce_mean(tf.cast(tf.equal(classes, labels), ‘float‘))
    
    global_step = tf.Variable(0, trainable=False)
    learning_rate = tf.train.exponential_decay(0.05, global_step, 150, 0.9)
    
    optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
    train_step = optimizer.minimize(loss, global_step)
    
    saver = tf.train.Saver()
    
    images, targets = get_train_data(FLAGS.images_path)
    
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
        
        for i in range(6000):
            batch_images, batch_labels = next_batch_set(images, targets)
            train_dict = {inputs: batch_images, labels: batch_labels}
            
            sess.run(train_step, feed_dict=train_dict)
            
            loss_, acc_,prob__,classes__ = sess.run([loss, acc, prob_,classes_], feed_dict=train_dict)
            
            train_text = ‘step: {}, loss: {}, acc: {},classes:{}‘.format(
                i+1, loss_, acc_,classes__)
            print(train_text)
            print (prob__)
        saver.save(sess, FLAGS.model_output_path)
        constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, [‘inputs‘,‘prob‘]) #训练完毕直接把模型保存为PB格式
        with tf.gfile.FastGFile(‘model3.pb‘, mode=‘wb‘) as f: #模型的名字是model.pb
            f.write(constant_graph.SerializeToString())    
if __name__ == ‘__main__‘:
    tf.app.run()

import tensorflow as tf
from tensorflow.python.framework import graph_util
def freeze_graph(input_checkpoint,output_graph):
    ‘‘‘
    :param input_checkpoint:
    :param output_graph: PB模型保存路径
    :return:
    ‘‘‘
    # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
    # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
 
    # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
    #input_node_names = "inputs"
    output_node_names = "inputs,classes"
    saver = tf.train.import_meta_graph(input_checkpoint + ‘.meta‘, clear_devices=True)
    graph = tf.get_default_graph() # 获得默认的图
    input_graph_def = graph.as_graph_def()  # 返回一个序列化的图代表当前的图
 
    with tf.Session() as sess:
        saver.restore(sess, input_checkpoint) #恢复图并得到数据
        output_graph_def = graph_util.convert_variables_to_constants(  # 模型持久化，将变量值固定
            sess=sess,
            input_graph_def=input_graph_def,# 等于:sess.graph_def
            output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
 
        with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
            f.write(output_graph_def.SerializeToString()) #序列化输出
        print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
 
        # for op in graph.get_operations():
        #     print(op.name, op.values())
# 输入ckpt模型路径
input_checkpoint=‘model/model.ckpt‘
# 输出pb模型的路径
out_pb_path="frozen_model.pb"
# 调用freeze_graph将ckpt转为pb
freeze_graph(input_checkpoint,out_pb_path)

import tensorflow as tf
#把pb文件路径改成自己的pb文件路径即可
path = "model2.pb"
 
#如果是不知道自己的模型的输入输出节点，建议用tensorboard做可视化查看计算图，计算图里有输入输出的节点名称
inputs = ["inputs"]
outputs = ["prob"]
#转换pb模型到tflite模型
converter = tf.lite.TFLiteConverter.from_frozen_graph(path, inputs, outputs)
#converter.post_training_quantize = True
tflite_model = converter.convert()
open("model3.tflite", "wb").write(tflite_model)

进入tensorflow源码目录，两步编译
bazel build tensorflow/python/tools:freeze_graph 
bazel build tensorflow/lite/toco:toco
./bazel-bin/tensorflow/contrib/lite/toco/toco 
--input_file=/media/bayes/69da5b29-ae56-4feb-93a1-2ce24323aa78/project/model2.pb 
--output_file=/media/bayes/69da5b29-ae56-4feb-93a1-2ce24323aa78/project/model2.tflite 
--input_format=TENSORFLOW_GRAPHDEF 
--output_format=TFLITE 
--inference_type=FLOAT 
--input_shape=1,28,28,3 
--input_array=inputs 
--output_array=prob

# -*- coding: utf-8 -*-

"""Evaluate the trained CNN model.
Example Usage:
---------------
python3 infrence_pb.py     --frozen_graph_path: Path to model frozen graph.
"""

import numpy as np
import tensorflow as tf

from captcha.image import ImageCaptcha

flags = tf.app.flags
flags.DEFINE_string(‘frozen_graph_path‘, None, ‘Path to model frozen graph.‘)
FLAGS = flags.FLAGS


def generate_captcha(text=‘1‘):
    capt = ImageCaptcha(width=28, height=28, font_sizes=[24])
    image = capt.generate_image(text)
    image = np.array(image, dtype=np.uint8)
    return image


def main(_):
    model_graph = tf.Graph()
    with model_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(FLAGS.frozen_graph_path, ‘rb‘) as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name=‘‘)
    
    with model_graph.as_default():
        with tf.Session(graph=model_graph) as sess:
            inputs = model_graph.get_tensor_by_name(‘inputs:0‘)
            classes = model_graph.get_tensor_by_name(‘classes:0‘)
            prob = model_graph.get_tensor_by_name(‘prob:0‘)
            for i in range(10):
                label = np.random.randint(0, 10)
                image = generate_captcha(str(label))
                image = 
                image_np = np.expand_dims(image, axis=0)
                predicted_label,probs = sess.run([classes,prob], 
                                           feed_dict={inputs: image_np})
                print(predicted_label, ‘ vs ‘, label)
                print(probs)
            
            
if __name__ == ‘__main__‘:
    tf.app.run()

# -*- coding:utf-8 -*-
import os
import cv2
import numpy as np
import time

import tensorflow as tf

test_image_dir = ‘./test_images/‘
#model_path = "./model/quantize_frozen_graph.tflite"
model_path = "./model3.tflite"

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
print(str(input_details))
output_details = interpreter.get_output_details()
print(str(output_details))

#with tf.Session( ) as sess:
if 1:
    file_list = os.listdir(test_image_dir)
    
    model_interpreter_time = 0
    start_time = time.time()
    # 遍历文件
    for file in file_list:
        print(‘=========================‘)
        full_path = os.path.join(test_image_dir, file)
        print(‘full_path:{}‘.format(full_path))
        

        img = cv2.imread(full_path )
        res_img = cv2.resize(img,(28,28),interpolation=cv2.INTER_CUBIC) 
        # 变成长784的一维数据
        #new_img = res_img.reshape((784))
        new_img = np.array(res_img, dtype=np.uint8)
        # 增加一个维度，变为 [1, 784]
        image_np_expanded = np.expand_dims(new_img, axis=0)
        image_np_expanded = image_np_expanded.astype(‘float32‘) # 类型也要满足要求
        
        # 填装数据
        model_interpreter_start_time = time.time()
        interpreter.set_tensor(input_details[0][‘index‘], image_np_expanded)
        
        # 注意注意，我要调用模型了
        interpreter.invoke()
        output_data = interpreter.get_tensor(output_details[0][‘index‘])
        model_interpreter_time += time.time() - model_interpreter_start_time
        
        # 出来的结果去掉没用的维度
        result = np.squeeze(output_data)
        print(‘result:{}‘.format(result))
        #print(‘result:{}‘.format(sess.run(output, feed_dict={newInput_X: image_np_expanded})))
        
        # 输出结果是长度为10（对应0-9）的一维数据，最大值的下标就是预测的数字
        #print(‘result:{}‘.format( (np.where(result==np.max(result)))[0][0]  ))
    used_time = time.time() - start_time
    print(‘used_time:{}‘.format(used_time))
    print(‘model_interpreter_time:{}‘.format(model_interpreter_time))

#模型的输入大小
private int[] ddims = {1, 3, 28, 28};
#模型的名称

private static final String[] PADDLE_MODEL = {
"model3",
"mobilenet_quant_v1_224",
"mobilenet_v1_1.0_224",
"mobilenet_v2"
};

#标签的名称

BufferedReader reader = new BufferedReader(new InputStreamReader(assetManager.open("cacheLabel1.txt")));
#模型输出的数据类型，在PC端可以清楚地看到

float[][] labelProbArray = new float[1][10];

#输入数据预处理工作是否已经包含在模型里面

//  imgData.putFloat(((((val >> 16) & 0xFF) - 128f) / 128f));
//  imgData.putFloat(((((val >> 8) & 0xFF) - 128f) / 128f));
//  imgData.putFloat((((val & 0xFF) - 128f) / 128f));
    imgData.putFloat(((val >> 16) & 0xFF) );
    imgData.putFloat(((val >> 8) & 0xFF) );
    imgData.putFloat((val & 0xFF) );

#coding:utf-8
 
import tensorflow as tf
from tensorflow.python.framework import graph_util
tf.reset_default_graph()  # 重置计算图
output_graph_path = ‘model3.pb‘
with tf.Session() as sess:
 
    tf.global_variables_initializer().run()
    output_graph_def = tf.GraphDef()
    # 获得默认的图
    graph = tf.get_default_graph()
    with open(output_graph_path, "rb") as f:
        output_graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(output_graph_def, name="")
        # 得到当前图有几个操作节点
        print("%d ops in the final graph." % len(output_graph_def.node))
 
        tensor_name = [tensor.name for tensor in output_graph_def.node]
        print(tensor_name)
        print(‘---------------------------‘)
        # 在log_graph文件夹下生产日志文件，可以在tensorboard中可视化模型
        #summaryWriter = tf.summary.FileWriter(‘log_graph/‘, graph)
 
 
        for op in graph.get_operations():
            # print出tensor的name和值
            print(op.name, op.values())