猫狗大战

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目录

• 一.猫狗大战
  • 1.1 简介
  • 1.2 数据集
• 二.代码：
  • dataset.py
  • train.py
  • predict.py
• 三.程序输出：
  • train.py
  • predict.py

一.猫狗大战

1.1 简介

这是计算机视觉系列的第一篇博文，主要介绍用TensorFlow来实现猫狗分类、识别。该项目主要包括dataset、train、predict三部分。其中dataset.py主要是读取数据并对数据进行预处理；train.py主要是训练一个二分类模型;predict.py是用训练好的模型进行测试。GitHub地址:https://github.com/jx1100370217/dog-cat-master

1.2 数据集

该项目的数据主要包括1000张网上找的猫和狗的图片，其中猫，狗各500张。

二.代码：

dataset.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# author: JX
# data: 20181101

import cv2
import os
import glob
from sklearn.utils import shuffle
import numpy as np

def load_train(train_path,image_size,classes):
    images = []
    labels = []
    img_names = []
    cls = []

    print('Going to read training images')
    for fields in classes:
        index = classes.index(fields)
        print('Now going to read {} file (Index: {})'.format(fields,index))
        path = os.path.join(train_path,fields,'*g')
        files = glob.glob(path)
        for f1 in files:
            image = cv2.imread(f1)
            image = cv2.resize(image,(image_size,image_size),0,0,cv2.INTER_LINEAR)
            image = image.astype(np.float32)
            image = np.multiply(image,1.0 / 255.0)
            images.append(image)
            label = np.zeros(len(classes))
            label[index] = 1.0
            labels.append(label)
            flbase = os.path.basename(f1)
            img_names.append(flbase)
            cls.append(fields)
    images = np.array(images)
    labels = np.array(labels)
    img_names = np.array(img_names)
    cls = np.array(cls)

    return images,labels,img_names,cls

class DataSet(object):
    def __init__(self,images,labels,img_names,cls):
        self._num_examples = images.shape[0]

        self._images = images
        self._labels = labels
        self._img_names = img_names
        self._cls = cls
        self._epochs_done = 0
        self._index_in_epoch = 0
    @property
    def images(self):
        return self._images
    @property
    def labels(self):
        return self._labels
    @property
    def img_names(self):
        return self._img_names
    @property
    def cls(self):
        return self._cls
    @property
    def num_examples(self):
        return self._num_examples
    @property
    def epochs_done(self):
        return self._epochs_done

    def next_batch(self,batch_size):
        start = self._index_in_epoch
        self._index_in_epoch += batch_size

        if self._index_in_epoch > self._num_examples:
            self._epochs_done += 1
            start = 0
            self._index_in_epoch = batch_size
            assert  batch_size <= self._num_examples
        end = self._index_in_epoch

        return self._images[start:end],self._labels[start:end],self._img_names[start:end],self.cls[start:end]

def read_train_sets(train_path,image_size,classes,validation_size):
    class DataSets(object):
        pass
    data_sets = DataSets()

    images, labels, img_names, cls = load_train(train_path, image_size, classes)
    images, labels, img_names, cls = shuffle(images, labels, img_names, cls)

    if isinstance(validation_size, float):
        validation_size = int(validation_size * images.shape[0])

    validation_images = images[:validation_size]
    validation_labels = labels[:validation_size]
    validation_img_names = img_names[:validation_size]
    validation_cls = cls[:validation_size]

    train_images = images[validation_size:]
    train_labels = labels[validation_size:]
    train_img_names = img_names[validation_size:]
    train_cls = cls[validation_size:]

    data_sets.train = DataSet(train_images,train_labels,train_img_names,train_cls)
    data_sets.valid = DataSet(validation_images,validation_labels,validation_img_names,validation_cls)

    return data_sets

train.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# author: JX
# data: 20181101

import dataset
import tensorflow as tf
import time
from datetime import timedelta
import math
import random
import numpy as np
from tensorflow import set_random_seed
from numpy.random import seed
seed(10)
set_random_seed(20)

batch_size = 32

classes = ['dogs','cats']
num_classes = len(classes)

validation_size = 0.2
img_size = 64
num_channels = 3
train_path = 'training_data'

data = dataset.read_train_sets(train_path,img_size,classes,validation_size=validation_size)

print("Complete reading input data.Will Now print a snippet of it")
print("Number of files in Training-set:		{}".format(len(data.train.labels)))
print("Number of files in Validation-set:		{}".format(len(data.valid.labels)))

session = tf.Session()
x = tf.placeholder(tf.float32,shape=[None,img_size,img_size,num_channels],name='x')

y_true = tf.placeholder(tf.float32,shape=[None,num_classes],name='y_true')
y_true_cls = tf.argmax(y_true,dimension=1)

filter_size_conv1 = 3
num_filters_conv1 = 32

filter_size_conv2 = 3
num_filters_conv2 = 32

filter_size_conv3 = 3
num_filters_conv3 = 64

fc_layer_size = 1024

def create_weights(shape):
    return tf.Variable(tf.truncated_normal(shape,stddev=0.05))

def create_biases(size):
    return tf.Variable(tf.constant(0.05,shape=[size]))

def create_convolutional_layer(input,
                               num_input_channels,
                               conv_filter_size,
                               num_filters):
    weights = create_weights(shape=[conv_filter_size,conv_filter_size,num_input_channels,num_filters])
    biases = create_biases(num_filters)

    layer = tf.nn.conv2d(input=input,
                         filter=weights,strides=[1,1,1,1],
                         padding='SAME')
    layer += biases
    layer = tf.nn.relu(layer)

    layer = tf.nn.max_pool(value=layer,
                           ksize=[1,2,2,1],
                           strides=[1,2,2,1],
                           padding='SAME')
    return layer

def create_flatten_layer(layer):
    layer_shape = layer.get_shape()
    num_features = layer_shape[1:4].num_elements()
    layer = tf.reshape(layer,[-1,num_features])

    return layer

def create_fc_layer(input,
                    num_inputs,
                    num_outputs,
                    use_relu=True):
    weights = create_weights(shape=[num_inputs,num_outputs])
    biases = create_biases(num_outputs)
    layer = tf.matmul(input,weights) + biases
    layer = tf.nn.dropout(layer,keep_prob=0.7)
    if use_relu:
        layer = tf.nn.relu(layer)

    return layer

layer_conv1 = create_convolutional_layer(input=x,
                                         num_input_channels=num_channels,
                                         conv_filter_size=filter_size_conv1,
                                         num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
                                         num_input_channels=num_filters_conv1,
                                         conv_filter_size=filter_size_conv2,
                                         num_filters=num_filters_conv2)
layer_conv3 = create_convolutional_layer(input=layer_conv2,
                                         num_input_channels=num_filters_conv2,
                                         conv_filter_size=filter_size_conv3,
                                         num_filters=num_filters_conv3)

layer_flat = create_flatten_layer(layer_conv3)

layer_fc1 = create_fc_layer(input=layer_flat,
                            num_inputs=layer_flat.get_shape()[1:4].num_elements(),
                            num_outputs=fc_layer_size,
                            use_relu=True)

layer_fc2 = create_fc_layer(input=layer_fc1,
                            num_inputs=fc_layer_size,
                            num_outputs=num_classes,
                            use_relu=False)

y_pred = tf.nn.softmax(layer_fc2,name='y_pred')

y_pred_cls = tf.argmax(y_pred,dimension=1)
session.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,labels=y_true)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)
correct_prediction = tf.equal(y_pred_cls,y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

session.run(tf.global_variables_initializer())

def show_pregress(epoch,feed_dict_train,feed_dict_validata,val_loss,i):
    acc = session.run(accuracy,feed_dict=feed_dict_train)
    val_acc = session.run(accuracy,feed_dict=feed_dict_validata)
    msg = "Training Epoch {0}---iterations: {1}---Training Accuracy:{2:>6.1%}," 
          "Validation Accuracy:{3:>6.1%},Validation Loss:{4:.3f}"
    print(msg.format(epoch+1,i,acc,val_acc,val_loss))

total_iterations = 0

saver = tf.train.Saver()
def train(num_iteration):
    global  total_iterations

    for i in range(total_iterations,total_iterations + num_iteration):
        x_batch,y_true_batch,_,cls_batch = data.train.next_batch(batch_size)
        x_valid_batch,y_valid_batch,_,valid_cls_batch = data.valid.next_batch(batch_size)

        feed_dict_tr = {x: x_batch,y_true:y_true_batch}
        feed_dict_val = {x: x_valid_batch, y_true: y_valid_batch}

        session.run(optimizer,feed_dict=feed_dict_tr)

        if i % int(data.train.num_examples/batch_size) == 0:
            val_loss = session.run(cost,feed_dict=feed_dict_val)
            epoch = int(i / int(data.train.num_examples/batch_size))

            show_pregress(epoch,feed_dict_tr,feed_dict_val,val_loss,i)
            saver.save(session,'./dogs-cats-model/dog-cat.ckpt',global_step=i)

    total_iterations += num_iteration

train(num_iteration=10000)

predict.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# author: JX
# data: 20181101

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

image_size = 64
num_channels = 3
images = []

path = 'cat.1.jpg'
image = cv2.imread(path)
image = cv2.resize(image,(image_size,image_size),0,0,cv2.INTER_LINEAR)
images.append(image)
images = np.array(images,dtype=np.uint8)
images = images.astype('float32')
images = np.multiply(images,1.0/255.0)
x_batch = images.reshape(1,image_size,image_size,num_channels)

sess = tf.Session()
saver = tf.train.import_meta_graph('./dogs-cats-model/dog-cat.ckpt-9975.meta')
saver.restore(sess,'./dogs-cats-model/dog-cat.ckpt-9975')
graph = tf.get_default_graph()

y_pred = graph.get_tensor_by_name("y_pred:0")

x = graph.get_tensor_by_name("x:0")
y_true = graph.get_tensor_by_name("y_true:0")
y_test_images = np.zeros((1,2))

feed_dict_testing = {x:x_batch,y_true:y_test_images}
result = sess.run(y_pred,feed_dict=feed_dict_testing)

res_label = ['dog','cat']
print(res_label[result.argmax()])

三.程序输出：

train.py

predict.py