dataset_split.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jul 12 16:34:16 2018

@author: yy
"""

import random
from sklearn.cross_validation import train_test_split
from keras import backend as K

from dataset_load import load_dataset_cnn

image_size = (128, 32)

class Dataset:
    def __init__(self, path_name):
        #训练集
        self.train_images = None
        self.train_labels = None
        
        #验证集
        self.valid_images = None
        self.valid_labels = None
        
        #测试集
        self.test_images  = None            
        self.test_labels  = None
        
        #数据集加载路径
        self.path_name    = path_name
        
        #当前库采用的维度顺序
        self.input_shape = None
        
    #加载数据集并按照交叉验证的原则划分数据集并进行相关预处理工作
    def load(self, img_rows = image_size[1], img_cols = image_size[0], 
             img_channels = 1):
        #加载数据集到内存
        images, labels = load_dataset_cnn(self.path_name)        
        
        train_images, valid_images, train_labels, valid_labels = train_test_split(images, labels, test_size = 0.3, random_state = random.randint(0, 100))        
        _, test_images, _, test_labels = train_test_split(images, labels, test_size = 0.5, random_state = random.randint(0, 100))                
        
        #当前的维度顺序如果为'th'，则输入图片数据时的顺序为：channels,rows,cols，否则:rows,cols,channels
        #这部分代码就是根据keras库要求的维度顺序重组训练数据集
        if K.image_dim_ordering() == 'th':
            train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols)
            valid_images = valid_images.reshape(valid_images.shape[0], img_channels, img_rows, img_cols)
            test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols)
            self.input_shape = (img_channels, img_rows, img_cols)            
        else:
            train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels)
            valid_images = valid_images.reshape(valid_images.shape[0], img_rows, img_cols, img_channels)
            test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels)
            self.input_shape = (img_rows, img_cols, img_channels)            
            
            #输出训练集、验证集、测试集的数量
            print(train_images.shape[0], 'train samples')
            print(valid_images.shape[0], 'valid samples')
            print(test_images.shape[0], 'test samples')
        
            
            #像素数据浮点化以便归一化
            train_images = train_images.astype('float32')            
            valid_images = valid_images.astype('float32')
            test_images = test_images.astype('float32')
            
            #将其归一化,图像的各像素值归一化到0~1区间
            train_images /= 255
            valid_images /= 255
            test_images /= 255            
        
            self.train_images = train_images
            self.valid_images = valid_images
            self.test_images  = test_images
            self.train_labels = train_labels
            self.valid_labels = valid_labels
            self.test_labels  = test_labels
            

    
    
if __name__ == '__main__':
    dataset = Dataset('../img_data/all/')    
    dataset.load()

    '''
    #评估模型
    model = Model()
    model.load_model(file_path = './model/face_model')
    model.evaluate(dataset)
    '''