test.py

import numpy as np
import tensorflow as tf
import os
import scipy.io
import sys
from scipy.spatial.distance import cdist

try:
    import cPickle
except:
    import pickle as cPickle

'''
字典形式的数据：
cifar100 data content: 
    { 
    "data" : [(R,G,B, R,G,B ,....),(R,G,B, R,G,B, ...),...]    # 50000张图片，每张： 32 * 32 * 3
    "coarse_labels":[0,...,19],                         # 0~19 super category 
    "filenames":["volcano_s_000012.png",...],   # 文件名
    "batch_label":"", 
    "fine_labels":[0,1...99]          # 0~99 category 
    } 
'''

def unpickle(Cifar_file):
    file = open(Cifar_file, 'rb')
    dict = cPickle.load(file,encoding='bytes')
    file.close()
    return dict


def load_data(Cifar_train_file, Cifar_test_file, nb_per_cl_val=0):
    xs = []
    ys = []
    for j in range(1):
        d = unpickle(Cifar_train_file)
        x = d[b'data']  # 训练数据。
        y = d[b'fine_labels']  # 标签
        xs.append(x)
        ys.append(y)

    d = unpickle(Cifar_test_file)
    xs.append(d[b'data'])
    ys.append(d[b'fine_labels'])

    # 求解图像均值
    x = np.concatenate(xs) / np.float32(255)  # 将xs连接起来
    y = np.concatenate(ys)

    x = np.dstack((x[:, :1024], x[:, 1024:2048], x[:, 2048:])) # 原数据1*1024*3（RGB）
    x = x.reshape((x.shape[0], 32, 32, 3))  # 后三个位置的坐标转换 变成 5000*32*32*3

    # 减去每个像素的均值
    pixel_mean = np.mean(x[0:5000], axis=0)
    x -= pixel_mean

    # 创建训练集
    train_sample_cl = 500 - nb_per_cl_val
    X_train = np.zeros((train_sample_cl * 100, 32, 32, 3))
    Y_train = np.zeros(train_sample_cl * 100)
    X_vaild = np.zeros((nb_per_cl_val * 100, 32, 32, 3))
    Y_vaild = np.zeros(nb_per_cl_val * 100)
    for i in range(100):  # 一次性将所有样本都分配好了
        index_y = np.where(y[0:50000] == i)
        np.random.shuffle(index_y)
        X_train[i * train_sample_cl:(i + 1) * train_sample_cl] = x[index_y[0:train_sample_cl], :, :, :]
        Y_train[i * train_sample_cl:(i + 1) * train_sample_cl] = y[index_y[0:train_sample_cl]]
        #X_vaild[i * nb_per_cl_val:(i + 1) * nb_per_cl_val] = x[index_y[train_sample_cl:500], :, :, :]
        #Y_vaild[i * nb_per_cl_val:(i + 1) * nb_per_cl_val] = y[index_y[train_sample_cl:500]]

    X_test = x[50000:, :, :, :]
    Y_test = y[50000:]

    # 这里数据都有了将数据转化为tensor
    # 需要将数据转化为tensorflow的数据 还要考虑batch_size。

    train_images = tf.convert_to_tensor(X_train, dtype=tf.float32)
    train_labels = tf.convert_to_tensor(Y_train, dtype=tf.int32)
    test_images = tf.convert_to_tensor(X_test, dtype=tf.float32)
    test_labels= tf.convert_to_tensor(Y_test, dtype=tf.int32)
    train_images = tf.image.random_flip_left_right(train_images)

    print('win')
    return train_images, train_labels,test_images,test_labels

if __name__=="__main__" :
    train_dir='/media/magic/Ubuntu 16.0/IMDP/code_TF/SVM_net/SVM_cifar/cifar-100-python/train'
    test_dir='/media/magic/Ubuntu 16.0/IMDP/code_TF/SVM_net/SVM_cifar/cifar-100-python/test'
    load_data(train_dir,test_dir)