用自己的图片构建cifar10 binary格式的数据

标签（空格分隔）： 未分类

摘要

• 本文主要讨论用python构建cifar10 binary version数据
• 环境：windows， python3.6
• 本文最后更新日期是2018.04.13，因为版本差异造成的问题请见谅

正文

tensorflow教程中的卷积神经网络一节，利用cifar10数据做分类，效果还不错，那么我们能不能用这个模型去训练做其他的图片分类呢？
tensorflow教程的卷积神经网络教程中文网址
tensorflow教程的卷积神经网络教程英文网址
答案当然是可以，但是教程给的模型是利用cifar10 binary格式的，所以需要我们事先把我们的图片数据转成cifar10 binary格式。
教程给的模型代码
cifar10 dataset网址
cifar10 binary version如下：

cifar10 binary version.png

# -*- coding: UTF-8 -*-

import cv2
import os
import numpy as np

DATA_LEN = 3072
CHANNEL_LEN = 1024
SHAPE = 32


def imread(im_path, shape=None, color="RGB", mode=cv2.IMREAD_UNCHANGED):
    im = cv2.imread(im_path, cv2.IMREAD_UNCHANGED)
    if color == "RGB":
        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    if shape != None:
        assert isinstance(shape, int)
        im = cv2.resize(im, (shape, shape))
    return im


def read_data(filename, data_path, shape=None, color='RGB'):
    """
       filename (str): a file
         data file is stored in such format:
           image_name  label
       data_path (str): image data folder
       return (numpy): a array of image and a array of label
    """
    if os.path.isdir(filename):
        print("Can't found data file!")
    else:
        f = open(filename)
        lines = f.read().splitlines()
        count = len(lines)
        data = np.zeros((count, DATA_LEN), dtype=np.uint8)
        # label = np.zeros(count, dtype=np.uint8)
        lst = [ln.split(' ')[0] for ln in lines]
        label = [int(ln.split(' ')[1]) for ln in lines]

        idx = 0
        s, c = SHAPE, CHANNEL_LEN
        for ln in lines:
            fname, lab = ln.split(' ')
            im = imread(os.path.join(data_path, fname), shape=s, color='RGB')
            '''
            im = cv2.imread(os.path.join(data_path, fname), cv2.IMREAD_UNCHANGED)
            im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
            im = cv2.resize(im, (s, s))
            '''
            data[idx, :c] = np.reshape(im[:, :, 0], c)
            data[idx, c:2 * c] = np.reshape(im[:, :, 1], c)
            data[idx, 2 * c:] = np.reshape(im[:, :, 2], c)
            label[idx] = int(lab)
            idx = idx + 1

        return data, label, lst


def py2bin(data, label):
    label_arr = np.array(label).reshape(len(label), 1)
    label_uint8 = label_arr.astype(np.uint8)
    arr = np.hstack((label_uint8, data))

    with open('./bin/train_batch', 'wb') as f:
        for element in arr.flat:
            f.write(element)


def imagelist():
    directory_normal = r"data/normal"   #原始图片位置，32*32 pixel
    file_train_list = r"data/image_train_list.txt"  #构建imagelist输出位置
    with open(file_train_list, "a") as f:
        for filename in os.listdir(directory_normal):
            f.write(filename + " " + "0" + "\n")    #这里分类默认全为0


if __name__ == '__main__':
    data_path = './data/normal'
    file_list = './data/image_train_list.txt'
    save_path = './bin'
    imagelist() #构建imagelist
    data, label, lst = read_data(file_list, data_path, shape=32)    #将图片像素数据转成矩阵和标签列表
    py2bin(data, label) #将像素矩阵和标签列表转成cifar10 binary version

参考资料

[1]tensorflow教程的卷积神经网络教程

[2]cifar10 dataset

[3]制作自己的python版本的类CIFAR10数据集