（转）Cifar-10数据集的可视化保存

https://blog.csdn.net/hyqwmxsh/article/details/82629961

  学习Tensorflow或深度学习，难免用到各种数据集， 最近用到cifar10数据集，简单研究了下，然后把cifar-10数据集保存为jpg图片，
分别利用python和c++做了实现。

   CIFAR-10下载

关于cifar-10，网上介绍很多，这里主要用了python和binary版本：

python版 

    每个batch包含一个字典，该字典有data和labels两个key。其中，data是1000*3072（ 3 *32 *32）的图像数据。1000即图片数量，前1024个数据是red通道像素值，然后1024是个green通道像素值，最后啥blue通道。labels是1000个0~9表示数据类别的数据。

代码如下：

import numpy as np
from PIL import Image
import pickle
import os
CHANNEL = 3
WIDTH = 32
HEIGHT = 32
data = []
labels=[]
classification = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
for i in range(5):
with open("data/cifar-10-batches-py/data_batch_"+ str(i+1),mode='rb') as file:
data_dict = pickle.load(file, encoding='bytes')
data+= list(data_dict[b'data'])
labels+= list(data_dict[b'labels'])
img =  np.reshape(data,[-1,CHANNEL, WIDTH, HEIGHT])
data_path = "data/images/"
if not os.path.exists(data_path):
os.makedirs(data_path)
for i in range(img.shape[0]):
r = img[i][0]
g = img[i][1]
b = img[i][2]
ir = Image.fromarray(r)
ig = Image.fromarray(g)
ib = Image.fromarray(b)
rgb = Image.merge("RGB", (ir, ig, ib))
name = "img-" + str(i) +"-"+ classification[labels[i]]+ ".png"
rgb.save(data_path + name, "PNG")

结果截图：

C++版 

每个batch包括10000*（1 + 3072）大小数据，1代表label大小，3072是图像数据。存储方式同上。

代码如下：

#include<iostream>
#include<opencv2/opencv.hpp>
using namespace std;
using namespace cv;
#define WIDTH 32
#define HEIGHT 32
#define CHANNEL 3
#define PERNUM 1000
#define CLASS 10
char classification[CLASS][256] = { "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" };
int main(){
FILE *pBatch = fopen("data_batch_1.bin","rb");
if (!pBatch)
return -1;
unsigned char buf[CHANNEL * WIDTH * HEIGHT + 1];
memset(buf,0,sizeof(buf));
Mat bgr;
bgr.create(WIDTH,HEIGHT,CV_8UC3);
int index = 0;
while (!feof(pBatch)){
fread(buf, 1, CHANNEL * WIDTH * HEIGHT + 1, pBatch);
unsigned char* pBuf = buf + 1;
for (int i = 0; i < bgr.rows;i++){
Vec3b *pbgr = bgr.ptr<Vec3b>(i);
for (int j = 0; j < bgr.cols;j++){
//pBuf += (i * bgr.rows + j * bgr.cols);
for (int c = 0; c < 3;c++){
pbgr[j][c] = pBuf[(2 - c)* bgr.rows * bgr.cols + i * bgr.rows + j ];
}
}
}
imwrite("image/img" + to_string(index)+".jpg",bgr);
index++;
}
fclose(pBatch);
return 0;
}

结果截图：

 

 

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