MNIST图片可视化(Win7+python3.5)

1. 将%caffe_root%\data\mnist\get_mnist.sh转换成linux格式，并用cygwin运行（事先装好wget）。自动下载并解压，得到t10k-images-idx3-ubyte，t10k-labels-idx1-ubyte，train-images-idx3-ubyte，train-labels-idx1-ubyte
2. 创建%caffe_root%\data\mnist\mnist_images\test及%caffe_root%\data\mnist\mnist_images\train文件夹，以保存转换后的图片
3. 编写如下python脚本，并运行。在对应文件夹下得到图片文件。

import numpy as npimport structfrom PIL import Imagecaffe_root = r"D:/caffe"caffe_mnist_data = caffe_root + r"/data/mnist/"test_MNIST_data = caffe_mnist_data + r"t10k-images-idx3-ubyte"train_MNIST_data = caffe_mnist_data + r"train-images-idx3-ubyte"test_MNIST_label = caffe_mnist_data + r"t10k-labels-idx1-ubyte"train_MNIST_label = caffe_mnist_data + r"train-labels-idx1-ubyte"### LABEL ###index = 0print("TEST LABEL DATA:" + test_MNIST_label)binfile = open(test_MNIST_label, 'rb')buf = binfile.read()magic, numImages = struct.unpack_from('>II', buf, index)print(magic, numImages)index += struct.calcsize('>II')label_test = []for i in range(numImages):    if i%1000 == 0:        print("test label:" + str(i))    (l,) = struct.unpack_from('>B', buf, index)    index += struct.calcsize('>B')    label_test.append(l)index = 0print("TRAIN LABEL DATA:" + train_MNIST_label)binfile = open(train_MNIST_label, 'rb')buf = binfile.read()magic, numImages = struct.unpack_from('>II', buf, index)print(magic, numImages)index += struct.calcsize('>II')label_train = []for i in range(numImages):    if i%1000 == 0:        print("test label:" + str(i))    (l,) = struct.unpack_from('>B', buf, index)    index += struct.calcsize('>B')    label_train.append(l)### DATA #### TESTindex = 0print("TEST IMAGE DATA:" + test_MNIST_data)binfile = open(test_MNIST_data, 'rb')buf = binfile.read()magic, numImages, numRows, numColumns = struct.unpack_from('>IIII', buf, index) #4 * Intprint(magic, numImages, numRows, numColumns)index += struct.calcsize('>IIII')FileDir = caffe_mnist_data + r"mnist_images/test/"print(FileDir)for i in range(numImages):    if i%1000 == 0:        print("test data:" + str(i))    im = struct.unpack_from('>784B', buf, index)    #28*28=784 Byte    index += struct.calcsize('>784B')    im = np.array(im)    im = im.reshape(28, 28)    picName = FileDir + r'\test_%d_label_%d.bmp'%(i,label_test[i])    image = Image.new("L", (28,28))    for a in range(28):        for b in range(28):            image.putpixel((a,b), int(im[b][a]))    image.save(picName, 'bmp')# TRAINindex = 0print("TRAIN IMAGE DATA:" + train_MNIST_data)binfile = open(train_MNIST_data, 'rb')buf = binfile.read()magic, numImages, numRows, numColumns = struct.unpack_from('>IIII', buf, index)print(magic, numImages, numRows, numColumns)index += struct.calcsize('>IIII')FileDir = caffe_mnist_data + r"mnist_images/train/"print(FileDir)ls = os.listdir(FileDir)if len(ls) >= numImages:    numImages = 0for i in range(numImages):    if i%1000 == 0:        print("train:" + str(i))    im = struct.unpack_from('>784B', buf, index)    index += struct.calcsize('>784B')    im = np.array(im)    im = im.reshape(28, 28)    picName = FileDir + r'\train_%d_label_%d.bmp'%(i, label_train[i])    image = Image.new("L", (28,28))    for a in range(28):        for b in range(28):            image.putpixel((a,b), int(im[b][a]))    image.save(picName, 'bmp')

最后生成文件总占用空间273M。