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disposeData - count.py
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'''''
使用python解析二进制文件
'''
import numpy as np
import struct
def loadImageSet(filename):
binfile = open(filename, 'rb') # 读取二进制文件
buffers = binfile.read()
head = struct.unpack_from('>IIII', buffers, 0) # 取前4个整数，返回一个元组
offset = struct.calcsize('>IIII') # 定位到data开始的位置
imgNum = head[1]
width = head[2]
height = head[3]
bits = imgNum * width * height # data一共有60000*28*28个像素值
bitsString = '>' + str(bits) + 'B' # fmt格式：'>47040000B'
imgs = struct.unpack_from(bitsString, buffers, offset) # 取data数据，返回一个元组
binfile.close()
imgs = np.reshape(imgs, [imgNum, width * height]) # reshape为[60000,784]型数组
return imgs, head
def loadLabelSet(filename):
binfile = open(filename, 'rb') # 读二进制文件
buffers = binfile.read()
head = struct.unpack_from('>II', buffers, 0) # 取label文件前2个整形数
labelNum = head[1]
offset = struct.calcsize('>II') # 定位到label数据开始的位置
numString = '>' + str(labelNum) + "B" # fmt格式：'>60000B'
labels = struct.unpack_from(numString, buffers, offset) # 取label数据
binfile.close()
labels = np.reshape(labels, [labelNum]) # 转型为列表(一维数组)
return labels, head
def vectorized_result(j): #向量化
e = np.zeros((2, 1))
e[j] = 1.0
return e
if __name__ == "__main__":
file1 = 'E:/intelligence center/task_3/mnist data/train-images.idx3-ubyte'
file2 = 'E:/intelligence center/task_3/mnist data/train-labels.idx1-ubyte'
number = 60000
imgs1, data_head = loadImageSet(file1)
print('data_head:', data_head) #魔数（可以理解为文件id），图片数，图片纵向像素数，图片横向像素数
#print(type(imgs1))
print('imgs1_array:', imgs1)
#print(np.reshape(imgs[1, :], [28, 28])) # 取出其中一张图片的像素，转型为28*28，大致就能从图像上看出是几啦
#print(np.reshape(imgs[0], [28, 28])) #5
imgs = []
#for i in range(0, number):
#imgs.append(imgs1[i]/512)
#print('看看', np.reshape(imgs[0], [28, 28]))
print('----------我是分割线-----------')
labels, labels_head = loadLabelSet(file2)
print('labels_head:', labels_head)
print(type(labels))
print(labels[0]) #5
mark = [] #记录位置
label_list = []
count0 = 0
count1 = 0
for i in range(0, number):
#label_list.append(vectorized_result(labels[i]))
if labels[i] == 0 :
count0 +=1
imgs.append(np.reshape(imgs1[i] / 512, [28, 28]))
label_list.append(vectorized_result(labels[i]))
mark.append(i)
elif labels[i] == 1:
imgs.append(imgs1[i] / 512)
label_list.append(vectorized_result(labels[i]))
mark.append(i)
np.savez("img01_8000_train.npz",np.array(imgs[:8000])) #(8000,28,28)
np.savez("label01_8000_train.npz", np.array(label_list[:8000])) #(8000,2,1)
np.savez("img01_2000_test.npz", np.array(imgs[8000:10000])) #(2000,28,28)
np.savez("label01_2000_test.npz", np.array(label_list[8000:10000])) #(2000,2,1)
#print("0:",count0)
#print("1:", count1)
#print("location:", mark[0],mark[1],mark[2],mark[3],mark[4])
#print(np.reshape(imgs1[1], [28, 28])) #0
#print(np.reshape(imgs1[6], [28, 28])) #1
#print(label_list[6])