生成h5文件_h5实现文件下载

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生成训练h5文件

import h5py
import os
import cv2
import math
import numpy as np
import random
import 

root_path = "/home/tyd/caffe_case/HDF5/image"

with open("/home/tyd/caffe_case/HDF5/hdf5.txt","r") as f:
    lines = f.readlines()

num = len(lines)
random.shuffle(lines)

imgAccu = 0
imgs = np.zeros([num,3,224,224])
labels = np.zeros([num,10])
for i in range(num):
    line=lines[i]
    segments=re.split('\s+',line)[:-1]
    print segments[0]
    img = cv2.imread(os.path.join(root_path,segments[0]))
    img = cv2.resize(img,(224,224))
    img = img.transpose(2,0,1)
    imgs[i,:,:,:]=img.astype(np.float32)
    for j in range(10):
        lables[i,j]=float(segments[j+1])*224/256
batchSize = 1
batchNum = int(math.ceil(1.0*num/batchSize))

imgsMean = np.mean(imgs,axis=0)
imgs = (imgs -imgsMean)/255.0
labelsMean = np.mean(labels,axis=0)
labels = (labels-labelsMean)/10

if os.path.exists('trainlist.txt'):
    os.remove('trainlist.txt')
    
if os.path.exists('testlist.txt'):
    os.remove('testlist.txt')
comp_kwargs={'compression':'gzip','compression_opts':1}
for i in range(batchNum):
    start = i*batchSize
    end=min((i+1)*batchSize,num)
    if i<batchNum-1:
        filename='/home/tyd/caffe_case/HDF5/h5/train{0}.h5'.format(i)
    else:
        filename='/home/tyd/caffe_case/HDF5/h5/test{0}.h5'.format(i-batchNum+1)
    print filename
    with h5py.File(filename,'w') as f:
        f.create_dataset('data',data=np.array((imgs[start:end]-imgsMean)/255.0).astype(np.float32),**comp_kwargs)
        f.create_dataset('label',data=np.array(labels[start:end]).astype(np.float32),**comp_kwargs)
        
    if i < batchNum-1:
        with open('/home/tyd/caffe_case/HDF5/h5/trainlist.txt','a') as f:
            f.write(os.path.join(os.getcwd(),'train{0}.h5'.format(i)+'\n')
    else:
        with open('/home/tyd/caffe_case/HDF5/h5/testlist.txt','a') as f:
            f.write(os.path.join(os.getcwd(),'train{0}.h5'.format(i-batchNum+1)+'\n')
imgsMean = np.mean(imgsMean,axis=(1,2))
with open('mean.txt','w') as f:
    f.write(str(imgsMean[0])+'\n'+str(imgsMean[2]))