基于h5py的使用及数据封装代码

Python  /  管理员 发布于 5年前   263

1. h5py简单介绍

h5py文件是存放两类对象的容器，数据集(dataset)和组(group)，dataset类似数组类的数据集合，和numpy的数组差不多。group是像文件夹一样的容器，它好比python中的字典，有键(key)和值(value)。group中可以存放dataset或者其他的group。”键”就是组成员的名称，”值”就是组成员对象本身(组或者数据集)，下面来看下如何创建组和数据集。

1.1 创建一个h5py文件

import h5py#要是读取文件的话，就把w换成rf=h5py.File("myh5py.hdf5","w")

在当前目录下会生成一个myh5py.hdf5文件。

2. 创建dataset数据集

import h5pyf=h5py.File("myh5py.hdf5","w")#deset1是数据集的name，（20,）代表数据集的shape，i代表的是数据集的元素类型d1=f.create_dataset("dset1", (20,), 'i')for key in f.keys(): print(key) print(f[key].name) print(f[key].shape) print(f[key].value)

输出：

dset1/dset1(20,)[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]import h5pyimport numpy as npf=h5py.File("myh5py.hdf5","w")a=np.arange(20)d1=f.create_dataset("dset1",data=a)for key in f.keys(): print(f[key].name) print(f[key].value)

输出：

/dset1[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]2. hpf5用于封装训练集和测试集#============================================================# This prepare the hdf5 datasets of the DRIVE database#============================================================ import osimport h5pyimport numpy as npfrom PIL import Image def write_hdf5(arr,outfile): with h5py.File(outfile,"w") as f: f.create_dataset("image", data=arr, dtype=arr.dtype) #------------Path of the images --------------------------------------------------------------#trainoriginal_imgs_train = "./DRIVE/training/images/"groundTruth_imgs_train = "./DRIVE/training/1st_manual/"borderMasks_imgs_train = "./DRIVE/training/mask/"#testoriginal_imgs_test = "./DRIVE/test/images/"groundTruth_imgs_test = "./DRIVE/test/1st_manual/"borderMasks_imgs_test = "./DRIVE/test/mask/"#--------------------------------------------------------------------------------------------- Nimgs = 20channels = 3height = 584width = 565dataset_path = "./DRIVE_datasets_training_testing/" def get_datasets(imgs_dir,groundTruth_dir,borderMasks_dir,train_test="null"): imgs = np.empty((Nimgs,height,width,channels)) groundTruth = np.empty((Nimgs,height,width)) border_masks = np.empty((Nimgs,height,width)) for path, subdirs, files in os.walk(imgs_dir): #list all files, directories in the path  for i in range(len(files)):   #original   print "original image: " +files[i]   img = Image.open(imgs_dir+files[i])   imgs[i] = np.asarray(img)   #corresponding ground truth   groundTruth_name = files[i][0:2] + "_manual1.gif"   print "ground truth name: " + groundTruth_name   g_truth = Image.open(groundTruth_dir + groundTruth_name)   groundTruth[i] = np.asarray(g_truth)   #corresponding border masks   border_masks_name = ""   if train_test=="train":    border_masks_name = files[i][0:2] + "_training_mask.gif"   elif train_test=="test":    border_masks_name = files[i][0:2] + "_test_mask.gif"   else:    print "specify if train or test!!"    exit()   print "border masks name: " + border_masks_name   b_mask = Image.open(borderMasks_dir + border_masks_name)   border_masks[i] = np.asarray(b_mask)  print "imgs max: " +str(np.max(imgs)) print "imgs min: " +str(np.min(imgs)) assert(np.max(groundTruth)==255 and np.max(border_masks)==255) assert(np.min(groundTruth)==0 and np.min(border_masks)==0) print "ground truth and border masks are correctly withih pixel value range 0-255 (black-white)" #reshaping for my standard tensors imgs = np.transpose(imgs,(0,3,1,2)) assert(imgs.shape == (Nimgs,channels,height,width)) groundTruth = np.reshape(groundTruth,(Nimgs,1,height,width)) border_masks = np.reshape(border_masks,(Nimgs,1,height,width)) assert(groundTruth.shape == (Nimgs,1,height,width)) assert(border_masks.shape == (Nimgs,1,height,width)) return imgs, groundTruth, border_masks if not os.path.exists(dataset_path): os.makedirs(dataset_path)#getting the training datasetsimgs_train, groundTruth_train, border_masks_train = get_datasets(original_imgs_train,groundTruth_imgs_train,borderMasks_imgs_train,"train")print "saving train datasets"write_hdf5(imgs_train, dataset_path + "DRIVE_dataset_imgs_train.hdf5")write_hdf5(groundTruth_train, dataset_path + "DRIVE_dataset_groundTruth_train.hdf5")write_hdf5(border_masks_train,dataset_path + "DRIVE_dataset_borderMasks_train.hdf5") #getting the testing datasetsimgs_test, groundTruth_test, border_masks_test = get_datasets(original_imgs_test,groundTruth_imgs_test,borderMasks_imgs_test,"test")print "saving test datasets"write_hdf5(imgs_test,dataset_path + "DRIVE_dataset_imgs_test.hdf5")write_hdf5(groundTruth_test, dataset_path + "DRIVE_dataset_groundTruth_test.hdf5")write_hdf5(border_masks_test,dataset_path + "DRIVE_dataset_borderMasks_test.hdf5")

遍历文件夹下的所有文件 os.walk( dir )

for parent, dir_names, file_names in os.walk(parent_dir):  for i in file_names:   print file_name 

parent: 父路径

dir_names: 子文件夹

file_names: 文件名

以上这篇基于h5py的使用及数据封装代码就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。