python制作图片数据集 h5py_基于h5py的使用及数据封装代码

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r

f=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 h5py

f=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 h5py

import numpy as np

f=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 os

import h5py

import numpy as np

from 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 --------------------------------------------------------------

#train

original_imgs_train = "./DRIVE/training/images/"

groundTruth_imgs_train = "./DRIVE/training/1st_manual/"

borderMasks_imgs_train = "./DRIVE/training/mask/"

#test

original_imgs_test = "./DRIVE/test/images/"

groundTruth_imgs_test = "./DRIVE/test/1st_manual/"

borderMasks_imgs_test = "./DRIVE/test/mask/"

#---------------------------------------------------------------------------------------------

Nimgs = 20

channels = 3

height = 584

width = 565

dataset_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 datasets

imgs_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 datasets

imgs_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

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