import DeePore as dp
# Explore the dataset:
# If you want to open images of dataset and visualize them
# 1. check or download the complete dataset
Data_complete='Data\DeePore_Dataset.h5'
# Data_complete='..\..\..\BigData\DeePore\DeePore_Dataset.h5'
dp.check_get('https://zenodo.org/record/4297035/files/DeePore_Dataset.h5?download=1',Data_complete)
# 2. read the first image out of 17700
A=dp.readh5slice(Data_complete,'X',[0])
# 3. show mid-slices of the loaded image
dp.showentry(A)
# 4. show and save the properties of this image which assumed to be the ground truth as text file
props=dp.readh5slice(Data_complete,'Y',[0])
dp.prettyresult(props,'sample_gt.txt',units='px')
import DeePore as dp
# Comparing statistics of the training, validation and testing data:
# 1. check or download the compact data
Data_compact = 'Data\DeePore_Compact_Data.h5'
# Data_compact='..\..\..\BigData\DeePore\DeePore_Compact_Data.h5'
dp.check_get(
'https://zenodo.org/record/4297035/files/DeePore_Compact_Data.h5?download=1',
Data_compact)
# 2. prepare the dataset by removing outliers and creating list of training, validation and test samples
List = dp.prep(Data_compact)
TrainList, EvalList, TestList = dp.splitdata(List)
# 3. read datasets 'Y' into arrays
Data_Eval = dp.readh5slice(Data_compact, 'Y', EvalList)
Data_Train = dp.readh5slice(Data_compact, 'Y', TrainList)
Data_Test = dp.readh5slice(Data_compact, 'Y', TestList)
# exporting to MATLAB for extra postprocessing if you needed
# import scipy.io as sio
# sio.savemat('All_Data.mat',{'train':Data_Train,'eval':Data_Eval,'test':Data_Test})
# 4. plot histograms
import matplotlib.pyplot as plt
FN = 5 # feature id number, you can select 0 to 14
h = plt.hist(Data_Eval[:, FN, 0],
50,
histtype='step',
density=True,
label='validation')
h = plt.hist(Data_Train[:, FN, 0],
50,