sits_func.py

#!/usr/bin/python


"""
	Reading train and test files.
	Pre-processing steps.
"""


import os, sys
import argparse

import numpy as np
import pandas as pd
import math
import random
import itertools
import time

#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
#-----------------			PREPROCESSING			--------------------
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------

#-----------------------------------------------------------------------
def readSITSData(name_file):
	"""
		Read the data contained in name_file
		INPUT:
			- name_file: file where to read the data
		OUTPUT:
			- X: variable vectors for each example
			- polygon_ids: id polygon (use e.g. for validation set)
			- Y: label for each example
	"""
	
	data = pd.read_table(name_file, sep=',', header=0) #-- one header
	
	y_data = data.iloc[:,0]
	y = np.asarray(y_data.values, dtype='uint8')
	
	polygonID_data = data.iloc[:,1]
	polygon_ids = polygonID_data.values
	polygon_ids = np.asarray(polygon_ids, dtype='uint16')
	
	X_data = data.iloc[:,2:]
	X = X_data.values
	X = np.asarray(X, dtype='float32')
	
	return  X, polygon_ids, y

#-----------------------------------------------------------------------
def reshape_data(X, nchannels):
	"""
		Reshaping (feature format (3 bands): d1.b1 d1.b2 d1.b3 d2.b1 d2.b2 d2.b3 ...)
		INPUT:
			-X: original feature vector ()
			-feature_strategy: used features (options: SB, NDVI, SB3feat)
			-nchannels: number of channels
		OUTPUT:
			-new_X: data in the good format for Keras models
	"""
	
	return X.reshape(X.shape[0],int(X.shape[1]/nchannels),nchannels)

#-----------------------------------------------------------------------
def computingMinMax(X, per=2):
	min_per = np.percentile(X, per, axis=(0,1))
	max_per = np.percentile(X, 100-per, axis=(0,1))
	return min_per, max_per

#-----------------------------------------------------------------------
def normalizingData(X, min_per, max_per):
	return (X-min_per)/(max_per-min_per)

#EOF