currentString = 'SEN'+str(i)+'_'+str(currentSNR)+'_'+noiseType
allStrings.append(currentString)
if (i == 10):
SNRIndex = 1
print("LOADING DATA")
resultsHIT = list()
resultsFA = list()
resultsHIT_FA = list()
#GENERATE FILE OUTPUTS
for j in allStrings:
inputTest = io.loadmat(imagesFolder + j + '.mat')
inputTest = np.array(inputTest['AIMImages'])
inputTest = np.swapaxes(inputTest,2,1)
inputTest = np.swapaxes(inputTest,0,1)
inputTest = inputTest.astype("float32")
inputTest = np.expand_dims(inputTest,1)
target = io.loadmat(targetsFolder + j + '.mat')
target = np.transpose(target['target'])
prediction = model.predict(inputTest)
HIT,FA,HIT_FA = computeHIT_FA(target,prediction,threshold)
resultsHIT.append(HIT)
resultsFA.append(FA)
resultsHIT_FA.append(HIT_FA)
resultsHIT = np.mean(resultsHIT)
resultsFA = np.mean(resultsFA)
resultsHIT_FA = np.mean(resultsHIT_FA)
print('HIT : ' + str(resultsHIT))
print('FA : ' + str(resultsFA))
print('HIT_FA : ' + str(resultsHIT_FA))
SNRs = [-5,-2]
threshold = 0.5
filesForTesting = list()
SNRs = [-5,-2]
for i in SNRs:
for x in range(1,11):
filesForTesting.append('Testing' + str(x) + 'SNR' + str(i))
resultsHIT = list()
resultsFA = list()
resultsHIT_FA = list()
#GENERATE FILE OUTPUTS
for j in filesForTesting:
fileToRead = sio.loadmat(readFolder + j + '.mat')
inputTest = np.transpose(fileToRead.get('input_tst_data'))
prediction = model.predict(inputTest)
trueMask = np.transpose(fileToRead.get('target_tst_data'))
HIT,FA,HIT_FA = computeHIT_FA(trueMask,prediction,threshold)
resultsHIT.append(HIT)
resultsFA.append(FA)
resultsHIT_FA.append(HIT_FA)
resultsHIT = np.mean(resultsHIT)
resultsFA = np.mean(resultsFA)
resultsHIT_FA = np.mean(resultsHIT_FA)
print("OVERALL HIT : " + np.array_str(resultsHIT))
print("OVERALL FA : " + np.array_str(resultsFA))
print("OVERALL HIT_FA : " + np.array_str(resultsHIT_FA))
import numpy as np
from usefulMethodsSE import computeHIT_FA
import scipy.io as sio
loadingFile = "./TestHIT_FAMethod/sen1.mat"
loadingFile = sio.loadmat(loadingFile)
estimation = np.transpose(loadingFile.get("estimation"))
trueMask = np.transpose(loadingFile.get("realTarget"))
HIT_FA = computeHIT_FA(trueMask, estimation, 0.5)
print "HIT-FA: {}".format(HIT_FA)
