def main(trainset, window, nfeatures, image, savetodir):
output = savetodir+"temp.xpl"
#building the xpl file
ensemble.build_xpl(trainset,window,output)
#reading the "just" created xpl file
result = xplutil.read_xpl(output)
XPL_data = result.data
w0 = result.freq0.copy()
w1 = result.freq1.copy()
#normalizing the table frequency values
w0,w1 = cl.normalize_table(w0, w1)
#calculating features and indexes
indices, feature_list, _ = ft.cmim(XPL_data, w0, w1, nfeatures)
#saving window file
tw.to_window_file(indices, result.winshape, savetodir+"window.win")
#applying the feature selection algorithm
w0, w1, updated_decision, cls_error = cl.apply_feature_selection(XPL_data, indices, w0, w1)
#calculating the unique array and their indexes
indices = np.sort(indices)
unique_array, unique_index = cl._apply_projection(XPL_data, indices)
#writing a mimterm file to disk
xplutil.write_minterm_file(savetodir+"mintermfile.mtm",indices, result.winshape, unique_array,updated_decision[unique_index])
#building the operator based on the mintermfile
ensemble.build_operator(savetodir+"window.win", savetodir+"mintermfile.mtm", savetodir+"operator")
#building a new XPL according to the learned window
output = savetodir+"Learned.xpl"
ensemble.build_xpl(trainset,savetodir+"window.win",output)
result_img = savetodir+"Image_applied"
#applying the operator on the image
ensemble.apply_operator(savetodir+"operator", image, result_img)
def main(level1trainset,level2trainset, testset, window, nfeatures, niterations, savetodir):
output = savetodir+"temp.xpl"
#building a XPL to start processing
ensemble.build_xpl(level1trainset,window,output)
XPL_filepath = output
#now let's read the XPL
XPL_data = xplutil.read_xpl(XPL_filepath)
#writing the minimal empirical error to file
ensemble.write_min_empirical_error(savetodir+"min_emp_err.txt",ensemble.min_empirical_error(XPL_data))
#training the ensemble
ensemble.train(XPL_data, nfeatures, niterations, savetodir)
#building first level operators
print
print "... Building first level operators ...\n"
ensemble.build_operators(savetodir , niterations)
#creating the operators combinations
for i in range(1,niterations):
print
print "...Building operators combination : 0 to %s ... \n" %str(i)
ensemble.build_operator_combination(level2trainset, np.array(range(i+1)), savetodir, savetodir+"twoLevel_0_to_"+str(i))
#combining all the operators
#ensemble.build_operator_combination(trainset, np.array(range(niterations)), savetodir, savetodir+"twoLevel")
#Writing MAE from training set to file.
set = imageset.Imageset()
trainimgset = set.read(level1trainset)
mae_t = ensemble.mae(trainimgset)
file = open(savetodir+"MAE_TrainingSet.txt", "w")
file.write(str(mae_t))
file.close()
#Writing MAE from test set to file.
set = imageset.Imageset()
testimgset = set.read(testset)
mae_test = ensemble.mae(testimgset)
file = open(savetodir+"MAE_TestSet.txt", "w")
file.write(str(mae_test))
file.close()
img_list = _get_imgList(testimgset)
#applying the first level operators on the test set images
for i in range(niterations):
for j in img_list:
ensemble.apply_operator(savetodir+"mtm"+str(i)+"-op", j, savetodir+"mtm"+str(i)+"-op-files/"+j.split("/")[-1][:-3]+"proc.pnm")
ensemble.trios_test(savetodir+"mtm"+str(i)+"-op", testset, savetodir+"mtm"+str(i)+"-op-files/MAE.txt")
#applying the second level operators combinations on the test set images
for i in range(1,niterations):
for j in img_list:
ensemble.apply_operator(savetodir+"twoLevel_0_to_"+str(i),j, savetodir+"twoLevel_0_to_"+str(i)+"-files/level1/operator0/"+j.split("/")[-1][:-3]+"proc.pnm")
ensemble.trios_test(savetodir+"twoLevel_0_to_"+str(i), testset, savetodir+"twoLevel_0_to_"+str(i)+"-files/level1/operator0/MAE.txt")
def main(numwindows, npixels, winshape, level1trainset, level2trainset, testset, savetodir):
winshape = winshape.split("x")
height = int(winshape[0])
width = int(winshape[1])
make_windows(savetodir, (height, width), npixels, numwindows)
# this is where i run the single level operators training
for i in range(numwindows):
print
print "...Building operator %s ...\n" % str(i)
build_operator(savetodir + "window_" + str(i) + ".win", level1trainset, savetodir + "window_" + str(i) + "_op")
# this is where i combine the single level operators to create a second level one
for i in range(1, numwindows):
print
print "...Building operators combination : 0 to %s ... \n" % str(i)
build_operators_combination(
level2trainset, np.array(range(i + 1)), savetodir, savetodir + "twoLevel_0_to_" + str(i)
)
triostestset = imageset.Imageset()
testimgset = triostestset.read(testset)
img_list = _get_imgList(testimgset)
# applying the first level operators on the test set images
for i in range(numwindows):
for j in img_list:
print
print "...Applying operator %s on image : %s ...\n" % (str(i), j)
ensemble.apply_operator(
savetodir + "window_" + str(i) + "_op",
j,
savetodir + "window_" + str(i) + "_op-files/" + j.split("/")[-1][:-3] + "proc.pnm",
)
ensemble.trios_test(
savetodir + "window_" + str(i) + "_op", testset, savetodir + "window_" + str(i) + "_op-files/MAE.txt"
)
# applying the second level operators combinations on the test set images
for i in range(1, int(numwindows)):
for j in img_list:
ensemble.apply_operator(
savetodir + "twoLevel_0_to_" + str(i),
j,
savetodir + "twoLevel_0_to_" + str(i) + "-files/level1/operator0/" + j.split("/")[-1][:-3] + "proc.pnm",
)
# running trios_test tool to calculate MAE for current operator combination
ensemble.trios_test(
savetodir + "twoLevel_0_to_" + str(i),
testset,
savetodir + "twoLevel_0_to_" + str(i) + "-files/level1/operator0/MAE.txt",
)
return 0
def main(trainset, window, image, savetodir):
output = savetodir+"temp.xpl"
fname = savetodir+"operator"
#building the xpl file
ensemble.build_xpl(trainset,window,output)
#writing minterm file to disk
ensemble.trios_build_mtm(window, trainset, savetodir+"mintermfile.mtm")
#building an operator with trios_build tool
ensemble.trios_build(window, trainset, fname)
result_img = savetodir+"Image_applied"
#applying the operator on the image
ensemble.apply_operator(savetodir+"operator", image, result_img)
