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(trainset, window, save_todir):
XPL = save_todir+'file.xpl'
#building xpl file
ensemble.build_xpl(trainset,window,XPL)
#reading the xpl file
xpl_data = xplutil.read_xpl(XPL)
decision = cl.make_decision(xpl_data.freq0,xpl_data.freq1)
size = xpl_data.winshape[0]*xpl_data.winshape[1]
xplutil.write_minterm_file(save_todir+'filename.mtm', np.array([range(size)]), xpl_data.winshape, xpl_data.data, decision)
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(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)
def main(trainset, window, save_todir):
XPL = window+'.xpl'
#building xpl file
ensemble.build_xpl(trainset,window,XPL)
#reading xpl file
xpl_data = xplutil.read_xpl(XPL)
indices = np.array([0,1,3,4,5,7,8])
w0 = xpl_data.freq0.copy()
w1 = xpl_data.freq1.copy()
w0, w1 = cl.normalize_table(w0, w1)
hash, unique_array = project(xpl_data.data, indices)
sum0 = []
sum1 = []
for row in unique_array:
arr = hash.get(tuple(row.reshape(1,-1)[0]))
indexes = tuple(arr[0].reshape(1,-1)[0])
sum0.append(w0[[np.array(indexes)]].sum())
sum1.append(w1[[np.array(indexes)]].sum())
decision = cl.make_decision(sum0, sum1)
xplutil.write_minterm_file(save_todir+"mtmFile.mtm",indices, xpl_data.winshape, unique_array,decision)
