def retrieval():
global feature_extraction_method
global distance
global number_of_images
global parameters_frame
number_of_images = np.int16(input_number_of_images.get())
feature_extraction_method = feature_extraction_method_name(var1.get())
distance = distance_name(var2.get())
#ajeitar para pegar esse parametro da interface
#path_cnn_trained = '/Users/flavio/Dropbox/Compartilhadas/Romuere/CBIR/inception-2015-12-05/classify_image_graph_def.pb'
#print(path_database,path_folder_retrieval,path_image,extension_classes,feature_extraction_method,distance,number_of_images,list_of_parameters)
if( (feature_extraction_method == 'cnn' or feature_extraction_method == 'cnn_probability') and var3.get() == 0 ):
feature_extraction_method = feature_extraction_method + '_training'
#get the list of names and labels
name_images_database, labels_database, name_images_query, labels_query = convert_database_to_files.get_name_labels(path_database,path_folder_retrieval)
path_output = path_database + 'features/'
path_cnn_trained = ''
if(feature_extraction_method == 'cnn'):
path_cnn_trained = path_output + 'inception_resnet_v2_2016_08_30.ckpt'
elif(feature_extraction_method == 'cnn_training'):
path_cnn_trained = path_output + 'model.ckpt'
run.run_command_line(name_images_database, labels_database, name_images_query, labels_query, path_cnn_trained, path_output, feature_extraction_method,distance, number_of_images,list_of_parameters, 'simple', searching_method = 'bf', isEvaluation = False)
def start_Saxsgen():
#flavio machine
path_database = '/Users/flavio/Desktop/Saxsgen/'
path_cnn_trained = '/Users/flavio/Desktop/Saxsgen_features/model.ckpt'
path_output = '/Users/flavio/Desktop/Saxsgen_features/'
#flavio dresden
#path_database = '/home/users/flavio/databases/Saxsgen/'
#path_cnn_trained = '/home/users/flavio/databases/Saxsgen_features/model.ckpt'
#path_output = '/home/users/flavio/databases/Saxsgen_features/'
feature_extraction_method = 'cnn_training'
searching_method = 'kd'
preprocessing_method = 'log'
distance = 'ed'
number_of_images = 10
list_of_parameters = ['0.01', '50000']
folders = glob.glob(path_database + '*/')
#get the name and labels for the database
cont = 0
name_images_database = []
labels_database = []
name_images_query = []
labels_query = []
for folder in folders:
name_images = glob.glob(folder + "/*.jpg")
labels = np.zeros(len(name_images), dtype=np.int)
labels[:] = cont
name_images_database.extend(name_images)
labels_database.extend(labels)
name_query = name_images_database.pop()
label_query = labels_database.pop()
name_images_query.append(name_query)
labels_query.append(label_query)
cont += 1
labels_database = np.asarray(labels_database)
labels_query = np.asarray(labels_query)
run.run_command_line(name_images_database, labels_database,
name_images_query, labels_query, path_cnn_trained,
path_output, feature_extraction_method, distance,
number_of_images, list_of_parameters,
preprocessing_method, searching_method, False)
def main(argv):
path_database = ''
path_cnn_trained = ''
path_folder_retrieval = ''
feature_extraction_method = ''
distance = ''
searching_method = ''
number_of_images = 0
list_of_parameters = []
try:
opts, args = getopt.getopt(argv,"hd:c:r:f:s:p:n:m:")
except getopt.GetoptError:
print ('cbir_cl.py -d <path_database> -c <path_cnn_trained> -r <path_folder_retrieval> -f <feature_extraction_method> -s <distance-similarity metric> -n <number_of_images> -m <list_of_parameters>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('cbir_cl.py -d <path_database> -c <path_cnn_trained> -r <path_folder_retrieval> -f <feature_extraction_method> -s <distance-similarity metric> -n <number_of_images> -m <list_of_parameters>')
sys.exit()
elif opt == '-d':
path_database = arg
elif opt == '-c':
path_cnn_trained = arg
elif opt == '-r':
path_folder_retrieval = arg
elif opt == '-f':
feature_extraction_method = arg
elif opt == '-s':
distance = arg
elif opt == '-p':
searching_method = arg
elif opt == '-n':
number_of_images = int(float(arg))
elif opt == '-m':
parameters = arg.split(',')
for i in parameters:
list_of_parameters.append(i)
run.run_command_line(path_database,path_folder_retrieval,path_cnn_trained,feature_extraction_method,distance,number_of_images,list_of_parameters)
def run_retrieval_process_using_folders_structure():
############################# Paths ############################################
#dani machine
path_database = '/Users/dani/Desktop/site_cric/test_pequeno/'
path_cnn_pre_trained = '/Users/dani/Desktop/celulas_6_classes/celulas_6_classes_train_png/tensor/files/inception_resnet_v2_2016_08_30.ckpt'
#path_cnn_pre_trained = '/Users/dani/Desktop/site_cric/test_pequeno/output/model.ckpt' #Only to initializing the cnn
#path_save_cnn = '/Users/dani/Desktop/celulas_6_classes/celulas_6_classes_train_png/tensor/files/inception_resnet_v2_2016_08_30.ckpt' #This variable will be used on the inception resnet pre trained
path_save_cnn = '/Users/dani/Desktop/site_cric/test_pequeno/output/model.ckpt' #To save and use in the retrieval process
path_retrieval = '/Users/dani/Desktop/site_cric/test_pequeno/query/'
#path_database = '/Users/dani/Desktop/site/celulas299_train/'
#path_cnn_trained = '/Users/dani/Desktop/cells_small/features/inception_resnet_v2_2016_08_30.ckpt'
#path_cnn_trained = '/Users/dani/Desktop/site/celulas299_2_classes/output/model.ckpt-65043'
#path_retrieval = '/Users/dani/Desktop/site/celulas299_train/query/'
#path_output = path_database + 'output/'
#cnn machine
#path_database = '/home/users/dani/databases/cells/cells_train_augmentation/database/'
#path_cnn_trained = '/home/users/dani/databases/cells/cells_train_augmentation/features/model.ckpt'
#path_retrieval = '/home/users/dani/databases/cells/cells_train_augmentation/query'
path_output = path_database + 'output/'
############################# Parameters #######################################
feature_extraction_method = 'cnn_training_inception'
preprocessing_method = 'None'
searching_method = 'bf'
distance = 'ed'
number_of_images = 10
#list_of_parameters = ['0.1','100']
#list_of_parameters = ['2','8','8']#distance, number of bins
#list_of_parameters = ['4', '3', '2', '8']
#list_of_parameters = ['0.01','1']
list_of_parameters = ['0.05','3']
#get the list of names and labels
name_images_database, labels_database, name_images_query, labels_query = convert_database_to_files.get_name_labels(path_database,path_retrieval)
#inception_feature_extraction.features_extraction_new(name_images_database,labels_database,path_cnn_trained,feature_extraction_method)
############################ Calling function ##################################
start = timeit.default_timer()
_, train_time, _ = run.run_command_line(name_images_database,labels_database,name_images_query,labels_query,path_cnn_pre_trained,path_save_cnn,path_output,feature_extraction_method,distance,number_of_images,list_of_parameters,preprocessing_method,searching_method, False)
stop = timeit.default_timer()
print("Total time = ", stop - start)
print("Train time = ", train_time)
def run_retrieval_process_using_folders_structure():
############################# Paths ############################################
#flavio machine
#path_database = '/Users/flavio/Desktop/cells/'
#path_cnn_trained = '/Users/flavio/Desktop/cells/features/model_wavelet.ckpt'
#path_cnn_trained = '/Users/flavio/Desktop/fibers_small/features/inception_resnet_v2_2016_08_30.ckpt'
#path_retrieval = '/Users/flavio/Desktop/cells/query/'
#path_output = path_database + 'features/'
#cnn machine
path_database = '/home/users/flavio/databases/cells/cells_train/'
path_cnn_trained = '/home/users/flavio/databases/cells/cells_train/features/model_wavelet.ckpt'
path_retrieval = '/home/users/flavio/databases/cells/cells_train/query/'
path_output = path_database + 'features/'
############################# Parameters #######################################
feature_extraction_method = 'daisy'
preprocessing_method = 'simple'
searching_method = 'bf'
distance = 'ed'
number_of_images = 10
#list_of_parameters = ['0.1','100']
#list_of_parameters = ['2','8','8']#distance, number of bins
list_of_parameters = ['4', '3', '2', '8']
#list_of_parameters = ['0.1','0']
#list_of_parameters = ['2']
#get the list of names and labels
name_images_database, labels_database, name_images_query, labels_query = convert_database_to_files.get_name_labels(
path_database, path_retrieval)
#inception_feature_extraction.features_extraction_new(name_images_database,labels_database,path_cnn_trained,feature_extraction_method)
############################ Calling function ##################################
start = timeit.default_timer()
_, train_time, _ = run.run_command_line(
name_images_database, labels_database, name_images_query, labels_query,
path_cnn_trained, path_output, feature_extraction_method, distance,
number_of_images, list_of_parameters, preprocessing_method,
searching_method, False)
stop = timeit.default_timer()
print("Total time = ", stop - start)
print("Train time = ", train_time)
def run_create_graph():
#cnn
path_database_train = '/home/users/romuere/Saxsgen/new_database_split/new_database_split_train/'
path_database_test = '/home/users/romuere/Saxsgen/new_database_split/new_database_split_test/'
path_retrieval = '/home/users/flavio/databases/fiberFlaRom/fiberFlaRom_train/query/'
path_cnn_trained = '/home/users/romuere/Saxsgen/new_database_split/new_database_split_train/features/model.ckpt'
path_output_train = path_database_train + 'features/'
path_output_test = path_database_test + 'features/'
preprocessing_method = 'log'
distance = 'ed'
searching_method = 'kd'
percent_database = 1
percent_query = 1
number_of_images = 10
feature_extraction_method = 'cnn_training'
#jump_num_epoch = [1,4,5,10,20,30,30]#cells
#learning_rate =[0.1,0.1,0.08,0.04,0.02,0.01,0.009]#cells
#jump_num_epoch = [1,9,10,20,30,30,50,100,100]#fmd
#learning_rate =[0.1,0.1,0.03,0.02,0.01,0.008,0.004,0.002,0.001]#fmd
jump_num_epoch = [1, 4, 5, 10, 20, 30, 50, 100] #fibers
learning_rate = [0.1, 0.1, 0.1, 0.1, 0.08, 0.06, 0.04, 0.04] #fibers
NUM_LEVEL = [0]
for num_level in NUM_LEVEL:
remove_files_cnn(path_output_train)
list_train_time = []
list_map = []
#removing files
remove_files_pickle(path_output_train)
cont_index = 0
for num_epoch in jump_num_epoch:
list_of_parameters = [
str(learning_rate[cont_index]),
str(num_epoch),
str(num_level)
]
#train
#get the list of names and labels
name_images_database, labels_database, name_images_query, labels_query = convert_database_to_files.get_name_labels(
path_database_train, path_retrieval)
_, train_time, _ = run.run_command_line(
name_images_database,
labels_database,
name_images_query,
labels_query,
path_cnn_trained,
path_output_train,
feature_extraction_method,
distance,
number_of_images,
list_of_parameters,
preprocessing_method,
searching_method,
isEvaluation=True,
do_searching_processing=False,
save_csv=False)
if (not list_train_time):
list_train_time.append([num_epoch, train_time[0]])
else:
list_train_time.append([
np.sum(jump_num_epoch[0:cont_index + 1]),
train_time[0] + list_train_time[-1][1]
])
#evaluation
list_of_parameters = ['0.1', '0', str(num_level)]
name_images_database, labels_database = convert_database_to_files.get_name_labels(
path_database_test)
MAP, fig = evaluation.evaluation(name_images_database,
labels_database,
name_images_database,
labels_database,
path_output_test,
feature_extraction_method,
distance,
list_of_parameters,
preprocessing_method,
searching_method,
path_cnn_trained=path_cnn_trained,
percent_query=percent_query,
percent_database=percent_database)
list_map.append(
[np.sum(jump_num_epoch[0:cont_index + 1]),
np.mean(MAP)])
print('Num_epoch =', np.sum(jump_num_epoch[0:cont_index + 1]),
'train_time =', list_train_time[-1][1], 'MAP =',
np.mean(MAP))
#removing files
remove_files_pickle(path_output_test)
cont_index += 1
np.savetxt(path_output_test + feature_extraction_method +
'_train_time_' + preprocessing_method + '_' +
str(num_level) + '_level' + '.csv',
np.asarray(list_train_time),
delimiter=',')
np.savetxt(path_output_test + feature_extraction_method + '_Map_' +
preprocessing_method + '_' + str(num_level) + '_level' +
'.csv',
np.asarray(list_map),
delimiter=',')
def run_retrieval_process_using_txt_file():
#dani machine
root = '/Users/dani/Desktop/kyager_data_raw'
path_database_class0 = '/Users/dani/Desktop/kyager_data_raw/WAXS.txt'
path_database_class1 = '/Users/dani/Desktop/kyager_data_raw/SAXS.txt'
path_query_class0 = '/Users/dani/Desktop/kyager_data_raw/WASXS_query.txt'
path_query_class1 = '/Users/dani/Desktop/kyager_data_raw/SAXS_query.txt'
path_cnn_trained = '/Users/dani/Desktop/kyager_data_raw/model.ckpt'
path_output = '/Users/dani/Desktop/kyager_data_raw/features/'
#dani cnn
#root = '/home/users/dani/databases/kyager_data'
#path_database_class0 = '/home/users/dani/databases/kyager_data/ringisotropic2_database.txt'
#path_database_class1 = '/home/users/dani/databases/kyager_data/ringtextured2_database.txt'
#path_query_class0 = '/home/users/dani/databases/kyager_data/ringisotropic2_query.txt'
#path_query_class1 = '/home/users/dani/databases/kyager_data/ringtextured2_query.txt'
#path_cnn_trained = '/home/users/dani/databases/kyager_data/model.ckpt'
#path_output = '/home/users/dani/databases/kyager_data/features/'
feature_extraction_method = 'cnn_training'
searching_method = 'kd'
preprocessing_method = 'simple'
distance = 'ed'
number_of_images = 10
list_of_parameters = ['0.01','50000']
with open(path_database_class0) as f:
name_database_class0 = f.read().splitlines()
with open(path_database_class1) as f:
name_database_class1 = f.read().splitlines()
with open(path_query_class0) as f:
name_query_class0 = f.read().splitlines()
with open(path_query_class1) as f:
name_query_class1 = f.read().splitlines()
for i in range(len(name_database_class0)):
name_database_class0[i] = root + name_database_class0[i]
for i in range(len(name_database_class1)):
name_database_class1[i] = root + name_database_class1[i]
for i in range(len(name_query_class0)):
name_query_class0[i] = root + name_query_class0[i]
for i in range(len(name_query_class1)):
name_query_class1[i] = root + name_query_class1[i]
labels_database_class0 = np.zeros(len(name_database_class0),dtype=np.int)
labels_database_class1 = np.ones(len(name_database_class1),dtype=np.int)
labels_query_class0 = np.zeros(len(name_query_class0),dtype=np.int)
labels_query_class1 = np.ones(len(name_query_class1),dtype=np.int)
name_images_database = []
name_images_query = []
name_images_database.extend(name_database_class0)
name_images_database.extend(name_database_class1)
labels_database = np.concatenate((labels_database_class0,labels_database_class1))
name_images_query.extend(name_query_class0)
name_images_query.extend(name_query_class1)
labels_query = np.concatenate((labels_query_class0,labels_query_class1))
#preprocessing to remove files inexisting
name_images_database, labels_database = convert_database_to_files.preprocessing(name_images_database, labels_database)
name_images_query, labels_query = convert_database_to_files.preprocessing(name_images_query, labels_query)
run.run_command_line(name_images_database,labels_database,name_images_query,labels_query,path_cnn_trained,path_output,feature_extraction_method,distance,number_of_images,list_of_parameters,preprocessing_method,searching_method, False)
def returnInformation(self, pyCBIR):
#path_output = self.returnPathOutput(self)
feature_extraction_method = ''
if self.checkBox.isChecked():
feature_extraction_method = 'glcm'
if self.checkBox_2.isChecked():
feature_extraction_method = 'hog'
if self.checkBox_3.isChecked():
feature_extraction_method = 'fotf'
if self.checkBox_4.isChecked():
feature_extraction_method = 'lbp'
if self.checkBox_5.isChecked():
feature_extraction_method = 'cnn'
if self.checkBox_6.isChecked():
feature_extraction_method = 'daisy'
similarity_metric = ''
if self.radioButton_7.isChecked():
similarity_metric = 'ed'
elif self.radioButton_8.isChecked():
similarity_metric = 'id'
elif self.radioButton_9.isChecked():
similarity_metric = 'cs'
elif self.radioButton_10.isChecked():
similarity_metric = 'pcc'
elif self.radioButton_16.isChecked():
similarity_metric = 'csd'
elif self.radioButton_11.isChecked():
similarity_metric = 'kld'
elif self.radioButton_13.isChecked():
similarity_metric = 'ld'
elif self.radioButton_12.isChecked():
similarity_metric = 'ksd'
elif self.radioButton_15.isChecked():
similarity_metric = 'cmd'
elif self.radioButton_14.isChecked():
similarity_metric = 'cd'
searching_method = ''
if self.radioButton_17.isChecked():
searching_method = 'bf'
elif self.radioButton_18.isChecked():
searching_method = 'r'
elif self.radioButton_19.isChecked():
searching_method = 'kd'
elif self.radioButton_20.isChecked():
searching_method = 'lsh'
retrieval_number = int(self.lineEdit.text())
preprocessing_method = 'simple'
path_cnn_trained = self.path_output + 'model.ckpt'
list_of_parameters = ['0', '0', '0']
if (self.lineEdit_6.text() != ""):
fname_database, labels_database = informationPath(
self.lineEdit_6.text())
fname_retrieval, labels_retrieval = informationPath(
self.lineEdit_4.text())
elif (self.lineEdit_9.text() != ""):
fname_database, labels_database = informationFile(
self.lineEdit_9.text())
fname_retrieval, labels_retrieval = informationFile(
self.lineEdit_8.text())
_, _, file = run.run_command_line(fname_database,
labels_database,
fname_retrieval,
labels_retrieval,
path_cnn_trained,
self.path_output,
feature_extraction_method,
similarity_metric,
retrieval_number,
list_of_parameters,
preprocessing_method,
searching_method,
isEvaluation=False)
print(file)
#file = '/Users/romuere/Dropbox/new_Database/todas.jpg'
# Create widget
self.w = QtWidgets.QWidget(self.centralwidget)
self.w.setGeometry(QtCore.QRect(270, 30, 951, 791))
self.w.setEnabled(True)
self.w.setAcceptDrops(True)
self.w.setObjectName("image")
label = QLabel(self.w)
pixmap = QPixmap(file)
if (pixmap.height() < 700):
pixmap = pixmap.scaled(900, pixmap.height())
else:
pixmap = pixmap.scaled(900, 800)
label.setPixmap(pixmap)
self.w.show()
def run_create_graph_map():
#cnn machine
path_database_train = '/home/users/flavio/databases/fmd/fmd_train_resize_augmentation/'
path_database_test = '/home/users/flavio/databases/cells/cells_test/'
path_retrieval = '/home/users/flavio/databases/fiberFlaRom/fiberFlaRom_train/query/'
path_cnn_trained = '/home/users/flavio/databases/fmd/fmd_train_resize_augmentation/features/model_test.ckpt'
path_output_train = path_database_train + 'features/'
path_output_test = path_database_test + 'features/'
preprocessing_method = 'None'
distance = 'ed'
searching_method = 'kd'
percent_database = 0.1
percent_query = 0.001
number_of_images = 10
feature_extraction_method = 'cnn_training'
#jump_num_epoch = [1,4,5,10,20,30,30]#cells
#learning_rate =[0.1,0.1,0.08,0.04,0.02,0.01,0.009]#cells
#jump_num_epoch = [1,9,10,20,30,30,50,100,100]#fmd
#learning_rate =[0.1,0.1,0.03,0.02,0.01,0.008,0.004,0.002,0.001]#fmd
#jump_num_epoch = [1,4,5,5,5]#fibers
#learning_rate =[0.1,0.1,0.08,0.06,0.004]#fibers
#learning_rate =[0.1,0.1,0.1,0.08,0.06,0.02,0.008,0.006,0.004,0.001]#cells
#learning_rate =[0.001,0.001,0.1,0.08,0.06,0.04,0.02,0.02,0.01,0.01]#cells
NUM_LEVEL = [0]
learning_rate_0 = 0.1
factor_dec = 0.01
learning_rate_f = 0.05
for num_level in NUM_LEVEL:
#remove_files_cnn(path_output_train)
list_train_time = []
list_map = []
list_accuracy = []
list_number_epoch = []
list_error_total = []
#removing files
remove_files_pickle(path_output_train)
cont_index=0
#for num_epoch in jump_num_epoch:
for num_epoch in range(1,61,1):
if(num_epoch == 1 or num_epoch ==2):
new_learning_rate = learning_rate_0
list_of_parameters = [str(new_learning_rate),str(1),str(num_level)]
else:
new_learning_rate = new_learning_new(new_learning_rate,factor_dec,num_epoch,list_error_total[-2][2],list_error_total[-1][2],num_epoch)
if(new_learning_rate < learning_rate_f):
new_learning_rate = learning_rate_f
list_of_parameters = [str(new_learning_rate),str(1),str(num_level)]
#train
#get the list of names and labels
name_images_database, labels_database, name_images_query, labels_query = convert_database_to_files.get_name_labels(path_database_train,path_retrieval)
_, train_time, _, error = run.run_command_line(name_images_database,labels_database,name_images_query,labels_query,path_cnn_trained,path_output_train,feature_extraction_method,distance,number_of_images,list_of_parameters,preprocessing_method,searching_method, isEvaluation=True,do_searching_processing=False,save_csv=False)
if(not list_train_time):
list_train_time.append(train_time[0])
else:
list_train_time.append(train_time[0] + list_train_time[-1])
print('train time epoch', num_epoch, '=', list_train_time[-1])
list_error_total.append([num_epoch, new_learning_rate, (error[0][1] + error[1][1])/2 ])
print('Num_epoch =', list_error_total[-1][0],'Learning rate =', list_error_total[-1][1], 'Error =',list_error_total[-1][2])
'''
if(not list_train_time):
list_train_time.append(train_time[0])
else:
list_train_time.append(train_time[0] + list_train_time[-1])
#evaluation
list_of_parameters = ['0.1','0',str(num_level)]
name_images_database, labels_database = convert_database_to_files.get_name_labels(path_database_test)
MAP, ACCURACY, fig = evaluation.evaluation(name_images_database, labels_database, name_images_database, labels_database,path_output_test,feature_extraction_method,distance,list_of_parameters,preprocessing_method,searching_method,path_cnn_trained=path_cnn_trained,percent_query=percent_query,percent_database=percent_database)
list_number_epoch.append(np.sum(jump_num_epoch[0:cont_index+1]))
list_map.append(MAP)
list_accuracy.append(ACCURACY)
print('Num_epoch =', list_number_epoch[-1],'train_time =', list_train_time[-1], 'MAP =',np.mean(MAP), 'Accuracy =',np.mean(ACCURACY))
for i in range(len(list_map[-1])):
print('Map for class ', i, list_map[-1][i])
for i in range(len(list_map[-1])):
print('Accuracy for class ', i, list_accuracy[-1][i])
#removing files
remove_files_pickle(path_output_test)
cont_index+=1
np.savetxt(path_output_test + feature_extraction_method + '_train_time_' + preprocessing_method + '_' + str(num_level) + '_level' + '.csv', np.asarray(list_train_time),delimiter = ',')
np.savetxt(path_output_test + feature_extraction_method + '_Map_' + preprocessing_method + '_' + str(num_level) + '_level' + '.csv', np.asarray(list_map),delimiter = ',')
np.savetxt(path_output_test + feature_extraction_method + '_number_epoch_' + preprocessing_method + '_' + str(num_level) + '_level' + '.csv', np.asarray(list_number_epoch),delimiter = ',')
np.savetxt(path_output_test + feature_extraction_method + '_accuracy_' + preprocessing_method + '_' + str(num_level) + '_level' + '.csv', np.asarray(list_accuracy),delimiter = ',')
'''
np.savetxt(path_output_train + feature_extraction_method + '_learning_rate_error_' + '.csv', np.asarray(list_error_total),delimiter = ',')
def run_sorting():
"""
This is just a test function, to avoid run the GUI every time.
"""
import csv
import itertools
"""
##To run fibers/cells/fmd/dtd/...
folders = ['/Users/romuere/Dropbox/CBIR/fibers/database/no_fibers/*','/Users/romuere/Dropbox/CBIR/fibers/database/yes_fibers/*']
fname_database = []
labels_database = np.empty(0)
for id,f in enumerate(folders):
files = glob.glob(f)
labels_database = np.append(labels_database, np.zeros(len(files))+id)
fname_database = fname_database+files
print(files)
print(len(fname_database))
preprocessing_method = 'log'
feature_extraction_method = 'glcm'
searching_method = 'lsh'
retrieval_number = 10
similarity_metric = 'ed'
path_output = '/Users/romuere/Dropbox/CBIR/fibers/results/'
list_of_parameters = ['1','2']
path_cnn_trained = ''
fname_retrieval = fname_database[0:3] + fname_database[2001:2003]
labels_retrieval = np.concatenate((labels_database[0:3],labels_database[2001:2003]))
"""
##To run scattering images
path = '/Users/romuere/Desktop/als/kyager_data_raw'
files_database_class0 = '/Users/romuere/Desktop/als/kyager_data_raw/SAXS.txt'
files_database_class1 = '/Users/romuere/Desktop/als/kyager_data_raw/WAXS.txt'
files_retrieval_class0 = '/Users/romuere/Desktop/als/kyager_data_raw/SAXS_query.txt'
files_retrieval_class1 = '/Users/romuere/Desktop/als/kyager_data_raw/WAXS_query.txt'
#------#
reader = csv.reader(open(files_database_class0))
fname_database_class0 = list(reader)
fname_database_class0 = list(itertools.chain(*fname_database_class0))
labels_class_0 = np.zeros(len(fname_database_class0))
reader = csv.reader(open(files_database_class1))
fname_database_class1 = list(reader)
fname_database_class1 = list(itertools.chain(*fname_database_class1))
labels_class_1 = np.zeros(len(fname_database_class1)) + 1
fname_database = fname_database_class0 + fname_database_class1
fname_database = [path + x for x in fname_database]
labels_database = np.concatenate((labels_class_0, labels_class_1))
#------#
reader = csv.reader(open(files_retrieval_class0))
fname_retrieval_class0 = list(reader)
fname_retrieval_class0 = list(itertools.chain(*fname_retrieval_class0))
labels_retrieval_class0 = np.zeros(len(fname_retrieval_class0))
reader = csv.reader(open(files_retrieval_class1))
fname_retrieval_class1 = list(reader)
fname_retrieval_class1 = list(itertools.chain(*fname_retrieval_class1))
labels_retrieval_class1 = np.zeros(len(fname_retrieval_class1))
fname_retrieval = fname_retrieval_class0 + fname_retrieval_class1
fname_retrieval = [path + x for x in fname_retrieval]
labels_retrieval = np.concatenate(
(labels_retrieval_class0, labels_retrieval_class1))
#------#
preprocessing_method = 'log'
feature_extraction_method = 'lbp'
searching_method = 'lsh'
retrieval_number = 10
similarity_metric = 'ed'
path_output = '/Users/romuere/Desktop/als/output/'
list_of_parameters = ['2'] #['2','8','8']
path_cnn_trained = ''
run.run_command_line(fname_database,
labels_database,
fname_retrieval,
labels_retrieval,
path_cnn_trained,
path_output,
feature_extraction_method,
similarity_metric,
retrieval_number,
list_of_parameters,
preprocessing_method,
searching_method,
isEvaluation=False)
searching_method = 'lsh'
retrieval_number = 2000
similarity_metric = 'ed'
path_output = '/Users/romuere/Dropbox/CBIR/fibers/results/'
list_of_parameters = []
path_cnn_trained = ''
fname_retrieval = fname_database
labels_retrieval = labels_database
result = run.run_command_line(fname_database,
labels_database,
fname_retrieval,
labels_retrieval,
path_cnn_trained,
path_output,
feature_extraction_method,
similarity_metric,
retrieval_number,
list_of_parameters,
searching_method,
isEvaluation=True)
result = np.array(result[1])
#result = result[1]
fscore = []
#fscore = np.zeros(())
print(len(labels_retrieval))
print(len(fname_database))
for id_label, i in enumerate(labels_retrieval):
for j in range(retrieval_number):
nm = j + 1
def returnInformation(self, pyCBIR):
#import sys
#sys.path.insert(0, '../src')
import run
if self.radioButton.isChecked():
self.feature_extraction_method = 'glcm'
if self.radioButton_2.isChecked():
self.feature_extraction_method = 'hog'
if self.radioButton_3.isChecked():
self.feature_extraction_method = 'fotf'
if self.radioButton_4.isChecked():
self.feature_extraction_method = 'lbp'
if self.radioButton_6.isChecked():
self.feature_extraction_method = 'daisy'
searching_method = ''
if self.radioButton_7.isChecked():
searching_method = 'bf'
elif self.radioButton_8.isChecked():
searching_method = 'kd'
elif self.radioButton_9.isChecked():
searching_method = 'bt'
retrieval_number = int(self.lineEdit.text())
preprocessing_method = 'simple'
if (self.lineEdit_3.text() != ""):
fname_database, labels_database = informationPath(
self.lineEdit_3.text())
fname_retrieval, labels_retrieval = informationPath(
self.lineEdit_4.text())
elif (self.lineEdit_5.text() != ""):
fname_database, labels_database = informationFile(
self.lineEdit_5.text())
fname_retrieval, labels_retrieval = informationFile(
self.lineEdit_6.text())
print(self.path_cnn_trained)
_, _, file = run.run_command_line(fname_database,
labels_database,
fname_retrieval,
labels_retrieval,
self.path_cnn_pre_trained,
self.path_save_cnn,
self.lineEdit_2.text(),
self.feature_extraction_method,
self.similarity_metric,
retrieval_number,
self.list_of_parameters,
preprocessing_method,
searching_method,
isEvaluation=False)
self.w = QtWidgets.QWidget(self.centralwidget)
self.w.setGeometry(QtCore.QRect(140, 0, 951, 791))
self.w.setEnabled(True)
self.w.setAcceptDrops(True)
self.w.setObjectName("image")
label = QLabel(self.w)
pixmap = QPixmap(file)
if (pixmap.height() < 680):
pixmap = pixmap.scaled(800, pixmap.height())
else:
pixmap = pixmap.scaled(800, 680)
label.setPixmap(pixmap)
self.w.show()