def run(self, session):
self.model.setup_testing(session)
if self.args.use_dataset:
test_data= data_parser(self.args)
n_data = len(test_data[1])
else:
test_data=get_single_image(self.args)
n_data = len(test_data)
print_info('Writing PNGs at {}'.format(self.args.base_dir_results))
if self.args.batch_size_test==1 and self.args.use_dataset:
for i in range(n_data):
im, em, file_name = get_testing_batch(self.args,
[test_data[0][test_data[1][i]], test_data[1][i]], use_batch=False)
self.img_info = file_name
edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]})
self.save_egdemaps(edgemap, single_image=True)
print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))
# for individual images
elif self.args.batch_size_test==1 and not self.args.use_dataset:
for i in range(n_data):
im, file_name = get_single_image(self.args,file_path=test_data[i])
self.img_info = file_name
edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]})
self.save_egdemaps(edgemap, single_image=True)
print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))
def run(self, session):
self.model.setup_testing(session)
if self.args.use_dataset:
test_data= data_parser(self.args)
n_data = len(test_data[1])
else:
test_data=get_single_image(self.args)
n_data = len(test_data)
print_info('Writing PNGs at {}'.format(self.args.base_dir_results))
if self.args.batch_size_test==1 and self.args.use_dataset:
for i in range(n_data):
im, em, file_name = get_testing_batch(self.args,
[test_data[0][test_data[1][i]], test_data[1][i]], use_batch=False)
self.img_info = file_name
#Dexi Start Time
startDexi = time.time()
#Edge map creation from the pretrained model
edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]})
#Dexi End Time
endDexi = time.time()
secondsDexi = endDexi - startDexi
print_info('Time taken for DexiNED: {} seconds'.format(secondsDexi))
self.save_egdemaps(edgemap, single_image=True)
print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))
# for individual images
elif self.args.batch_size_test==1 and not self.args.use_dataset:
for i in range(n_data):
im, file_name = get_single_image(self.args,file_path=test_data[i])
self.img_info = file_name
#Dexi Start Time
startDexi = time.time()
edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]})
#Dexi End Time
endDexi = time.time()
secondsDexi = endDexi - startDexi
print_info('Time taken for DexiNED: {} seconds'.format(secondsDexi))
self.save_egdemaps(edgemap, single_image=True)
print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))