def print_image(image_file, title, text, show_image=False, save_graph=False):
img = load_img(image_file)
format = img.format
mode = img.mode
(width, height) = img.size
text = '%s - %s - %sx%s\n%s' % (format, mode, width, height, text)
plt.figure(figsize=(10, 6))
plt.imshow(img)
plt.title(title)
plt.text(
s=text,
fontsize=8,
x=width - round(width * 0.025),
y=round(width * 0.025),
color='green',
bbox=dict(facecolor='white', alpha=0.6, edgecolor='grey', boxstyle='round,pad=0.5'),
horizontalalignment='right',
verticalalignment='top'
)
# save graph
if save_graph:
graph_file = add_file_extension(add_file_extension(image_file, 'graph', True), 'png')
plt.savefig(graph_file)
# show graph
if show_image:
plt.show()
def rebuild_model_dict(self, config_file=None):
model_file = self.get_data('model_file')
# convert model file if needed (from other training system)
if not os.path.isfile(model_file) and config_file is not None and os.path.isfile(config_file):
self.set_data('config_file', config_file)
model_file_tmp = add_file_extension(config_file, 'h5')
if os.path.isfile(model_file_tmp):
model_file = model_file_tmp
self.set_data('config_file', config_file)
if not os.path.isfile(model_file):
raise AssertionError('model file "%s" was not found' % model_file)
model_path = os.path.dirname(model_file)
model_base = add_file_extension(os.path.basename(model_file), '*', True)
model_files = []
model_file_best = None
model_file_best_val_acc = 0
# search for all best models
os.chdir(model_path)
for file in glob.glob(model_base):
output = re.search('\.([0-9]{1,})-([0-9]{1,}\.[0-9]{1,})\.', file, flags=re.IGNORECASE)
if output is not None:
epoch = int(output.group(1))
val_acc = float(output.group(2))
# build current model dict
model_file_current = {
'val_acc': val_acc,
'epoch': epoch,
'model_file': '%s/%s' % (model_path, file)
}
# remember the best model
if val_acc > model_file_best_val_acc:
model_file_best_val_acc = val_acc
model_file_best = model_file_current
model_files.append(model_file_current)
# set new model dict
self.set_data('model_files', model_files)
self.set_data('model_file_best', model_file_best)
def POST_prediction_hook(self, argument, upload_data, models):
# some configs
show_image = False
save_image = True
# get model
model = models[argument]['model']
model_json_config = models[argument]['json_config']
# load json
self.config.load_json(model_json_config, True)
# get file to evaluate
evaluation_file = upload_data['upload_path']
graph_file = add_file_extension(
add_file_extension(evaluation_file, 'graph', True), PNG_EXTENSION)
evaluation_file_web = upload_data['upload_path_web']
graph_file_web = add_file_extension(
add_file_extension(evaluation_file_web, 'graph', True),
PNG_EXTENSION)
self.start_timer('prediction')
evaluation_data = self.evaluate_file(model, evaluation_file,
show_image, save_image)
self.finish_timer('prediction')
prediction_overview_array = evaluation_data[
'prediction_overview_array']
prediction_class = evaluation_data['prediction_class']
prediction_accuracy = evaluation_data['prediction_accuracy']
return_value = {
'evaluated_file': evaluation_file,
'graph_file': graph_file,
'evaluated_file_web': evaluation_file_web,
'graph_file_web': graph_file_web,
'prediction_overview_array': prediction_overview_array,
'prediction_class': prediction_class,
'prediction_accuracy': prediction_accuracy,
'prediction_time': '%.4f' % self.get_timer('prediction')
}
return return_value
def build_data(self):
add_transfer_learning_name = self.configs['data']['add_transfer_learning_name'] if 'add_transfer_learning_name' in self.configs['data'] else None
if 'transfer_learning' in self.configs and add_transfer_learning_name:
transfer_learning_model = self.gettl('transfer_learning_model').lower()
else:
transfer_learning_model = None
data_files = [
'model_file',
'config_file',
'best_model_file',
'accuracy_file',
'log_file',
'csv_file'
]
extension_wrapper = {
'config_file': 'json',
'model_file': 'h5',
'best_model_file': 'best.{epoch:02d}-{val_accuracy:.2f}.h5',
'accuracy_file': 'png',
'log_file': 'log',
'csv_file': 'csv'
}
for data_file_main in data_files:
if data_file_main in self.configs['data']:
if transfer_learning_model is not None:
self.set_data(
data_files[0],
add_file_extension(self.get_data(data_files[0]), transfer_learning_model, True)
)
data_files.remove(data_file_main)
for data_file in data_files:
self.set_data(
data_file,
add_file_extension(self.get_data(data_file_main), extension_wrapper[data_file])
)
return None