Charlie Hewitt
49 linhas
2.3 KiB
Python
Arquivo Executável
49 linhas
2.3 KiB
Python
Arquivo Executável
#! /usr/bin/env python
|
|
|
|
import sys
|
|
import coremltools
|
|
|
|
|
|
def do(t, path):
|
|
if t == 'C':
|
|
coreml_model = coremltools.converters.keras.convert(path,
|
|
input_names=['image'],
|
|
image_input_names=['image'],
|
|
output_names=['emotion_p'],
|
|
image_scale=1 / 255.0,
|
|
class_labels=['0', '1', '2', '3', '4', '5', '6', '7'],
|
|
predicted_feature_name='emotion')
|
|
coreml_model.short_description = 'Predicts the emotion present in an image of a human face.'
|
|
coreml_model.input_description['image'] = '128x128 image of human face'
|
|
coreml_model.output_description['emotion'] = 'Predicted emotion - 1 of 8 basic emotions'
|
|
else:
|
|
coreml_model = coremltools.converters.keras.convert(path,
|
|
input_names=['image'],
|
|
image_input_names=['image'],
|
|
image_scale=1 / 255.0,
|
|
output_names=['valence/arousal'],
|
|
predicted_feature_name='emotion')
|
|
coreml_model.short_description = 'Predicts the valence/arousal present in an image of a human face.'
|
|
coreml_model.input_description['image'] = '128x128 image of human face'
|
|
coreml_model.output_description['valence/arousal'] = 'Predicted valence and arousal between -1 and 1'
|
|
coreml_model.author = 'Charlie Hewitt'
|
|
coreml_model.license = 'BSD'
|
|
coreml_model.save('MobAffNet' + t + '.mlmodel')
|
|
|
|
|
|
def main(argv):
|
|
if len(argv) == 2:
|
|
if argv[0] == 'c' or argv[0] == 'r':
|
|
do(argv[0], argv[1])
|
|
return
|
|
raise(Exception('INPUT ERROR'))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if len(sys.argv) != 3:
|
|
print('Usage:')
|
|
print('convert.py -c path_to_keras_classifier_model')
|
|
print('convert.py -r path_to_keras_regressor_model')
|
|
else:
|
|
main(sys.argv[1:])
|