def __init__(self, weights_file_path: str, image_file_path: Path,
user_name: str, image_name: str, complete_event: Event):
super(OptimizeThread, self).__init__()
self.rank_net = RankNet(IMAGE_SHAPE, use_vgg16=False)
self.rank_net.trainable_model.load_weights(weights_file_path)
self.enhancer = IE.ImageEnhancer(str(image_file_path),
(IMAGE_WIDTH, IMAGE_HEIGHT))
enhance_generator = EnhanceGenerator(self.enhancer)
self.optimizer = ParameterOptimizer(self.rank_net, enhance_generator,
EnhanceDecorder())
self.image_file_path = image_file_path
self.user_name, self.image_name = user_name, image_name
self.complete_event = complete_event
import EnhanceGenerator, EnhanceDecorder, Predictor, ParameterOptimizer
from pathlib import Path
from gdrive_scripts import config
if __name__ == "__main__":
optimize_dir_path = Path(__file__).parent / 'optimize'
weights_dir_path = Path(__file__).parent / 'weights'
optimizable_dir_path = Path(__file__).parent / 'optimizable'
for category_name in ['flower']:
weight_path = weights_dir_path / f'{category_name}.h5'
for image_path in optimizable_dir_path.iterdir():
print(f'optimize {image_path.name} used {category_name}.h5')
generator = EnhanceGenerator(str(image_path),
config.ImageInfo.size)
enhance_decoder = EnhanceDecorder()
predictor = Predictor(str(weight_path))
optimizer = \
ParameterOptimizer.Optimizer(predictor, generator,
enhance_decoder)
best_param_list, logbook = optimizer.optimize(ngen=20,
param_list_num=1)
save_dir_path = optimize_dir_path / category_name
save_dir_path.mkdir(exist_ok=True, parents=True)
image_name = image_path.stem
save_path = str(save_dir_path / f'{image_name}.png')
user_name_list = ['oba', 'sakao', 'tamiya']
for user_name in user_name_list:
for category_dir_path in optimizable_dir_path.iterdir():
category_name = category_dir_path.name
weight_path = weights_dir_path / user_name / f'{category_name}.h5'
if not weight_path.exists():
print(f'{category_name}.h5 not found in {user_name}')
continue
for image_path in category_dir_path.iterdir():
print(f'{user_name} - {image_path.name} in {category_name}')
generator = EnhanceGenerator(str(image_path), IMAGE_SIZE)
enhance_decoder = EnhanceDecorder()
predictor = Predictor(str(weight_path))
optimizer = \
ParameterOptimizer.Optimizer(predictor, generator,
enhance_decoder)
best_param_list, logbook = \
optimizer.optimize(ngen=20, param_list_num=1)
save_dir_path = optimize_dir_path / user_name / category_name
save_dir_path.mkdir(parents=True, exist_ok=True)
image_name = image_path.stem
save_path = str(save_dir_path / f'{image_name}.png')
if __name__ == "__main__":
katsudon_path_list = [
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\Questionnaire\image\katsudon\1\1.jpg',
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\Questionnaire\image\katsudon\2\2.png']
salad_path_list = [
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\Questionnaire\image\salad\1\1.jpg',
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\Questionnaire\image\salad\2\2.jpg',
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\Questionnaire\image\salad\3\3.jpg']
path_dict = {'katsudon': katsudon_path_list, 'salad': salad_path_list}
key = 'salad'
for index, image_path in enumerate(path_dict[key], start=1):
enhancer = ResizableEnhancer(image_path, config.IMAGE_SIZE)
enhance_generator = EnhanceGenerator(enhancer)
enhance_decoder = EnhanceDecorder()
# cnn = RankNet(config.IMAGE_SHAPE, use_vgg16=False)
vgg = RankNet(config.IMAGE_SHAPE, use_vgg16=True)
records_num = 4000
weights_dir_path = Path(
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\improve_tournament\weights')
optimize_dir_path = Path(
r'C:\Users\init\Documents\PythonScripts\ImageEnhancementFromUserPreference\Experiment\improve_tournament\optimize')/key/str(index)
# cnn_weights_path = str(weights_dir_path/f'{records_num}.h5')
vgg_weights_path = str(weights_dir_path/'vgg'/f'{records_num}.h5')