def start(self):
env_path = self.config['path']
sys_data_path = f"{env_path['predictions']}{self.config['predictions']}/"
image_ref_csv_path, ref_data_path, _, _ = PathUtils.get_paths(
self.config)
starting_index, ending_index = JsonLoader.get_data_size(self.config)
irb = ImgRefBuilder(image_ref_csv_path)
img_refs = irb.get_img_ref(starting_index, ending_index)
data = MediforData.get_data(img_refs, sys_data_path, ref_data_path)
self.model_scoring(data)
def start(self):
img_ref_csv_path, ref_data_path, targets_path, indicators_path = PathUtils.get_paths(
self.config)
irb = ImgRefBuilder(img_ref_csv_path)
starting_index, ending_index = JsonLoader.get_data_size(self.config)
img_refs = irb.get_img_ref(starting_index, ending_index)
patches_folder = self.config['path']['outputs'] + "patches/"
output_dir = FolderUtils.create_patch_output_folder(
self.patch_shape, self.img_downscale_factor, patches_folder,
PathUtils.get_indicator_directories(indicators_path),
self.config['tuning'], self.config['data_year'],
self.config['data_prefix'])
LogUtils.init_log(output_dir)
pg = PatchGenerator(output_dir=output_dir,
indicators_path=indicators_path,
img_downscale_factor=self.img_downscale_factor,
patch_shape=self.patch_shape,
tuning=self.config["tuning"])
pg.create_img_patches(img_refs, targets_path)
def __init__(self, config, model_name=None, output_dir=None):
super().__init__(config, model_name, output_dir)
img_ref_csv_path, self.ref_data_path, self.targets_path, self.indicators_path = PathUtils.get_paths(self.config)
self.indicator_directories = PathUtils.get_indicator_directories(self.indicators_path)
self.image_downscale_factor = config['image_downscale_factor']
self._prepare_img_refs(img_ref_csv_path)