# ignore warnings warnings.filterwarnings("ignore") # configuration config = ImageConfig() config.model_description = '' config.time = '' config.lr = 3e-4 config.operation = ConfigOpt.TRAIN config.images_dir = '../data/golgi images 0' config.masks_dir = '../data/golgi masks 0' config.mean_map = 'mean_map_{}.pic'.format('') # prepare data (augmentation) if config.operation == ConfigOpt.AUGMENTATION: augmentation(config) if isinstance(config.operation, ConfigOpt) and \ config.operation is not ConfigOpt.AUGMENTATION: # create model model = Unet(config=config) # load data x, y = load_images_train_data(model, img_num=8000) # date generator date_gen = image_data_generator() # run model model.run_model(x, y, use_generator=False, date_gen=date_gen)
config.mean_map = os.path.join(root_path, 'code/results/mean_map', 'mean_map_{}.pic'.format(config.time)) # prepare data # run if config.operation is not ConfigOpt.AUGMENTATION: # create model model = Unet(config=config) # load data if config.operation is ConfigOpt.TRAIN: x, y = load_images_train_data(model) # data generator data_gen = image_data_generator() # run model model.run_model(x, y, use_generator=False, data_gen=data_gen) elif config.operation is ConfigOpt.PREDICT: x = load_image_test_data(model, config.images_dir, 100) y_gt = load_image_test_data(model, config.masks_dir, 100) # data generator data_gen = image_data_generator() # run model config.time = '117_229' weights = os.path.join(config.root_path, 'code/results/weights', config._weights) y = model.run_model(x, weights=weights) print(y.shape) for i in range(y.shape[0]): cv2.imshow('predicted image', np.concatenate((x[i][:,:,0], y[i][:,:,0], y_gt[i][:,:,0]), axis=1)) cv2.waitKey()