def create_predict_mask_lake(modis_utils_obj, data_type='test'):
for idx in range(modis_utils_obj.get_n_tests(data_type)):
pred = modis_utils_obj.get_inference(data_type, idx)
pred = modis_utils_obj._preprocess_strategy_context.inverse(pred)
predict_mask_lake_path = os.path.join(
modis_utils_obj._predict_mask_lake_dir, data_type, '{}.dat'.format(idx))
cache_data(mask_lake_img(pred), predict_mask_lake_path)
def get_groundtruth_mask_lake(data_dir, used_band, time_steps,
reservoir_index, test_index, data_type='test'):
target_file_path = get_data_file_path(data_dir, reservoir_index, used_band,
time_steps, data_type, 'target')
test_path = get_target_paths(target_file_path)[test_index]
token = test_path.split('/')
raw_path = '/'.join(['raw_data'] + token[3:])
img = get_im(raw_path[:-4] + '.tif')
return mask_lake_img(img, band=used_band)
def get_predict_mask_lake(data_dir, used_band, crop_size, time_steps, filters,
kernel_size, n_hidden_layers, mask_cloud_loss,
reservoir_index, test_index):
predict_dir = get_predict_dir(data_dir, reservoir_index, used_band, crop_size, time_steps,
filters, kernel_size, n_hidden_layers, mask_cloud_loss)
predict = restore_data(os.path.join(predict_dir, '{}.dat'.format(test_index)))
if 'div' not in data_dir:
reservoir_min, reservoir_max = get_reservoir_min_max(data_dir, reservoir_index)
mean, std = get_reservoir_mean_std(data_dir, reservoir_index)
predict = np.interp(predict, (np.min(predict), np.max(predict)),
(reservoir_min, reservoir_max))
predict = predict*std + mean
else:
predict = np.interp(predict, (np.min(predict), np.max(predict)), (-2001, 10000))
predict_mask = mask_lake_img(predict, band=used_band)
return predict_mask
def find_boundaries_mask_lake(x, water_threshold):
x1 = mask_lake_img(x, offset=water_threshold)
x1 = np.logical_or(x1, permanent_water_area)
return find_boundaries(x1)
def find_boundaries_mask_lake(x, water_threshold):
x1 = mask_lake_img(x, offset=water_threshold)
return find_boundaries(x1)