rf_simi = RandomForest() model_path = "data/model/rf_same_region.pkl" rf_simi.load_model(model_path) rf_sal = RandomForest() model_path = "data/model/rf_salience.pkl" rf_sal.load_model(model_path) im_data.get_multi_segs(rf_simi) segs_num = len(im_data.rlists) height = im_data.rmat.shape[0] width = im_data.rmat.shape[1] salience_map = np.zeros([segs_num, height, width]) for i, rlist in enumerate(im_data.rlists): Y = rf_sal.predict(im_data.feature93s[i])[:, 1] for j, r in enumerate(rlist): salience_map[i][r] = Y[j] X_test = salience_map.reshape([-1, height*width]).T mlp = MLP() model_path = "data/model/mlp.pkl" mlp.load_model(model_path) Y = mlp.predict(X_test).reshape([height, width])*255 img = np.zeros([height, width*2, 3], dtype=np.uint8) img[:, :width, :] = cv2.imread(img_path) img[:, width:, :] = Y.repeat(3).reshape([height, width, 3]) print("finished~( •̀ ω •́ )y") cv2.imshow("result", img) cv2.waitKey(0)
parser.add_argument('--test_data', default=PATH_PARSED_DATA_TEST, type=str, help='Test Data Path') parser.add_argument('--dataset_size', default=DEFAULT_DATASET_SIZE, type=int, help='Train Dataset size') parser.add_argument('--ctx', default=DEFAULT_CTX, type=str, help='Context') args = parser.parse_args() net = MLP(drop_out=args.drop_out, hidden_units=args.hidden_units) net.set_ctx(args.ctx) net.load_model() data_attr = { 'path': args.test_data, 'dataset_size': args.dataset_size, 'batch_size': args.batch_size, 'shuffle_data': False, } cumulative_accuracy = 0 set_count = 0 data_gen = net.prepare_data(**data_attr) for test_data in data_gen: set_count += 1 set_acc = net.evaluation(test_data)