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)
