diff --git a/predit_single.py b/predit_single.py
new file mode 100644
index 0000000..ee875e4
--- /dev/null
+++ b/predit_single.py
@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+import cv2 as cv
+import keras.backend as K
+import numpy as np
+
+from model import build_encoder_decoder, build_refinement
+from utils import get_final_output, create_patches, patch_dims, assemble_patches
+import tensorflow as tf
+import time
+
+config = tf.ConfigProto(device_count = {"GPU": 1, "CPU": 1})
+sess = tf.Session(config=config)
+K.set_session(sess)
+
+if __name__ == '__main__':
+    # load network
+    PATCH_SIZE = 320
+    PRETRAINED_PATH = 'models/final.42-0.0398.hdf5'
+    TRIMAP_PATH = "images/trimap2.png"
+    IMG_PATH = "images/frame2.png"
+
+    encoder_decoder = build_encoder_decoder()
+    final = build_refinement(encoder_decoder)
+    final.load_weights(PRETRAINED_PATH)
+    print(final.summary())
+
+    # loading input files
+    trimap = cv.imread(TRIMAP_PATH, cv.IMREAD_GRAYSCALE)
+    img = cv.imread(IMG_PATH)
+    result = np.zeros(trimap.shape, dtype=np.uint8)
+
+    img_size = np.array(trimap.shape)
+
+    # create patches
+    x = np.dstack((img, np.expand_dims(trimap, axis=2))) / 255.
+    patches = create_patches(x, PATCH_SIZE)
+
+    # create mat for patches predictions
+    patches_count = np.product(
+        patch_dims(mat_size=trimap.shape, patch_size=PATCH_SIZE)
+    )
+    patches_predictions = np.zeros(shape=(patches_count, PATCH_SIZE, PATCH_SIZE))
+
+    # predicting
+    for i in range(patches.shape[0]):
+        print("Predicting patches {}/{}".format(i + 1, patches_count))
+
+        patch_prediction = final.predict(np.expand_dims(patches[i, :, :, :], axis=0))
+        patches_predictions[i] = np.reshape(patch_prediction, (PATCH_SIZE, PATCH_SIZE)) * 255.
+
+    # assemble
+    result = assemble_patches(patches_predictions, trimap.shape, PATCH_SIZE)
+    result = result[:img_size[0], :img_size[1]]
+
+    prediction = get_final_output(result, trimap).astype(np.uint8)
+
+    # save into files
+    cv.imshow("result", prediction)
+    cv.imshow("image", img)
+    cv.waitKey(0)
+
+    K.clear_session()
+
diff --git a/utils.py b/utils.py
index 3006e37..549a536 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,5 @@
 import multiprocessing
-
+import math
 import cv2 as cv
 import keras.backend as K
 import numpy as np
@@ -88,6 +88,45 @@ def get_final_output(out, trimap):
     mask = np.equal(trimap, unknown_code).astype(np.float32)
     return (1 - mask) * trimap + mask * out
 
+def patch_dims(mat_size, patch_size):
+    return np.ceil(np.array(mat_size) / patch_size).astype(int)
+
+def create_patches(mat, patch_size):
+    mat_size = mat.shape
+    assert len(mat_size) == 3, "Input mat need to have 4 channels (R, G, B, trimap)"
+    assert mat_size[-1] == 4 , "Input mat need to have 4 channels (R, G, B, trimap)"
+
+    patches_dim = patch_dims(mat_size=mat_size[:2], patch_size=patch_size)
+    patches_count = np.product(patches_dim)
+
+    patches = np.zeros(shape=(patches_count, patch_size, patch_size, 4), dtype=np.float32)
+    for y in range(patches_dim[0]):
+        y_start = y * patch_size
+        for x in range(patches_dim[1]):
+            x_start = x * patch_size
+
+            # extract patch from input mat
+            single_patch = mat[y_start: y_start + patch_size, x_start: x_start + patch_size, :]
+
+            # zero pad patch in bottom and right side if real patch size is smaller than patch size
+            real_patch_h, real_patch_w = single_patch.shape[:2]
+            patch_id = y + x * patches_dim[0]
+            patches[patch_id, :real_patch_h, :real_patch_w, :] = single_patch
+
+    return patches
+
+def assemble_patches(pred_patches, mat_size, patch_size):
+    patch_dim_h, patch_dim_w = patch_dims(mat_size=mat_size, patch_size=patch_size)
+    result = np.zeros(shape=(patch_size * patch_dim_h, patch_size * patch_dim_w), dtype=np.uint8)
+    patches_count = pred_patches.shape[0]
+
+    for i in range(patches_count):
+        y = (i % patch_dim_h) * patch_size
+        x = int(math.floor(i / patch_dim_h)) * patch_size
+
+        result[y:y+patch_size, x:x+patch_size] = pred_patches[i]
+
+    return result
 
 def safe_crop(mat, x, y, crop_size=(img_rows, img_cols)):
     crop_height, crop_width = crop_size