added tests and image scoring

antialiasing/src/image_handling.py

@@ -13,7 +13,7 @@ def imp(file):
     :return: a three dimesional numpy array representing the image
     :rtype: ndarray
     """
-    return np.asarray(im.open(file), dtype=np.intc)
+    return np.asarray(im.open(file), dtype=np.int16)
 
 
 def exp(array, file):
@@ -26,4 +26,27 @@ def exp(array, file):
     :param file: file path
     :type file: string
     """
-    im.fromarray(array).save(file)
+    im.fromarray(array.astype(np.uint8)).save(file)
+
+
+def score(old, new):
+    """
+    Scores two images of same dimensions on similarity from 0 to 255.
+    The smaller the score the more similar they are.
+
+    :param old: a three dimesional numpy array representing the image
+    :type array: ndarray
+
+    :param new: a three dimesional numpy array representing the image
+    :type array: ndarray
+
+    :return: score from 0 to 255
+    :rtype: double
+    """
+    assert(old.shape[0] == new.shape[0] and old.shape[1] == new.shape[1])
+    sum = 0
+    for i in range(new.shape[0]):
+        for j in range(new.shape[1]):
+            for k in range(3):
+                sum += abs(new[i][j][k] - old[i][j][k])
+    return sum/(new.shape[0]*new.shape[1]*3)

antialiasing/src/supersampling/bicubic.py

@@ -8,7 +8,7 @@
 
 def bicub_val(m):
     """
-    computation for bicubic interpolation
+    Compututes derivatives to calculate coefficients.
 
     :m: 4 by 4 matrix
     :type file: ndarray
@@ -38,7 +38,7 @@ def bicub_val(m):
 
 def compute_coeff(col):
     """
-    computes coefficients for bicubic interpolation
+    Computes coefficients for bicubic interpolation.
 
     :col: a numpy array representing a single color
     :type file: ndarray
@@ -58,7 +58,7 @@ def compute_coeff(col):
 
 def bicub_inter(array, factor):
     """
-    Scales an image by a factor using bicubic interpolation
+    Scales an image by a factor using bicubic interpolation.
 
     :param array: a numpy array representing the image
     :type file: ndarray
@@ -69,7 +69,7 @@ def bicub_inter(array, factor):
     old_shape = array.shape
     new_arr = np.zeros(
         (round(old_shape[0] * factor), round(old_shape[1] * factor), 3),
-        np.uint8)
+        np.int16)
     new_shape = new_arr.shape
     coeffs = []
     coeffs.append(compute_coeff(array[:, :, 0]))

antialiasing/src/supersampling/uniform_grid.py

@@ -19,7 +19,7 @@ def unif_grid(array, factor):
     """
     old_shape = array.shape
     new_arr = np.zeros(
-        (int(old_shape[0] * factor), int(old_shape[1] * factor), 3), np.uint8
+        (int(old_shape[0] * factor), int(old_shape[1] * factor), 3), np.int16
     )
     new_shape = new_arr.shape
     for i in range(new_shape[0]):

antialiasing/src/supersampling/uniform_grid_2.py

@@ -20,7 +20,7 @@ def unif_grid_2(array, factor):
     old_shape = array.shape
     new_arr = np.zeros(
         (round(old_shape[0] * factor), round(old_shape[1] * factor), 3),
-        np.uint8)
+        np.int16)
     new_shape = new_arr.shape
     for i in range(new_shape[0]):
         for j in range(new_shape[1]):

antialiasing/tests/unit-tests/test_image_handling.py

@@ -1,4 +1,6 @@
 from src.image_handling import imp
+from src.image_handling import score
+from src.supersampling.uniform_grid import unif_grid
 import pathlib as pl
 import numpy as np
 
@@ -28,3 +30,10 @@ def test_valid_val():
             for k in range(shape[2]):
                 test_val = array[i][j][k]
                 assert test_val < 256 and test_val >= 0
+
+
+def test_valid_score():
+    small = unif_grid(array, 0.5)
+    modified = unif_grid(small, 2)
+    s = score(array, modified)
+    assert s >= 0 and s <= 255

antialiasing/tests/unit-tests/test_supersampling.py

@@ -1,14 +1,14 @@
 from src.image_handling import imp
 from src.supersampling.uniform_grid import unif_grid
 from src.supersampling.uniform_grid_2 import unif_grid_2
+from src.supersampling.bicubic import bicub_inter
 import pathlib as pl
 import numpy as np
 
 images_path = pl.Path("./antialiasing/tests/images")
 array = imp(images_path / "triangle.jpeg")
 shape = array.shape
 
-
 def test_unif_grid_shape():
     factor = 1.5
     new_arr = unif_grid(array, factor)
@@ -22,7 +22,7 @@ def test_unif_grid_shape():
 
 def test_unit_grid_type():
     new_arr = unif_grid(array, 1.5)
-    assert new_arr.dtype == np.uint8
+    assert new_arr.dtype == np.int16
 
 
 def test_unit_grid_val():
@@ -48,12 +48,38 @@ def test_unif_grid_2_shape():
 
 def test_unit_grid_2_type():
     new_arr = unif_grid_2(array, 1.5)
-    assert new_arr.dtype == np.uint8
+    assert new_arr.dtype == np.int16
 
 
 def test_unit_grid_2_val():
     new_arr = unif_grid_2(array, 1.5)
     new_shape = new_arr.shape
+    for i in range(new_shape[0]):
+        for j in range(new_shape[1]):
+            for k in range(shape[2]):
+                test_val = new_arr[i][j][k]
+                assert test_val < 256 and test_val >= 0
+
+
+def test_bicubic_shape():
+    factor = 1.5
+    new_arr = bicub_inter(array, factor)
+    new_shape = new_arr.shape
+    assert (
+        int(shape[0] * factor) == new_shape[0]
+        and int(shape[1] * factor) == new_shape[1]
+        and new_shape[2] == 3
+    )
+
+
+def test_bicubic_type():
+    new_arr = bicub_inter(array, 1.5)
+    assert new_arr.dtype == np.int16
+
+
+def test_bicubic_type():
+    new_arr = bicub_inter(array, 1.5)
+    new_shape = new_arr.shape
     for i in range(new_shape[0]):
         for j in range(new_shape[1]):
             for k in range(shape[2]):