Resolve "In painting has different output based on computer OS"

Closes #2 (closed)

As discussed in #2 (closed), small differences coming from difference linear algebra libraries can be amplified during further computation (especially in uint8 precision) and lead to slightly different output on different systems. This is not something that can be easily worked around, therefore the chosen solution is to increase the tolerance of the unit tests to allow for these small modifications.

bob/ip/stereo/test/test.py

@@ -62,8 +62,9 @@ def canberra_distance(array1, array2):
 def is_close_enough(image1, image2):
     """Checks if the 2 inputs are close enough to pass the test, using different metrics.
 
-    The test is considered passed if the difference between the inputs is not more than the a small value for each pixel.
-    Eg: the first test is considered passed if "distance(image1, image2) < distance(image1, image1 + 2)"
+    The test is considered passed if the difference between the inputs is not more than the a small value for each 
+    pixel on average.
+    Eg: the first test is considered passed if "distance(image1, image2) < distance(image1, image1 + epsilon)"
 
     The images are expected to be in unsigned int format (so positive).
 
@@ -73,11 +74,13 @@ def is_close_enough(image1, image2):
 
     nb_elements = np.prod(image1.shape)
 
+    epsilon = 0.5  # allowed average difference between image
+
     return (
-        sum_of_squared_absolute_difference(image1, image2) < 2 * nb_elements
-        and euclidian_distance(image1, image2) < 4 * nb_elements
-        and manhattan_distance(image1, image2) < 2 * nb_elements
-        and canberra_distance(image1, image2) < 2 * nb_elements / (np.mean(image1) + np.mean(image2))
+        sum_of_squared_absolute_difference(image1, image2) < epsilon * nb_elements
+        and euclidian_distance(image1, image2) < (epsilon ** 2) * nb_elements
+        and manhattan_distance(image1, image2) < epsilon * nb_elements
+        and canberra_distance(image1, image2) < epsilon * nb_elements / (np.mean(image1) + np.mean(image2))
     )
 
 
@@ -116,18 +119,5 @@ def test_stereo_mapping_and_project():
     assert groundtruth_color_image.shape == projected_image.shape
     assert groundtruth_map_3d.shape == map_3d.shape
 
-    # assert np.allclose(groundtruth_color_image, projected_image)
-    # assert np.allclose(groundtruth_map_3d, map_3d)
-
-    print("ssad", sum_of_squared_absolute_difference(groundtruth_color_image, projected_image))
-    print("n2", euclidian_distance(groundtruth_color_image, projected_image))
-    print("manhattan", manhattan_distance(groundtruth_color_image, projected_image))
-    print("canberra", canberra_distance(groundtruth_color_image, projected_image))
-
-    print("ssad", sum_of_squared_absolute_difference(groundtruth_map_3d, map_3d))
-    print("n2", euclidian_distance(groundtruth_map_3d, map_3d))
-    print("manhattan", manhattan_distance(groundtruth_map_3d, map_3d))
-    print("canberra", canberra_distance(groundtruth_map_3d, map_3d))
-
     assert is_close_enough(groundtruth_color_image, projected_image)
     assert is_close_enough(groundtruth_map_3d, map_3d)