test: add image conversion properties for improved implementation (#112)

* test: add image conversion properties for improved implementation

* pr-fix: install hypothesis in CI build

* pr-sug: remove unnecessary imports

arcus/ml/images/conversion.py

@@ -16,24 +16,30 @@ def to_blackwhite(image_list: np.array, threshold = 128, keep_3d_shape = False)
     Args:
         image_list (np.array): A numpy array that contains all selected images represented as np.array
         threshold (int): The threshold (between 0 and 255) that decides a pixel will be 0 or 255 (W/B)
+        keep_3d_shape (bool): The flag indicating whether to keep the images in shape (H,W,1) or not when encoutering 2D arrays
     Returns: 
         np.array: A numpy array that contains all black & white images represented as np.array
     '''
+    if threshold < 0 or threshold > 255:
+        raise ValueError("Requires a threshold for black/white conversion between 0-255 (inconclusive)")
+
+    def to_gray_scale(img):
+        return np.dot(img, [0.3, 0.59, 0.11])
+    def make_black_or_white(img):
+        return  1.0 * (img > threshold)
+
     images = []
     for img in image_list:
-        # Check if the image is scaled down by 255 or not
-        if(np.count_nonzero(img > 1)==0):
+        is_scaled_down_by_255 = np.count_nonzero(img > 1) == 0
+        if is_scaled_down_by_255:
             img *= 255
-        if(len(img.shape) == 3):
-            if(img.shape[2] == 3):
-                # Convert to grey scale
-                img = np.dot(img, [0.3, 0.59, 0.11])
+        if(len(img.shape) == 3 and img.shape[2] == 3):
+            img = to_gray_scale(img)
         if (len(img.shape) == 2 and keep_3d_shape):
             img = np.expand_dims(img, -1)
-        # make all pixels < threshold black
-        img = 1.0 * (img > threshold) 
+        img = make_black_or_white(img) 
         images.append(img)
-    
+
     return np.array(images)
 
 def prepare(image: np.array, image_size = None, 

build/ci-build.yml

@@ -79,6 +79,7 @@ stages:
           - script: |
               pip install pytest
               pip install pytest-cov
+              pip install hypothesis
               pip install lazydocs
               pytest tests --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
             displayName: 'Unit tests (pytest)'

tests/test_images_conversion.py

@@ -5,25 +5,66 @@
 import arcus.ml.images.io as ami
 import arcus.ml.images.conversion as conv
 import logging
-import numpy as np 
+import numpy as np
+from hypothesis import given, strategies as st
 
 image_path = 'tests/resources/images/lungs'
 
+def is_black_or_white(cell):
+    return cell == 0 or cell == 1
+def all_black_and_white(arr):
+    result = []
+    for cell_z in arr:
+        for cell_y in cell_z:
+            if isinstance(cell_y, np.ndarray):
+                for cell_x in cell_y:
+                    result.append(is_black_or_white(cell_x))
+            else:
+                result.append(is_black_or_white(cell_y))
+    return all(result)
+
+@st.composite
+def images(draw, dimension = 3):
+    def create_array_rec(strategy, lengths, index = 0):
+        if index == len(lengths):
+            return strategy
+        length = lengths[index]
+        arr = st.lists(strategy, min_size=length, max_size=length).map(lambda x : np.array(x))
+        return create_array_rec(arr, lengths, index + 1)
+
+    num = st.integers(min_value=0, max_value=255)
+    amount = st.integers(min_value=1, max_value=10)
+    return draw(st.lists(amount, min_size=dimension, max_size=dimension).flatmap(lambda lengths : create_array_rec(num, lengths)))
+
+@given(images())
+def test_black_white_property(image_list):
+    image_converted = conv.to_blackwhite(image_list)
+    assert all_black_and_white(image_converted)
+
+@given(images(dimension=2))
+def test_black_white_2D_property(image_list):
+    image_converted = conv.to_blackwhite(image_list)
+    assert all_black_and_white(image_converted)
+
+def test_black_white_empty_stays_empty():
+    image_converted = conv.to_blackwhite(np.array([]))
+    image_converted = []
+
+@given(images(), st.integers().filter(lambda x : x < 0 or x > 255))
+def test_black_white_outofbounds_threshold(image_list, threshold):
+    with pytest.raises(ValueError, match=r".* threshold .*"):
+        conv.to_blackwhite(image_list, threshold)
+
 def test_black_white_conversion():
     image_list = ami.load_images(image_path)
-    ls = conv.to_blackwhite(image_list)
-    pixel_value = ls[0][0][0]
-    assert pixel_value == 0 or pixel_value == 1
-    assert len(ls[0].shape)==2
+    image_converted = conv.to_blackwhite(image_list)
+    assert all_black_and_white(image_converted)
 
 def test_black_white_conversion_double_arrays():
     image_list = ami.load_images(image_path)
-    test_list = list()
-    for img in image_list:
-        test_list.append(img / 255)
-    ls = conv.to_blackwhite(np.array(test_list))
-    pixel_value = ls[0][0][0]
-    assert pixel_value == 0 or pixel_value == 1
+    test_list = image_list / 255
+    image_converted = conv.to_blackwhite(np.array(test_list))
+    assert all_black_and_white(image_converted)
 
 def test_crop_image():
     image = ami.load_images(image_path, max_images=1, image_size=50)[0]
@@ -32,10 +73,8 @@ def test_crop_image():
 
 def test_black_white_conversion_3dshape():
     image_list = ami.load_images(image_path)
-    ls = conv.to_blackwhite(image_list, keep_3d_shape=True)
-    pixel_value = ls[0][0][0]
-    assert pixel_value == 0 or pixel_value == 1
-    assert len(ls[0].shape)==3
+    image_converted = conv.to_blackwhite(image_list, keep_3d_shape=True)
+    assert all_black_and_white(image_converted)
 
 def test_black_white_conversion_2x3dshape():
     lung = ami.load_images(image_path, convert_to_grey=True, image_size = 30, keep_3d_shape=True)[0]