Feat/use sh testing utils

features/eolearn/tests/conftest.py

@@ -8,6 +8,8 @@
 
 from eolearn.core import EOPatch
 
+pytest.register_assert_rewrite("sentinelhub.testing_utils")  # makes asserts in helper functions work with pytest
+
 EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data")
 EXAMPLE_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch")
 

features/eolearn/tests/test_blob.py

@@ -11,51 +11,50 @@
 import copy
 import sys
 
-import numpy as np
 import pytest
-from numpy.testing import assert_array_equal
 from pytest import approx
 from skimage.feature import blob_dog
 
+from sentinelhub.testing_utils import test_numpy_data
+
 from eolearn.core import FeatureType
-from eolearn.core.eodata_io import FeatureIO
 from eolearn.features import BlobTask, DoGBlobTask, DoHBlobTask, LoGBlobTask
 
 FEATURE = (FeatureType.DATA, "NDVI", "blob")
+BLOB_FEATURE = (FeatureType.DATA, "blob")
 
 
-def test_blob_feature(small_ndvi_eopatch):
+def test_dog_blob_task(small_ndvi_eopatch):
     eopatch = small_ndvi_eopatch
     BlobTask(FEATURE, blob_dog, sigma_ratio=1.6, min_sigma=1, max_sigma=30, overlap=0.5, threshold=0)(eopatch)
     DoGBlobTask((FeatureType.DATA, "NDVI", "blob_dog"), threshold=0)(eopatch)
-    assert eopatch.data["blob"] == approx(
-        eopatch.data["blob_dog"]
-    ), "DoG derived class result not equal to base class result"
+    assert eopatch[BLOB_FEATURE] == approx(eopatch.data["blob_dog"])
 
 
 BLOB_TESTS = [
-    [DoGBlobTask(FEATURE, threshold=0), 0.0, 37.9625, 0.0854, 0.0],
+    (DoGBlobTask(FEATURE, threshold=0), {"exp_min": 0.0, "exp_max": 37.9625, "exp_mean": 0.08545, "exp_median": 0.0}),
 ]
 if sys.version_info >= (3, 8):  # For Python 3.7 scikit-image returns less accurate result for this test
-    BLOB_TESTS.append([DoHBlobTask(FEATURE, num_sigma=5, threshold=0), 0.0, 21.9203, 0.05807, 0.0])
-    BLOB_TESTS.append([LoGBlobTask(FEATURE, log_scale=True, threshold=0), 0, 42.4264, 0.0977, 0.0])
-
-
-@pytest.mark.parametrize("task, expected_min, expected_max, expected_mean, expected_median", BLOB_TESTS)
-def test_blob(small_ndvi_eopatch, task, expected_min, expected_max, expected_mean, expected_median):
+    BLOB_TESTS.extend(
+        [
+            (
+                DoHBlobTask(FEATURE, num_sigma=5, threshold=0),
+                {"exp_min": 0.0, "exp_max": 21.9203, "exp_mean": 0.05807, "exp_median": 0.0},
+            ),
+            (
+                LoGBlobTask(FEATURE, log_scale=True, threshold=0),
+                {"exp_min": 0, "exp_max": 42.4264, "exp_mean": 0.09767, "exp_median": 0.0},
+            ),
+        ]
+    )
+
+
+@pytest.mark.parametrize("task, expected_statistics", BLOB_TESTS)
+def test_blob_task(small_ndvi_eopatch, task, expected_statistics):
     eopatch = copy.deepcopy(small_ndvi_eopatch)
     task.execute(eopatch)
 
-    # Test that no other features were modified
-    for feature, value in small_ndvi_eopatch.data.items():
-        if isinstance(value, FeatureIO):
-            value = value.load()
-        assert_array_equal(value, eopatch.data[feature], err_msg=f"EOPatch data feature '{feature}' has changed")
-
-    delta = 1e-4
+    test_numpy_data(eopatch[BLOB_FEATURE], exp_shape=(10, 20, 20, 1), **expected_statistics, delta=1e-4)
 
-    blob = eopatch.data[FEATURE[-1]]
-    assert np.min(blob) == approx(expected_min, abs=delta)
-    assert np.max(blob) == approx(expected_max, abs=delta)
-    assert np.mean(blob) == approx(expected_mean, abs=delta)
-    assert np.median(blob) == approx(expected_median, abs=delta)
+    del eopatch[BLOB_FEATURE]
+    assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected."

features/eolearn/tests/test_haralick.py

@@ -10,56 +10,38 @@
 
 import numpy as np
 import pytest
-from numpy.testing import assert_array_equal
-from pytest import approx
+
+from sentinelhub.testing_utils import test_numpy_data
 
 from eolearn.core import FeatureType
-from eolearn.core.eodata_io import FeatureIO
 from eolearn.features import HaralickTask
 
 FEATURE = (FeatureType.DATA, "NDVI", "haralick")
+OUTPUT_FEATURE = (FeatureType.DATA, "haralick")
 
 
 @pytest.mark.parametrize(
-    "task, expected_min, expected_max, expected_mean, expected_median",
+    "task, expected_statistics",
     (
         [
             HaralickTask(FEATURE, texture_feature="contrast", angle=0, levels=255, window_size=3),
-            3.5,
-            9079.0,
-            965.8295,
-            628.5833,
+            {"exp_min": 3.5, "exp_max": 9079.0, "exp_mean": 965.8295, "exp_median": 628.5833},
         ],
         [
             HaralickTask(FEATURE, texture_feature="sum_of_square_variance", angle=np.pi / 2, levels=8, window_size=5),
-            0.96899,
-            48.7815,
-            23.0229,
-            23.8987,
+            {"exp_min": 0.96899, "exp_max": 48.7815, "exp_mean": 23.0229, "exp_median": 23.8987},
         ],
         [
             HaralickTask(FEATURE, texture_feature="sum_entropy", angle=-np.pi / 2, levels=8, window_size=7),
-            0,
-            1.7463,
-            0.5657,
-            0.5055,
+            {"exp_min": 0, "exp_max": 1.7463, "exp_mean": 0.5657, "exp_median": 0.50558},
         ],
     ),
 )
-def test_haralick(small_ndvi_eopatch, task, expected_min, expected_max, expected_mean, expected_median):
+def test_haralick(small_ndvi_eopatch, task, expected_statistics):
     eopatch = copy.deepcopy(small_ndvi_eopatch)
     task.execute(eopatch)
 
-    # Test that no other features were modified
-    for feature, value in small_ndvi_eopatch.data.items():
-        if isinstance(value, FeatureIO):
-            value = value.load()
-        assert_array_equal(value, eopatch.data[feature], err_msg=f"EOPatch data feature '{feature}' has changed")
-
-    delta = 1e-4
+    test_numpy_data(eopatch[OUTPUT_FEATURE], exp_shape=(10, 20, 20, 1), **expected_statistics, delta=1e-4)
 
-    haralick = eopatch.data["haralick"]
-    assert np.min(haralick) == approx(expected_min, abs=delta)
-    assert np.max(haralick) == approx(expected_max, abs=delta)
-    assert np.mean(haralick) == approx(expected_mean, abs=delta)
-    assert np.median(haralick) == approx(expected_median, abs=delta)
+    del eopatch[OUTPUT_FEATURE]
+    assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected."

features/eolearn/tests/test_hog.py

@@ -8,12 +8,9 @@
 """
 import copy
 
-import numpy as np
-from numpy.testing import assert_array_equal
-from pytest import approx
+from sentinelhub.testing_utils import test_numpy_data
 
 from eolearn.core import FeatureType
-from eolearn.core.eodata_io import FeatureIO
 from eolearn.features import HOGTask
 
 
@@ -30,19 +27,12 @@ def test_hog(small_ndvi_eopatch):
     eopatch = copy.deepcopy(small_ndvi_eopatch)
     task.execute(eopatch)
 
-    # Test that no other features were modified
-    for feature, value in small_ndvi_eopatch.data.items():
-        if isinstance(value, FeatureIO):
-            value = value.load()
-        assert_array_equal(value, eopatch.data[feature], err_msg=f"EOPatch data feature '{feature}' has changed")
-
-    delta = 1e-4
-    for feature, expected_min, expected_max, expected_mean, expected_median in [
-        ("hog", 0.0, 0.5567, 0.0931, 0.0),
-        ("hog_visu", 0.0, 0.3241, 0.0105, 0.0),
+    for feature_name, expected_statistics in [
+        ("hog", {"exp_min": 0.0, "exp_max": 0.5567, "exp_mean": 0.09309, "exp_median": 0.0}),
+        ("hog_visu", {"exp_min": 0.0, "exp_max": 0.3241, "exp_mean": 0.010537, "exp_median": 0.0}),
     ]:
-        hog = eopatch.data[feature]
-        assert np.min(hog) == approx(expected_min, abs=delta)
-        assert np.max(hog) == approx(expected_max, abs=delta)
-        assert np.mean(hog) == approx(expected_mean, abs=delta)
-        assert np.median(hog) == approx(expected_median, abs=delta)
+        test_numpy_data(eopatch.data[feature_name], **expected_statistics, delta=1e-4)
+
+    del eopatch[(FeatureType.DATA, "hog")]
+    del eopatch[(FeatureType.DATA, "hog_visu")]
+    assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected."