Upload CT_2D images and adjust tests

Signed-off-by: Felix Schnabel <[email protected]>
Project-MONAI · Jan 29, 2023 · e4b89cf · e4b89cf
1 parent ce4fef5
commit e4b89cf
Show file tree

Hide file tree

Showing 4 changed files with 52 additions and 58 deletions.
diff --git a/monai/data/itk_torch_affine_matrix_bridge.py b/monai/data/itk_torch_affine_matrix_bridge.py
@@ -40,6 +40,7 @@
 ]
 
 
+# TODO remove
 def metatensor_to_array(metatensor: MetaTensor):
     metatensor = metatensor.squeeze()
     metatensor = metatensor.permute(*torch.arange(metatensor.ndim - 1, -1, -1))
@@ -106,8 +107,7 @@ def itk_to_monai_affine(image, matrix, translation, center_of_rotation=None) ->
     Converts an ITK affine matrix (2x2 for 2D or 3x3 for 3D matrix and translation vector) to a MONAI affine matrix.
 
     Args:
-        image: The ITK image object. This is used to extract the spacing and
-               direction information.
+        image: The ITK image object. This is used to extract the spacing and direction information.
         matrix: The 2x2 or 3x3 ITK affine matrix.
         translation: The 2-element or 3-element ITK affine translation vector.
         center_of_rotation: The center of rotation. If provided, the affine
@@ -150,8 +150,7 @@ def monai_to_itk_affine(image, affine_matrix, center_of_rotation=None):
     3D matrix and translation vector). See also 'itk_to_monai_affine'.
 
     Args:
-        image: The ITK image object. This is used to extract the spacing and
-               direction information.
+        image: The ITK image object. This is used to extract the spacing and direction information.
         affine_matrix: The 3x3 for 2D or 4x4 for 3D MONAI affine matrix.
         center_of_rotation: The center of rotation. If provided, the affine
                             matrix will be adjusted to account for the difference
@@ -208,8 +207,7 @@ def create_itk_affine_from_parameters(
     Args:
         image: The ITK image.
         translation: The translation (shift) to apply to the image.
-        rotation: The rotation to apply to the image, specified as angles in radians
-                around the x, y, and z axes.
+        rotation: The rotation to apply to the image, specified as angles in radians around the x, y, and z axes.
         scale: The scaling factor to apply to the image.
         shear: The shear to apply to the image.
         center_of_rotation: The center of rotation for the image. If not specified,
@@ -279,7 +277,9 @@ def itk_affine_resample(image, matrix, translation, center_of_rotation=None):
 
 
 def monai_affine_resample(metatensor: MetaTensor, affine_matrix: NdarrayOrTensor):
-    # TODO documentation
+    # TODO documentation, change to mode=3
+    #affine = Affine(affine=affine_matrix, mode=3, padding_mode='mirror', dtype=torch.float64, image_only=True)
+    #output_tensor = cast(MetaTensor, affine(metatensor))
     affine = Affine(affine=affine_matrix, padding_mode="zeros", dtype=torch.float64, image_only=True)
     output_tensor = cast(MetaTensor, affine(metatensor, mode="bilinear"))
 

diff --git a/tests/test_itk_torch_affine_matrix_bridge.py b/tests/test_itk_torch_affine_matrix_bridge.py
@@ -33,37 +33,40 @@
     monai_to_itk_affine,
 )
 
-TEST_CASES = [
+TESTS = [
     "CT_2D_head_fixed.mha",
     "CT_2D_head_moving.mha",
     #"copd1_highres_INSP_STD_COPD_img.nii.gz"
 ]
-# Download URL: https://data.kitware.com/api/v1/file/62a0f067bddec9d0c4175c5a/download
+# Download URL:
 # SHA-521: 60193cd6ef0cf055c623046446b74f969a2be838444801bd32ad5bedc8a7eeecb343e8a1208769c9c7a711e101c806a3133eccdda7790c551a69a64b9b3701e9
-#TEST_CASE_3D = {"filepath": "copd1_highres_INSP_STD_COPD_img.nii.gz"}
+#TEST_CASE_3D_1 = [
+# "copd1_highres_INSP_STD_COPD_img.nii.gz" # https://data.kitware.com/api/v1/file/62a0f067bddec9d0c4175c5a/download
+# "copd1_highres_INSP_STD_COPD_img.nii.gz" # https://data.kitware.com/api/v1/item/62a0f045bddec9d0c4175c44/download
+# ]
+
 
 
 @unittest.skipUnless(has_itk, "Requires `itk` package.")
 class TestITKTorchAffineMatrixBridge(unittest.TestCase):
     def setUp(self):
         # TODO: which data should be used
         self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
-        self.reader = ITKReader(pixel_type=itk.F)
-        #data_dir = os.path.join(self.data_dir, "MedNIST")
-        #dataset_file = os.path.join(self.data_dir, "MedNIST.tar.gz")
-#
-        #if not os.path.exists(data_dir):
-        #    with skip_if_downloading_fails():
-        #        data_spec = testing_data_config("images", "mednist")
-        #        download_and_extract(
-        #            data_spec["url"],
-        #            dataset_file,
-        #            self.data_dir,
-        #            hash_val=data_spec["hash_val"],
-        #            hash_type=data_spec["hash_type"],
-        #        )
-
-    @parameterized.expand(TEST_CASES)
+        self.reader = ITKReader()
+
+        #for filepath in TEST_CASES:
+        #    if not os.path.exists(os.path.join(self.data_dir, filepath)):
+        #        with skip_if_downloading_fails():
+        #            data_spec = testing_data_config("images", filepath)
+        #            download_and_extract(
+        #                data_spec["url"],
+        #                filepath,
+        #                self.data_dir,
+        #                #hash_val=data_spec["hash_val"],
+        #                #hash_type=data_spec["hash_type"],
+        #            )
+
+    @parameterized.expand(TESTS)
     def test_setting_affine_parameters(self, filepath):
         # Read image
         image = self.reader.read(os.path.join(self.data_dir, filepath))
@@ -91,10 +94,18 @@ def test_setting_affine_parameters(self, filepath):
         ###########################################################################
         # Make sure that the array conversion of the inputs is the same
         input_array_monai = metatensor_to_array(metatensor)
-        assert np.array_equal(input_array_monai, np.asarray(image))
+        #output_array_monai = ITKWriter.create_backend_obj(
+        #    metatensor.array,
+        #    channel_dim=None,
+        #    affine=affine_matrix_for_monai,
+        #    affine_lps_to_ras=False,  # False if the affine is in itk convention
+        #)
+        np.testing.assert_array_equal(input_array_monai, np.asarray(image))
 
         # Compare outputs
-        print("MONAI-ITK: ", np.allclose(output_array_monai, output_array_itk))
+        percentage = 100 * np.isclose(output_array_monai, output_array_itk).sum() / output_array_itk.size
+        print("MONAI equals result: ", percentage, "%")
+        self.assertGreaterEqual(percentage, 99.0)
 
         diff_output = output_array_monai - output_array_itk
         print(f"[Min, Max] MONAI: [{output_array_monai.min()}, {output_array_monai.max()}]")
@@ -107,7 +118,7 @@ def test_setting_affine_parameters(self, filepath):
         # itk.imwrite(itk.GetImageFromArray(output_array_itk), "./output/output_itk.tif")
         ###########################################################################
 
-    @parameterized.expand(TEST_CASES)
+    @parameterized.expand(TESTS)
     def test_arbitary_center_of_rotation(self, filepath):
         # Read image
         image = self.reader.read(os.path.join(self.data_dir, filepath))
@@ -141,19 +152,21 @@ def test_arbitary_center_of_rotation(self, filepath):
 
         # Make sure that the array conversion of the inputs is the same
         input_array_monai = metatensor_to_array(metatensor)
-        assert np.array_equal(input_array_monai, np.asarray(image))
+        np.testing.assert_array_equal(input_array_monai, np.asarray(image))
 
         ###########################################################################
         # Compare outputs
-        print("MONAI-ITK: ", np.allclose(output_array_monai, output_array_itk))
+        percentage = 100 * np.isclose(output_array_monai, output_array_itk).sum() / output_array_itk.size
+        print("MONAI equals result: ", percentage, "%")
+        self.assertGreaterEqual(percentage, 99.0)
 
         diff_output = output_array_monai - output_array_itk
         print(f"[Min, Max] MONAI: [{output_array_monai.min()}, {output_array_monai.max()}]")
         print(f"[Min, Max] ITK: [{output_array_itk.min()}, {output_array_itk.max()}]")
         print(f"[Min, Max] diff: [{diff_output.min()}, {diff_output.max()}]")
         ###########################################################################
 
-    @parameterized.expand(TEST_CASES)
+    @parameterized.expand(TESTS)
     def test_monai_to_itk(self, filepath):
         print("\nTEST: MONAI affine matrix -> ITK matrix + translation vector -> transform")
         # Read image
@@ -192,19 +205,21 @@ def test_monai_to_itk(self, filepath):
 
         # Make sure that the array conversion of the inputs is the same
         input_array_monai = metatensor_to_array(metatensor)
-        assert np.array_equal(input_array_monai, np.asarray(image))
+        np.testing.assert_array_equal(input_array_monai, np.asarray(image))
 
         ###########################################################################
         # Compare outputs
-        print("MONAI-ITK: ", np.allclose(output_array_monai, output_array_itk))
+        percentage = 100 * np.isclose(output_array_monai, output_array_itk).sum() / output_array_itk.size
+        print("MONAI equals result: ", percentage, "%")
+        self.assertGreaterEqual(percentage, 99.0)
 
         diff_output = output_array_monai - output_array_itk
         print(f"[Min, Max] MONAI: [{output_array_monai.min()}, {output_array_monai.max()}]")
         print(f"[Min, Max] ITK: [{output_array_itk.min()}, {output_array_itk.max()}]")
         print(f"[Min, Max] diff: [{diff_output.min()}, {diff_output.max()}]")
         ###########################################################################
 
-    @parameterized.expand(TEST_CASES)
+    @parameterized.expand(TESTS)
     def test_cyclic_conversion(self, filepath):
         image = self.reader.read(os.path.join(self.data_dir, filepath))
         ndim = image.ndim
@@ -232,30 +247,9 @@ def test_cyclic_conversion(self, filepath):
 
         matrix_result, translation_result = monai_to_itk_affine(image, affine_matrix, center_of_rotation)
 
-        print("Matrix cyclic conversion: ", np.allclose(matrix, matrix_result))
-        print("Translation cyclic conversion: ", np.allclose(translation, translation_result))
+        np.testing.assert_allclose(matrix, matrix_result)
+        np.testing.assert_allclose(translation, translation_result)
 
 
 if __name__ == "__main__":
-
-    # test_utils.download_test_data()
-
-    ## 2D cases
-    # filepath0 = str(test_utils.TEST_DATA_DIR / 'CT_2D_head_fixed.mha')
-    # filepath1 = str(test_utils.TEST_DATA_DIR / 'CT_2D_head_moving.mha')
-    #
-    # test_setting_affine_parameters(filepath=filepath0)
-    # test_arbitary_center_of_rotation(filepath=filepath0)
-    # test_monai_to_itk(filepath=filepath0)
-    # test_cyclic_conversion(filepath=filepath0)
-    #
-    ## 3D cases
-    # filepath2 = str(test_utils.TEST_DATA_DIR / 'copd1_highres_INSP_STD_COPD_img.nii.gz')
-    # filepath3 = str(test_utils.TEST_DATA_DIR / 'copd1_highres_EXP_STD_COPD_img.nii.gz')
-    #
-    # test_setting_affine_parameters(filepath=filepath2)
-    # test_arbitary_center_of_rotation(filepath=filepath2)
-    # test_monai_to_itk(filepath=filepath2)
-    # test_cyclic_conversion(filepath=filepath2)
-
     unittest.main()
diff --git a/tests/testing_data/CT_2D_head_fixed.mha b/tests/testing_data/CT_2D_head_fixed.mha
diff --git a/tests/testing_data/CT_2D_head_moving.mha b/tests/testing_data/CT_2D_head_moving.mha