adding tests, for bscan images

.gitignore

@@ -1,7 +1,8 @@
 # Ignore
 
-# Cache
+# Caches
 __pycache__/
+.pytest_cache/
 
 # Virtual environments
 venv*/

dicomparser/DICOMParser.py

@@ -134,18 +134,55 @@ def extract_common_metadata(self):
 
     @staticmethod
     def get_bscan_images_from_pixel_array(pixel_arr):
-        bscan_count = pixel_arr.shape[0]
-        bscan_images = {}
+        """
+        Converts a 3D numpy array to a dictionary of PIL Image objects.
+
+        Parameters:
+        -----------
+        pixel_arr : numpy.ndarray
+            3D array with shape (n_scans, height, width)
+            First dimension represents individual B-scan slices
+
+        Returns:
+        --------
+        dict
+            Dictionary with keys "bscan{i+1}" and values as PIL Image objects
+            B-scans are numbered starting from 1 in the output keys
+        """
+        bscan_count = pixel_arr.shape[0]  # Get number of B-scans
+        bscan_images = {}  # Initialize empty dictionary
+
+        # Process each B-scan slice
         for i in range(bscan_count):
+            # Convert 2D array slice to PIL Image
             bscan_image = Image.fromarray(pixel_arr[i, :, :])
+
+            # Store in dictionary with 1-based indexing
             bscan_images[f"bscan{i+1}"] = bscan_image
 
         return bscan_images
 
     @staticmethod
     def save_bscan_images(meta, output_pth):
+        """
+        Saves B-scan images from the metadata dictionary to the specified output path.
+
+        Parameters:
+        -----------
+        meta : dict
+            Dictionary containing 'SOP Instance' ID and 'bscan_images' dictionary
+            'bscan_images' should contain PIL Image objects
+
+        output_pth : str
+            Base directory where images will be saved
+        """
+        # Create folder path using SOP Instance ID
         sop_path = os.path.join(output_pth, f"{meta['SOP Instance']}")
-        if not os.path.exists(sop_path): os.makedirs(sop_path) # make pdf (png) folder
+
+        # Create directory if it doesn't exist
+        if not os.path.exists(sop_path): os.makedirs(sop_path)
+
+        # Save each B-scan image as PNG
         for bscan in meta['bscan_images'].keys():
             meta['bscan_images'][bscan].save(os.path.join(sop_path, f"{bscan}.png"))
 

pyproject.toml

@@ -6,12 +6,15 @@ authors = [
     {name = "bbearce",email = "[email protected]"}
 ]
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 dependencies = [
     "hvf-extraction-script (>=0.0.4,<0.0.5)",
     "matplotlib (>=3.10.1,<4.0.0)",
     "pymupdf (>=1.25.4,<2.0.0)",
-    "oct-converter (>=0.6.0,<0.6.3)"
+    "oct-converter (>=0.6.0,<0.6.3)",
+    "pytest (>=8.3.5, <8.3.6)",
+    "pytest-cov (>=6.1.1,<6.1.2)",
+    "tox (>=4.25.0,<4.26.0)"
 ]
 
 

tests/dicomparser/base_class/test_get_bscan_images_from_pixel_array.py

@@ -0,0 +1,41 @@
+import pytest
+import numpy as np
+from PIL import Image
+from dicomparser.DICOMParser import DICOMParser
+
+@pytest.mark.filterwarnings("ignore")
+@pytest.mark.parametrize("pixel_arr, expected_keys, expected_shapes", [
+    # Test case 1: Single B-scan, 2x2 image
+    (np.array([[[1, 2],
+                [3, 4]]], dtype=np.uint8),
+     ["bscan1"],
+     [(2, 2)]),
+
+    # Test case 2: Two B-scans, 3x3 images
+    (np.array([[[1, 2, 3],
+                [4, 5, 6],
+                [7, 8, 9]],
+               [[9, 8, 7],
+                [6, 5, 4],
+                [3, 2, 1]]], dtype=np.uint8),
+     ["bscan1", "bscan2"],
+     [(3, 3), (3, 3)]),
+
+    # Test case 3: Empty input (0 B-scans)
+    (np.empty((0, 2, 2), dtype=np.uint8),
+     [],
+     []),
+])
+def test_get_bscan_images_from_pixel_array(pixel_arr, expected_keys, expected_shapes):
+    """
+    Test the conversion of a 3D numpy array to a dictionary of PIL Images.
+    """
+    dicom_path = '/persist/QTIM/Active/23-0284/dashboard/Data/topcon_samples_from_steve_03_27_2025/OCT/100100_10142024_151925_OP_R_001.dcm'
+    dicom_parser = DICOMParser(dicom_path=dicom_path)
+    # Act
+    result = dicom_parser.get_bscan_images_from_pixel_array(pixel_arr)
+
+    # Assert
+    assert list(result.keys()) == expected_keys
+    assert all(isinstance(result[k], Image.Image) for k in expected_keys)
+    assert [result[k].size for k in expected_keys] == [(w, h) for h, w in expected_shapes]

tests/dicomparser/base_class/test_save_bscan_images.py

@@ -0,0 +1,50 @@
+import pytest
+import numpy as np
+import os
+from PIL import Image
+from dicomparser.DICOMParser import DICOMParser
+
+@pytest.fixture
+def dicom_parser():
+    dicom_path = '/persist/QTIM/Active/23-0284/dashboard/Data/topcon_samples_from_steve_03_27_2025/OCT/100100_10142024_151925_OP_R_001.dcm'
+    return DICOMParser(dicom_path=dicom_path)
+
+@pytest.mark.parametrize("bscan_count, img_shape", [
+    (1, (10, 10)),
+    (3, (5, 5)),
+    (19, (8, 8))
+])
+def test_save_bscan_images(dicom_parser, tmp_path, bscan_count, img_shape):
+    """
+    Test that B-scan images are correctly saved to disk and match the original image content.
+    """
+    # Create dummy pixel data and images
+    bscan_images = {}
+    original_arrays = []
+
+    for i in range(bscan_count):
+        arr = np.random.randint(0, 255, img_shape, dtype=np.uint8)
+        original_arrays.append(arr)
+        bscan_images[f"bscan{i+1}"] = Image.fromarray(arr)
+
+    meta = {
+        "SOP Instance": "TEST123",
+        "bscan_images": bscan_images
+    }
+
+    # Use temporary directory for output
+    dicom_parser.save_bscan_images(meta, tmp_path)
+
+    # Check that all images were saved correctly
+    saved_dir = os.path.join(tmp_path, "TEST123")
+    assert os.path.isdir(saved_dir)
+
+    for i in range(bscan_count):
+        fname = f"bscan{i+1}.png"
+        fpath = os.path.join(saved_dir, fname)
+        assert os.path.exists(fpath)
+
+        # Open saved image and compare pixel values
+        saved_img = Image.open(fpath).convert("L")  # Ensure grayscale
+        saved_arr = np.array(saved_img)
+        np.testing.assert_array_equal(saved_arr, original_arrays[i])

tests/example/test_example.py

@@ -0,0 +1,19 @@
+import pytest
+
+
+@pytest.mark.parametrize("num1, num2, expected", [
+    (2, 3, 6),
+    (5, 4, 20),
+    (10, 0, 0),
+    (-2, 3, -6),
+    (2.5, 2, 5.0),
+])
+def test_multiplication(num1, num2, expected):
+    """
+    Test the multiplication of two numbers.
+    """
+    # Act
+    result = num1 * num2
+
+    # Assert
+    assert result == expected