(face) parameterize test

image_recognition_face_recognition/package.xml

@@ -29,6 +29,7 @@
   <exec_depend>rospy</exec_depend>
 
   <test_depend>catkin_lint_cmake</test_depend>
+  <test_depend>python3-parameterized</test_depend>
 
   <doc_depend>python3-sphinx</doc_depend>
   <doc_depend>python-sphinx-autoapi-pip</doc_depend>

image_recognition_face_recognition/test/test_face_recognizer.py

@@ -6,6 +6,7 @@
 import cv2
 import numpy as np
 import torch
+from parameterized import parameterized
 from image_recognition_face_recognition.face_recognizer import FaceRecognizer, RecognizedFace, ROI
 
 
@@ -57,29 +58,25 @@ def test_update_with_categorical_distribution(self):
         updated_face = self.face_recognizer._update_with_categorical_distribution(recognized_face)
         self.assertIsInstance(updated_face, RecognizedFace)
 
-    def test_detect(self):
+    @parameterized.expand([
+        ("doc/1.jpg", 1),
+        ("doc/example.png", 6),
+    ])
+    def test_detect(self, image_name, expected_faces):
         """
-        Test the detect method with different images (single/multiple faces, no faces)
+        Test the detect method with different images (single/multiple faces)
         """
-        # Test with an image containing one face
-        image_path = Path(__file__).parent / "doc" / "1.jpg"
-        single_face_image = cv2.imread(str(image_path))
-        self.assertIsNotNone(single_face_image, f"Image not found at {image_path}")
-        recognized_faces = self.face_recognizer.detect(single_face_image)
+        image_path = Path(__file__).parent / image_name
+        image = cv2.imread(str(image_path))
+        self.assertIsNotNone(image, f"Image not found at {image_path}")
+        recognized_faces = self.face_recognizer.detect(image)
         self.assertIsInstance(recognized_faces, list)
-        self.assertEqual(len(recognized_faces), 1)  # Expect exactly 1 face
-
-        # Test with an image containing multiple faces
-        image_path = Path(__file__).parent / "doc" / "example.png"
-        multiple_faces_image = cv2.imread(str(image_path))
-        self.assertIsNotNone(multiple_faces_image, f"Image not found at {image_path}")
-        recognized_faces = self.face_recognizer.detect(multiple_faces_image)
-        self.assertIsInstance(recognized_faces, list)
-        self.assertGreaterEqual(len(recognized_faces), 6)  # Expect at least 2 faces
+        self.assertEqual(len(recognized_faces), expected_faces)
 
+    def test_detect_no_faces(self):
         # Test with an image containing no faces
-        no_face_image = np.zeros((160, 160, 3), dtype=np.uint8)
-        recognized_faces = self.face_recognizer.detect(no_face_image)
+        image = np.zeros((160, 160, 3), dtype=np.uint8)
+        recognized_faces = self.face_recognizer.detect(image)
         self.assertEqual(len(recognized_faces), 0)
 
     def test_get_min_l2_distance(self):