Merge pull request #1310 from serengil/feat-task-1708-exception-messa…

…ges-refactored-for-1339

Feat task 1708 exception messages refactored for 1339

deepface/commons/logger.py

@@ -8,7 +8,7 @@ class Logger:
     A Logger class for logging messages with a specific log level.
 
     The class follows the singleton design pattern, ensuring that only one
-    instance of the Logger is created. The parameters of the first instance 
+    instance of the Logger is created. The parameters of the first instance
     are preserved across all instances.
     """
 
@@ -21,7 +21,7 @@ def __new__(cls):
 
     def __init__(self):
         if not hasattr(self, "_singleton_initialized"):
-            self._singleton_initialized = True # to prevent multiple initializations
+            self._singleton_initialized = True  # to prevent multiple initializations
             log_level = os.environ.get("DEEPFACE_LOG_LEVEL", str(logging.INFO))
             try:
                 self.log_level = int(log_level)

deepface/modules/verification.py

@@ -115,22 +115,21 @@ def verify(
     }
 
     def extract_embeddings_and_facial_areas(
-            img_path: Union[str, np.ndarray, List[float]],
-            index: int
-        ) -> Tuple[List[List[float]], List[dict]]:
+        img_path: Union[str, np.ndarray, List[float]], index: int
+    ) -> Tuple[List[List[float]], List[dict]]:
         """
         Extracts facial embeddings and corresponding facial areas from an
         image or returns pre-calculated embeddings.
 
         Depending on the type of img_path, the function either extracts
-        facial embeddings from the provided image 
+        facial embeddings from the provided image
         (via a path or NumPy array) or verifies that the input is a list of
         pre-calculated embeddings and validates them.
 
         Args:
-            img_path (Union[str, np.ndarray, List[float]]): 
-                - A string representing the file path to an image, 
-                - A NumPy array containing the image data, 
+            img_path (Union[str, np.ndarray, List[float]]):
+                - A string representing the file path to an image,
+                - A NumPy array containing the image data,
                 - Or a list of pre-calculated embedding values (of type `float`).
             index (int): An index value used in error messages and logging
             to identify the number of the image.
@@ -150,15 +149,15 @@ def extract_embeddings_and_facial_areas(
 
             if silent is False:
                 logger.warn(
-                    "You passed 1st image as pre-calculated embeddings."
+                    f"You passed {index}-th image as pre-calculated embeddings."
                     "Please ensure that embeddings have been calculated"
                     f" for the {model_name} model."
                 )
 
             if len(img_path) != dims:
                 raise ValueError(
                     f"embeddings of {model_name} should have {dims} dimensions,"
-                    f" but it has {len(img_path)} dimensions input"
+                    f" but {index}-th image has {len(img_path)} dimensions input"
                 )
 
             img_embeddings = [img_path]

tests/test_verify.py

@@ -153,7 +153,7 @@ def test_verify_with_precalculated_embeddings_for_incorrect_model():
 
     with pytest.raises(
         ValueError,
-        match="embeddings of Facenet should have 128 dimensions, but it has 4096 dimensions input",
+        match="embeddings of Facenet should have 128 dimensions, but 1-th image has 4096 dimensions input",
     ):
         _ = DeepFace.verify(
             img1_path=img1_embedding, img2_path=img2_embedding, model_name="Facenet", silent=True
@@ -171,4 +171,18 @@ def test_verify_for_broken_embeddings():
         match="When passing img1_path as a list, ensure that all its items are of type float.",
     ):
         _ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_embeddings)
-    logger.info("✅ test verify for broken embeddings content is done")
+    logger.info("✅ test verify for broken embeddings content is done")
+
+
+def test_verify_for_nested_embeddings():
+    """
+    batch embeddings not supported
+    """
+    img1_embeddings = [[1, 2, 3], [4, 5, 6]]
+    img2_path = "dataset/img1.jpg"
+
+    with pytest.raises(
+        ValueError,
+        match="When passing img1_path as a list, ensure that all its items are of type float",
+    ):
+        _ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_path)