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Merge pull request #1310 from serengil/feat-task-1708-exception-messages-refactored-for-1339

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Feat task 1708 exception messages refactored for 1339
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Sefik Ilkin Serengil 2024-08-17 08:26:54 +01:00 committed by GitHub
commit f2c2bb27e4
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GPG Key ID: B5690EEEBB952194
3 changed files with 26 additions and 13 deletions
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2
deepface/commons/logger.py
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@ -21,7 +21,7 @@ class Logger:
def __init__(self): def __init__(self):
if not hasattr(self, "_singleton_initialized"): 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)) log_level = os.environ.get("DEEPFACE_LOG_LEVEL", str(logging.INFO))
try: try:
self.log_level = int(log_level) self.log_level = int(log_level)

9
deepface/modules/verification.py
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@ -115,9 +115,8 @@ def verify(
} }
def extract_embeddings_and_facial_areas( def extract_embeddings_and_facial_areas(
img_path: Union[str, np.ndarray, List[float]], img_path: Union[str, np.ndarray, List[float]], index: int
index: int ) -> Tuple[List[List[float]], List[dict]]:
) -> Tuple[List[List[float]], List[dict]]:
""" """
Extracts facial embeddings and corresponding facial areas from an Extracts facial embeddings and corresponding facial areas from an
image or returns pre-calculated embeddings. image or returns pre-calculated embeddings.
@ -150,7 +149,7 @@ def verify(
if silent is False: if silent is False:
logger.warn( 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" "Please ensure that embeddings have been calculated"
f" for the {model_name} model." f" for the {model_name} model."
) )
@ -158,7 +157,7 @@ def verify(
if len(img_path) != dims: if len(img_path) != dims:
raise ValueError( raise ValueError(
f"embeddings of {model_name} should have {dims} dimensions," 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] img_embeddings = [img_path]

16
tests/test_verify.py
View File

@ -153,7 +153,7 @@ def test_verify_with_precalculated_embeddings_for_incorrect_model():
with pytest.raises( with pytest.raises(
ValueError, 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( _ = DeepFace.verify(
img1_path=img1_embedding, img2_path=img2_embedding, model_name="Facenet", silent=True img1_path=img1_embedding, img2_path=img2_embedding, model_name="Facenet", silent=True
@ -172,3 +172,17 @@ def test_verify_for_broken_embeddings():
): ):
_ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_embeddings) _ = 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)