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Update minimum distance between embeddings calculation

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This commit is contained in:
kremnik 2024-08-16 04:56:58 +03:00
parent 6b75d54f98
commit ca1db5fac7
2 changed files with 44 additions and 79 deletions
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121
deepface/modules/verification.py
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@ -105,79 +105,47 @@ def verify(
) )
dims = model.output_shape dims = model.output_shape
# extract faces from img1 def extract_faces_from_img(img_path, index=1):
if isinstance(img1_path, list): # extract faces from img
# given image is already pre-calculated embedding if isinstance(img_path, list):
if not all(isinstance(dim, float) for dim in img1_path): # given image is already pre-calculated embedding
raise ValueError( if not all(isinstance(dim, float) for dim in img_path):
"When passing img1_path as a list, ensure that all its items are of type float." raise ValueError(
) f"When passing img{index}_path as a list, ensure that all its items are of type float."
)
if silent is False: if silent is False:
logger.warn( logger.warn(
"You passed 1st image as pre-calculated embeddings." "You passed 1st image as pre-calculated embeddings."
f"Please ensure that embeddings have been calculated for the {model_name} model." f"Please ensure that embeddings have been calculated for the {model_name} model."
) )
if len(img1_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(img1_path)} dimensions input" f" but it has {len(img_path)} dimensions input"
) )
img1_embeddings = [img1_path] img_embeddings = [img_path]
img1_facial_areas = [None] img_facial_areas = [None]
else: else:
try: try:
img1_embeddings, img1_facial_areas = __extract_faces_and_embeddings( img_embeddings, img_facial_areas = __extract_faces_and_embeddings(
img_path=img1_path, img_path=img_path,
model_name=model_name, model_name=model_name,
detector_backend=detector_backend, detector_backend=detector_backend,
enforce_detection=enforce_detection, enforce_detection=enforce_detection,
align=align, align=align,
expand_percentage=expand_percentage, expand_percentage=expand_percentage,
normalization=normalization, normalization=normalization,
anti_spoofing=anti_spoofing, anti_spoofing=anti_spoofing,
) )
except ValueError as err: except ValueError as err:
raise ValueError("Exception while processing img1_path") from err raise ValueError(f"Exception while processing img{index}_path") from err
return img_embeddings, img_facial_areas
# extract faces from img2 img1_embeddings, img1_facial_areas = extract_faces_from_img(img1_path, 1)
if isinstance(img2_path, list): img2_embeddings, img2_facial_areas = extract_faces_from_img(img2_path, 2)
# given image is already pre-calculated embedding
if not all(isinstance(dim, float) for dim in img2_path):
raise ValueError(
"When passing img2_path as a list, ensure that all its items are of type float."
)
if silent is False:
logger.warn(
"You passed 2nd image as pre-calculated embeddings."
f"Please ensure that embeddings have been calculated for the {model_name} model."
)
if len(img2_path) != dims:
raise ValueError(
f"embeddings of {model_name} should have {dims} dimensions,"
f" but it has {len(img2_path)} dimensions input"
)
img2_embeddings = [img2_path]
img2_facial_areas = [None]
else:
try:
img2_embeddings, img2_facial_areas = __extract_faces_and_embeddings(
img_path=img2_path,
model_name=model_name,
detector_backend=detector_backend,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
normalization=normalization,
anti_spoofing=anti_spoofing,
)
except ValueError as err:
raise ValueError("Exception while processing img2_path") from err
no_facial_area = { no_facial_area = {
"x": None, "x": None,
@ -188,21 +156,18 @@ def verify(
"right_eye": None, "right_eye": None,
} }
distances = [] min_distance, min_idx, min_idy = float("inf"), None, None
facial_areas = []
for idx, img1_embedding in enumerate(img1_embeddings): for idx, img1_embedding in enumerate(img1_embeddings):
for idy, img2_embedding in enumerate(img2_embeddings): for idy, img2_embedding in enumerate(img2_embeddings):
distance = find_distance(img1_embedding, img2_embedding, distance_metric) distance = find_distance(img1_embedding, img2_embedding, distance_metric)
distances.append(distance) if distance < min_distance:
facial_areas.append( min_distance, min_idx, min_idy = distance, idx, idy
(img1_facial_areas[idx] or no_facial_area, img2_facial_areas[idy] or no_facial_area)
)
# find the face pair with minimum distance # find the face pair with minimum distance
threshold = threshold or find_threshold(model_name, distance_metric) threshold = threshold or find_threshold(model_name, distance_metric)
min_index = np.argmin(distances) distance = float(min_distance)
distance = float(distances[min_index]) # best distance facial_areas = (no_facial_area, no_facial_area) if None in (min_idx, min_idy) else \
facial_areas = facial_areas[min_index] (img1_facial_areas[min_idx], img2_facial_areas[min_idy])
toc = time.time() toc = time.time()