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Update color format for `__find_bulk_embeddings`.
This commit is contained in:
dakotah-jones 2025-01-28 10:41:16 -05:00
parent 41cabea7a6
commit 5d4146b248
3 changed files with 26 additions and 4 deletions
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1
deepface/modules/recognition.py
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@ -398,6 +398,7 @@ def __find_bulk_embeddings(
enforce_detection=enforce_detection, enforce_detection=enforce_detection,
align=align, align=align,
expand_percentage=expand_percentage, expand_percentage=expand_percentage,
color_face='bgr' # `represent` expects images in bgr format.
) )
except ValueError as err: except ValueError as err:

7
deepface/modules/representation.py
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@ -74,6 +74,7 @@ def represent(
# we have run pre-process in verification. so, this can be skipped if it is coming from verify. # we have run pre-process in verification. so, this can be skipped if it is coming from verify.
target_size = model.input_shape target_size = model.input_shape
if detector_backend != "skip": if detector_backend != "skip":
# Images are returned in RGB format.
img_objs = detection.extract_faces( img_objs = detection.extract_faces(
img_path=img_path, img_path=img_path,
detector_backend=detector_backend, detector_backend=detector_backend,
@ -88,12 +89,12 @@ def represent(
# Try load. If load error, will raise exception internal # Try load. If load error, will raise exception internal
img, _ = image_utils.load_image(img_path) img, _ = image_utils.load_image(img_path)
# bgr to rgb
img = img[:, :, ::-1]
if len(img.shape) != 3: if len(img.shape) != 3:
raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}") raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
# Convert to RGB format to keep compatability with `extract_faces`.
img = img[:, :, ::-1]
# make dummy region and confidence to keep compatibility with `extract_faces` # make dummy region and confidence to keep compatibility with `extract_faces`
img_objs = [ img_objs = [
{ {

20
tests/test_represent.py
View File

@ -81,3 +81,23 @@ def test_max_faces():
max_faces = 1 max_faces = 1
results = DeepFace.represent(img_path="dataset/couple.jpg", max_faces=max_faces) results = DeepFace.represent(img_path="dataset/couple.jpg", max_faces=max_faces)
assert len(results) == max_faces assert len(results) == max_faces
def test_represent_detector_backend():
# Results using a detection backend.
results_1 = DeepFace.represent(img_path="dataset/img1.jpg")
assert len(results_1) == 1
# Results performing face extraction first.
faces = DeepFace.extract_faces(img_path="dataset/img1.jpg", color_face='bgr')
assert len(faces) == 1
# Images sent into represent need to be in BGR format.
img = faces[0]['face']
results_2 = DeepFace.represent(img_path=img, detector_backend="skip")
assert len(results_2) == 1
# The embeddings should be the exact same for both cases.
embedding_1 = results_1[0]['embedding']
embedding_2 = results_2[0]['embedding']
assert embedding_1 == embedding_2