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Reformat with black and pylint.

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This commit is contained in:
Vincent STRAGIER 2023-03-01 13:40:46 +01:00
parent 0d176360ad
commit 3a325f5540
2 changed files with 35 additions and 36 deletions
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28
.vscode/settings.json vendored
View File

@ -1,17 +1,13 @@
{ {
"python.linting.pylintEnabled": true, "python.linting.pylintEnabled": true,
"python.linting.enabled": true, "python.linting.enabled": true,
"python.linting.pylintUseMinimalCheckers": false, "python.linting.pylintUseMinimalCheckers": false,
"editor.formatOnSave": true, "editor.formatOnSave": true,
"editor.renderWhitespace": "all", "editor.renderWhitespace": "all",
"files.autoSave": "afterDelay", "files.autoSave": "afterDelay",
"python.analysis.typeCheckingMode": "basic", "python.analysis.typeCheckingMode": "basic",
"python.formatting.provider": "black", "python.formatting.provider": "autopep8",
"python.formatting.blackArgs": [ "python.formatting.blackArgs": ["--line-length=100"],
"--line-length=100" "editor.fontWeight": "normal",
], "python.analysis.extraPaths": ["./deepface"]
"editor.fontWeight": "normal", }
"python.analysis.extraPaths": [
"./deepface"
]
}

43
deepface/commons/functions.py
View File

@ -85,29 +85,25 @@ def load_image(img):
Returns: Returns:
numpy array: the loaded image. numpy array: the loaded image.
""" """
exact_image = False
# The image is already a numpy array # The image is already a numpy array
if type(img).__module__ == np.__name__: if type(img).__module__ == np.__name__:
# exact_image = True
return img return img
# The image is a base64 string # The image is a base64 string
elif img.startswith("data:image/"): if img.startswith("data:image/"):
return loadBase64Img(img) return loadBase64Img(img)
# The image is a url # The image is a url
elif img.startswith("http"): if img.startswith("http"):
return np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))[:, :, ::-1] return np.array(
Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB")
)[:, :, ::-1]
# The image is a path # The image is a path
if exact_image is not True: # image path passed as input if os.path.isfile(img) is not True:
if os.path.isfile(img) is not True: raise ValueError(f"Confirm that {img} exists")
raise ValueError(f"Confirm that {img} exists")
return cv2.imread(img) return cv2.imread(img)
return img
# -------------------------------------------------- # --------------------------------------------------
@ -125,9 +121,11 @@ def extract_faces(
Args: Args:
img: a path, url, base64 or numpy array. img: a path, url, base64 or numpy array.
target_size (tuple, optional): the target size of the extracted faces. Defaults to (224, 224). target_size (tuple, optional): the target size of the extracted faces.
Defaults to (224, 224).
detector_backend (str, optional): the face detector backend. Defaults to "opencv". detector_backend (str, optional): the face detector backend. Defaults to "opencv".
grayscale (bool, optional): whether to convert the extracted faces to grayscale. Defaults to False. grayscale (bool, optional): whether to convert the extracted faces to grayscale.
Defaults to False.
enforce_detection (bool, optional): whether to enforce face detection. Defaults to True. enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
align (bool, optional): whether to align the extracted faces. Defaults to True. align (bool, optional): whether to align the extracted faces. Defaults to True.
@ -150,7 +148,8 @@ def extract_faces(
else: else:
face_detector = FaceDetector.build_model(detector_backend) face_detector = FaceDetector.build_model(detector_backend)
face_objs = FaceDetector.detect_faces( face_objs = FaceDetector.detect_faces(
face_detector, detector_backend, img, align) face_detector, detector_backend, img, align
)
# in case of no face found # in case of no face found
if len(face_objs) == 0 and enforce_detection is True: if len(face_objs) == 0 and enforce_detection is True:
@ -164,7 +163,6 @@ def extract_faces(
for current_img, current_region, confidence in face_objs: for current_img, current_region, confidence in face_objs:
if current_img.shape[0] > 0 and current_img.shape[1] > 0: if current_img.shape[0] > 0 and current_img.shape[1] > 0:
if grayscale is True: if grayscale is True:
current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY) current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
@ -175,7 +173,9 @@ def extract_faces(
factor = min(factor_0, factor_1) factor = min(factor_0, factor_1)
dsize = ( dsize = (
int(current_img.shape[1] * factor), int(current_img.shape[0] * factor)) int(current_img.shape[1] * factor),
int(current_img.shape[0] * factor),
)
current_img = cv2.resize(current_img, dsize) current_img = cv2.resize(current_img, dsize)
diff_0 = target_size[0] - current_img.shape[0] diff_0 = target_size[0] - current_img.shape[0]
@ -194,8 +194,10 @@ def extract_faces(
else: else:
current_img = np.pad( current_img = np.pad(
current_img, current_img,
((diff_0 // 2, diff_0 - diff_0 // 2), (
(diff_1 // 2, diff_1 - diff_1 // 2)), (diff_0 // 2, diff_0 - diff_0 // 2),
(diff_1 // 2, diff_1 - diff_1 // 2),
),
"constant", "constant",
) )
@ -233,7 +235,8 @@ def normalize_input(img, normalization="base"):
Args: Args:
img (numpy array): the input image. img (numpy array): the input image.
normalization (str, optional): the normalization technique. Defaults to "base", for no normalization. normalization (str, optional): the normalization technique. Defaults to "base",
for no normalization.
Returns: Returns:
numpy array: the normalized image. numpy array: the normalized image.