code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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|---|---|---|---|---|---|---|---|
def find_image_hash(file_path: str) -> str:
"""
Find the hash of given image file with its properties
finding the hash of image content is costly operation
Args:
file_path (str): exact image path
Returns:
hash (str): digest with sha1 algorithm
"""
file_stats = os.stat(fil... |
Find the hash of given image file with its properties
finding the hash of image content is costly operation
Args:
file_path (str): exact image path
Returns:
hash (str): digest with sha1 algorithm
| find_image_hash | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def load_image(img: Union[str, np.ndarray, IO[bytes]]) -> Tuple[np.ndarray, str]:
"""
Load image from path, url, file object, base64 or numpy array.
Args:
img: a path, url, file object, base64 or numpy array.
Returns:
image (numpy array): the loaded image in BGR format
image name... |
Load image from path, url, file object, base64 or numpy array.
Args:
img: a path, url, file object, base64 or numpy array.
Returns:
image (numpy array): the loaded image in BGR format
image name (str): image name itself
| load_image | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def load_image_from_io_object(obj: IO[bytes]) -> np.ndarray:
"""
Load image from an object that supports being read
Args:
obj: a file like object.
Returns:
img (np.ndarray): The decoded image as a numpy array (OpenCV format).
"""
try:
_ = obj.seek(0)
except (Attribute... |
Load image from an object that supports being read
Args:
obj: a file like object.
Returns:
img (np.ndarray): The decoded image as a numpy array (OpenCV format).
| load_image_from_io_object | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def load_image_from_base64(uri: str) -> np.ndarray:
"""
Load image from base64 string.
Args:
uri: a base64 string.
Returns:
numpy array: the loaded image.
"""
encoded_data_parts = uri.split(",")
if len(encoded_data_parts) < 2:
raise ValueError("format error in base6... |
Load image from base64 string.
Args:
uri: a base64 string.
Returns:
numpy array: the loaded image.
| load_image_from_base64 | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def load_image_from_file_storage(file: FileStorage) -> np.ndarray:
"""
Loads an image from a FileStorage object and decodes it into an OpenCV image.
Args:
file (FileStorage): The FileStorage object containing the image file.
Returns:
img (np.ndarray): The decoded image as a numpy array (... |
Loads an image from a FileStorage object and decodes it into an OpenCV image.
Args:
file (FileStorage): The FileStorage object containing the image file.
Returns:
img (np.ndarray): The decoded image as a numpy array (OpenCV format).
| load_image_from_file_storage | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def load_image_from_web(url: str) -> np.ndarray:
"""
Loading an image from web
Args:
url: link for the image
Returns:
img (np.ndarray): equivalent to pre-loaded image from opencv (BGR format)
"""
response = requests.get(url, stream=True, timeout=60)
response.raise_for_status(... |
Loading an image from web
Args:
url: link for the image
Returns:
img (np.ndarray): equivalent to pre-loaded image from opencv (BGR format)
| load_image_from_web | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def find_file_hash(file_path: str, hash_algorithm: str = "sha256") -> str:
"""
Find the hash of a given file with its content
Args:
file_path (str): exact path of a given file
hash_algorithm (str): hash algorithm
Returns:
hash (str)
"""
hash_func = hashlib.new(hash_algori... |
Find the hash of a given file with its content
Args:
file_path (str): exact path of a given file
hash_algorithm (str): hash algorithm
Returns:
hash (str)
| find_file_hash | python | serengil/deepface | deepface/commons/package_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/package_utils.py | MIT |
def load_model_weights(model: Sequential, weight_file: str) -> Sequential:
"""
Load pre-trained weights for a given model
Args:
model (keras.models.Sequential): pre-built model
weight_file (str): exact path of pre-trained weights
Returns:
model (keras.models.Sequential): pre-buil... |
Load pre-trained weights for a given model
Args:
model (keras.models.Sequential): pre-built model
weight_file (str): exact path of pre-trained weights
Returns:
model (keras.models.Sequential): pre-built model with
updated weights
| load_model_weights | python | serengil/deepface | deepface/commons/weight_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py | MIT |
def download_all_models_in_one_shot() -> None:
"""
Download all model weights in one shot
"""
# import model weights from module here to avoid circular import issue
from deepface.models.facial_recognition.VGGFace import WEIGHTS_URL as VGGFACE_WEIGHTS
from deepface.models.facial_recognition.Face... |
Download all model weights in one shot
| download_all_models_in_one_shot | python | serengil/deepface | deepface/commons/weight_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py | MIT |
def _predict_internal(self, img_batch: np.ndarray) -> np.ndarray:
"""
Predict for single image or batched images.
This method uses legacy method while receiving single image as input.
And switch to batch prediction if receives batched images.
Args:
img_batch:
... |
Predict for single image or batched images.
This method uses legacy method while receiving single image as input.
And switch to batch prediction if receives batched images.
Args:
img_batch:
Batch of images as np.ndarray (n, x, y, c)
with ... | _predict_internal | python | serengil/deepface | deepface/models/Demography.py | https://github.com/serengil/deepface/blob/master/deepface/models/Demography.py | MIT |
def _preprocess_batch_or_single_input(
self, img: Union[np.ndarray, List[np.ndarray]]
) -> np.ndarray:
"""
Preprocess single or batch of images, return as 4-D numpy array.
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.nd... |
Preprocess single or batch of images, return as 4-D numpy array.
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
Returns:
Four-dimensional numpy ... | _preprocess_batch_or_single_input | python | serengil/deepface | deepface/models/Demography.py | https://github.com/serengil/deepface/blob/master/deepface/models/Demography.py | MIT |
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.float64, np.ndarray]:
"""
Predict apparent age(s) for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of... |
Predict apparent age(s) for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
Returns:
np.ndarray (age_classes,) if single i... | predict | python | serengil/deepface | deepface/models/demography/Age.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Age.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct age model, download its weights and load
Returns:
model (Model)
"""
model = VGGFace.base_model()
# --------------------------
classes = 101
base_model_output = Sequential()
base_model_output = Convolution2D(cla... |
Construct age model, download its weights and load
Returns:
model (Model)
| load_model | python | serengil/deepface | deepface/models/demography/Age.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Age.py | MIT |
def find_apparent_age(age_predictions: np.ndarray) -> np.float64:
"""
Find apparent age prediction from a given probas of ages
Args:
age_predictions (age_classes,)
Returns:
apparent_age (float)
"""
assert (
len(age_predictions.shape) == 1
), f"Input should be a list o... |
Find apparent age prediction from a given probas of ages
Args:
age_predictions (age_classes,)
Returns:
apparent_age (float)
| find_apparent_age | python | serengil/deepface | deepface/models/demography/Age.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Age.py | MIT |
def _preprocess_image(self, img: np.ndarray) -> np.ndarray:
"""
Preprocess single image for emotion detection
Args:
img: Input image (224, 224, 3)
Returns:
Preprocessed grayscale image (48, 48)
"""
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
... |
Preprocess single image for emotion detection
Args:
img: Input image (224, 224, 3)
Returns:
Preprocessed grayscale image (48, 48)
| _preprocess_image | python | serengil/deepface | deepface/models/demography/Emotion.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Emotion.py | MIT |
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
"""
Predict emotion probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np... |
Predict emotion probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
Returns:
np.ndarray (n, n_emotions)
... | predict | python | serengil/deepface | deepface/models/demography/Emotion.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Emotion.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Sequential:
"""
Consruct emotion model, download and load weights
"""
num_classes = 7
model = Sequential()
# 1st convolution layer
model.add(Conv2D(64, (5, 5), activation="relu", input_shape=(48, 48, 1)))
model.add(MaxPooling2D(pool_size=(5, 5... |
Consruct emotion model, download and load weights
| load_model | python | serengil/deepface | deepface/models/demography/Emotion.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Emotion.py | MIT |
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
"""
Predict gender probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.... |
Predict gender probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
Returns:
np.ndarray (n, 2)
| predict | python | serengil/deepface | deepface/models/demography/Gender.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Gender.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct gender model, download its weights and load
Returns:
model (Model)
"""
model = VGGFace.base_model()
# --------------------------
classes = 2
base_model_output = Sequential()
base_model_output = Convolution2D(cl... |
Construct gender model, download its weights and load
Returns:
model (Model)
| load_model | python | serengil/deepface | deepface/models/demography/Gender.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Gender.py | MIT |
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
"""
Predict race probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.nd... |
Predict race probabilities for single or multiple faces
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
Returns:
np.ndarray (n, n_races)
... | predict | python | serengil/deepface | deepface/models/demography/Race.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Race.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct race model, download its weights and load
"""
model = VGGFace.base_model()
# --------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name="predictions")(mo... |
Construct race model, download its weights and load
| load_model | python | serengil/deepface | deepface/models/demography/Race.py | https://github.com/serengil/deepface/blob/master/deepface/models/demography/Race.py | MIT |
def build_model(self):
"""
Download pre-trained weights of CenterFace model if necessary and load built model
"""
weights_path = weight_utils.download_weights_if_necessary(
file_name="centerface.onnx", source_url=WEIGHTS_URL
)
return CenterFace(weight_path=we... |
Download pre-trained weights of CenterFace model if necessary and load built model
| build_model | python | serengil/deepface | deepface/models/face_detection/CenterFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/CenterFace.py | MIT |
def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
"""
Detect and align face with CenterFace
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
... |
Detect and align face with CenterFace
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/CenterFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/CenterFace.py | MIT |
def build_model(self) -> dict:
"""
Build a dlib hog face detector model
Returns:
model (Any)
"""
# this is not a must dependency. do not import it in the global level.
try:
import dlib
except ModuleNotFoundError as e:
raise Impo... |
Build a dlib hog face detector model
Returns:
model (Any)
| build_model | python | serengil/deepface | deepface/models/face_detection/Dlib.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Dlib.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with dlib
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp ... |
Detect and align face with dlib
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/Dlib.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Dlib.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with mtcnn
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp... |
Detect and align face with mtcnn
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/FastMtCnn.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/FastMtCnn.py | MIT |
def build_model(self) -> Any:
"""
Build a fast mtcnn face detector model
Returns:
model (Any)
"""
# this is not a must dependency. do not import it in the global level.
try:
from facenet_pytorch import MTCNN as fast_mtcnn
import torch
... |
Build a fast mtcnn face detector model
Returns:
model (Any)
| build_model | python | serengil/deepface | deepface/models/face_detection/FastMtCnn.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/FastMtCnn.py | MIT |
def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
"""
Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
Args:
regions (list or tuple): facial area coordinates as x, y, x+w, y+h
Returns:
regions (tuple): facial area coordinates as x, y, w, h
"""
x, y, x_plus_w, y_plus_... |
Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
Args:
regions (list or tuple): facial area coordinates as x, y, x+w, y+h
Returns:
regions (tuple): facial area coordinates as x, y, w, h
| xyxy_to_xywh | python | serengil/deepface | deepface/models/face_detection/FastMtCnn.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/FastMtCnn.py | MIT |
def build_model(self) -> Any:
"""
Build a mediapipe face detector model
Returns:
model (Any)
"""
# this is not a must dependency. do not import it in the global level.
try:
import mediapipe as mp
except ModuleNotFoundError as e:
... |
Build a mediapipe face detector model
Returns:
model (Any)
| build_model | python | serengil/deepface | deepface/models/face_detection/MediaPipe.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/MediaPipe.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with mediapipe
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
... |
Detect and align face with mediapipe
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/MediaPipe.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/MediaPipe.py | MIT |
def build_model(self):
"""
Build opencv's face and eye detector models
Returns:
model (dict): including face_detector and eye_detector keys
"""
detector = {}
detector["face_detector"] = self.__build_cascade("haarcascade")
detector["eye_detector"] = sel... |
Build opencv's face and eye detector models
Returns:
model (dict): including face_detector and eye_detector keys
| build_model | python | serengil/deepface | deepface/models/face_detection/OpenCv.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/OpenCv.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with opencv
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
res... |
Detect and align face with opencv
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/OpenCv.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/OpenCv.py | MIT |
def find_eyes(self, img: np.ndarray) -> tuple:
"""
Find the left and right eye coordinates of given image
Args:
img (np.ndarray): given image
Returns:
left and right eye (tuple)
"""
left_eye = None
right_eye = None
# if image has u... |
Find the left and right eye coordinates of given image
Args:
img (np.ndarray): given image
Returns:
left and right eye (tuple)
| find_eyes | python | serengil/deepface | deepface/models/face_detection/OpenCv.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/OpenCv.py | MIT |
def __build_cascade(self, model_name="haarcascade") -> Any:
"""
Build a opencv face&eye detector models
Returns:
model (Any)
"""
opencv_path = self.__get_opencv_path()
if model_name == "haarcascade":
face_detector_path = os.path.join(opencv_path, "... |
Build a opencv face&eye detector models
Returns:
model (Any)
| __build_cascade | python | serengil/deepface | deepface/models/face_detection/OpenCv.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/OpenCv.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with retinaface
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
... |
Detect and align face with retinaface
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/RetinaFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/RetinaFace.py | MIT |
def build_model(self) -> dict:
"""
Build a ssd detector model
Returns:
model (dict)
"""
# model structure
output_model = weight_utils.download_weights_if_necessary(
file_name="deploy.prototxt",
source_url=MODEL_URL,
)
... |
Build a ssd detector model
Returns:
model (dict)
| build_model | python | serengil/deepface | deepface/models/face_detection/Ssd.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Ssd.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with ssd
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
# Bec... |
Detect and align face with ssd
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/Ssd.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Ssd.py | MIT |
def build_model(self, model: YoloModel) -> Any:
"""
Build a yolo detector model
Returns:
model (Any)
"""
# Import the optional Ultralytics YOLO model
try:
from ultralytics import YOLO
except ModuleNotFoundError as e:
raise Impo... |
Build a yolo detector model
Returns:
model (Any)
| build_model | python | serengil/deepface | deepface/models/face_detection/Yolo.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Yolo.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with yolo
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp ... |
Detect and align face with yolo
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/Yolo.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/Yolo.py | MIT |
def build_model(self) -> Any:
"""
Build a yunet detector model
Returns:
model (Any)
"""
opencv_version = cv2.__version__.split(".")
if not len(opencv_version) >= 2:
raise ValueError(
f"OpenCv's version must have major and minor val... |
Build a yunet detector model
Returns:
model (Any)
| build_model | python | serengil/deepface | deepface/models/face_detection/YuNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/YuNet.py | MIT |
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with yunet
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
# Fa... |
Detect and align face with yunet
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
| detect_faces | python | serengil/deepface | deepface/models/face_detection/YuNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/face_detection/YuNet.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct ArcFace model, download its weights and load
Returns:
model (Model)
"""
base_model = ResNet34()
inputs = base_model.inputs[0]
arcface_model = base_model.outputs[0]
arcface_model = BatchNormalization(momentum=0.9, epsi... |
Construct ArcFace model, download its weights and load
Returns:
model (Model)
| load_model | python | serengil/deepface | deepface/models/facial_recognition/ArcFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/ArcFace.py | MIT |
def preprocess(self, img: np.ndarray) -> np.ndarray:
"""
Preprocess the input image or batch of images.
Args:
img: Input image or batch with shape (112, 112, 3)
or (batch_size, 112, 112, 3).
Returns:
Preprocessed image(s) with RGB converted to BGR.
... |
Preprocess the input image or batch of images.
Args:
img: Input image or batch with shape (112, 112, 3)
or (batch_size, 112, 112, 3).
Returns:
Preprocessed image(s) with RGB converted to BGR.
| preprocess | python | serengil/deepface | deepface/models/facial_recognition/Buffalo_L.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Buffalo_L.py | MIT |
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
"""
Extract facial embedding(s) from the input image or batch of images.
Args:
img: Input image or batch with shape (112, 112, 3)
or (batch_size, 112, 112, 3).
Returns:
Emb... |
Extract facial embedding(s) from the input image or batch of images.
Args:
img: Input image or batch with shape (112, 112, 3)
or (batch_size, 112, 112, 3).
Returns:
Embedding as a list of floats (single image)
or list of lists of floats (batch... | forward | python | serengil/deepface | deepface/models/facial_recognition/Buffalo_L.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Buffalo_L.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct DeepId model, download its weights and load
"""
myInput = Input(shape=(55, 47, 3))
x = Conv2D(20, (4, 4), name="Conv1", activation="relu", input_shape=(55, 47, 3))(myInput)
x = MaxPooling2D(pool_size=2, strides=2, name="Pool1")(x)
... |
Construct DeepId model, download its weights and load
| load_model | python | serengil/deepface | deepface/models/facial_recognition/DeepID.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/DeepID.py | MIT |
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
"""
Find embeddings with Dlib model.
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image(s) in BGR
... |
Find embeddings with Dlib model.
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image(s) in BGR
Returns
embeddings (list of lists or list of floats): multi-dimensional... | forward | python | serengil/deepface | deepface/models/facial_recognition/Dlib.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Dlib.py | MIT |
def InceptionResNetV1(dimension: int = 128) -> Model:
"""
InceptionResNetV1 model heavily inspired from
github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
As mentioned in Sandberg's repo's readme, pre-trained models are using Inception ResNet v1
Besides training process i... |
InceptionResNetV1 model heavily inspired from
github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
As mentioned in Sandberg's repo's readme, pre-trained models are using Inception ResNet v1
Besides training process is documented at
sefiks.com/2018/09/03/face-recognition-wi... | InceptionResNetV1 | python | serengil/deepface | deepface/models/facial_recognition/Facenet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Facenet.py | MIT |
def load_facenet128d_model(
url=FACENET128_WEIGHTS,
) -> Model:
"""
Construct FaceNet-128d model, download weights and then load weights
Args:
dimension (int): construct FaceNet-128d or FaceNet-512d models
Returns:
model (Model)
"""
model = InceptionResNetV1()
weight_fil... |
Construct FaceNet-128d model, download weights and then load weights
Args:
dimension (int): construct FaceNet-128d or FaceNet-512d models
Returns:
model (Model)
| load_facenet128d_model | python | serengil/deepface | deepface/models/facial_recognition/Facenet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Facenet.py | MIT |
def load_facenet512d_model(
url=FACENET512_WEIGHTS,
) -> Model:
"""
Construct FaceNet-512d model, download its weights and load
Returns:
model (Model)
"""
model = InceptionResNetV1(dimension=512)
weight_file = weight_utils.download_weights_if_necessary(
file_name="facenet51... |
Construct FaceNet-512d model, download its weights and load
Returns:
model (Model)
| load_facenet512d_model | python | serengil/deepface | deepface/models/facial_recognition/Facenet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/Facenet.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Construct DeepFace model, download its weights and load
"""
# we have some checks for this dependency in the init of client
# putting this in global causes library initialization
if tf_major == 1:
from keras.layers import LocallyConnec... |
Construct DeepFace model, download its weights and load
| load_model | python | serengil/deepface | deepface/models/facial_recognition/FbDeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/FbDeepFace.py | MIT |
def GhostFaceNetV1() -> Model:
"""
Build GhostFaceNetV1 model. Refactored from
github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
Returns:
model (Model)
"""
inputs = Input(shape=(112, 112, 3))
out_channel = 20
nn = Conv2D(
out_channel,
(3, 3... |
Build GhostFaceNetV1 model. Refactored from
github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
Returns:
model (Model)
| GhostFaceNetV1 | python | serengil/deepface | deepface/models/facial_recognition/GhostFaceNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/GhostFaceNet.py | MIT |
def se_module(inputs, reduction):
"""
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
"""
# get the channel axis
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
# filters = channel axis shape
filters = inputs.shape[channel_axis]
... |
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
| se_module | python | serengil/deepface | deepface/models/facial_recognition/GhostFaceNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/GhostFaceNet.py | MIT |
def replace_relu_with_prelu(model) -> Model:
"""
Replaces relu activation function in the built model with prelu.
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
Args:
model (Model): built model with relu activation functions
Returns
model (Mod... |
Replaces relu activation function in the built model with prelu.
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
Args:
model (Model): built model with relu activation functions
Returns
model (Model): built model with prelu activation functions
... | replace_relu_with_prelu | python | serengil/deepface | deepface/models/facial_recognition/GhostFaceNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/GhostFaceNet.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Consturct OpenFace model, download its weights and load
Returns:
model (Model)
"""
myInput = Input(shape=(96, 96, 3))
x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
x = Conv2D(64, (7, 7), strides=(2, 2), name=... |
Consturct OpenFace model, download its weights and load
Returns:
model (Model)
| load_model | python | serengil/deepface | deepface/models/facial_recognition/OpenFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/OpenFace.py | MIT |
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
"""
Find embeddings with SFace model
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image in BGR
... |
Find embeddings with SFace model
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image in BGR
Returns
embeddings (list): multi-dimensional vector
| forward | python | serengil/deepface | deepface/models/facial_recognition/SFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/SFace.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Any:
"""
Construct SFace model, download its weights and load
"""
weight_file = weight_utils.download_weights_if_necessary(
file_name="face_recognition_sface_2021dec.onnx", source_url=url
)
model = SFaceWrapper(model_path=weight_file)
retu... |
Construct SFace model, download its weights and load
| load_model | python | serengil/deepface | deepface/models/facial_recognition/SFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/SFace.py | MIT |
def __init__(self, model_path):
"""
SFace wrapper covering model construction, layer infos and predict
"""
try:
self.model = cv.FaceRecognizerSF.create(
model=model_path, config="", backend_id=0, target_id=0
)
except Exception as err:
... |
SFace wrapper covering model construction, layer infos and predict
| __init__ | python | serengil/deepface | deepface/models/facial_recognition/SFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/SFace.py | MIT |
def forward(self, img: np.ndarray) -> List[float]:
"""
Generates embeddings using the VGG-Face model.
This method incorporates an additional normalization layer.
Args:
img (np.ndarray): pre-loaded image in BGR
Returns
embeddings (list): multi-dimensio... |
Generates embeddings using the VGG-Face model.
This method incorporates an additional normalization layer.
Args:
img (np.ndarray): pre-loaded image in BGR
Returns
embeddings (list): multi-dimensional vector
| forward | python | serengil/deepface | deepface/models/facial_recognition/VGGFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/VGGFace.py | MIT |
def base_model() -> Sequential:
"""
Base model of VGG-Face being used for classification - not to find embeddings
Returns:
model (Sequential): model was trained to classify 2622 identities
"""
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(224, 224, 3)))
model.add(... |
Base model of VGG-Face being used for classification - not to find embeddings
Returns:
model (Sequential): model was trained to classify 2622 identities
| base_model | python | serengil/deepface | deepface/models/facial_recognition/VGGFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/VGGFace.py | MIT |
def load_model(
url=WEIGHTS_URL,
) -> Model:
"""
Final VGG-Face model being used for finding embeddings
Returns:
model (Model): returning 4096 dimensional vectors
"""
model = base_model()
weight_file = weight_utils.download_weights_if_necessary(
file_name="vgg_face_weights.... |
Final VGG-Face model being used for finding embeddings
Returns:
model (Model): returning 4096 dimensional vectors
| load_model | python | serengil/deepface | deepface/models/facial_recognition/VGGFace.py | https://github.com/serengil/deepface/blob/master/deepface/models/facial_recognition/VGGFace.py | MIT |
def analyze(self, img: np.ndarray, facial_area: Union[list, tuple]):
"""
Analyze a given image spoofed or not
Args:
img (np.ndarray): pre loaded image
facial_area (list or tuple): facial rectangle area coordinates with x, y, w, h respectively
Returns:
... |
Analyze a given image spoofed or not
Args:
img (np.ndarray): pre loaded image
facial_area (list or tuple): facial rectangle area coordinates with x, y, w, h respectively
Returns:
result (tuple): a result tuple consisting of is_real and score
| analyze | python | serengil/deepface | deepface/models/spoofing/FasNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/spoofing/FasNet.py | MIT |
def to_tensor(pic):
"""Convert a ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
import torch
# handle numpy array
# IR image channel=1: modi... | Convert a ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
| to_tensor | python | serengil/deepface | deepface/models/spoofing/FasNet.py | https://github.com/serengil/deepface/blob/master/deepface/models/spoofing/FasNet.py | MIT |
def analyze(
img_path: Union[str, np.ndarray, IO[bytes], List[str], List[np.ndarray], List[IO[bytes]]],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent... |
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
Args:
img_path (str, np.ndarray, IO[bytes], list): The exact path to the image,
a numpy array in BGR format, or a base64 encoded image. If the source image
contains multiple faces, the resul... | analyze | python | serengil/deepface | deepface/modules/demography.py | https://github.com/serengil/deepface/blob/master/deepface/modules/demography.py | MIT |
def extract_faces(
img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False... |
Extract faces from a given image
Args:
img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), a file object that supports at least `.read` and is
opened in binary mode, or base64 encoded images.
dete... | extract_faces | python | serengil/deepface | deepface/modules/detection.py | https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py | MIT |
def detect_faces(
detector_backend: str,
img: np.ndarray,
align: bool = True,
expand_percentage: int = 0,
max_faces: Optional[int] = None,
) -> List[DetectedFace]:
"""
Detect face(s) from a given image
Args:
detector_backend (str): detector name
img (np.ndarray): pre-loa... |
Detect face(s) from a given image
Args:
detector_backend (str): detector name
img (np.ndarray): pre-loaded image
align (bool): enable or disable alignment after detection
expand_percentage (int): expand detected facial area with a percentage (default is 0).
Returns:
... | detect_faces | python | serengil/deepface | deepface/modules/detection.py | https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py | MIT |
def extract_sub_image(
img: np.ndarray, facial_area: Tuple[int, int, int, int]
) -> Tuple[np.ndarray, int, int]:
"""
Get the sub image with given facial area while expanding the facial region
to ensure alignment does not shift the face outside the image.
This function doubles the height and wid... |
Get the sub image with given facial area while expanding the facial region
to ensure alignment does not shift the face outside the image.
This function doubles the height and width of the face region,
and adds black pixels if necessary.
Args:
- img (np.ndarray): pre-loaded image with ... | extract_sub_image | python | serengil/deepface | deepface/modules/detection.py | https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py | MIT |
def align_img_wrt_eyes(
img: np.ndarray,
left_eye: Optional[Union[list, tuple]],
right_eye: Optional[Union[list, tuple]],
) -> Tuple[np.ndarray, float]:
"""
Align a given image horizantally with respect to their left and right eye locations
Args:
img (np.ndarray): pre-loaded image with d... |
Align a given image horizantally with respect to their left and right eye locations
Args:
img (np.ndarray): pre-loaded image with detected face
left_eye (list or tuple): coordinates of left eye with respect to the person itself
right_eye(list or tuple): coordinates of right eye with res... | align_img_wrt_eyes | python | serengil/deepface | deepface/modules/detection.py | https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py | MIT |
def project_facial_area(
facial_area: Tuple[int, int, int, int], angle: float, size: Tuple[int, int]
) -> Tuple[int, int, int, int]:
"""
Update pre-calculated facial area coordinates after image itself
rotated with respect to the eyes.
Inspried from the work of @UmutDeniz26 - github.com/serengil... |
Update pre-calculated facial area coordinates after image itself
rotated with respect to the eyes.
Inspried from the work of @UmutDeniz26 - github.com/serengil/retinaface/pull/80
Args:
facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
x2 is equal... | project_facial_area | python | serengil/deepface | deepface/modules/detection.py | https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py | MIT |
def build_model(task: str, model_name: str) -> Any:
"""
This function loads a pre-trained models as singletonish way
Parameters:
task (str): facial_recognition, facial_attribute, face_detector, spoofing
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace,... |
This function loads a pre-trained models as singletonish way
Parameters:
task (str): facial_recognition, facial_attribute, face_detector, spoofing
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
ArcFace, SFace and... | build_model | python | serengil/deepface | deepface/modules/modeling.py | https://github.com/serengil/deepface/blob/master/deepface/modules/modeling.py | MIT |
def resize_image(img: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:
"""
Resize an image to expected size of a ml model with adding black pixels.
Args:
img (np.ndarray): pre-loaded image as numpy array
target_size (tuple): input shape of ml model
Returns:
img (np.ndarra... |
Resize an image to expected size of a ml model with adding black pixels.
Args:
img (np.ndarray): pre-loaded image as numpy array
target_size (tuple): input shape of ml model
Returns:
img (np.ndarray): resized input image
| resize_image | python | serengil/deepface | deepface/modules/preprocessing.py | https://github.com/serengil/deepface/blob/master/deepface/modules/preprocessing.py | MIT |
def find(
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalizat... |
Identify individuals in a database
Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
db_path ... | find | python | serengil/deepface | deepface/modules/recognition.py | https://github.com/serengil/deepface/blob/master/deepface/modules/recognition.py | MIT |
def __find_bulk_embeddings(
employees: Set[str],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
) -> List[Dict["str", Any]]:
"""
Fin... |
Find embeddings of a list of images
Args:
employees (list): list of exact image paths
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_ba... | __find_bulk_embeddings | python | serengil/deepface | deepface/modules/recognition.py | https://github.com/serengil/deepface/blob/master/deepface/modules/recognition.py | MIT |
def find_batched(
representations: List[Dict[str, Any]],
source_objs: List[Dict[str, Any]],
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
threshold: Optional[float] = None,
normalization: str = "base",
anti_spoofing... |
Perform batched face recognition by comparing source face embeddings with a set of
target embeddings. It calculates pairwise distances between the source and target
embeddings using the specified distance metric.
The function uses batch processing for efficient computation of distances.
Args:
... | find_batched | python | serengil/deepface | deepface/modules/recognition.py | https://github.com/serengil/deepface/blob/master/deepface/modules/recognition.py | MIT |
def analysis(
db_path: str,
model_name="VGG-Face",
detector_backend="opencv",
distance_metric="cosine",
enable_face_analysis=True,
source=0,
time_threshold=5,
frame_threshold=5,
anti_spoofing: bool = False,
output_path: Optional[str] = None,
debug: bool = False,
):
"""
... |
Run real time face recognition and facial attribute analysis
Args:
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Fa... | analysis | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def build_facial_recognition_model(model_name: str) -> None:
"""
Build facial recognition model
Args:
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
Returns... |
Build facial recognition model
Args:
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
Returns
input_shape (tuple): input shape of given facial recognitio... | build_facial_recognition_model | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def search_identity(
detected_face: np.ndarray,
db_path: str,
model_name: str,
detector_backend: str,
distance_metric: str,
) -> Tuple[Optional[str], Optional[np.ndarray]]:
"""
Search an identity in facial database.
Args:
detected_face (np.ndarray): extracted individual facial im... |
Search an identity in facial database.
Args:
detected_face (np.ndarray): extracted individual facial image
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Mod... | search_identity | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def build_demography_models(enable_face_analysis: bool) -> None:
"""
Build demography analysis models
Args:
enable_face_analysis (bool): Flag to enable face analysis (default is True).
Returns:
None
"""
if enable_face_analysis is False:
return
DeepFace.build_model(tas... |
Build demography analysis models
Args:
enable_face_analysis (bool): Flag to enable face analysis (default is True).
Returns:
None
| build_demography_models | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def highlight_facial_areas(
img: np.ndarray,
faces_coordinates: List[Tuple[int, int, int, int, bool, float]],
anti_spoofing: bool = False,
) -> np.ndarray:
"""
Highlight detected faces with rectangles in the given image
Args:
img (np.ndarray): image itself
faces_coordinates (list... |
Highlight detected faces with rectangles in the given image
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with x, y, w and h
also is_real and antispoof_score keys
anti_spoofing (boolean): Flag to enable anti spoofing (defaul... | highlight_facial_areas | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def countdown_to_freeze(
img: np.ndarray,
faces_coordinates: List[Tuple[int, int, int, int, bool, float]],
frame_threshold: int,
num_frames_with_faces: int,
) -> np.ndarray:
"""
Highlight time to freeze in the image's facial areas
Args:
img (np.ndarray): image itself
faces_co... |
Highlight time to freeze in the image's facial areas
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with x, y, w and h
frame_threshold (int): how many sequantial frames required with face(s) to freeze
num_frames_with_faces (int):... | countdown_to_freeze | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def countdown_to_release(
img: Optional[np.ndarray], tic: float, time_threshold: int
) -> Optional[np.ndarray]:
"""
Highlight time to release the freezing in the image top left area
Args:
img (np.ndarray): image itself
tic (float): time specifying when freezing started
time_thres... |
Highlight time to release the freezing in the image top left area
Args:
img (np.ndarray): image itself
tic (float): time specifying when freezing started
time_threshold (int): freeze time threshold
Returns:
img (np.ndarray): image with time to release the freezing
| countdown_to_release | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def grab_facial_areas(
img: np.ndarray, detector_backend: str, threshold: int = 130, anti_spoofing: bool = False
) -> List[Tuple[int, int, int, int, bool, float]]:
"""
Find facial area coordinates in the given image
Args:
img (np.ndarray): image itself
detector_backend (string): face det... |
Find facial area coordinates in the given image
Args:
img (np.ndarray): image itself
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'ski... | grab_facial_areas | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def extract_facial_areas(
img: np.ndarray, faces_coordinates: List[Tuple[int, int, int, int, bool, float]]
) -> List[np.ndarray]:
"""
Extract facial areas as numpy array from given image
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of facial area coordinates as tup... |
Extract facial areas as numpy array from given image
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of facial area coordinates as tuple with
x, y, w and h values also is_real and antispoof_score keys
Returns:
detected_faces (list): list of detected f... | extract_facial_areas | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def perform_facial_recognition(
img: np.ndarray,
detected_faces: List[np.ndarray],
faces_coordinates: List[Tuple[int, int, int, int, bool, float]],
db_path: str,
detector_backend: str,
distance_metric: str,
model_name: str,
) -> np.ndarray:
"""
Perform facial recognition
Args:
... |
Perform facial recognition
Args:
img (np.ndarray): image itself
detected_faces (list): list of extracted detected face images as numpy
faces_coordinates (list): list of facial area coordinates as tuple with
x, y, w and h values also is_real and antispoof_score keys
d... | perform_facial_recognition | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def perform_demography_analysis(
enable_face_analysis: bool,
img: np.ndarray,
faces_coordinates: List[Tuple[int, int, int, int, bool, float]],
detected_faces: List[np.ndarray],
) -> np.ndarray:
"""
Perform demography analysis on given image
Args:
enable_face_analysis (bool): Flag to ... |
Perform demography analysis on given image
Args:
enable_face_analysis (bool): Flag to enable face analysis.
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with
x, y, w and h values also is_real and antispoof_score keys
dete... | perform_demography_analysis | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def overlay_identified_face(
img: np.ndarray,
target_img: np.ndarray,
label: str,
x: int,
y: int,
w: int,
h: int,
) -> np.ndarray:
"""
Overlay the identified face onto image itself
Args:
img (np.ndarray): image itself
target_img (np.ndarray): identified face's ima... |
Overlay the identified face onto image itself
Args:
img (np.ndarray): image itself
target_img (np.ndarray): identified face's image
label (str): name of the identified face
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the give... | overlay_identified_face | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def overlay_emotion(
img: np.ndarray, emotion_probas: dict, x: int, y: int, w: int, h: int
) -> np.ndarray:
"""
Overlay the analyzed emotion of face onto image itself
Args:
img (np.ndarray): image itself
emotion_probas (dict): probability of different emotionas dictionary
x (int)... |
Overlay the analyzed emotion of face onto image itself
Args:
img (np.ndarray): image itself
emotion_probas (dict): probability of different emotionas dictionary
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the given image
w (i... | overlay_emotion | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def overlay_age_gender(
img: np.ndarray, apparent_age: float, gender: str, x: int, y: int, w: int, h: int
) -> np.ndarray:
"""
Overlay the analyzed age and gender of face onto image itself
Args:
img (np.ndarray): image itself
apparent_age (float): analyzed apparent age
gender (st... |
Overlay the analyzed age and gender of face onto image itself
Args:
img (np.ndarray): image itself
apparent_age (float): analyzed apparent age
gender (str): analyzed gender
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the give... | overlay_age_gender | python | serengil/deepface | deepface/modules/streaming.py | https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py | MIT |
def verify(
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
... |
Verify if an image pair represents the same person or different persons.
The verification function converts facial images to vectors and calculates the similarity
between those vectors. Vectors of images of the same person should exhibit higher similarity
(or lower distance) than vectors of images of ... | verify | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def extract_embeddings_and_facial_areas(
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 ty... |
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
(via a path or NumPy array) or verifies that the input is a li... | extract_embeddings_and_facial_areas | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def __extract_faces_and_embeddings(
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
) -> Tuple[List[... |
Extract facial areas and find corresponding embeddings for given image
Returns:
embeddings (List[float])
facial areas (List[dict])
| __extract_faces_and_embeddings | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def find_cosine_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> Union[np.float64, np.ndarray]:
"""
Find cosine distance between two given vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or bat... |
Find cosine distance between two given vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or batch of vectors.
test_representation (np.ndarray or list): 2nd vector or batch of vectors.
Returns
np.float64 or np.ndarray: Calculated cosine dista... | find_cosine_distance | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def find_angular_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> Union[np.float64, np.ndarray]:
"""
Find angular distance between two vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or batch ... |
Find angular distance between two vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or batch of vectors.
test_representation (np.ndarray or list): 2nd vector or batch of vectors.
Returns:
np.float64 or np.ndarray: angular distance(s).
... | find_angular_distance | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def find_euclidean_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> Union[np.float64, np.ndarray]:
"""
Find Euclidean distance between two vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or ba... |
Find Euclidean distance between two vectors or batches of vectors.
Args:
source_representation (np.ndarray or list): 1st vector or batch of vectors.
test_representation (np.ndarray or list): 2nd vector or batch of vectors.
Returns:
np.float64 or np.ndarray: Euclidean distance(s).
... | find_euclidean_distance | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def l2_normalize(
x: Union[np.ndarray, list], axis: Union[int, None] = None, epsilon: float = 1e-10
) -> np.ndarray:
"""
Normalize input vector with l2
Args:
x (np.ndarray or list): given vector
axis (int): axis along which to normalize
Returns:
np.ndarray: l2 normalized vect... |
Normalize input vector with l2
Args:
x (np.ndarray or list): given vector
axis (int): axis along which to normalize
Returns:
np.ndarray: l2 normalized vector
| l2_normalize | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def find_distance(
alpha_embedding: Union[np.ndarray, list],
beta_embedding: Union[np.ndarray, list],
distance_metric: str,
) -> Union[np.float64, np.ndarray]:
"""
Wrapper to find the distance between vectors based on the specified distance metric.
Args:
alpha_embedding (np.ndarray or l... |
Wrapper to find the distance between vectors based on the specified distance metric.
Args:
alpha_embedding (np.ndarray or list): 1st vector or batch of vectors.
beta_embedding (np.ndarray or list): 2nd vector or batch of vectors.
distance_metric (str): The type of distance to compute
... | find_distance | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def find_threshold(model_name: str, distance_metric: str) -> float:
"""
Retrieve pre-tuned threshold values for a model and distance metric pair
Args:
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace an... |
Retrieve pre-tuned threshold values for a model and distance metric pair
Args:
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
distance_metric (str): distan... | find_threshold | python | serengil/deepface | deepface/modules/verification.py | https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py | MIT |
def is_form_data_file_testable() -> bool:
"""
Sending a file from form data fails in unit test with
415 unsupported media type error for flask 3.X
but it is working for flask 2.0.2
Returns:
is_form_data_file_testable (bool)
"""
flask_version = version.parse(flask.__version__)... |
Sending a file from form data fails in unit test with
415 unsupported media type error for flask 3.X
but it is working for flask 2.0.2
Returns:
is_form_data_file_testable (bool)
| is_form_data_file_testable | python | serengil/deepface | tests/test_api.py | https://github.com/serengil/deepface/blob/master/tests/test_api.py | MIT |
def test_filetype_for_find():
"""
only images as jpg and png can be loaded into database
"""
img_path = os.path.join("dataset", "img1.jpg")
dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True)
df = dfs[0]
# img47 is webp even though its extension is jpg
assert df[df["... |
only images as jpg and png can be loaded into database
| test_filetype_for_find | python | serengil/deepface | tests/test_find.py | https://github.com/serengil/deepface/blob/master/tests/test_find.py | MIT |
def har_to_jar(cookiejar, har_cookies, request_cookies=None):
""" Add HAR cookies to the cookiejar.
If request_cookies is given, remove cookies absent from har_cookies
but present in request_cookies (they were removed). """
har_cookie_keys = set()
for c in har_cookies:
cookie = har_to_cookie... | Add HAR cookies to the cookiejar.
If request_cookies is given, remove cookies absent from har_cookies
but present in request_cookies (they were removed). | har_to_jar | python | scrapy-plugins/scrapy-splash | scrapy_splash/cookies.py | https://github.com/scrapy-plugins/scrapy-splash/blob/master/scrapy_splash/cookies.py | BSD-3-Clause |
def har_to_cookie(har_cookie):
"""
Convert a cookie dict in HAR format to a Cookie instance.
>>> har_cookie = {
... "name": "TestCookie",
... "value": "Cookie Value",
... "path": "/foo",
... "domain": "www.janodvarko.cz",
... "expires": "2009-07-24T19:20:30Z",
.... |
Convert a cookie dict in HAR format to a Cookie instance.
>>> har_cookie = {
... "name": "TestCookie",
... "value": "Cookie Value",
... "path": "/foo",
... "domain": "www.janodvarko.cz",
... "expires": "2009-07-24T19:20:30Z",
... "httpOnly": True,
... "... | har_to_cookie | python | scrapy-plugins/scrapy-splash | scrapy_splash/cookies.py | https://github.com/scrapy-plugins/scrapy-splash/blob/master/scrapy_splash/cookies.py | BSD-3-Clause |
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