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Merge branch 'serengil:master' into distances
This commit is contained in:
Golubkov Alexander
GitHub
commit
1d3de83c2a
@ -25,7 +25,7 @@
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DeepFace is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), `SFace` and `GhostFaceNet`.
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[`Experiments`](https://github.com/serengil/deepface/tree/master/benchmarks) show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
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[`Experiments`](https://github.com/serengil/deepface/tree/master/benchmarks) show that **human beings have 97.53% accuracy** on facial recognition tasks whereas those models already reached and passed that accuracy level.
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## Installation [](https://pypi.org/project/deepface/)
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@ -35,7 +35,7 @@ The easiest way to install deepface is to download it from [`PyPI`](https://pypi
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$ pip install deepface
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```
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You can also install deepface from its source code.
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Alternatively, you can also install deepface from its source code. Source code may have new features not published in pip release yet.
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```shell
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$ git clone https://github.com/serengil/deepface.git
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@ -43,7 +43,7 @@ $ cd deepface
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$ pip install -e .
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```
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Then you will be able to import the library and use its functionalities.
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Once you installed the library, then you will be able to import it and use its functionalities.
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```python
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from deepface import DeepFace
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@ -48,17 +48,23 @@ if tf_version == 2:
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folder_utils.initialize_folder()
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def build_model(model_name: str) -> Any:
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def build_model(model_name: str, task: str = "facial_recognition") -> Any:
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"""
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This function builds a deepface model
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This function builds a pre-trained model
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Args:
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model_name (string): face recognition or facial attribute model
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VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition
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Age, Gender, Emotion, Race for facial attributes
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model_name (str): model identifier
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- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
|
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ArcFace, SFace, GhostFaceNet for face recognition
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- Age, Gender, Emotion, Race for facial attributes
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- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yunet,
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fastmtcnn or centerface for face detectors
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- Fasnet for spoofing
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task (str): facial_recognition, facial_attribute, face_detector, spoofing
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default is facial_recognition
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Returns:
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built_model
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"""
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return modeling.build_model(model_name=model_name)
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return modeling.build_model(task=task, model_name=model_name)
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def verify(
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@ -1 +1 @@
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__version__ = "0.0.92"
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__version__ = "0.0.93"
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@ -1,64 +1,13 @@
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from typing import Any, List, Tuple
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from typing import List, Tuple
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import numpy as np
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import cv2
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from deepface.modules import detection
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from deepface.modules import detection, modeling
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from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
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from deepface.detectors import (
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FastMtCnn,
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MediaPipe,
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MtCnn,
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OpenCv,
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Dlib,
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RetinaFace,
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Ssd,
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Yolo,
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YuNet,
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CenterFace,
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)
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from deepface.commons.logger import Logger
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logger = Logger()
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def build_model(detector_backend: str) -> Any:
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"""
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Build a face detector model
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Args:
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detector_backend (str): backend detector name
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Returns:
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built detector (Any)
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"""
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global face_detector_obj # singleton design pattern
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backends = {
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"opencv": OpenCv.OpenCvClient,
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"mtcnn": MtCnn.MtCnnClient,
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"ssd": Ssd.SsdClient,
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"dlib": Dlib.DlibClient,
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"retinaface": RetinaFace.RetinaFaceClient,
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"mediapipe": MediaPipe.MediaPipeClient,
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"yolov8": Yolo.YoloClient,
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"yunet": YuNet.YuNetClient,
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"fastmtcnn": FastMtCnn.FastMtCnnClient,
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"centerface": CenterFace.CenterFaceClient,
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}
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if not "face_detector_obj" in globals():
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face_detector_obj = {}
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built_models = list(face_detector_obj.keys())
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if detector_backend not in built_models:
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face_detector = backends.get(detector_backend)
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if face_detector:
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face_detector = face_detector()
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face_detector_obj[detector_backend] = face_detector
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else:
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raise ValueError("invalid detector_backend passed - " + detector_backend)
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return face_detector_obj[detector_backend]
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def detect_faces(
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detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
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) -> List[DetectedFace]:
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@ -87,7 +36,9 @@ def detect_faces(
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"""
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height, width, _ = img.shape
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face_detector: Detector = build_model(detector_backend)
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face_detector: Detector = modeling.build_model(
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task="face_detector", model_name=detector_backend
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)
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# validate expand percentage score
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if expand_percentage < 0:
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@ -33,7 +33,9 @@ class ApparentAgeClient(Demography):
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self.model_name = "Age"
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def predict(self, img: np.ndarray) -> np.float64:
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age_predictions = self.model.predict(img, verbose=0)[0, :]
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# model.predict causes memory issue when it is called in a for loop
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# age_predictions = self.model.predict(img, verbose=0)[0, :]
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age_predictions = self.model(img, training=False).numpy()[0, :]
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return find_apparent_age(age_predictions)
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@ -52,7 +52,10 @@ class EmotionClient(Demography):
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img_gray = cv2.resize(img_gray, (48, 48))
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img_gray = np.expand_dims(img_gray, axis=0)
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emotion_predictions = self.model.predict(img_gray, verbose=0)[0, :]
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# model.predict causes memory issue when it is called in a for loop
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# emotion_predictions = self.model.predict(img_gray, verbose=0)[0, :]
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emotion_predictions = self.model(img_gray, training=False).numpy()[0, :]
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return emotion_predictions
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@ -41,7 +41,9 @@ class GenderClient(Demography):
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self.model_name = "Gender"
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def predict(self, img: np.ndarray) -> np.ndarray:
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return self.model.predict(img, verbose=0)[0, :]
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# model.predict causes memory issue when it is called in a for loop
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# return self.model.predict(img, verbose=0)[0, :]
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return self.model(img, training=False).numpy()[0, :]
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def load_model(
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@ -40,7 +40,9 @@ class RaceClient(Demography):
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self.model_name = "Race"
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def predict(self, img: np.ndarray) -> np.ndarray:
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return self.model.predict(img, verbose=0)[0, :]
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# model.predict causes memory issue when it is called in a for loop
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# return self.model.predict(img, verbose=0)[0, :]
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return self.model(img, training=False).numpy()[0, :]
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def load_model(
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@ -158,7 +158,9 @@ def analyze(
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pbar.set_description(f"Action: {action}")
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if action == "emotion":
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emotion_predictions = modeling.build_model("Emotion").predict(img_content)
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emotion_predictions = modeling.build_model(
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task="facial_attribute", model_name="Emotion"
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).predict(img_content)
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sum_of_predictions = emotion_predictions.sum()
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obj["emotion"] = {}
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@ -169,12 +171,16 @@ def analyze(
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obj["dominant_emotion"] = Emotion.labels[np.argmax(emotion_predictions)]
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elif action == "age":
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apparent_age = modeling.build_model("Age").predict(img_content)
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apparent_age = modeling.build_model(
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task="facial_attribute", model_name="Age"
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).predict(img_content)
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# int cast is for exception - object of type 'float32' is not JSON serializable
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obj["age"] = int(apparent_age)
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elif action == "gender":
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gender_predictions = modeling.build_model("Gender").predict(img_content)
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gender_predictions = modeling.build_model(
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task="facial_attribute", model_name="Gender"
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).predict(img_content)
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obj["gender"] = {}
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for i, gender_label in enumerate(Gender.labels):
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gender_prediction = 100 * gender_predictions[i]
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@ -183,7 +189,9 @@ def analyze(
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obj["dominant_gender"] = Gender.labels[np.argmax(gender_predictions)]
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elif action == "race":
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race_predictions = modeling.build_model("Race").predict(img_content)
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race_predictions = modeling.build_model(
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task="facial_attribute", model_name="Race"
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).predict(img_content)
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sum_of_predictions = race_predictions.sum()
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obj["race"] = {}
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@ -25,7 +25,7 @@ def extract_faces(
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align: bool = True,
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expand_percentage: int = 0,
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grayscale: bool = False,
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color_face: str = 'rgb',
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color_face: str = "rgb",
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normalize_face: bool = True,
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anti_spoofing: bool = False,
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) -> List[Dict[str, Any]]:
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@ -126,16 +126,14 @@ def extract_faces(
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logger.warn("Parameter grayscale is deprecated. Use color_face instead.")
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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else:
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if color_face == 'rgb':
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if color_face == "rgb":
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current_img = current_img[:, :, ::-1]
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elif color_face == 'bgr':
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elif color_face == "bgr":
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pass # image is in BGR
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elif color_face == 'gray':
|
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elif color_face == "gray":
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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else:
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raise ValueError(
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f"The color_face can be rgb, bgr or gray, but it is {color_face}."
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)
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raise ValueError(f"The color_face can be rgb, bgr or gray, but it is {color_face}.")
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if normalize_face:
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current_img = current_img / 255 # normalize input in [0, 1]
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@ -159,7 +157,7 @@ def extract_faces(
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}
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if anti_spoofing is True:
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antispoof_model = modeling.build_model(model_name="Fasnet")
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antispoof_model = modeling.build_model(task="spoofing", model_name="Fasnet")
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is_real, antispoof_score = antispoof_model.analyze(img=img, facial_area=(x, y, w, h))
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resp_obj["is_real"] = is_real
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resp_obj["antispoof_score"] = antispoof_score
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@ -13,51 +13,88 @@ from deepface.basemodels import (
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Facenet,
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GhostFaceNet,
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)
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from deepface.detectors import (
|
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FastMtCnn,
|
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MediaPipe,
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MtCnn,
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OpenCv,
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Dlib as DlibDetector,
|
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RetinaFace,
|
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Ssd,
|
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Yolo,
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YuNet,
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CenterFace,
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)
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.spoofmodels import FasNet
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|
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|
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def build_model(model_name: str) -> Any:
|
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def build_model(task: str, model_name: str) -> Any:
|
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"""
|
||||
This function builds a deepface model
|
||||
This function loads a pre-trained models as singletonish way
|
||||
Parameters:
|
||||
model_name (string): face recognition or facial attribute model
|
||||
VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition
|
||||
Age, Gender, Emotion, Race for facial attributes
|
||||
|
||||
task (str): facial_recognition, facial_attribute, face_detector, spoofing
|
||||
model_name (str): model identifier
|
||||
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
|
||||
ArcFace, SFace, GhostFaceNet for face recognition
|
||||
- Age, Gender, Emotion, Race for facial attributes
|
||||
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yunet,
|
||||
fastmtcnn or centerface for face detectors
|
||||
- Fasnet for spoofing
|
||||
Returns:
|
||||
built model class
|
||||
"""
|
||||
|
||||
# singleton design pattern
|
||||
global model_obj
|
||||
global cached_models
|
||||
|
||||
models = {
|
||||
"VGG-Face": VGGFace.VggFaceClient,
|
||||
"OpenFace": OpenFace.OpenFaceClient,
|
||||
"Facenet": Facenet.FaceNet128dClient,
|
||||
"Facenet512": Facenet.FaceNet512dClient,
|
||||
"DeepFace": FbDeepFace.DeepFaceClient,
|
||||
"DeepID": DeepID.DeepIdClient,
|
||||
"Dlib": Dlib.DlibClient,
|
||||
"ArcFace": ArcFace.ArcFaceClient,
|
||||
"SFace": SFace.SFaceClient,
|
||||
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
|
||||
"Emotion": Emotion.EmotionClient,
|
||||
"Age": Age.ApparentAgeClient,
|
||||
"Gender": Gender.GenderClient,
|
||||
"Race": Race.RaceClient,
|
||||
"Fasnet": FasNet.Fasnet,
|
||||
"facial_recognition": {
|
||||
"VGG-Face": VGGFace.VggFaceClient,
|
||||
"OpenFace": OpenFace.OpenFaceClient,
|
||||
"Facenet": Facenet.FaceNet128dClient,
|
||||
"Facenet512": Facenet.FaceNet512dClient,
|
||||
"DeepFace": FbDeepFace.DeepFaceClient,
|
||||
"DeepID": DeepID.DeepIdClient,
|
||||
"Dlib": Dlib.DlibClient,
|
||||
"ArcFace": ArcFace.ArcFaceClient,
|
||||
"SFace": SFace.SFaceClient,
|
||||
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
|
||||
},
|
||||
"spoofing": {
|
||||
"Fasnet": FasNet.Fasnet,
|
||||
},
|
||||
"facial_attribute": {
|
||||
"Emotion": Emotion.EmotionClient,
|
||||
"Age": Age.ApparentAgeClient,
|
||||
"Gender": Gender.GenderClient,
|
||||
"Race": Race.RaceClient,
|
||||
},
|
||||
"face_detector": {
|
||||
"opencv": OpenCv.OpenCvClient,
|
||||
"mtcnn": MtCnn.MtCnnClient,
|
||||
"ssd": Ssd.SsdClient,
|
||||
"dlib": DlibDetector.DlibClient,
|
||||
"retinaface": RetinaFace.RetinaFaceClient,
|
||||
"mediapipe": MediaPipe.MediaPipeClient,
|
||||
"yolov8": Yolo.YoloClient,
|
||||
"yunet": YuNet.YuNetClient,
|
||||
"fastmtcnn": FastMtCnn.FastMtCnnClient,
|
||||
"centerface": CenterFace.CenterFaceClient,
|
||||
},
|
||||
}
|
||||
|
||||
if not "model_obj" in globals():
|
||||
model_obj = {}
|
||||
if models.get(task) is None:
|
||||
raise ValueError(f"unimplemented task - {task}")
|
||||
|
||||
if not model_name in model_obj.keys():
|
||||
model = models.get(model_name)
|
||||
if not "cached_models" in globals():
|
||||
cached_models = {current_task: {} for current_task in models.keys()}
|
||||
|
||||
if cached_models[task].get(model_name) is None:
|
||||
model = models[task].get(model_name)
|
||||
if model:
|
||||
model_obj[model_name] = model()
|
||||
cached_models[task][model_name] = model()
|
||||
else:
|
||||
raise ValueError(f"Invalid model_name passed - {model_name}")
|
||||
raise ValueError(f"Invalid model_name passed - {task}/{model_name}")
|
||||
|
||||
return model_obj[model_name]
|
||||
return cached_models[task][model_name]
|
||||
|
||||
@ -65,7 +65,9 @@ def represent(
|
||||
"""
|
||||
resp_objs = []
|
||||
|
||||
model: FacialRecognition = modeling.build_model(model_name)
|
||||
model: FacialRecognition = modeling.build_model(
|
||||
task="facial_recognition", model_name=model_name
|
||||
)
|
||||
|
||||
# ---------------------------------
|
||||
# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
|
||||
|
||||
@ -171,7 +171,7 @@ def build_facial_recognition_model(model_name: str) -> None:
|
||||
Returns
|
||||
input_shape (tuple): input shape of given facial recognitio n model.
|
||||
"""
|
||||
_ = DeepFace.build_model(model_name=model_name)
|
||||
_ = DeepFace.build_model(task="facial_recognition", model_name=model_name)
|
||||
logger.info(f"{model_name} is built")
|
||||
|
||||
|
||||
@ -267,11 +267,11 @@ def build_demography_models(enable_face_analysis: bool) -> None:
|
||||
"""
|
||||
if enable_face_analysis is False:
|
||||
return
|
||||
DeepFace.build_model(model_name="Age")
|
||||
DeepFace.build_model(task="facial_attribute", model_name="Age")
|
||||
logger.info("Age model is just built")
|
||||
DeepFace.build_model(model_name="Gender")
|
||||
DeepFace.build_model(task="facial_attribute", model_name="Gender")
|
||||
logger.info("Gender model is just built")
|
||||
DeepFace.build_model(model_name="Emotion")
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||||
DeepFace.build_model(task="facial_attribute", model_name="Emotion")
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||||
logger.info("Emotion model is just built")
|
||||
|
||||
|
||||
|
||||
@ -100,7 +100,9 @@ def verify(
|
||||
|
||||
tic = time.time()
|
||||
|
||||
model: FacialRecognition = modeling.build_model(model_name)
|
||||
model: FacialRecognition = modeling.build_model(
|
||||
task="facial_recognition", model_name=model_name
|
||||
)
|
||||
dims = model.output_shape
|
||||
|
||||
# extract faces from img1
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
{
|
||||
"version": "0.0.92"
|
||||
"version": "0.0.93"
|
||||
}
|
||||
@ -16,7 +16,7 @@ logger = Logger()
|
||||
|
||||
model_name = "VGG-Face"
|
||||
|
||||
model: FacialRecognition = DeepFace.build_model(model_name=model_name)
|
||||
model: FacialRecognition = DeepFace.build_model(task="facial_recognition", model_name=model_name)
|
||||
|
||||
target_size = model.input_shape
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user