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retinaface as default detector

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Sefik Ilkin Serengil 2021-06-13 17:12:38 +03:00
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commit 4267953dc3
3 changed files with 14 additions and 14 deletions
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2
README.md
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@ -144,7 +144,7 @@ The both face recognition and facial attribute analysis are covered in the API.
**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
Face detection and alignment are early stages of a modern face recognition pipeline. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) and [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/) methods are wrapped in deepface as a facial detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.
Face detection and alignment are early stages of a modern face recognition pipeline. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) and [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/) methods are wrapped in deepface as a facial detector. You can optionally pass a custom detector to functions in deepface interface. RetinaFace is the default detector if you won't pass any detector.
```python
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']

22
deepface/DeepFace.py
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@ -59,7 +59,7 @@ def build_model(model_name):
else:
raise ValueError('Invalid model_name passed - {}'.format(model_name))
def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'retinaface', align = True):
"""
This function verifies an image pair is same person or different persons.
@ -82,7 +82,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images.
detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib
detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
Returns:
Verify function returns a dictionary. If img1_path is a list of image pairs, then the function will return list of dictionary.
@ -253,7 +253,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
return resp_obj
def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models = {}, enforce_detection = True, detector_backend = 'mtcnn'):
def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models = {}, enforce_detection = True, detector_backend = 'retinaface'):
"""
This function analyzes facial attributes including age, gender, emotion and race
@ -273,7 +273,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images.
detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib.
detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib.
Returns:
The function returns a dictionary. If img_path is a list, then it will return list of dictionary.
@ -460,7 +460,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
return resp_obj
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'retinaface', align = True):
"""
This function applies verification several times and find an identity in a database
@ -480,7 +480,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images.
detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib
detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
Returns:
This function returns pandas data frame. If a list of images is passed to img_path, then it will return list of pandas data frame.
@ -705,7 +705,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
return None
def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'retinaface', align = True):
"""
This function represents facial images as vectors.
@ -721,7 +721,7 @@ def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection
enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images.
detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib
detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
Returns:
Represent function returns a multidimensional vector. The number of dimensions is changing based on the reference model. E.g. FaceNet returns 128 dimensional vector; VGG-Face returns 2622 dimensional vector.
@ -777,12 +777,12 @@ def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', ena
if frame_threshold < 1:
raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold))
functions.initialize_detector(detector_backend = 'opencv')
functions.initialize_detector(detector_backend = 'opencv') #stream uses opencv by default!
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis
, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)
def detectFace(img_path, detector_backend = 'mtcnn', enforce_detection = True):
def detectFace(img_path, detector_backend = 'retinaface', enforce_detection = True):
"""
This function applies pre-processing stages of a face recognition pipeline including detection and alignment
@ -790,7 +790,7 @@ def detectFace(img_path, detector_backend = 'mtcnn', enforce_detection = True):
Parameters:
img_path: exact image path, numpy array or base64 encoded image
detector_backend (string): face detection backends are mtcnn, opencv, ssd or dlib
detector_backend (string): face detection backends are retinaface, mtcnn, opencv, ssd or dlib
Returns:
deteced and aligned face in numpy format

4
deepface/commons/functions.py
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@ -87,7 +87,7 @@ def load_image(img):
return img
def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_detection = True, align = True):
def detect_face(img, detector_backend = 'retinaface', grayscale = False, enforce_detection = True, align = True):
img_region = [0, 0, img.shape[0], img.shape[1]]
@ -106,7 +106,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
else:
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv', return_region = False, align = True):
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'retinaface', return_region = False, align = True):
#img might be path, base64 or numpy array. Convert it to numpy whatever it is.
img = load_image(img)