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align argument

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This commit is contained in:
Sefik Ilkin Serengil 2021-04-29 21:38:08 +03:00
parent 73d1950133
commit 1812391a3c
8 changed files with 35 additions and 26 deletions
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21
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'):
def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
"""
This function verifies an image pair is same person or different persons.
@ -153,11 +153,13 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
#img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'mtcnn'
img1_representation = represent(img_path = img1_path
, model_name = model_name, model = custom_model
, enforce_detection = enforce_detection, detector_backend = detector_backend)
, enforce_detection = enforce_detection, detector_backend = detector_backend
, align = align)
img2_representation = represent(img_path = img2_path
, model_name = model_name, model = custom_model
, enforce_detection = enforce_detection, detector_backend = detector_backend)
, enforce_detection = enforce_detection, detector_backend = detector_backend
, align = align)
#----------------------
#find distances between embeddings
@ -458,7 +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'):
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
"""
This function applies verification several times and find an identity in a database
@ -569,7 +571,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
representation = represent(img_path = employee
, model_name = model_name, model = custom_model
, enforce_detection = enforce_detection, detector_backend = detector_backend)
, enforce_detection = enforce_detection, detector_backend = detector_backend
, align = align)
instance.append(representation)
@ -610,7 +613,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
target_representation = represent(img_path = img_path
, model_name = model_name, model = custom_model
, enforce_detection = enforce_detection, detector_backend =detector_backend)
, enforce_detection = enforce_detection, detector_backend = detector_backend
, align = align)
for k in metric_names:
distances = []
@ -701,7 +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'):
def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'mtcnn', align = True):
"""
This function represents facial images as vectors.
@ -737,7 +741,8 @@ def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection
img = functions.preprocess_face(img = img_path
, target_size=(input_shape_y, input_shape_x)
, enforce_detection = enforce_detection
, detector_backend = detector_backend)
, detector_backend = detector_backend
, align = align)
#represent
embedding = model.predict(img)[0].tolist()

8
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):
def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_detection = True, align = True):
img_region = [0, 0, img.shape[0], img.shape[1]]
@ -95,7 +95,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
if not "face_detector" in globals():
initialize_detector(detector_backend = detector_backend)
detected_face, img_region = FaceDetector.detect_face(face_detector, detector_backend, img)
detected_face, img_region = FaceDetector.detect_face(face_detector, detector_backend, img, align)
if (isinstance(detected_face, np.ndarray)):
return detected_face, img_region
@ -106,13 +106,13 @@ 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):
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv', return_region = False, align = True):
#img might be path, base64 or numpy array. Convert it to numpy whatever it is.
img = load_image(img)
base_img = img.copy()
img, region = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection)
img, region = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection, align = align)
#--------------------------

7
deepface/detectors/DlibWrapper.py
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@ -32,7 +32,7 @@ def build_model():
detector["sp"] = sp
return detector
def detect_face(detector, img):
def detect_face(detector, img, align = True):
import dlib #this requirement is not a must that's why imported here
@ -55,7 +55,8 @@ def detect_face(detector, img):
img_region = [left, top, right - left, bottom - top]
break #get the first one
img_shape = sp(img, detections[0])
detected_face = dlib.get_face_chip(img, img_shape, size = detected_face.shape[0])
if align:
img_shape = sp(img, detections[0])
detected_face = dlib.get_face_chip(img, img_shape, size = detected_face.shape[0])
return detected_face, img_region

4
deepface/detectors/FaceDetector.py
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@ -23,7 +23,7 @@ def build_model(detector_backend):
return face_detector
def detect_face(face_detector, detector_backend, img):
def detect_face(face_detector, detector_backend, img, align = True):
backends = {
'opencv': OpenCvWrapper.detect_face,
@ -36,7 +36,7 @@ def detect_face(face_detector, detector_backend, img):
detect_face = backends.get(detector_backend)
if detect_face:
face, region = detect_face(face_detector, img)
face, region = detect_face(face_detector, img, align)
else:
raise ValueError("invalid detector_backend passed - " + detector_backend)

5
deepface/detectors/MtcnnWrapper.py
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@ -6,7 +6,7 @@ def build_model():
face_detector = MTCNN()
return face_detector
def detect_face(face_detector, img):
def detect_face(face_detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
@ -24,6 +24,7 @@ def detect_face(face_detector, img):
left_eye = keypoints["left_eye"]
right_eye = keypoints["right_eye"]
detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
if align:
detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
return detected_face, img_region

7
deepface/detectors/OpenCvWrapper.py
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@ -35,7 +35,7 @@ def build_cascade(model_name = 'haarcascade'):
eye_detector = cv2.CascadeClassifier(eye_detector_path)
return eye_detector
def detect_face(detector, img):
def detect_face(detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
@ -50,13 +50,14 @@ def detect_face(detector, img):
x,y,w,h = faces[0] #focus on the 1st face found in the image
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
detected_face = align_face(detector["eye_detector"], detected_face)
if align:
detected_face = align_face(detector["eye_detector"], detected_face)
img_region = [x, y, w, h]
return detected_face, img_region
def align_face(eye_detector, img):
detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #eye detector expects gray scale image
eyes = eye_detector.detectMultiScale(detected_face_gray)

4
deepface/detectors/RetinaFaceWrapper.py
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@ -5,14 +5,14 @@ def build_model():
face_detector = RetinaFace.build_model()
return face_detector
def detect_face(face_detector, img):
def detect_face(face_detector, img, align = True):
face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #retinaface expects RGB but OpenCV read BGR
faces = RetinaFace.extract_faces(img_rgb, model = face_detector, align = True)
faces = RetinaFace.extract_faces(img_rgb, model = face_detector, align = align)
if len(faces) > 0:
face = faces[0][:, :, ::-1]

5
deepface/detectors/SsdWrapper.py
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@ -45,7 +45,7 @@ def build_model():
return detector
def detect_face(detector, img):
def detect_face(detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
@ -94,6 +94,7 @@ def detect_face(detector, img):
detected_face = base_img[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)]
img_region = [int(left*aspect_ratio_x), int(top*aspect_ratio_y), int(right*aspect_ratio_x) - int(left*aspect_ratio_x), int(bottom*aspect_ratio_y) - int(top*aspect_ratio_y)]
detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
if align:
detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
return detected_face, img_region