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Merge pull request #580 from Andrea-Oliveri/confidence

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Added confidence to FaceDetector.detect_face and .detect_faces methods
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Sefik Ilkin Serengil 2022-10-23 19:27:48 +03:00 committed by GitHub
commit bbc3c6d59b
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9 changed files with 31 additions and 18 deletions
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2
deepface/commons/functions.py
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@ -110,7 +110,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
face_detector = FaceDetector.build_model(detector_backend)
try:
detected_face, img_region = FaceDetector.detect_face(face_detector, detector_backend, img, align)
detected_face, img_region, _ = FaceDetector.detect_face(face_detector, detector_backend, img, align)
except: #if detected face shape is (0, 0) and alignment cannot be performed, this block will be run
detected_face = None

2
deepface/commons/realtime.py
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@ -151,7 +151,7 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
detected_faces = []
face_index = 0
for face, (x, y, w, h) in faces:
for face, (x, y, w, h), _ in faces:
if w > 130: #discard small detected faces
face_detected = True

8
deepface/detectors/DlibWrapper.py
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@ -46,9 +46,12 @@ def detect_face(detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
confidence = None
face_detector = detector["face_detector"]
detections = face_detector(img, 1)
#note that, by design, dlib's fhog face detector scores are >0 but not capped at 1
detections, scores, _ = face_detector.run(img, 1)
if len(detections) > 0:
@ -60,12 +63,13 @@ def detect_face(detector, img, align = True):
detected_face = img[max(0, top): min(bottom, img.shape[0]), max(0, left): min(right, img.shape[1])]
img_region = [left, top, right - left, bottom - top]
confidence = scores[idx]
if align:
img_shape = sp(img, detections[idx])
detected_face = dlib.get_face_chip(img, img_shape, size = detected_face.shape[0])
resp.append((detected_face, img_region))
resp.append((detected_face, img_region, confidence))
return resp

11
deepface/detectors/FaceDetector.py
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@ -14,7 +14,7 @@ def build_model(detector_backend):
'dlib': DlibWrapper.build_model,
'mtcnn': MtcnnWrapper.build_model,
'retinaface': RetinaFaceWrapper.build_model,
'mediapipe': MediapipeWrapper.build_model
'mediapipe': MediapipeWrapper.build_model
}
if not "face_detector_obj" in globals():
@ -37,12 +37,13 @@ def detect_face(face_detector, detector_backend, img, align = True):
obj = detect_faces(face_detector, detector_backend, img, align)
if len(obj) > 0:
face, region = obj[0] #discard multiple faces
face, region, confidence = obj[0] #discard multiple faces
else: #len(obj) == 0
face = None
region = [0, 0, img.shape[0], img.shape[1]]
confidence = None
return face, region
return face, region, confidence
def detect_faces(face_detector, detector_backend, img, align = True):
@ -52,14 +53,14 @@ def detect_faces(face_detector, detector_backend, img, align = True):
'dlib': DlibWrapper.detect_face,
'mtcnn': MtcnnWrapper.detect_face,
'retinaface': RetinaFaceWrapper.detect_face,
'mediapipe': MediapipeWrapper.detect_face
'mediapipe': MediapipeWrapper.detect_face
}
detect_face = backends.get(detector_backend)
if detect_face:
obj = detect_face(face_detector, img, align)
#obj stores list of detected_face and region pair
#obj stores list of (detected_face, region, confidence)
return obj
else:

4
deepface/detectors/MediapipeWrapper.py
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@ -20,7 +20,7 @@ def detect_face(face_detector, img, align = True):
if results.detections:
for detection in results.detections:
confidence = detection.score
confidence, = detection.score
bounding_box = detection.location_data.relative_bounding_box
landmarks = detection.location_data.relative_keypoints
@ -44,6 +44,6 @@ def detect_face(face_detector, img, align = True):
if align:
detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
resp.append((detected_face,img_region))
resp.append((detected_face, img_region, confidence))
return resp

4
deepface/detectors/MtcnnWrapper.py
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@ -12,6 +12,7 @@ def detect_face(face_detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
confidence = None
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
detections = face_detector.detect_faces(img_rgb)
@ -22,6 +23,7 @@ def detect_face(face_detector, img, align = True):
x, y, w, h = detection["box"]
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
img_region = [x, y, w, h]
confidence = detection["confidence"]
if align:
keypoints = detection["keypoints"]
@ -29,6 +31,6 @@ def detect_face(face_detector, img, align = True):
right_eye = keypoints["right_eye"]
detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
resp.append((detected_face, img_region))
resp.append((detected_face, img_region, confidence))
return resp

9
deepface/detectors/OpenCvWrapper.py
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@ -41,17 +41,20 @@ def detect_face(detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
confidence = None
faces = []
try:
#faces = detector["face_detector"].detectMultiScale(img, 1.3, 5)
faces = detector["face_detector"].detectMultiScale(img, 1.1, 10)
#note that, by design, opencv's haarcascade scores are >0 but not capped at 1
faces, _, scores = detector["face_detector"].detectMultiScale3(img, 1.1, 10, outputRejectLevels = True)
except:
pass
if len(faces) > 0:
for x,y,w,h in faces:
for (x,y,w,h), confidence in zip(faces, scores):
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
if align:
@ -59,7 +62,7 @@ def detect_face(detector, img, align = True):
img_region = [x, y, w, h]
resp.append((detected_face, img_region))
resp.append((detected_face, img_region, confidence))
return resp

3
deepface/detectors/RetinaFaceWrapper.py
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@ -44,6 +44,7 @@ def detect_face(face_detector, img, align = True):
x = facial_area[0]
w = facial_area[2] - x
img_region = [x, y, w, h]
confidence = identity["score"]
#detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
detected_face = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]
@ -58,6 +59,6 @@ def detect_face(face_detector, img, align = True):
detected_face = postprocess.alignment_procedure(detected_face, right_eye, left_eye, nose)
resp.append((detected_face, img_region))
resp.append((detected_face, img_region, confidence))
return resp

4
deepface/detectors/SsdWrapper.py
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@ -52,6 +52,7 @@ def detect_face(detector, img, align = True):
detected_face = None
img_region = [0, 0, img.shape[0], img.shape[1]]
confidence = None
ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"]
@ -93,10 +94,11 @@ def detect_face(detector, img, align = True):
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)]
confidence = instance["confidence"]
if align:
detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
resp.append((detected_face, img_region))
resp.append((detected_face, img_region, confidence))
return resp