mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 20:15:21 +00:00
Merge branch 'master' into regions
This commit is contained in:
Sefik Ilkin Serengil
GitHub
commit
db3a0f2fe8
@ -301,7 +301,9 @@ You can also support this work on [Patreon](https://www.patreon.com/serengil?rep
|
||||
|
||||
## Citation
|
||||
|
||||
Please cite deepface in your publications if it helps your research for facial recognition purposes. Here are its BibTex entries:
|
||||
Please cite deepface in your publications if it helps your research. Here are its BibTex entries:
|
||||
|
||||
If you use deepface for facial recogntion purposes, please cite the this publication.
|
||||
|
||||
```BibTeX
|
||||
@inproceedings{serengil2020lightface,
|
||||
@ -316,7 +318,7 @@ Please cite deepface in your publications if it helps your research for facial r
|
||||
}
|
||||
```
|
||||
|
||||
If you use deepface for facial attribute analysis purposes such as age, gender, emotion or ethnicity, please cite the this publication.
|
||||
If you use deepface for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction, please cite the this publication.
|
||||
|
||||
```BibTeX
|
||||
@inproceedings{serengil2021lightface,
|
||||
|
||||
@ -395,7 +395,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
|
||||
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
|
||||
img, region = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
|
||||
|
||||
emotion_predictions = models['emotion'].predict(img)[0,:]
|
||||
emotion_predictions = models['emotion'].predict(img, verbose=0)[0,:]
|
||||
|
||||
sum_of_predictions = emotion_predictions.sum()
|
||||
|
||||
@ -412,7 +412,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
|
||||
if img_224 is None:
|
||||
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
|
||||
|
||||
age_predictions = models['age'].predict(img_224)[0,:]
|
||||
age_predictions = models['age'].predict(img_224, verbose=0)[0,:]
|
||||
apparent_age = Age.findApparentAge(age_predictions)
|
||||
|
||||
resp_obj["age"] = int(apparent_age) #int cast is for the exception - object of type 'float32' is not JSON serializable
|
||||
@ -422,7 +422,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
|
||||
if img_224 is None:
|
||||
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
|
||||
|
||||
gender_predictions = models['gender'].predict(img_224)[0,:]
|
||||
gender_predictions = models['gender'].predict(img_224, verbose=0)[0,:]
|
||||
|
||||
gender_labels = ["Woman", "Man"]
|
||||
resp_obj["gender"] = {}
|
||||
@ -436,7 +436,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
|
||||
elif action == 'race':
|
||||
if img_224 is None:
|
||||
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True) #just emotion model expects grayscale images
|
||||
race_predictions = models['race'].predict(img_224)[0,:]
|
||||
race_predictions = models['race'].predict(img_224, verbose=0)[0,:]
|
||||
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
|
||||
|
||||
sum_of_predictions = race_predictions.sum()
|
||||
@ -765,6 +765,11 @@ def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection
|
||||
#---------------------------------
|
||||
|
||||
#represent
|
||||
if "keras" in str(type(model)):
|
||||
#new tf versions show progress bar and it is annoying
|
||||
embedding = model.predict(img, verbose=0)[0].tolist()
|
||||
else:
|
||||
#SFace is not a keras model and it has no verbose argument
|
||||
embedding = model.predict(img)[0].tolist()
|
||||
|
||||
return embedding
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -45,10 +45,13 @@ def detect_face(detector, img, align = True):
|
||||
sp = detector["sp"]
|
||||
|
||||
detected_face = None
|
||||
|
||||
img_region = [0, 0, img.shape[1], img.shape[0]]
|
||||
|
||||
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
|
||||
|
||||
@ -37,12 +37,12 @@ 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[1], img.shape[0]]
|
||||
|
||||
return face, region
|
||||
return face, region, confidence
|
||||
|
||||
def detect_faces(face_detector, detector_backend, img, align = True):
|
||||
|
||||
@ -59,7 +59,7 @@ def detect_faces(face_detector, detector_backend, img, align = True):
|
||||
|
||||
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:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -22,6 +22,7 @@ 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 +30,6 @@ 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
|
||||
|
||||
@ -45,13 +45,15 @@ def detect_face(detector, img, align = True):
|
||||
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 +61,7 @@ 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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -93,10 +93,11 @@ 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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user