def deepFaceAnalysis(retina_model, deepface_models, image_path):
age_list = {}
gender_list = {}
race_list = {}
emotion_list = {}
# Pass all images through DeepFace
faces = RetinaFace.extract_faces(image_path, model=retina_model)
if len(faces) > 0:
if len(faces) > 1:
removeBackgroundFaces(faces)
for i, face in enumerate(faces, start=1):
dict = "face_" + str(i)
face_feature = DeepFace.analyze(
face,
actions=['age', 'gender', 'race', 'emotion'],
models=deepface_models,
enforce_detection=False)
age_list[dict] = (face_feature["age"])
gender_list[dict] = (face_feature["gender"])
race_list[dict] = (face_feature["dominant_race"])
emotion_list[dict] = (face_feature["dominant_emotion"])
return len(faces), age_list, gender_list, race_list, emotion_list
else:
return 0, "None", "None", "None", "None"
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 = align)
if len(faces) > 0:
face = faces[0][:, :, ::-1]
return face, img_region
def detect_face(img,
detector_backend='opencv',
grayscale=False,
enforce_detection=True):
home = str(Path.home())
img_region = [0, 0, img.shape[0], img.shape[1]]
#if functions.preproces_face is called directly, then face_detector global variable might not been initialized.
if not "face_detector" in globals():
initialize_detector(detector_backend=detector_backend)
if detector_backend == 'opencv':
faces = []
try:
faces = face_detector.detectMultiScale(img, 1.3, 5)
except:
pass
if len(faces) > 0:
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)]
return detected_face, [x, y, w, h]
else: #if no face detected
if enforce_detection != True:
return img, img_region
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."
)
elif detector_backend == 'ssd':
ssd_labels = [
"img_id", "is_face", "confidence", "left", "top", "right", "bottom"
]
target_size = (300, 300)
base_img = img.copy() #we will restore base_img to img later
original_size = img.shape
img = cv2.resize(img, target_size)
aspect_ratio_x = (original_size[1] / target_size[1])
aspect_ratio_y = (original_size[0] / target_size[0])
imageBlob = cv2.dnn.blobFromImage(image=img)
face_detector.setInput(imageBlob)
detections = face_detector.forward()
detections_df = pd.DataFrame(detections[0][0], columns=ssd_labels)
detections_df = detections_df[detections_df['is_face'] ==
1] #0: background, 1: face
detections_df = detections_df[detections_df['confidence'] >= 0.90]
detections_df['left'] = (detections_df['left'] * 300).astype(int)
detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int)
detections_df['right'] = (detections_df['right'] * 300).astype(int)
detections_df['top'] = (detections_df['top'] * 300).astype(int)
if detections_df.shape[0] > 0:
#TODO: sort detections_df
#get the first face in the image
instance = detections_df.iloc[0]
left = instance["left"]
right = instance["right"]
bottom = instance["bottom"]
top = instance["top"]
detected_face = base_img[int(top *
aspect_ratio_y):int(bottom *
aspect_ratio_y),
int(left *
aspect_ratio_x):int(right *
aspect_ratio_x)]
return detected_face, [
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)
]
else: #if no face detected
if enforce_detection != True:
img = base_img.copy()
return img, img_region
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."
)
elif detector_backend == 'dlib':
detections = face_detector(img, 1)
if len(detections) > 0:
for idx, d in enumerate(detections):
left = d.left()
right = d.right()
top = d.top()
bottom = d.bottom()
detected_face = img[top:bottom, left:right]
return detected_face, [left, top, right - left, bottom - top]
else: #if no face detected
if enforce_detection != True:
return img, img_region
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."
)
elif detector_backend == 'mtcnn':
img_rgb = cv2.cvtColor(
img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
detections = face_detector.detect_faces(img_rgb)
if len(detections) > 0:
detection = detections[0]
x, y, w, h = detection["box"]
detected_face = img[int(y):int(y + h), int(x):int(x + w)]
return detected_face, [x, y, w, h]
else: #if no face detected
if not enforce_detection:
return img, img_region
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."
)
elif detector_backend == 'retinaface':
img_rgb = cv2.cvtColor(
img,
cv2.COLOR_BGR2RGB) #retinaface expects RGB but OpenCV read BGR
from retinaface import RetinaFace
faces = RetinaFace.extract_faces(img_rgb, align=True)
if len(faces) > 0:
face = faces[0]
return face, img_region
else: #if no face detected
if not enforce_detection:
return img, img_region
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."
)
else:
detectors = ['opencv', 'ssd', 'dlib', 'mtcnn']
raise ValueError("Valid backends are ", detectors, " but you passed ",
detector_backend)
