def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
print("Loading WideResNet model...")
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "models").replace("//", "\\")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
print("Loaded WideResNet model")
# Load emotion models
print("Loading emotion model...")
self.emotion_model = emotion.load_model_dir("models")
print("Loaded emotion model")
if RECOGNIZE_FACES:
print("Loading face recognizer...")
self.face_recognizer = FaceRecognizer()
print("Loaded face recognizer")
class FaceImage(object):
"""
Singleton class for face recognition task
"""
CASE_PATH = "./models/haarcascade_frontalface_alt.xml"
WRN_WEIGHTS_PATH = "https://github.com/Tony607/Keras_age_gender/releases/download/V1.0/weights.18-4.06.hdf5"
def __new__(cls, weight_file=None, depth=16, width=8, face_size=64):
if not hasattr(cls, 'instance'):
cls.instance = super(FaceImage, cls).__new__(cls)
return cls.instance
def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
print("Loading WideResNet model...")
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "models").replace("//", "\\")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
print("Loaded WideResNet model")
# Load emotion models
print("Loading emotion model...")
self.emotion_model = emotion.load_model_dir("models")
print("Loaded emotion model")
if RECOGNIZE_FACES:
print("Loading face recognizer...")
self.face_recognizer = FaceRecognizer()
print("Loaded face recognizer")
@classmethod
def draw_label_top(cls,
image,
point,
label,
font=cv2.FONT_HERSHEY_SIMPLEX,
font_scale=1,
thickness=2,
alpha=OVERLAY_ALPHA):
size = cv2.getTextSize(label, font, font_scale, thickness)[0]
x, y = point
overlay = image.copy()
cv2.rectangle(overlay, (x, y - size[1]), (x + size[0], y), (255, 0, 0),
cv2.FILLED)
cv2.putText(overlay, label, point, font, font_scale, (255, 255, 255),
thickness)
cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
@classmethod
def draw_label_bottom(cls,
image,
point,
label,
font=cv2.FONT_HERSHEY_SIMPLEX,
font_scale=0.5,
thickness=1,
row_index=0,
alpha=OVERLAY_ALPHA):
size = cv2.getTextSize(label, font, font_scale, thickness)[0]
point = (point[0], point[1] + (row_index * size[1]))
x, y = point
overlay = image.copy()
cv2.rectangle(overlay, (x, y), (x + size[0], y + size[1]), (255, 0, 0),
cv2.FILLED)
point = x, y + size[1]
cv2.putText(overlay, label, point, font, font_scale, (255, 255, 255),
thickness)
cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
def get_regular_face(self, img, bb):
return img[bb.top():bb.bottom() + 1, bb.left():bb.right() + 1, :]
def get_expanded_face(self, img, bb):
img_h, img_w, _ = np.shape(img)
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
xw1 = max(int(x1 - 0.4 * w), 0)
yw1 = max(int(y1 - 0.4 * h), 0)
xw2 = min(int(x2 + 0.4 * w), img_w - 1)
yw2 = min(int(y2 + 0.4 * h), img_h - 1)
return cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :],
(self.face_size, self.face_size))
def detect_face(self, img):
# workaround for CV2 bug
img = copy.deepcopy(img)
# for face detection
detector = dlib.get_frontal_face_detector()
input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_h, img_w, _ = np.shape(input_img)
# detect faces using dlib detector
if RECOGNIZE_FACES == True:
face_bbs, identities = self.face_recognizer.identify_image_faces(
img)
else:
face_bbs = detector(input_img, 1)
expanded_face_imgs = np.empty(
(len(face_bbs), self.face_size, self.face_size, 3))
emotion2_results = []
# Get face images
for i, bb in enumerate(face_bbs):
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
expanded_face_imgs[i, :, :, :] = self.get_expanded_face(img, bb)
reg_face = self.get_regular_face(img, bb)
#reg_face = copy.deepcopy(reg_face)
emotion2_results.append(
emotion.emotionof(self.emotion_model, reg_face)[0])
if len(expanded_face_imgs) > 0:
# predict ages and genders of the detected faces
results = self.model.predict(expanded_face_imgs)
predicted_genders = results[0]
ages = np.arange(0, 101).reshape(101, 1)
predicted_ages = results[1].dot(ages).flatten()
# draw results
for i, bb in enumerate(face_bbs):
if RECOGNIZE_FACES == True:
# Display name
label1 = "{}".format(identities[i])
self.draw_label_bottom(img, (bb.left(), bb.bottom()), label1)
## Display age, gender and emotion
if identities[i] == "Unknown" or "customer" in identities[i]:
label2 = "{}, {}, {}".format(
int(predicted_ages[i]),
"F" if predicted_genders[i][0] > 0.5 else "M",
emotion2_results[i])
else:
label2 = "{}".format(emotion2_results[i])
self.draw_label_bottom(img, (bb.left(), bb.bottom() + 1),
label2,
row_index=1)
else:
## Display age, gender and emotion
label2 = "{}, {}, {}".format(
int(predicted_ages[i]),
"F" if predicted_genders[i][0] > 0.5 else "M",
emotion2_results[i])
self.draw_label_bottom(img, (bb.left(), bb.bottom()),
label2,
row_index=0)
# draw face rectangles
for i, bb in enumerate(face_bbs):
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
return img