def main():
args = get_args()
depth = args.depth
k = args.width
weight_file = args.weight_file
global margin
margin = args.margin
image_dir = args.image_dir
global celeb_model
celeb_model = args.model
global classifier
classifier = args.classifier
if not weight_file:
weight_file = get_file("weights.28-3.73.hdf5",
pretrained_model,
cache_subdir="pretrained_models",
file_hash=modhash,
cache_dir=str(Path(__file__).resolve().parent))
# for face detection
global detector
detector = dlib.get_frontal_face_detector()
# load model and weights
global model
img_size = 64
model = WideResNet(img_size, depth=depth, k=k)()
model.load_weights(weight_file)
model._make_predict_function()
class FaceCV(object):
"""
Singleton class for face recongnition task
"""
CASE_PATH = ".\\harcascade\\haarcascade_frontalface_alt.xml"
WRN_WEIGHTS_PATH = ".\\model_weights\\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(FaceCV, cls).__new__(cls)
return cls.instance
def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "model_weights").replace("//", "\\")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
self.model._make_predict_function()
@classmethod
def draw_label(cls, image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,
font_scale=1, thickness=2):
size = cv2.getTextSize(label, font, font_scale, thickness)[0]
x, y = point
cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness)
def crop_face(self, imgarray, section, margin=40, size=64):
"""
:param imgarray: full image
:param section: face detected area (x, y, w, h)
:param margin: add some margin to the face detected area to include a full head
:param size: the result image resolution with be (size x size)
:return: resized image in numpy array with shape (size x size x 3)
"""
img_h, img_w, _ = imgarray.shape
if section is None:
section = [0, 0, img_w, img_h]
(x, y, w, h) = section
margin = int(min(w, h) * margin / 100)
x_a = x - margin
y_a = y - margin
x_b = x + w + margin
y_b = y + h + margin
if x_a < 0:
x_b = min(x_b - x_a, img_w - 1)
x_a = 0
if y_a < 0:
y_b = min(y_b - y_a, img_h - 1)
y_a = 0
if x_b > img_w:
x_a = max(x_a - (x_b - img_w), 0)
x_b = img_w
if y_b > img_h:
y_a = max(y_a - (y_b - img_h), 0)
y_b = img_h
cropped = imgarray[y_a: y_b, x_a: x_b]
resized_img = cv2.resize(cropped, (size, size), interpolation=cv2.INTER_AREA)
resized_img = np.array(resized_img)
return resized_img, (x_a, y_a, x_b - x_a, y_b - y_a)
def detect_face(self,frame):
age = 'Detecting...'
gender = 'Detecting...'
face_cascade = cv2.CascadeClassifier(self.CASE_PATH)
with session1.as_default():
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=10,
minSize=(self.face_size, self.face_size)
)
if faces is not():
# placeholder for cropped faces
face_imgs = np.empty((len(faces), self.face_size, self.face_size, 3))
for i, face in enumerate(faces):
face_img, cropped = self.crop_face(frame, face, margin=40, size=self.face_size)
(x, y, w, h) = cropped
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 200, 0), 2)
face_imgs[i, :, :, :] = face_img
if len(face_imgs) > 0:
# predict ages and genders of the detected faces
results = self.model.predict(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, face in enumerate(faces):
label = "{}, {}".format(int(predicted_ages[i]-3),
"F" if predicted_genders[i][0] > 0.5 else "M")
self.draw_label(frame, (face[0], face[1]), label)
print(int(predicted_ages[i]))
if(predicted_genders[i][0] > 0.5):
gender = "Female"
else:
gender = "Male"
age = (int(predicted_ages[i])-3)
return age , gender
face_size = 64
age_gender_model = WideResNet(face_size, depth=16, k=8)()
age_gender_model_dir = os.path.join(os.getcwd(),
'pretrained_models').replace('//', '\\')
image_width = 1280
image_height = 720
fpath = get_file(
'weights.18-4.06.hdf5',
'https://github.com/Tony607/Keras_age_gender/releases/download/V1.0/weights.18-4.06.hdf5',
cache_dir=age_gender_model_dir)
age_gender_model.load_weights(fpath)
# Address the error: ValueError: Tensor Tensor("dense_1/Softmax:0", shape=(?, 2), dtype=float32) is not an element of this graph.
age_gender_model._make_predict_function()
# Start code from facenet/src/compare.py
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = facenet.src.align.detect_face.create_mtcnn(
sess, None)
cap = cv2.VideoCapture(0)
cap.set(3, image_width)
