def main(face_recognition_method: str = None):
webcam = cv.VideoCapture(0)
data_aquisition = DataAquisition(dataset_dir='images/training/',
resources_dir='resources/')
known_people = data_aquisition.create_dataset(webcam)
if len(known_people) == 1:
print('Should have any face to recognition')
return
print('Training model...')
data_aquisition.train(face_recognition_method)
print('Initialzing face recognizer...')
face_recognizer = FaceRecognizer(resources_dir='resources/',
recognizer_name=face_recognition_method)
while True:
if cv.waitKey(1) & 0xFF == ord('q'):
break
_, c_frame = webcam.read()
c_frame, face_locations, labels, factors = face_recognizer.predict(
c_frame, known_people)
cv.imshow('webcam', c_frame)
webcam.release()
cv.destroyAllWindows()
def __init__(self):
self.node_name = rospy.get_name()
rospy.loginfo("[%s] Initializing......" % (self.node_name))
self.bridge = CvBridge()
self.visualization = True
self.image_msg = None
self.pub_detections = rospy.Publisher("~image_face",
CompressedImage,
queue_size=1)
self.recognizer = FaceRecognizer(scale=3)
rospy.Service('~detect_face_locations', GetFaceDetections,
self.cbDetectFaceLocations)
rospy.Service('~detect_face_labels', GetFaceDetections,
self.cbDetectFaceLabels)
rospy.Service('~list_face_labels', GetStrings, self.cbListFaceLabels)
rospy.Service('~add_face_label', SetString, self.cbAddFaceLabel)
rospy.Service('~remove_face_label', SetString, self.cbRemoveFaceLabel)
# self.sub_image = rospy.Subscriber("~image_raw", Image, self.cbImg , queue_size=1)
self.sub_image = rospy.Subscriber("~image_raw/compressed",
CompressedImage,
self.cbImg,
queue_size=1)
# rospy.loginfo("[%s] wait_for_service : camera_get_frame..." % (self.node_name))
# rospy.wait_for_service('~camera_get_frame')
# self.get_frame = rospy.ServiceProxy('~camera_get_frame', GetFrame)
rospy.loginfo("[%s] Initialized." % (self.node_name))
def memorize_face(self):
""" Learn face model.
Returns:
"""
# FaceRecognizer.fit(n_epoch=5)
FaceRecognizer.fit(
n_epoch=self.N_EPOCH, batch_size=self.BATCH_SIZE,
image_size=self.RECOGNITION_IMAGE_SIZE,)
def initialize(self):
if self.network_name == 'YOLO':
self.network = YOLO_TF()
elif self.network_name == 'SSD-mobilenet-face':
self.network = ssd_mobilenet_face_wrapper.ssd_mobilenet_face(
self.threshold)
self.faceRecognizer = FaceRecognizer()
else:
raise Exception('Network name not recognized')
def run_animation(self):
"""
Run the animation of the face
:return:
"""
self.runText.set("Press 'Esc' to stop")
self.rc = FaceRecognizer(self.root, self.canvas, self.runText,
self.runBtn, self.chatVar)
self.rc.start()
self.runText.set("Start Animation")
def history_recollection():
history = FaceRecognizer(user_interface)
history.build_imagecsv()
user_number = history.RecognizeFace()
user_name = history.names[user_number]
if user_name is None:
#print "did not recognize user ", user_name
chatbot_response = "I don't think we've met before, what's your name?"
user_interface.update_sprites(chatbot_response, " ".join(("Emotion: ", meeting_emotion)), " ".join(("User: "******"Unknown")), "Primary Topics: ")
user_interface.render()
text_to_speech(chatbot_response)
user_name = speech.recognize_speech()
user_interface.update_sprites(chatbot_response, " ".join(("Emotion: ", meeting_emotion)), " ".join(("User: "******"Primary Topics: ")
user_interface.render()
#print "Name entered as: ", user_name
history.retrain(user_name)
else:
#print "recognized user ", user_name
chatbot_response = "It's good to see you again, " + user_name
user_interface.update_sprites(chatbot_response, " ".join(("Emotion: ", meeting_emotion)), " ".join(("User: "******"Primary Topics: ")
user_interface.render()
text_to_speech(chatbot_response)
history.exit()
return user_name
def __init__(self, url, faces_path, tolerance):
self.url = url
# load stream video
self.camera = cv2.VideoCapture(self.url)
if self.camera.isOpened() is False:
print("Error: Failed to open the video source.")
self.video_frame = self.camera.get(cv2.CAP_PROP_FRAME_COUNT)
self.video_fps = self.camera.get(cv2.CAP_PROP_FPS)
self.image_x = int(self.camera.get(cv2.CAP_PROP_FRAME_WIDTH))
self.image_y = int(self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.face_recognizer = FaceRecognizer(faces_path, tolerance)
def make_one_to_more_test(self, max_size):
self._load_dataset_more(max_size)
for i, (picture, name) in enumerate(self.dataset_more_base):
# print(picture)
try:
self.face_recognizer.fingerprints[
name + "?" + str(i)] = self._get_face_feature(picture)
except:
pass
# print("ok")
correct_num = 0
cnt = 0
for picture, name in self.dataset_more_test:
# print(f"{cnt} / {len(self.dataset_more_test)}")
cnt += 1
feature = self._get_face_feature(picture)
similarity_max = 0
name_of_max = ""
for p, vector in self.face_recognizer.fingerprints.items():
similarity = FaceRecognizer._cosine_similarity(feature, vector)
if similarity > similarity_max:
name_of_max = p
similarity_max = similarity
# print(f"{similarity_max}, {nam e_of_max}, {p}")
if name_of_max.startswith(name):
correct_num += 1
return correct_num / len(self.dataset_more_test)
def test_gender_recognizer(args):
print 'Identity Recognizer:'
input_shape = (args.image_size, args.image_size, args.num_channels)
print 'Input shape:', input_shape
X, y = get_30_people_chunk(args.image_path, 0, gender_label=True)
print 'images shape:', X.shape
print 'labels shape:', y.shape
identity_recognizer = FaceRecognizer(args.model_path + '_gender',
y.shape[1], input_shape,
args.num_channels)
identity_recognizer.model.compile(loss=TEST_SOFTMAX,
optimizer=TEST_SGD,
metrics=['accuracy'])
y_pred = identity_recognizer.model.predict(X)
y_pred_ct = np.argmax(y_pred, axis=1)
y_true_ct = np.argmax(y, axis=1)
acc = np.sum(y_pred_ct == y_true_ct) / y_true_ct.shape[0]
print '\tAccuracy on training data: %.4f' % acc
X_val, y_val = get_30_people_chunk(args.image_path, 1, gender_label=True)
y_pred = identity_recognizer.model.predict(X_val)
y_pred_ct = np.argmax(y_pred, axis=1)
y_true_ct = np.argmax(y_val, axis=1)
acc = np.sum(y_pred_ct == y_true_ct) / y_true_ct.shape[0]
print '\tAccuracy on validation data: %.4f' % acc
def initialize(self):
if self.network_name == 'YOLO':
self.network = YOLO_TF()
elif self.network_name == 'SSD-brainlab':
self.brainlab_args = UpdatePredictConfiguration()
assert self.brainlab_args.batch_size == 1, 'Batch size must be 1'
self.network = BrainLabNet.BrainLabNet(self.brainlab_args,
'interactive')
self.network.start_interactive_session(self.brainlab_args)
self.faceRecognizer = FaceRecognizer()
elif self.network_name == 'SSD-mobilenet-face':
self.network = ssd_mobilenet_face_wrapper.ssd_mobilenet_face(
self.threshold)
self.faceRecognizer = FaceRecognizer()
else:
raise Exception('Network name not recognized')
class IPCamera:
"""
Face recognition for IPCamera
"""
def __init__(self, url, faces_path, tolerance):
self.url = url
# load stream video
self.camera = cv2.VideoCapture(self.url)
if self.camera.isOpened() is False:
print("Error: Failed to open the video source.")
self.video_frame = self.camera.get(cv2.CAP_PROP_FRAME_COUNT)
self.video_fps = self.camera.get(cv2.CAP_PROP_FPS)
self.image_x = int(self.camera.get(cv2.CAP_PROP_FRAME_WIDTH))
self.image_y = int(self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.face_recognizer = FaceRecognizer(faces_path, tolerance)
def __del__(self):
self.camera.release()
def get_frame(self):
""" Returns estimated image """
ret, frame = self.camera.read()
if not ret:
return None
res_img = self.face_recognizer.recognize(frame)
_, jpeg = cv2.imencode('.jpg', res_img)
return jpeg.tobytes()
def __init__(self, args: Dict):
# todo proper handling of self.modes
self.ie = IECore()
self.modes = self.__determine_processing_mode(args)
net_face_detect = net_landmarks_detect = net_recognize_face = None
if not self.modes['detect']:
raise ValueError('detection model undefined')
# load networks from file
net_face_detect = self.__prepare_network(args['detection_model'])
# put it to corresponding class
# self.face_locator = FaceLocator(net_face_detect, args['detection_model_threshold'])
self.face_locator = FaceLocator(net_face_detect,
args['detection_model_threshold'],
NetworkType(args['detection_model']))
# setup device plugins
if next(iter(args['device'])) == 'CPU':
# CPU
self.ie.set_config(config={
"CPU_THROUGHPUT_STREAMS": "1",
"CPU_THREADS_NUM": "8",
},
device_name='CPU')
elif next(iter(args['device'])) == 'GPU':
# GPU
pass
self.ie.set_config(config={"GPU_THROUGHPUT_STREAMS": "1"},
device_name='GPU')
elif next(iter(args['device'])) == 'MYRIAD':
pass
# load to device for inferencing
self.face_locator.deploy_network(next(iter(args['device'])), self.ie)
if self.modes['landmark']:
net_landmarks_detect = self.__prepare_network(
args['landmarks_model'])
self.landmarks_locator = LandmarksLocator(net_landmarks_detect)
self.landmarks_locator.deploy_network(next(iter(args['device'])),
self.ie)
if self.modes['recognize']:
net_recognize_face = self.__prepare_network(
args['recognition_model'])
self.face_recognizer = FaceRecognizer(net_recognize_face)
self.face_recognizer.deploy_network(next(iter(args['device'])),
self.ie)
class InvaderDetectorWithFace:
def __init__(self):
self.face_finder = FaceFinder()
self.recognizer = FaceRecognizer()
self.recognizer.load('trainer.yml')
self.security_trigger = Trigger(20, lambda similarity: similarity > 80)
def on_enabled_start(self):
pass
def on_disabled_update(self):
pass
def detect(self, frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_finder.find(gray)
best_face = None
best_confidence = 1000
for coordinates in faces:
region_of_interest = get_region_of_interest(gray, coordinates)
id_, conf = self.recognizer.predict(region_of_interest)
if conf < best_confidence:
best_face = region_of_interest
best_confidence = conf
print('{}, {}, {}'.format(datetime.now(), id_, conf))
# save_region_of_interest(gray, coordinates)
self.highlight_face(frame, coordinates)
if best_face is not None:
if self.security_trigger.update(best_confidence):
print('Face not match!')
return True
return False
def highlight_face(self, image, coordinates, color=(0, 0, 255)):
x, y, w, h = coordinates
x_end = x + w
y_end = y + h
stroke = 2
cv2.rectangle(image, (x, y), (x_end, y_end), color, stroke)
def __init__(self):
FaceRecognizer.__init__(self)
logger.info('Creating AWS Rekognition client.')
self._aws_client = boto3.client('rekognition')
self._face_registries = self._get_aws_collections()
self._active_face_registry = None
# Holds current registry details
self._registry_faces = []
self._registry_face_names = []
self._registry_face_ids = []
self._detection_attributes = ['DEFAULT']
self._detection_threshold = 80.0
self._matching_threshold = 70.0
# Enabling this will get more facial attributes such as age, gender.
# self._detection_attributes = ['DEFAULT', 'ALL']
logger.info('Created face recognizer.')
logger.info('Existing face registries {}'.format(
self._face_registries))
def __init__(self):
"""Initialize the AppEngine
Input: None
Output: Creates an instance of the AppEngine.
"""
# init QObject
super().__init__()
# create an instance of the database
self.database = Database('../data/face_data.db')
# ensure appropriate table is created
create_sql = "CREATE TABLE IF NOT EXISTS faces (FaceName TEXT PRIMARY KEY," \
"FaceEncodings BLOB NOT NULL)"
self.database.createDatabase(create_sql)
# create an instance of the VideoStreamer
self.videoStreamer = VideoStreamer()
# connect the VideoStreamer's newPiFrame signal to the processNewFrame method
self.videoStreamer.newPiFrame.connect(self.processNewFrame)
# create an instance of the FaceRecognizer
self.faceRecognizer = FaceRecognizer()
# bool which determines if facial recognition is performed on each incoming frame from the Pi
self.performFacialRecognition = False
# current frame sent from the Pi, used for adding a new face
self.currentFrame = None
# current face name and encodings, used for adding a new face
self.currentAddFaceName = None
self.currentAddFaceEncodings = list()
# create an instance of the AlertObserver
self.alertObserver = AlertObserver()
# bool determines if an alert has already been sent (only one is sent per application run)
self.alertSent = False
def time_fr(image_folder=None):
embedding_file = 'embeddings.npy'
if os.path.exists(embedding_file):
os.remove(embedding_file)
face_recog = FaceRecognizer(model_path='model/20180402-114759')
if image_folder is None:
image_path = 'faces.jpg'
image_paths = [image_path for i in range(10)]
else:
image_paths = os.listdir(image_folder)
image_paths = [os.path.join(image_folder, p) for p in image_paths if p.endswith('jpg')]
for i, image_path in enumerate(image_paths):
print('running: %d'%i)
image = face_recog.read_image_sp(image_path)
image = resize_image(image)
det_start = time.time()
bboxes, scores, keypoints = face_recog.detect_faces_and_keypoints(image)
det_stop = time.time()
print('detection time = ', det_stop - det_start)
if len(bboxes) == 0:
print('no face detected')
else:
rec_start = time.time()
face_patches = face_recog.prepare_image_patches(image, bboxes, 160, 20)
embeddings = face_recog.extract_embedding(face_patches)
rec_stop = time.time()
print('recognition time = ', rec_stop - rec_start)
def __init__(self, detection_method, embedding_model):
''' function constructor
Constructor for FaceDetector
Args:
detection_method (DetectionMethod): Method to use for detection
embedding_model (FaceEmbeddingModelEnum): The model to use for generating
embeddings for face images
Returns:
None
'''
# load face detection engine
self.face_detection_engine = FaceDetectionEngine(detection_method)
self.face_recognizer = FaceRecognizer(embedding_model)
self.embedding_image_dimensions = get_image_dimensions_for_embedding_model(embedding_model)
self.start_time_stamp = None
self.fps_font = ImageFont.truetype(font="/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", size=20)
self.face_label_font = ImageFont.truetype(font="/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", size=20)
class Detector:
def __init__(self, network_name, threshold=0.7):
self.network_name = network_name
self.threshold = threshold
def initialize(self):
if self.network_name == 'YOLO':
self.network = YOLO_TF()
elif self.network_name == 'SSD-mobilenet-face':
self.network = ssd_mobilenet_face_wrapper.ssd_mobilenet_face(
self.threshold)
self.faceRecognizer = FaceRecognizer()
else:
raise Exception('Network name not recognized')
def detect(self, image):
if self.network_name == 'YOLO':
yolo_result = self.network.detect_from_cvmat2(image)
all_detections = []
for i in range(len(yolo_result)):
if yolo_result[i][0] == 'person':
this_detection = Detection()
this_detection.class_name = yolo_result[i][0]
this_detection.x_center = yolo_result[i][1]
this_detection.y_center = yolo_result[i][2]
this_detection.width = yolo_result[i][3]
this_detection.height = yolo_result[i][4]
this_detection.conf = yolo_result[i][5]
all_detections.append(this_detection)
elif self.network_name == 'SSD-mobilenet-face':
boxes = self.network.forward_image(image)
all_detections = []
for i in range(len(boxes)):
this_detection = Detection()
this_detection.class_name = boxes[i][0]
this_detection.x_center = boxes[i][1]
this_detection.y_center = boxes[i][2]
this_detection.width = boxes[i][3] * 2
this_detection.height = boxes[i][4] * 2
this_detection.conf = boxes[i][5]
this_detection.class_name = self.faceRecognizer.get_person_name(
image, this_detection)
all_detections.append(this_detection)
else:
raise Exception('Network name not recognized')
#print('Found ' + str(len(all_detections)) + ' detections')
return all_detections
def run(self):
import dlib
from people import People
from gender_recognizer_tf import GenderRecognizer
from face_recognizer import FaceRecognizer
self.gender_recognizer = GenderRecognizer()
self.p = People("./known_people")
self.face_recognizer = FaceRecognizer(
self.p.people
) #this is not ok , we should pass onlu self.p as object
self.predictor = dlib.shape_predictor(
"shape_predictor_68_face_landmarks.dat")
self.fa = FaceAligner(self.predictor, desiredFaceWidth=160)
self.state = IdleState()
while True:
label, data = self.recv()
if label == 'video_feed':
frame = data[0]
face = None
aligned_image, face, rect_nums, XY = self.face_extractor(frame)
if face is None:
continue
face_locations, face_names, percent = self.face_recognizer.recognize(
face)
in_the_frame = self.__update_state(self.p.people, percent)
if (self.state.__class__ == MatchState):
path, gender, age = self.gender_recognizer.recognize(
aligned_image, face, rect_nums, XY)
self.broadcast(["Match", face, in_the_frame, gender, age])
else:
path, gender, age = self.gender_recognizer.recognize(
aligned_image, face, rect_nums, XY)
if path is None:
continue
print("FaceFinder path = " + path)
self.broadcast(["NoMatch", path])
def __init__(self):
#observer = Observable()
#motion_detector = MotionObserver('Motion Detector')
#observer.register(motion_detector)
self.camera = cv2.VideoCapture(0)
time.sleep(0.5)
self.motion_detector = MotionDetector()
self.face_detector = FaceDetector()
self.face_recognizer = FaceRecognizer()
self.detectingstate = DetectingMotion(self)
self.scanningstate = Scanning(self)
self.facestate = FacialRecognition(self)
self.greetingstate = GreetRoommate(self)
self.waitingstate = WaitingForTask(self)
self.servingstate = Serving(self)
self.state = self.detectingstate
class PersonFinder:
def __init__(self, dataset):
self.detector = FaceDetector()
self.recognizer = FaceRecognizer(dataset)
self.dataset = dataset
def find_all(self, frame):
rects = self.detector.get_rects(frame)
labels = self.recognizer.get_names_from_rects(frame, rects)
return list(zip(labels, rects))
def open_window(self):
cv2.namedWindow(self.dataset)
def close_window(self):
cv2.destroyWindow(self.dataset)
def render(self, frame):
copy = frame.copy()
for name, rect in self.find_all(frame):
y = rect[1] - 10 if rect[1] - 10 > 10 else rect[1] + 10
cv2.rectangle(copy, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255),
2)
cv2.putText(copy, name, (rect[0], y), cv2.FONT_HERSHEY_SIMPLEX,
0.45, (0, 0, 255), 2)
cv2.imshow(self.dataset, copy)
return chr(cv2.waitKey(1) & 0xFF)
@staticmethod
def cycle(dataset=None, port=0):
if dataset is None:
dataset = input("Enter The Dataset Name: ")
finder = PersonFinder(dataset)
vid = cv2.VideoCapture(port)
ok, frame = vid.read()
finder.open_window()
while vid.isOpened() and finder.render(frame) not in "qQ":
ok, frame = vid.read()
finder.close_window()
vid.release()
def load_face_recognition_model(self):
# person.Person.objects().distinct('_id')
# self.face_recognizer = FaceRecognizer.objects(
# n_epoch=self.N_EPOCH,
# batch_size=self.BATCH_SIZE,
# image_size=self.RECOGNITION_IMAGE_SIZE,
# person_ids=Person.ascendind_ids(),
# ).first()
# if self.face_recognizer is None:
# return None
person_ids = [obj._id for obj in Person.objects]
self.face_recognizer = FaceRecognizer(
n_epoch=self.N_EPOCH,
batch_size=self.BATCH_SIZE,
image_size=self.RECOGNITION_IMAGE_SIZE,
person_ids=person_ids,
person_num=len(person_ids)
)
self.face_recognizer.generate_filename()
print(self.face_recognizer.filename)
self.face_recognition_model = \
self.face_recognizer.load_model()
return self.face_recognition_model
parser.add_argument(
'--predictor_path',
type=str,
required=True,
help=
'location of the dlib facial landmark predictor where shape_predictor_68_face_landmarks.dat is located'
)
parser.add_argument('--gallery_path',
type=str,
required=True,
help='location of the gallery')
parser.add_argument('--port', type=int, default=8000, help='which port to use')
args = parser.parse_args()
face_aligner = FaceAligner(args.predictor_path)
face_recognizer = FaceRecognizer(args.gallery_path, OpenCVAlgorithm,
face_aligner)
register_handler = RegisterHandler(args.gallery_path, face_aligner)
recognize_handler = RecognizeHandler(args.gallery_path, face_aligner,
face_recognizer)
class S(BaseHTTPRequestHandler):
def _set_response(self, message=None):
self.send_response(200)
if message is not None:
#self.send_header('Content-type', 'text/html')
self.send_header('Content-type', 'application/json')
self.end_headers()
def do_GET(self):
def process_image(self, cv_image):
if not self.enable:
grey = cv.CreateImage(self.image_size, 8, 1)
cv.CvtColor(cv_image, grey, cv.CV_BGR2GRAY)
return grey
self.frame_count = self.frame_count + 1
haar = False
# Use HAAR if no faces are tracked yet or every 5
if (self.use_haar_only or not self.detect_box.any_trackable_faces()
) and self.auto_face_tracking:
self.detect_face(cv_image)
haar = True
elif self.frame_count % 5 == 0:
self.detect_face(cv_image)
haar = True
""" Otherwise, track the face using Good Features to Track and
Lucas-Kanade Optical Flow """
if not self.use_haar_only:
for fkey in self.detect_box.faces.keys():
face = self.detect_box.faces[fkey]
if not face.is_trackable():
continue
# Let's try to identify the person by this face.
# Note: face is of type FaceBox
x1, y1 = face.pt1[0], face.pt1[1]
x2, y2 = face.pt2[0], face.pt2[1]
pad = 10 # Just to relax the face boundary
# crop the face
face_cv_image = cv_image[y1:y2 + pad, x1:x2 + pad]
# DEBUG cv.SaveImage('/tmp/face.png', face_cv_image)
bridge = CvBridge()
r = FaceRecognizer().infer(np.asarray(face_cv_image))
face.name = r[0][0]
if not face.track_box or not self.is_rect_nonzero(
face.track_box):
face.features = []
face.update_box(face.face_box())
track_box = self.track_lk(cv_image, face)
if track_box and len(track_box) != 3:
face.update_box(track_box)
else:
face.update_box_elipse(track_box)
""" Prune features that are too far from the main cluster """
if len(face.features) > 0:
# Consider to move face class
((mean_x, mean_y, mean_z), mse_xy, mse_z,
score) = self.prune_features(
min_features=face.abs_min_features,
outlier_threshold=self.std_err_xy,
mse_threshold=self.max_mse,
face=face)
if score == -1:
face.lost_face()
continue
""" Add features if the number is getting too low """
if len(face.features) < face.min_features:
face.expand_roi = self.expand_roi_init * face.expand_roi
self.add_features(cv_image, face)
else:
face.expand_roi = self.expand_roi_init
self.detect_box.nextFrame(haar)
self.detect_box.publish_faces()
return cv_image
class FaceDetector():
''' class FaceDetector
Purpose: detect (and locate if present) faces in an image
'''
def __init__(self, detection_method, embedding_model):
''' function constructor
Constructor for FaceDetector
Args:
detection_method (DetectionMethod): Method to use for detection
embedding_model (FaceEmbeddingModelEnum): The model to use for generating
embeddings for face images
Returns:
None
'''
# load face detection engine
self.face_detection_engine = FaceDetectionEngine(detection_method)
self.face_recognizer = FaceRecognizer(embedding_model)
self.embedding_image_dimensions = get_image_dimensions_for_embedding_model(embedding_model)
self.start_time_stamp = None
self.fps_font = ImageFont.truetype(font="/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", size=20)
self.face_label_font = ImageFont.truetype(font="/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", size=20)
# With the input frame, use the EdgeTPU Detection engine along with the
# tflite model to detect any faces. If any faces are detected the image
# will be updated with boxes drawn around each identified face.
# Note that the frame_as_image object is updated itself.
def identify_faces_in_frame(self, rgb_array, detect_only=False):
''' function identify_faces_in_frame
Detect any faces that are present in the given image.
For each detected face, call FaceRecognizer to try to
identfy it, then draw a box around the face including
an identification label (or "Unknown" if the face was
not identified).
Args:
rgb_array (numpy.ndarray): The frame that we should try to detect
faces in.
Returns:
A PIL Image with an enclosing red box and label for each face
detected in the given frame
'''
# record start of main ML processing
self.start_time_stamp = time.monotonic()
# Delegate the face detection to the face detection engine
detection_start_time = time.monotonic()
detected_faces = self.face_detection_engine.detect_faces(rgb_array)
detection_end_time = time.monotonic()
if PRINT_PERFORMANCE_INFO:
print("Face detection time: {:.3f}s".format(detection_end_time - detection_start_time))
# convert to a PIL Image
frame_as_image = Image.fromarray(rgb_array)
# draw a box drawn around each face detected
self.draw_face_boxes(frame_as_image, detected_faces, detect_only)
return frame_as_image
# draw boxes around each identified face in the image
def draw_face_boxes(self, frame_as_image, detected_faces, detect_only=False):
''' function draw_face_boxes
For each detected face, try to identify it, then draw a
bounding box around the face and add a label.
Args:
frame_as_image (PIL Image): Original full image containing the faces
detected_faces (array of Tuples): bounding box for each detected face
that includes: top left corner position (x,y) as well as width and height
Returns:
A PIL Image with the original image overlayed with the bounding boxes and labels
for each detected face
'''
# We need these local variables, so turn off Lint's complaint
# pylint: disable=too-many-locals
draw = ImageDraw.Draw(frame_as_image)
for face in detected_faces:
# get the top-left and lower-right coordinates of the bounding box for the face
x_1, y_1, width, height = tuple(face)
x_2 = x_1 + width
y_2 = y_1 + height
# generate a cropped image of the face with proper size to pass to the recognizer
cropped_face = frame_as_image.crop((x_1, y_1, x_2, y_2))
cropped_face = cropped_face.resize(self.embedding_image_dimensions)
# This can be uncommented and used to see exactly what the cropped image looks like
# cropped_face.save("cropped_face.jpg")
# bounding box around face
draw.rectangle(((x_1, y_1), (x_2, y_2)), outline='red')
if not detect_only:
# run the face recognizer on the image here
name_for_face, process_time = self.face_recognizer.get_name_for_face(cropped_face)
if name_for_face == "":
name_for_face = "Unknown"
# label the face
face_label = name_for_face + ' {:.3f}s'.format(process_time)
face_label_width = self.face_label_font.getsize(face_label)
face_label_start_x = x_1 + (x_2-x_1)/2 - face_label_width[0]/2
draw.text((face_label_start_x, y_2 + 5), face_label, fill='red', font=self.face_label_font)
# label the current FPS as well
annotate_text = 'Processing time: {:.3f}s'.format(time.monotonic() - self.start_time_stamp)
draw.text((175, 10), annotate_text, fill="red", font=self.fps_font)
def __init__(self, dataset):
self.detector = FaceDetector()
self.recognizer = FaceRecognizer(dataset)
self.dataset = dataset
import os
import numpy as np
import cv2
from face_finder import FaceFinder, get_region_of_interest
from face_recognizer import FaceRecognizer
face_finder = FaceFinder()
recognizer = FaceRecognizer()
dataset_dir = os.path.join('dataset', 'train')
x_train = []
y_labels = []
label = 1
def recursive_append(path, x_train, y_labels, current_label):
for file in os.listdir(path):
filepath = os.path.join(path, file)
if os.path.isdir(filepath):
recursive_append(filepath, x_train, y_labels, current_label)
elif file.endswith('png') or file.endswith('jpg'):
image_array = cv2.imread(filepath)
image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)
# image_array = cv2.equalizeHist(image_array)
# normalized_image_array = np.zeros(image_array.shape)
# normalized_image_array = cv2.normalize(image_array, normalized_image_array)
# while True:
# cv2.imshow('image', image_array)
# if cv2.waitKey(20) & 0xFF == ord('q'):
# break
faces = face_finder.find(image_array)
def create_faces_image():
images = cv2.imread(load_face_images())
face_recognize = FaceRecognizer(images)
face_recognize.face_recoginaze()
class ProcessFrame:
def __init__(self, args: Dict):
# todo proper handling of self.modes
self.ie = IECore()
self.modes = self.__determine_processing_mode(args)
net_face_detect = net_landmarks_detect = net_recognize_face = None
if not self.modes['detect']:
raise ValueError('detection model undefined')
# load networks from file
net_face_detect = self.__prepare_network(args['detection_model'])
# put it to corresponding class
# self.face_locator = FaceLocator(net_face_detect, args['detection_model_threshold'])
self.face_locator = FaceLocator(net_face_detect,
args['detection_model_threshold'],
NetworkType(args['detection_model']))
# setup device plugins
if next(iter(args['device'])) == 'CPU':
# CPU
self.ie.set_config(config={
"CPU_THROUGHPUT_STREAMS": "1",
"CPU_THREADS_NUM": "8",
},
device_name='CPU')
elif next(iter(args['device'])) == 'GPU':
# GPU
pass
self.ie.set_config(config={"GPU_THROUGHPUT_STREAMS": "1"},
device_name='GPU')
elif next(iter(args['device'])) == 'MYRIAD':
pass
# load to device for inferencing
self.face_locator.deploy_network(next(iter(args['device'])), self.ie)
if self.modes['landmark']:
net_landmarks_detect = self.__prepare_network(
args['landmarks_model'])
self.landmarks_locator = LandmarksLocator(net_landmarks_detect)
self.landmarks_locator.deploy_network(next(iter(args['device'])),
self.ie)
if self.modes['recognize']:
net_recognize_face = self.__prepare_network(
args['recognition_model'])
self.face_recognizer = FaceRecognizer(net_recognize_face)
self.face_recognizer.deploy_network(next(iter(args['device'])),
self.ie)
# todo other models or load separately
@staticmethod
def __determine_processing_mode(args: Dict) -> Dict[str, bool]:
ret = {}
if args['detection_model']:
ret['detect'] = True
else:
ret['detect'] = False
if args['landmarks_model']:
ret['landmark'] = True
else:
ret['landmark'] = False
if args['recognition_model']:
ret['recognize'] = True
else:
ret['recognize'] = False
return ret
def __prepare_network(self, model_path: str) -> IENetwork:
model_path = os.path.abspath(model_path)
model = self.ie.read_network(model=model_path,
weights=os.path.splitext(model_path)[0] +
".bin")
return model
def process_frame(
self, frame: np.ndarray
) -> List[
Union[List[FaceLocator.FacePosition],
List[LandmarksLocator.FaceLandmarks],
List[FaceRecognizer.FaceIdentity]]]: # todo uniton with None
faces_landmarks = faces_identities = None
face_positions = self.face_locator.get_face_positions(frame)
if self.modes['landmark']:
faces_landmarks = self.landmarks_locator.get_landmarks(
frame, face_positions)
if self.modes['recognize']:
faces_identities = self.face_recognizer.get_identities(
frame, face_positions, faces_landmarks)
return [face_positions, faces_landmarks, faces_identities]
class ReceptionRobot():
# N_EPOCH = 50
N_EPOCH = 100
BATCH_SIZE = 96
# RECOGNITION_IMAGE_SIZE = (32, 32)
RECOGNITION_IMAGE_SIZE = (64, 64)
SAVE_IMAGE_SIZE = (64, 64)
PERSON_IMAGES_NUM = 10
PLATFORM = Platform.macos.value
def __init__(self):
self.load_face_recognition_model()
self.app = QApplication(sys.argv)
def run(self):
""" Start face recognize system.
"""
self.say("おはようございます")
self.capture()
def capture(self):
""" Capture video. Wait to detect face.
"""
def loop_func(**kwargs):
current_persons = self.recognize(kwargs.get('face_imgs'))
kwargs.get('past_recognized_persons').append(current_persons)
if len(kwargs.get('past_recognized_persons')) > 3:
kwargs.get('past_recognized_persons').pop(0)
# continuous person filtering.
person_ids = Person.objects().distinct(field='_id')
for persons in kwargs.get('past_recognized_persons')[-3:]:
tmp_person_ids = []
for person in persons:
tmp_person_ids.append(person._id)
person_ids = list(set(person_ids) & set(tmp_person_ids))
for person_id in person_ids:
self.greet(Person.objects(_id=person_id).first())
face_capture.FaceCapture.capture(
loop_func=loop_func,
continue_condition_func=lambda **kwargs: False,
break_condition_func=lambda **kwargs: False,
)
def recognize(self, imgs: [np.array((None, None, 3))]):
""" Recognize detected face.
Args:
img: numpy.array(), (?, ?, 3)
unknown size of rgb image.
Returns:
persons: [person.Person()]
"""
person_id_indexes = \
self.face_recognizer.recognize(imgs)
persons = []
for person_id_index in person_id_indexes:
person_id = self.face_recognizer.person_ids[person_id_index]
person = Person.objects(_id=person_id).first()
persons.append(person)
return persons
def greet(self, person: Person):
""" Greet to recognized person.
"""
self.say("こんにちは。{}さん".format(person.nickname))
@classmethod
def say(cls, message, speaker="kyoko"):
""" Speak with MacOSX talk application
"""
# print(os.path.dirname(__file__))
cls.print(message)
if cls.PLATFORM == Platform.macos.value:
cls.say_macos(message, speaker=speaker)
elif cls.PLATFORM == Platform.linux.value:
cls.say_linux(message, speaker=speaker)
@classmethod
def say_macos(cls, message, speaker="kyoko"):
talk_application = 'default'
# talk_application = 'open-jtalk'
# Text to voice.
voice_output_dir = os.path.dirname(__file__) + "/voices/"
if talk_application == 'default':
voice_filename = "{voice_output_dir}{message}.aiff".format(
voice_output_dir=voice_output_dir, message=message
)
say_command = "say -v {speaker} {message} -o {voice_dir}{message}.aiff".format(
speaker=speaker, message=message, voice_dir=voice_output_dir)
elif talk_application == 'open-jtalk':
dict_dir = '/usr/local/Cellar/open-jtalk/1.09/dic'
voice_dir = '/usr/local/Cellar/open-jtalk/1.09/voice/mei' \
'/mei_normal.htsvoice'
voice_filename = "{voice_outut_dir}{message}.wav".format(
voice_outut_dir=voice_output_dir, message=message
)
say_command = 'open_jtalk -x {dict_dir} -m {voice_dir} ' \
'-ow {voice_filename} ' \
'{message}'.format(
message=message, dict_dir=dict_dir, voice_dir=voice_dir,
voice_filename=voice_filename)
print(say_command)
else:
return False
# Convert to robotic voice.
pitch = 350
tempo = 1.15
sox_convert_command = "sox {voice_filename} " \
"{voice_dir}{message}.wav " \
"echo 0.8 0.8 5 0.7 " \
"echo 0.8 0.7 6 0.7 " \
"echo 0.8 0.7 10 0.7 " \
"echo 0.8 0.8 12 0.7 " \
"echo 0.8 0.88 30 0.7 " \
"pitch {pitch} tempo {tempo}".format(
message=message, pitch=pitch, tempo=tempo,
voice_dir=voice_output_dir, voice_filename=voice_filename,
)
play_command = "play {voice_dir}{message}.wav".format(
message=message, voice_dir=voice_output_dir)
# if not os.path.exists("{voice_dir}{message}.wav"):
os.system(say_command)
os.system(sox_convert_command)
os.system(play_command)
@classmethod
def say_linux(cls):
""" Linux platform say method.
"""
@classmethod
def print(cls, message):
""" Print message to application interface.
Args:
message:
Returns:
"""
print(message)
def load_face_recognition_model(self):
# person.Person.objects().distinct('_id')
# self.face_recognizer = FaceRecognizer.objects(
# n_epoch=self.N_EPOCH,
# batch_size=self.BATCH_SIZE,
# image_size=self.RECOGNITION_IMAGE_SIZE,
# person_ids=Person.ascendind_ids(),
# ).first()
# if self.face_recognizer is None:
# return None
person_ids = [obj._id for obj in Person.objects]
self.face_recognizer = FaceRecognizer(
n_epoch=self.N_EPOCH,
batch_size=self.BATCH_SIZE,
image_size=self.RECOGNITION_IMAGE_SIZE,
person_ids=person_ids,
person_num=len(person_ids)
)
self.face_recognizer.generate_filename()
print(self.face_recognizer.filename)
self.face_recognition_model = \
self.face_recognizer.load_model()
return self.face_recognition_model
@classmethod
def save_person_capture(cls, max_faces=100):
""" save person with video capture.
"""
nickname, last_name, first_name, company = \
cls.ask_personality()
cls.say("{}さんのことを覚えたいので5秒ほどビデオを撮りますね。".format(nickname))
cls.say("はい。とりまーす！")
def face_yield(face, faces):
if len(faces) > cls.PERSON_IMAGES_NUM:
yield face
yield face
all_face_imgs = face_capture.FaceCapture.capture(
loop_func=lambda **kwargs: True,
continue_condition_func=lambda **kwargs: len(
kwargs.get('face_positions')) > 1,
break_condition_func=lambda **kwargs:
len(kwargs.get('all_face_imgs')) > cls.PERSON_IMAGES_NUM)
person_obj = Person(
nickname=nickname,
last_name=last_name,
first_name=first_name,
company=company,
)
person_obj.set_face_imgs(all_face_imgs, cls.SAVE_IMAGE_SIZE)
cls.say("今{}さんのこと覚えてます。1分くらいかかるかもしれません。".format(
nickname))
# cls.say("たぶん大丈夫ですが、僕はロボットなのでデータの保存に失敗すると"
# "全て忘れてしまうので...")
person_obj.save()
cls.say("{}さんのことバッチリ覚えました！またお会いしましょう。".format(
nickname))
# cls.say("あ、でも一回寝ないと顔で思い出せない作りになっているので、"
# "また明日以降あったときはご挨拶させてもらいますね。")
@classmethod
def ask_personality(cls):
app = QApplication(sys.argv)
window = Window(title='Opus',
labels=['ニックネーム', '姓', '名', '会社名'])
# cls.say("はじめまして。私は人工知能のオーパスです。")
# cls.say("あなたのことを知りたいので名前と会社名を教えてください。"
# "ニックネームには、私に呼ばれたい名前を入れてください。")
cls.say("名前を教えてね。")
app.exit(app.exec_())
# cls.say("私に呼ばれたい名前を入れてください")
# nickname = input()
# cls.say("{} さん ですね。よろしくお願いします。".format(nickname))
# cls.say("あと苗字と名前、所属会社を教えてください。")
# cls.say("まずは苗字をお願いします。")
# last_name = input()
# cls.say("次に名前")
# first_name = input()
# cls.say("最後に所属会社をお願いします。")
# company = input()
# cls.say("{}の{} {}さんですね。登録しておきます。".format(
# company, last_name, first_name))
# return nickname, last_name, first_name, company
texts = window.texts
return texts
@classmethod
def memorize_face(self):
""" Learn face model.
Returns:
"""
# FaceRecognizer.fit(n_epoch=5)
FaceRecognizer.fit(
n_epoch=self.N_EPOCH, batch_size=self.BATCH_SIZE,
image_size=self.RECOGNITION_IMAGE_SIZE,)
class FaceFinder(Process, Broadcaster, Listener):
"""Face finder process"""
def __init__(self):
Process.__init__(self)
Broadcaster.__init__(self, 'face_finder')
Listener.__init__(self)
self.detector = cv2.CascadeClassifier(
"haarcascade_frontalface_default.xml")
def run(self):
import dlib
from people import People
from gender_recognizer_tf import GenderRecognizer
from face_recognizer import FaceRecognizer
self.gender_recognizer = GenderRecognizer()
self.p = People("./known_people")
self.face_recognizer = FaceRecognizer(
self.p.people
) #this is not ok , we should pass onlu self.p as object
self.predictor = dlib.shape_predictor(
"shape_predictor_68_face_landmarks.dat")
self.fa = FaceAligner(self.predictor, desiredFaceWidth=160)
self.state = IdleState()
while True:
label, data = self.recv()
if label == 'video_feed':
frame = data[0]
face = None
aligned_image, face, rect_nums, XY = self.face_extractor(frame)
if face is None:
continue
face_locations, face_names, percent = self.face_recognizer.recognize(
face)
in_the_frame = self.__update_state(self.p.people, percent)
if (self.state.__class__ == MatchState):
path, gender, age = self.gender_recognizer.recognize(
aligned_image, face, rect_nums, XY)
self.broadcast(["Match", face, in_the_frame, gender, age])
else:
path, gender, age = self.gender_recognizer.recognize(
aligned_image, face, rect_nums, XY)
if path is None:
continue
print("FaceFinder path = " + path)
self.broadcast(["NoMatch", path])
def face_extractor(self, frame):
import dlib
image = imutils.resize(frame, width=256)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = self.detector.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(15, 15),
flags=cv2.CASCADE_SCALE_IMAGE)
if len(rects) > 0:
(x, y, w, h) = rects[0]
rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
face = self.fa.align(image, gray, rect)
return [face], face, len(rects), [(x, y)]
return [], None, 0, []
def __update_state(self, people, percent):
if percent < 0.8:
if self.state.__class__ != IdleState:
self.state = IdleState()
return None
else:
in_the_frame = [p for p in people if p.in_the_frame]
if len(in_the_frame) == 0:
if percent > 0.8:
if self.state.__class__ != SearchingState:
self.state = SearchingState()
elif in_the_frame[0].id is None:
if self.state.__class__ != NoMatchState:
self.state = NoMatchState()
else:
if self.state.__class__ != MatchState:
self.state = MatchState()
return in_the_frame
def train():
opt = opts.parse_opt()
opt.input_data = "MNIST"
img_size = (opt.img_dim, opt.img_dim)
print 'Dimension of images:', img_size
train_data, train_label, id_gender = \
get_30_people_chunk(opt.image_path, 1, gender_meta=True, img_size=img_size)
test_data, test_label = get_30_people_chunk(opt.image_path,
2,
img_size=img_size)
names = get_people_names(opt.image_path, 30)
if opt.balance_data:
ratio = opt.balance_ratio
print 'Balancing dataset with ratio %f' % ratio
train_data, train_label = balance_dataset(train_data, train_label)
test_data, test_label = balance_dataset(test_data, test_label)
if opt.balance_gender:
print train_data.shape, train_label.shape
print test_data.shape, test_label.shape
print 'Balancing genders'
selected_people = []
for i in range(id_gender.shape[1]):
indices, = np.where(id_gender[:, i] == 1)
selected_people.append(np.random.choice(indices, 5, replace=False))
selected_people = np.concatenate(selected_people)
print 'Selected people are:'
print np.array(names)[selected_people]
selected_imgs = train_label[:, selected_people].sum(axis=1) != 0
train_data = train_data[selected_imgs, :]
train_label = train_label[selected_imgs, :]
selected_imgs = test_label[:, selected_people].sum(axis=1) != 0
test_data = test_data[selected_imgs, :]
test_label = test_label[selected_imgs, :]
print 'Shape of data:'
print '\tTraining data: ' + str(train_data.shape)
print '\tTraining label: ' + str(train_label.shape)
print '\tMax, Min Train: %.4f, %.4f' % (np.max(train_data),
np.min(train_data))
print '\tTest data: ' + str(test_data.shape)
print '\tTest label: ' + str(test_label.shape)
print '\tMax, Min Test: %.4f, %.4f' % (np.max(test_data),
np.min(test_data))
x_dim = train_data.shape[1]
y_dim = train_label.shape[1]
opt.input_c_dim = 3
opt.output_c_dim = 3
opt.input_dim = x_dim
opt.label_dim = y_dim
input_shape = (x_dim, x_dim, opt.input_c_dim)
batch_size = opt.batch_size
print 'Batch size: %d' % batch_size
NUM_REPR = 5
NUM_SAMPLES_EACH = int(batch_size / NUM_REPR / 2)
output_samples = get_output_samples(train_data, train_label, id_gender,
NUM_REPR, NUM_SAMPLES_EACH)
NUM_THREADS = 2
tf_config = tf.ConfigProto()
tf_config.intra_op_parallelism_threads = NUM_THREADS
tf_config.gpu_options.allow_growth = True
iteration_time = []
with tf.Session(config=tf_config) as sess:
id_model_path = '%s_%d_id_0' % (opt.lfw_base_path, x_dim)
print '\tRetrieving evil model from "%s"' % id_model_path
evil_model = FaceRecognizer(id_model_path, train_label.shape[1],
input_shape, opt.input_c_dim)
gender_model_path = '%s_%d_gender_0' % (opt.lfw_base_path, x_dim)
print '\tRetrieving good model from "%s"' % gender_model_path
good_model = FaceRecognizer(gender_model_path, 2, input_shape,
opt.input_c_dim)
model = advGAN(good_model, evil_model, opt, sess, mnist=False)
iteration = 0
if opt.resnet_gen:
generator_mode = 'ResNet'
else:
generator_mode = 'Regular'
summary_dir = "logs/LFW/g_%d_ld_%d_gl_%d_L2_%.2f_lr_%.4f_%s/" % (
opt.G_lambda, opt.ld, opt.good_loss_coeff, opt.L2_lambda,
opt.learning_rate, generator_mode)
if os.path.isdir(summary_dir) is False:
print 'Creating directory %s for logs.' % summary_dir
os.mkdir(summary_dir)
# else:
# print 'Removing all files in %s' % (summary_dir + '*')
# shutil.rmtree(summary_dir)
writer = tf.summary.FileWriter(summary_dir, sess.graph)
loader = Dataset2(train_data, train_label)
print 'Training data loaded.'
print 'Maximum iterations: %d' % opt.max_iteration
max_acc_diff = -1.0
while iteration < opt.max_iteration:
# this function returns (data, label, np.array(target)).
feed_data, evil_labels, real_data = loader.next_batch(
batch_size, negative=False)
good_labels = id_gender[np.argmax(evil_labels, axis=1)]
feed = {
model.source: feed_data,
model.target: real_data,
model.good_labels: good_labels,
model.evil_labels: evil_labels
}
# Training G once.
summary_str, G_loss, _ = sess.run(
[model.total_loss_merge_sum, model.g_loss, model.G_train_op],
feed)
writer.add_summary(summary_str, iteration)
# Training G twice.
summary_str, G_loss, gan_loss, hinge_loss, l1_loss, l2_loss, \
good_fn_loss, evil_fn_loss, adv_loss, total_loss, _ = sess.run([
model.total_loss_merge_sum,
model.g_loss,
model.gan_loss,
model.hinge_loss,
model.l1_loss,
model.l2_loss,
model.good_fn_loss,
model.evil_fn_loss,
model.adv_loss,
model.total_loss,
model.G_train_op], feed)
writer.add_summary(summary_str, iteration)
# Training D.
summary_str, D_loss, _ = \
sess.run([model.total_loss_merge_sum, model.d_loss, model.D_pre_train_op], feed)
writer.add_summary(summary_str, iteration)
if iteration % opt.losses_log_every == 0:
print "iteration: ", iteration
print '\tD: %.4f, G: %.4f\n\thinge(%.2f): %.4f, L1(%.2f): %.4f, L2(%.2f): %.4f' % (
D_loss, G_loss, opt.H_lambda, hinge_loss, opt.L1_lambda,
l1_loss, opt.L2_lambda, l2_loss)
print '\t\tGAN total loss: %.4f' % gan_loss
print '\tGood: %.4f, Evil: %.4f' % (good_fn_loss, evil_fn_loss)
print '\tAdv: %.4f, Total: %.4f' % (adv_loss, total_loss)
new_test_data = []
new_pred_data = []
head = 0
last_batch = False
while head < test_data.shape[0]:
if head + batch_size <= test_data.shape[0]:
tail = head + batch_size
else:
tail = test_data.shape[0]
head = test_data.shape[0] - batch_size
last_batch = True
cur_data, pred_data = sess.run(
[model.fake_images_output, model.prediction_ready],
{model.source: test_data[head:tail, :]})
if last_batch:
new_test_data.append(
cur_data[-(test_data.shape[0] % batch_size):, :])
new_pred_data.append(
pred_data[-(test_data.shape[0] % batch_size):, :])
else:
new_test_data.append(cur_data)
new_pred_data.append(pred_data)
head += batch_size
new_test_data = np.concatenate(new_test_data)
new_pred_data = np.concatenate(new_pred_data)
good_pred = np.argmax(
model.good_model.model.predict(new_pred_data), axis=1)
evil_pred = np.argmax(
model.evil_model.model.predict(new_pred_data), axis=1)
evil_true = np.argmax(test_label, axis=1)
good_true = np.argmax(id_gender[evil_true, :], axis=1)
good_accuracy = accuracy_score(good_true, good_pred)
evil_accuracy = accuracy_score(evil_true, evil_pred)
total_good_confusion = confusion_matrix(good_true, good_pred)
total_evil_confusion = confusion_matrix(
evil_true, evil_pred, labels=range(opt.evil_label_num))
print '\tGood Accuracy: %.4f, Evil Accuracy: %.4f' % (
good_accuracy, evil_accuracy)
print '\tAccuracy diff: %f' % (good_accuracy - evil_accuracy)
print 'Good confusion matrix:'
print total_good_confusion
evil_misclass = total_evil_confusion.sum(
axis=0) - np.diag(total_evil_confusion)
evil_idxs = np.argsort(-evil_misclass)
print 'Top 3 Misclassifications:'
print np.array(names)[evil_idxs][:3]
print evil_misclass[evil_idxs][:3]
evil_tp = np.diag(total_evil_confusion)
evil_idxs = np.argsort(-evil_tp)
print 'Top 3 True classifications:'
print np.array(names)[evil_idxs][:3]
print evil_tp[evil_idxs][:3]
# print 'Selected people are:'
# print names[evil_idxs].tolist()
# print evil_tp
# print total_evil_confusion
# print evil_idxs
fake_samples, fake_noise = sess.run(
[model.fake_images_output, model.fake_noise_output],
{model.source: output_samples})
fakes = merge(fake_samples, [2 * NUM_REPR, NUM_SAMPLES_EACH])
original = merge(output_samples,
[2 * NUM_REPR, NUM_SAMPLES_EACH])
noise = merge(fake_noise, [2 * NUM_REPR, NUM_SAMPLES_EACH])
final_image = np.concatenate([fakes, noise, original], axis=1)
scipy_imsave('snapshot_%d.png' % iteration, final_image)
if (good_accuracy - evil_accuracy) > max(0.5, max_acc_diff):
print '\tSaving new training data at accuracy diff: %.4f' % (
good_accuracy - evil_accuracy),
max_acc_diff = good_accuracy - evil_accuracy
# other_good = FaceRecognizer('%s_%d_gender_0' % (opt.lfw_base_path, x_dim),
# 2, input_shape, opt.input_c_dim)
# other_pred = np.argmax(other_good.model.predict(new_pred_data), axis=1)
# print 'Other Good accuracy: %.4f' % accuracy_score(good_true, other_pred)
# other_pred = np.argmax(other_good.model.predict(
# preprocess_images(new_test_data * 255.0)), axis=1)
# print '\tTest data processeced accuracy: %.4f' % \
# accuracy_score(good_true, other_pred)
# other_evil = FaceRecognizer('%s_%d_id_0' % (opt.lfw_base_path, x_dim),
# 34, input_shape, opt.input_c_dim)
# other_pred = np.argmax(other_evil.model.predict(new_pred_data), axis=1)
# print 'Other Evil accuracy: %.4f' % accuracy_score(evil_true, other_pred)
# other_pred = np.argmax(other_evil.model.predict(
# preprocess_images(new_test_data * 255.0)), axis=1)
# print '\tTest data processeced accuracy: %.4f' % \
# accuracy_score(evil_true, other_pred)
new_train_data = []
head = 0
last_batch = False
while head < train_data.shape[0]:
if head + batch_size <= train_data.shape[0]:
tail = head + batch_size
else:
tail = train_data.shape[0]
head = train_data.shape[0] - batch_size
last_batch = True
cur_data = sess.run(
model.fake_images_output,
{model.source: train_data[head:tail, :]})
if last_batch:
new_train_data.append(
cur_data[-(train_data.shape[0] %
batch_size):, :])
else:
new_train_data.append(cur_data)
head += batch_size
new_train_data = np.concatenate(new_train_data)
np.savez_compressed(opt.output_path,
train_data=new_train_data,
org_train_data=train_data,
train_label=train_label,
test_data=new_test_data,
org_test_data=test_data,
test_label=test_label,
id_gender=id_gender)
print '\t[DONE]'
iteration += 1
def __init__(self):
self.face_finder = FaceFinder()
self.recognizer = FaceRecognizer()
self.recognizer.load('trainer.yml')
self.security_trigger = Trigger(20, lambda similarity: similarity > 80)