def __init__(self):
super(AUFR, self).__init__()
self.setupUi(self)
try:
self.setting = Setting(self)
conf = self.setting.get_config()
self.threshold = conf.threshold
self.model_name = 'mobile face' if conf.net_mode is None else conf.net_mode
self.net_mode = conf.net_mode
self.use_mtcnn = True if conf.use_mtcnn else False
self.camera_id = conf.video_source
self.face_recognize = face_recognize(conf)
except:
pass
conf = get_config(net_mode='ir_se', use_mtcnn=True, threshold=1.25)
self.threshold = conf.threshold
self.model_name = 'mobile face' if conf.net_mode is None else conf.net_mode
self.net_mode = conf.net_mode
self.use_mtcnn = True if conf.use_mtcnn else False
self.camera_id = conf.video_source
self.face_recognize = face_recognize(conf)
self.targets, self.names = self.face_recognize.get_facebank()
self.has_targ = len(self.targets) > 0
# Variables
self.camera_id = 'video.mp4' # can also be a url of Video
self.dataset_per_subject = 50
self.ret = False
self.trained_model = 0
self.image = cv2.imread("icon/default.jpg", 1)
self.modified_image = self.image.copy()
self.reload()
self.display()
# Actions
self.generate_dataset_btn.setCheckable(True)
# self.train_model_btn.setCheckable(True)
self.recognize_face_btn.setCheckable(True)
# Menu
self.about_menu = self.menu_bar.addAction("About")
self.help_menu = self.menu_bar.addAction("Help")
self.about_menu.triggered.connect(self.about_info)
self.help_menu.triggered.connect(self.help_info)
# Algorithms
self.generate_dataset_btn.clicked.connect(self.pressedGendataButton)
self.recognize_face_btn.clicked.connect(self.recognize)
self.video_recording_btn.clicked.connect(self.save_video)
self.adv_setting.clicked.connect(self.pressedSettingsButton)
self.capture_btn.clicked.connect(self.captureDialog_show)
if not os.path.exists(FACE_BANK):
os.mkdir(FACE_BANK)
self.createMenus()
def __init__(self, width=None, height=None):
# initialize the video camera stream and read the first frame
# from the stream
self.width = width
self.height = height
self.image_hub = imagezmq.ImageHub()
self.frame_ori = {}
self.last_active = {}
self.active_check_secon = 30
self.stop_process = {}
self.frame_process = {}
self.lst_thread = []
self.face_reg = face_recognize(conf)
self.targets, self.names = self.face_reg.load_facebank()
def process_two_image(data):
folder_name = str(uuid.uuid1())
command = 'mkdir %s' % folder_name
subprocess.call(command, shell=True)
image_path_origin = download_file_by_url(data['image_url_origin'],
folder_name)
image_path_detection = download_file_by_url(data['image_url_detection'],
folder_name)
from api import face_recognize
conf = get_config()
face_recognize = face_recognize(conf)
face_recognize._raw_load_single_face(image_path_origin)
targets = face_recognize.embeddings
image = Image.open(image_path_detection)
submiter = [['image_url', 'x1', 'y1', 'x2', 'y2', 'result']]
try:
bboxes, faces = face_recognize.align_multi(image)
except:
bboxes = []
faces = []
if len(bboxes) > 0:
bboxes = bboxes[:, :-1]
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1]
results, score = face_recognize.infer(faces, targets)
for id, (re, sc) in enumerate(zip(results, score)):
if re != -1:
temp = {
'x1': bboxes[id][0],
'y1': bboxes[id][1],
'x2': bboxes[id][2],
'y2': bboxes[id][3],
'result': 1
}
temp = [
data['image_url_detection'], bboxes[id][0], bboxes[id][1],
bboxes[id][2], bboxes[id][3], 1
]
submiter.append(temp)
command = 'rm -rf %s' % folder_name
subprocess.call(command, shell=True)
df = pd.DataFrame.from_records(submiter)
headers = df.iloc[0]
df = pd.DataFrame(df.values[1:], columns=headers)
df = df.sort_values(by=['result'], ascending=False)
results = df.to_json(orient='records')
return results
def process_images(image_path='', path=''):
from api import face_recognize
conf = get_config()
face_recognize = face_recognize(conf)
face_recognize._raw_load_single_face(image_path)
targets = face_recognize.embeddings
submiter = [['image', 'x1', 'y1', 'x2', 'y2', 'result']]
list_file = glob.glob(path + '/*')
if os.path.isfile(list_file[0]) == False:
path = list_file[0]
print(path)
for img in tqdm(glob.glob(path + '/*')):
temp = [img.split('/')[-1], 0, 0, 0, 0, 0]
image = Image.open(img)
try:
bboxes, faces = face_recognize.align_multi(image)
except:
bboxes = []
faces = []
if len(bboxes) > 0:
bboxes = bboxes[:, :-1]
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1]
results, score = face_recognize.infer(faces, targets)
for id, (re, sc) in enumerate(zip(results, score)):
if re != -1:
temp = [
img.split('/')[-1], bboxes[id][0], bboxes[id][1],
bboxes[id][2], bboxes[id][3], 1
]
submiter.append(temp)
df = pd.DataFrame.from_records(submiter)
headers = df.iloc[0]
df = pd.DataFrame(df.values[1:], columns=headers)
df = df.sort_values(by=['result'], ascending=False)
results = df.to_json(orient='records')
return results
@app.route('/video_feed/<camera_type>/<device_id>')
def video_feed(camera_type, device_id):
"""Video streaming route. Put this in the src attribute of an img tag."""
camera_stream = import_module('camera_' + camera_type).Camera
if camera_type == 'opencv':
try:
device = int(device)
except:
pass
camera_stream.set_video_source(link_video)
return Response(gen(camera_stream(int(device_id))),
mimetype='multipart/x-mixed-replace; boundary=frame')
link_video = ['rtsp://*****:*****@@10.0.20.226:554/profile2/media.smp']
# link_video = ['video.mp4']
# client = ObjectDetector()
conf = get_config(net_mode = 'mobi', threshold = 1.22, detect_id = 1)
face_recognize = face_recognize(conf)
targets, names = face_recognize.load_facebank()
lst_video = []
fps_init = FPS()
app.secret_key = 'super secret key'
app.config['SESSION_TYPE'] = 'filesystem'
if __name__ == '__main__':
app.secret_key = 'super secret key'
app.config['SESSION_TYPE'] = 'filesystem'
app.run(host='0.0.0.0', port=5001, debug=True, threaded=True)