def __init__(self):
rospy.init_node('face_recognizer', anonymous=True)
self.image_scraper_enable = rospy.get_param('image_scraper')
if self.image_scraper_enable != 0:
from image_scraper import image_scaper
self.user_agents = rospy.get_param('user_agents')
self.image_scraper = image_scaper(self.user_agents)
self.face_event_topic = rospy.get_param('publish_topic_event')
self.openface_loc = rospy.get_param('openface')
self.camera_topic = rospy.get_param('recognition_topic')
self.filtered_face_locations = rospy.get_param('filtered_face_locations')
self.shape_predictor_file = rospy.get_param("shape_predictor")
self.image_dir = rospy.get_param("image_locations")
self.face_recognizer = face_recognizer(self.openface_loc, self.image_dir)
self.bridge = CvBridge()
self.event_pub = rospy.Publisher( self.face_event_topic, FaceEvent, queue_size=10 )
self.image_sub = message_filters.Subscriber(self.camera_topic, Image)
self.cmt_sub = message_filters.Subscriber('tracker_results',Trackers)
self.face_sub = message_filters.Subscriber(self.filtered_face_locations, Objects)
self.temp_sub = message_filters.Subscriber('temporary_trackers',Trackers)
ts = message_filters.ApproximateTimeSynchronizer([self.image_sub, self.cmt_sub,self.face_sub,self.temp_sub], 1,0.25)
ts.registerCallback(self.callback)
self.srv = Server(RecognitionConfig, self.sample_callback)
self.faces_cmt_overlap = {}
self.logger = logging.getLogger('hr.cmt_tracker.face_reinforcer_node')
#This would hold the trackers that the system would hold.
self.save_tracker_images = []
#This query x results of this particular tracking instances.
self.get_tracker_results = []
self.service_ = rospy.Service('add_to_query', TrackerNames, self.add_to_query)
self.cmt_state = {}
# self.confidence = 0.85
self.states = ['results','save']
if not self.face_recognizer.get_state():
print ("No Classifier")
query_only = rospy.get_param('query_only', True)
if query_only:
print("Operating in Query Mode There is Model so No Queries would happen")
rospy.signal_shutdown("Query Mode; No Model Found. Shutting Down Node")
else:
print ("Classifier Found")
self.state ={'query_save': '00', 'save_only': '01','query_only': '10', 'ignore': '11'}
#format
#tracker_name : {state: state in self.state or 'query_save', count: int}
self.cmt_tracker_instances = {}
self.overall_state = ''
self.initial_run = True
self.google_query = []
def __init__(self):
self.save_faces = False
rospy.init_node('offline_viewer', anonymous=True)
self.openface_loc = rospy.get_param('openface')
self.camera_topic = rospy.get_param('recognition_topic')
self.filtered_face_locations = rospy.get_param('filtered_face_locations')
self.shape_predictor_file = rospy.get_param("shape_predictor")
self.image_dir = rospy.get_param("image_locations")
self.face_recognizer = face_recognizer(self.openface_loc, self.image_dir)
self.tracker_locations_pub = rospy.Publisher("tracking_locations", Trackers, queue_size=5)
self.bridge = CvBridge()
self.image_sub = message_filters.Subscriber(self.camera_topic, Imge)
self.face_sub = message_filters.Subscriber(self.filtered_face_locations, Objects)
ts = message_filters.ApproximateTimeSynchronizer([self.image_sub, self.face_sub],
10, 0.25)
ts.registerCallback(self.callback)
Server(RecogntionConfig, self.sample_callback)
#Set up GUI
self.window = tk.Tk() #Makes main window
self.window.wm_title("Offline Vizualizer For OpenFace ")
self.window.config(background="#FFFFFF")
#Graphics window
imageFrame = tk.Frame(self.window, width=600, height=500)
imageFrame.grid(row=0, column=0, padx=10, pady=2)
#Capture video frames
self.lmain = tk.Label(imageFrame)
self.lmain.grid(row=0, column=0)
buttonsFrame = tk.Frame(self.window,width=600, height=20)
buttonsFrame.grid(row = 600, column=0, padx=10, pady=2)
liveResultsButton = tk.Button(buttonsFrame,text="Live Results", fg="brown",command= self.switchToLive)
liveResultsButton.pack(side = tk.LEFT)
saveFacesButton = tk.Button(buttonsFrame, text="Save Faces", fg="brown", command = self.switchToSave)
saveFacesButton.pack(side = tk.LEFT)
trainFacesButton = tk.Button(buttonsFrame,text="Train Faces", fg="brown", command = self.switchToTrain)
trainFacesButton.pack(side = tk.LEFT)
trimFacesButton = tk.Button(buttonsFrame, text="Trim Faces", fg="brown", command = self.trimTrainingSet)
trimFacesButton.pack(side=tk.LEFT)
#add buttons
self.new_faces_array = []
self.window.mainloop() # Starts GUI
def __init__(self):
rospy.init_node('face_recognizer', anonymous=True)
self.openface_loc = rospy.get_param('openface')
self.camera_topic = rospy.get_param('camera_topic')
self.filtered_face_locations = rospy.get_param('filtered_face_locations')
self.shape_predictor_file = rospy.get_param("shape_predictor")
self.image_dir = rospy.get_param("image_locations")
self.face_recognizer = face_recognizer(self.openface_loc, self.image_dir)
self.bridge = CvBridge()
self.image_sub = message_filters.Subscriber(self.camera_topic, Image)
self.cmt_sub = message_filters.Subscriber('tracker_results',Trackers)
self.face_sub = message_filters.Subscriber(self.filtered_face_locations, Objects)
self.temp_sub = message_filters.Subscriber('temporary_trackers',Trackers)
ts = message_filters.ApproximateTimeSynchronizer([self.image_sub, self.cmt_sub,self.face_sub,self.temp_sub], 1,0.25)
ts.registerCallback(self.callback)
self.srv = Server(RecogntionConfig, self.sample_callback)
self.faces_cmt_overlap = {}
self.logger = logging.getLogger('hr.cmt_tracker.face_reinforcer_node')
#This would hold the trackers that the system would hold.
self.save_tracker_images = []
#This query x results of this particular tracking instances.
self.get_tracker_results = []
self.service_ = rospy.Service('add_to_query', TrackerNames, self.add_to_query)
self.cmt_state = {}
self.confidence = 0.85
self.states = ['results','save']
self.state ={'query_save': '00', 'save_only': '01','query_only': '10', 'ignore': '11'}
#format
#tracker_name : {state: state in self.state or 'query_save', count: int}
self.cmt_tracker_instances = {}
self.overall_state = ''
self.initial_run = True
def __init__(self):
self.save_faces = False
rospy.init_node('offline_viewer', anonymous=True)
self.openface_loc = rospy.get_param('openface')
self.camera_topic = rospy.get_param('recognition_topic')
self.filtered_face_locations = rospy.get_param(
'filtered_face_locations')
self.shape_predictor_file = rospy.get_param("shape_predictor")
self.image_dir = rospy.get_param("image_locations")
self.face_recognizer = face_recognizer(self.openface_loc,
self.image_dir)
self.tracker_locations_pub = rospy.Publisher("tracking_locations",
Trackers,
queue_size=5)
self.bridge = CvBridge()
self.image_sub = message_filters.Subscriber(self.camera_topic, Imge)
self.face_sub = message_filters.Subscriber(
self.filtered_face_locations, Objects)
ts = message_filters.ApproximateTimeSynchronizer(
[self.image_sub, self.face_sub], 10, 0.25)
ts.registerCallback(self.callback)
Server(RecognitionConfig, self.sample_callback)
#Set up GUI
self.window = tk.Tk() #Makes main window
self.window.wm_title("Offline Vizualizer For OpenFace ")
self.window.config(background="#FFFFFF")
#Graphics window
imageFrame = tk.Frame(self.window, width=600, height=500)
imageFrame.grid(row=0, column=0, padx=10, pady=2)
#Capture video frames
self.lmain = tk.Label(imageFrame)
self.lmain.grid(row=0, column=0)
buttonsFrame = tk.Frame(self.window, width=600, height=20)
buttonsFrame.grid(row=600, column=0, padx=10, pady=2)
liveResultsButton = tk.Button(buttonsFrame,
text="Live Results",
fg="brown",
command=self.switchToLive)
liveResultsButton.pack(side=tk.LEFT)
saveFacesButton = tk.Button(buttonsFrame,
text="Save Faces",
fg="brown",
command=self.switchToSave)
saveFacesButton.pack(side=tk.LEFT)
trainFacesButton = tk.Button(buttonsFrame,
text="Train Faces",
fg="brown",
command=self.switchToTrain)
trainFacesButton.pack(side=tk.LEFT)
trimFacesButton = tk.Button(buttonsFrame,
text="Trim Faces",
fg="brown",
command=self.trimTrainingSet)
trimFacesButton.pack(side=tk.LEFT)
#add buttons
self.new_faces_array = []
self.window.mainloop() # Starts GUI
