def crop_face(self,image_data):
image_batch = image_data
files = []
face_detect = face_detection_model('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')
face_files = face_detect.run(image_data)
#print(face_files)
return face_files
def __init__(self, wait=0.0):
image_topic = "/hsrb/head_rgbd_sensor/rgb/image_rect_color"
rospy.Subscriber(image_topic, Image, self.image_callback)
s = rospy.Service('/face_recognition_run', face_recognition_srv,
self.Isrunnging)
DATA_DIR = abspath(join(dirname(__file__), 'known_faces'))
self.string_pub = rospy.Publisher('/face_detected_name',
String,
queue_size=10)
self.bridge = CvBridge()
self.path = exists("known_faces")
print self.path
self.takepicture = False
self.threshold = 0.6
self.known_folder = DATA_DIR
self.known_imgs = [
f for f in listdir(self.known_folder)
if isfile(join(self.known_folder, f))
]
print self.known_imgs
self.known_img_names = []
self.known_encodings = []
self.count = 0
self.unknowncount = 0
self.Isrecognized = False
self.detected_name = "unknown"
self.detection_model = face_detection_model(
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml'
)
for img_name in self.known_imgs:
img = face_recognition.load_image_file(self.known_folder + '/' +
img_name)
print img_name
self.known_encodings.append(
face_recognition.face_encodings(img)[0])
dot_idx = img_name.rfind('.')
name = img_name[0:dot_idx]
self.known_img_names.append(name)