def init_vision(self):
self.set_default_context()
# read cascades
self.log.info("reading haar cascades")
self.face_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_frontalface_alt2.xml')
self.smile_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_smile.xml')
self.leye_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_lefteye.xml')
self.reye_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_righteye.xml')
self.profileface_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/lbpcascade_profileface.xml')
#######video streaming and fps ###########33
self.log.info("initializing videostream")
self.vs = eye(src=0).start()
self.fps = FPS().start()
# TODO distance from cam
self.rect = (161, 79, 150, 150) # random initial guess for foreground/face position for background extraction
self.distance = 0
# initialize the known distance from the camera to the reference eye pic, which
# in this case is 50cm from computer screen
self.KNOWN_DISTANCE = 50.0
# initialize the known object width, which in this case, the human eye is 24mm width average
self.KNOWN_WIDTH = 2.4
# initialize the reference eye image that we'll be using
self.REF_IMAGE_PATH = dirname(__file__) + "/ref/eye.jpg"
# load the image that contains an eye that is KNOWN TO BE 50cm
# from our camera, then find the eye marker in the image, and initialize
# the focal length
image = cv2.imread(self.REF_IMAGE_PATH)
self.radius = 24 # initialize radius for distance counter
marker = self.find_eye_radius(image)
# calculate camera focallenght
self.focalLength = (marker * self.KNOWN_DISTANCE) / self.KNOWN_WIDTH
# TODO read from config file
# bools
self.showfeed = True
self.showboundingboxes = True
self.showdetected = True
######## detected objects ######
self.detectedfaces = []
self.detectedmoods = []
# process first frame
self.feed = self.process_frame()
self.awake = True
if self.awake:
pass
# TODO start vision thread
self.filter = "None"
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