def __init__(self, i_viola_scale=0.5, i_img_resize_scale=1.0):
self.__params = {'filter_size': 0, 'viola_scale': i_viola_scale}
self.__normaliser = IplImageNormaliser()
self.__normaliser.setParams(i_resize_scale=i_img_resize_scale,
i_filter_size=self.__params['filter_size'],
i_eq=False,
i_roi=None)
self.__roi_detector = ViolaJonesRoi(
i_scale=self.__params['viola_scale'])
if i_n_jitter > 0:
y = numpy.vstack([y, transforms[j]])
o_images = x
o_transforms = y
o_n_jittered = n_jittered
return (o_images, o_transforms, o_n_jittered)
if __name__ == "__main__":
from PyQt4 import QtCore, QtGui
from sys import stdin, exit, argv
from qt_image_display import ImageDisplay
from roi_detector import ViolaJonesRoi
data = image_utils.Video2Numpy("recordings/calibration.avi", 1)
detector = ViolaJonesRoi()
#Compute a region of interest automatically
face_roi = detector.compute(data)
eye_roi = detector.convertFace2EyeRoi(face_roi)
detector.setRoi(eye_roi)
(min_row, min_col, max_row, max_col) = eye_roi
roi = image_utils.Numpy2CvRect(min_row, min_col, max_row, max_col)
#Setup normaliser
normaliser = IplImageNormaliser()
normaliser.setParams(i_resize_scale=1.,
i_filter_size=0,
i_eq=False,
i_roi=roi)
center = cv.cvPoint2D32f(roi.x + roi.width / 2, roi.y + roi.height / 2)
normaliser.setAffineTransform(center, i_scale=1., i_rot_angle=0)
