class HeadTracker(object):
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'])
def getParams(self):
return self.__params
def setGain(self, i_gain):
self.__xy_gain = float(i_gain)
def detectRoi(self, i_data, i_roi_scale_factor=1.2, i_track=True):
ipl_roi = self.__normaliser.getRoi()
if (ipl_roi is not None) and not (i_track):
x = numpy.float(ipl_roi.x) + 0.5 * ipl_roi.width
y = numpy.float(ipl_roi.y) + 0.5 * ipl_roi.height
self.__roi_detector.setPrev(x, y, ipl_roi.width, ipl_roi.height)
return (0., 0., x, y, ipl_roi.width, ipl_roi.height)
if ipl_roi is None:
face_roi = self.__roi_detector.compute([i_data], i_ipl=True)
if face_roi is None:
return (0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
face_roi = self.__roi_detector.scaleRoi(face_roi,
i_roi_scale_factor,
i_data.width - 1,
i_data.height - 1)
ipl_roi = image_utils.Numpy2CvRect(i_face_roi=face_roi)
self.__normaliser.setRoi(ipl_roi)
x = numpy.float(ipl_roi.x) + 0.5 * ipl_roi.width
y = numpy.float(ipl_roi.y) + 0.5 * ipl_roi.height
self.__roi_detector.setPrev(x, y, ipl_roi.width, ipl_roi.height)
return (0.0, 0.0, x, y, ipl_roi.width, ipl_roi.height)
#At this point i_track=True, ipl_roi is not None
return self.__roi_detector.trackRoi(i_data)
def update(self, i_ipl_image, i_track=False):
(delta_x, delta_y, x, y, w, h) = self.detectRoi(i_ipl_image,
i_track=i_track)
return (delta_x, delta_y, x, y, w, h)
def setRoi(self, i_roi):
self.__normaliser.setRoi(i_roi)
self.__roi_detector.setRoi(image_utils.Cv2NumpyRect)
def getRoi(self, i_ipl=False):
if i_ipl:
return self.__normaliser.getRoi()
return image_utils.Cv2NumpyRect(self.__normaliser.getRoi())
def clearRoi(self):
self.__normaliser.clearRoi()
class HeadTracker(object):
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'])
def getParams(self):
return self.__params
def setGain(self, i_gain):
self.__xy_gain = float(i_gain)
def detectRoi(self, i_data, i_roi_scale_factor=1.2, i_track=True):
ipl_roi = self.__normaliser.getRoi()
if (ipl_roi is not None) and not(i_track):
x = numpy.float(ipl_roi.x) + 0.5*ipl_roi.width
y = numpy.float(ipl_roi.y) + 0.5*ipl_roi.height
self.__roi_detector.setPrev(x, y, ipl_roi.width, ipl_roi.height)
return (0., 0., x,y, ipl_roi.width, ipl_roi.height)
if ipl_roi is None:
face_roi = self.__roi_detector.compute([i_data], i_ipl=True)
if face_roi is None:
return (0.0, 0.0, 0.0, 0.0,0.0, 0.0)
face_roi = self.__roi_detector.scaleRoi(face_roi, i_roi_scale_factor, i_data.width-1, i_data.height-1)
ipl_roi = image_utils.Numpy2CvRect( i_face_roi = face_roi)
self.__normaliser.setRoi(ipl_roi)
x = numpy.float(ipl_roi.x) + 0.5*ipl_roi.width
y = numpy.float(ipl_roi.y) + 0.5*ipl_roi.height
self.__roi_detector.setPrev(x, y, ipl_roi.width, ipl_roi.height)
return (0.0, 0.0, x, y, ipl_roi.width, ipl_roi.height)
#At this point i_track=True, ipl_roi is not None
return self.__roi_detector.trackRoi(i_data )
def update(self, i_ipl_image, i_track=False):
(delta_x, delta_y, x,y,w,h) = self.detectRoi(i_ipl_image,i_track=i_track)
return (delta_x, delta_y, x, y,w,h)
def setRoi(self, i_roi):
self.__normaliser.setRoi(i_roi)
self.__roi_detector.setRoi(image_utils.Cv2NumpyRect)
def getRoi(self, i_ipl=False):
if i_ipl:
return self.__normaliser.getRoi()
return image_utils.Cv2NumpyRect(self.__normaliser.getRoi())
def clearRoi(self):
self.__normaliser.clearRoi()
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)
nframe = 0
app = QtGui.QApplication(argv)
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)
nframe = 0
app = QtGui.QApplication(argv)
timer = QtCore.QTimer()
n_jitter_examples = 5
display = ImageDisplay(n_jitter_examples + 2)