def normalise(self, i_ipl_image):
#Do the affine transform
if self.__rot_mat == None:
warped_image = i_ipl_image
else:
warped_image = cv.cvCreateImage(
cv.cvSize(i_ipl_image.width, i_ipl_image.height), 8, 1)
cv.cvWarpAffine(i_ipl_image, warped_image, self.__rot_mat)
#Crop
if self.__roi == None:
self.__cropped_image = warped_image
else:
self.crop(warped_image)
#Scale
if self.__resize_scale == 1:
scaled_image = self.__cropped_image
else:
w = int(round(self.__cropped_image.width * self.__resize_scale))
h = int(round(self.__cropped_image.height * self.__resize_scale))
scaled_image = cv.cvCreateImage(cv.cvSize(w, h), 8, 1)
cv.cvResize(self.__cropped_image, scaled_image, cv.CV_INTER_LINEAR)
#Histogram equalisation
if self.__equalise_hist:
cv.cvEqualizeHist(scaled_image, scaled_image)
#Blur
if self.__filter_size == 0:
smoothed_image = scaled_image
else:
smoothed_image = cv.cvCreateImage(
cv.cvSize(scaled_image.width, scaled_image.height), 8, 1)
cv.cvSmooth(scaled_image, smoothed_image, cv.CV_GAUSSIAN,
self.__filter_size)
return smoothed_image
def normalise(self, i_ipl_image):
#Do the affine transform
if self.__rot_mat == None:
warped_image = i_ipl_image
else:
warped_image = cv.cvCreateImage(cv.cvSize(i_ipl_image.width,i_ipl_image.height), 8, 1)
cv.cvWarpAffine(i_ipl_image , warped_image, self.__rot_mat );
#Crop
if self.__roi == None:
self.__cropped_image = warped_image
else:
self.crop(warped_image)
#Scale
if self.__resize_scale == 1:
scaled_image = self.__cropped_image
else:
w = int(round( self.__cropped_image.width * self.__resize_scale))
h = int(round( self.__cropped_image.height * self.__resize_scale))
scaled_image = cv.cvCreateImage(cv.cvSize(w, h), 8, 1)
cv.cvResize( self.__cropped_image, scaled_image ,cv.CV_INTER_LINEAR)
#Histogram equalisation
if self.__equalise_hist:
cv.cvEqualizeHist(scaled_image,scaled_image)
#Blur
if self.__filter_size == 0:
smoothed_image = scaled_image
else:
smoothed_image = cv.cvCreateImage(cv.cvSize(scaled_image.width, scaled_image.height), 8, 1)
cv.cvSmooth(scaled_image, smoothed_image, cv.CV_GAUSSIAN, self.__filter_size)
return smoothed_image
screen.fill(white)
queryWebcam()
if time.time() > timer + 0.5:
switchMode(MODE_DETECT_OBSTACLES2)
elif mode == MODE_DETECT_OBSTACLES3:
screen.fill(white)
queryWebcam()
if time.time() > timer + 0.5:
action = ""
switchMode(MODE_READY)
elif mode == MODE_DETECT_OBSTACLES2:
before = time.time()
screen.fill(white)
im = queryWebcam()
opencv.cvCvtColor (im, igray, opencv.CV_BGR2GRAY)
opencv.cvSmooth(igray, igray, opencv.CV_GAUSSIAN, 3, 3)
opencv.cvAdaptiveThreshold(igray, iwhite, 255, opencv.CV_ADAPTIVE_THRESH_GAUSSIAN_C)
num, contours = opencv.cvFindContours (iwhite, stor, opencv.sizeof_CvContour, opencv.CV_RETR_LIST)
opencv.cvCvtColor(iwhite, im, opencv.CV_GRAY2BGR)
staticImage = im
retrieveObstacles(contours)
switchMode(MODE_DETECT_OBSTACLES3)
action = "Detection took: %.2f" % (time.time()-before)
elif mode == MODE_READY:
screen.fill(lgray)
im = queryWebcam()
opencv.cvCvtColor(im, igray, opencv.CV_BGR2GRAY)
opencv.cvSmooth(igray, igray, opencv.CV_GAUSSIAN, 5, 5)