def _mixImageSelfMask(self, wipeSettings, level, image1, image2, mixMat, whiteMode):
cv.CvtColor(image2, self._mixImageMask, cv.CV_BGR2GRAY)
if(whiteMode == True):
cv.CmpS(self._mixImageMask, 250, self._mixImageMask, cv.CV_CMP_LT)
else:
cv.CmpS(self._mixImageMask, 5, self._mixImageMask, cv.CV_CMP_GT)
return self._mixImageAlphaMask(wipeSettings, level, image1, image2, self._mixImageMask, mixMat)
def _mixImageSelfMaskHue(self, wipeSettings, level, image1, image2, mixMat):
cv.CvtColor(image1, mixMat, cv.CV_RGB2HSV)
cv.Split(mixMat, self._mixMixMask1, None, None, None)
cv.CvtColor(image2, mixMat, cv.CV_RGB2HSV)
cv.Split(mixMat, self._mixMixMask2, None, None, None)
cv.Sub(self._mixMixMask2, self._mixMixMask1, self._mixImageMask)
cv.CmpS(self._mixImageMask, 255-int((level*254)), self._mixImageMask, cv.CV_CMP_GT)
return self._mixImageAlphaMask(wipeSettings, level, image1, image2, self._mixImageMask, mixMat)
def cvCmpS(src, scalar, dst, code):
width1 = cv.GetImageROI(src)[2]
height1 = cv.GetImageROI(src)[3]
width2 = cv.GetImageROI(dst)[2]
height2 = cv.GetImageROI(dst)[3]
if not (width1 == width2 and height1 == height2):
print 'cvCmpS src, dst size error'
print (width1, height1), (width2, height2)
cv.CmpS(src, scalar, dst, code)
def precornerdetect(image):
# assume that the image is floating-point
corners = cv.CloneMat(image)
cv.PreCornerDetect(image, corners, 3)
dilated_corners = cv.CloneMat(image)
cv.Dilate(corners, dilated_corners, None, 1)
corner_mask = cv.CreateMat(image.rows, image.cols, cv.CV_8UC1)
cv.Sub(corners, dilated_corners, corners)
cv.CmpS(corners, 0, corner_mask, cv.CV_CMP_GE)
return (corners, corner_mask)
ds2 = ptdst(features[2], features[3])
if max(ds1, ds2) / min(ds1, ds2) > 1.4: tracking = 0
ds3 = ptdst(features[0], features[2])
ds4 = ptdst(features[1], features[3])
if max(ds3, ds4) / min(ds3, ds4) > 1.4: tracking = 0
if ds1 < 10 or ds2 < 10 or ds3 < 10 or ds4 < 10: tracking = 0
if tracking == 0: detected = 0
#detection mode
if tracking == 0:
detected = 0
cv.Smooth(grey, dst2, cv.CV_GAUSSIAN, 3)
cv.Laplace(dst2, d)
cv.CmpS(d, 8, d2, cv.CV_CMP_GT)
if onlyBlackCubes:
#can also detect on black lines for improved robustness
cv.CmpS(grey, 100, b, cv.CV_CMP_LT)
cv.And(b, d2, d2)
#these weights should be adaptive. We should always detect 100 lines
if lastdetected > dects: THR = THR + 1
if lastdetected < dects: THR = max(2, THR - 1)
li = cv.HoughLines2(d2, cv.CreateMemStorage(),
cv.CV_HOUGH_PROBABILISTIC, 1, 3.1415926 / 45, THR,
10, 5)
#store angles for later
angs = []
