def __call__(self, scale, gradient_threshold):
canny_image = _edgedetect.canny_edge_image(self, scale, gradient_threshold)
canny_image_onebit = canny_image.to_onebit()
"""
LAPLACIAN OF GAUSSIAN (recreated from convolution.py)
"""
smooth = _convolution.GaussianKernel(scale)
deriv = _convolution.GaussianDerivativeKernel(scale, 2)
fp = self.to_float()
tmp = fp.convolve_x(deriv)
tmp_x = tmp.convolve_y(smooth)
tmp = fp.convolve_x(smooth)
tmp_y = tmp.convolve_y(deriv)
lap = _arithmetic.add_images(tmp_x, tmp_y, False)
"""
Bias high-confidence background pixels
"""
gsmooth_image = self.convolve_xy(_convolution.GaussianKernel(scale), border_treatment = 3)
img2 = _arithmetic.subtract_images(self, gsmooth_image, False)
squared_img2 = _arithmetic.multiply_images(img2, img2, False)
gsmooth_img2 = img2.convolve_xy(_convolution.GaussianKernel(scale), border_treatment = 3)
gsmooth_squared_img2 = squared_img2.convolve_xy(_convolution.GaussianKernel(scale), border_treatment = 3)
rms = squareRoot(gsmooth_squared_img2)
threshold_img = checkThreshold(rms, 12)
return lap
def __call__(self, scale, gradient_threshold):
canny_image = _edgedetect.canny_edge_image(self, scale, gradient_threshold)
canny_image_onebit = canny_image.to_onebit()
"""
LAPLACIAN OF GAUSSIAN (recreated from convolution.py)
"""
smooth = _convolution.GaussianKernel(scale)
deriv = _convolution.GaussianDerivativeKernel(scale, 2)
fp = self.to_float()
tmp = fp.convolve_x(deriv)
tmp_x = tmp.convolve_y(smooth)
tmp = fp.convolve_x(smooth)
tmp_y = tmp.convolve_y(deriv)
lap = _arithmetic.add_images(tmp_x, tmp_y, False)
"""
Bias high-confidence background pixels
"""
gsmooth_image = self.convolve_xy(_convolution.GaussianKernel(scale), border_treatment=3)
img2 = _arithmetic.subtract_images(self, gsmooth_image, False)
squared_img2 = _arithmetic.multiply_images(img2, img2, False)
gsmooth_img2 = img2.convolve_xy(_convolution.GaussianKernel(scale), border_treatment=3)
gsmooth_squared_img2 = squared_img2.convolve_xy(_convolution.GaussianKernel(scale), border_treatment=3)
rms = squareRoot(gsmooth_squared_img2)
threshold_img = checkThreshold(rms, 12)
return lap
def __call__(self, scale=1.0):
smooth = _convolution.GaussianKernel(scale)
deriv = _convolution.GaussianDerivativeKernel(scale, 2)
fp = self.to_float()
tmp = fp.convolve_x(deriv)
tmp_x = tmp.convolve_y(smooth)
tmp = fp.convolve_x(smooth)
tmp_y = tmp.convolve_y(deriv)
result = _arithmetic.add_images(tmp_x, tmp_y, False)
if self.data.pixel_type == GREYSCALE:
return result.to_greyscale()
if self.data.pixel_type == GREY16:
return result.to_grey16()
return result
def __call__(self, scale=1.0):
smooth = _convolution.GaussianKernel(scale)
deriv = _convolution.GaussianDerivativeKernel(scale, 2)
fp = self.to_float()
tmp = fp.convolve_x(deriv)
tmp_x = tmp.convolve_y(smooth)
tmp = fp.convolve_x(smooth)
tmp_y = tmp.convolve_y(deriv)
result = _arithmetic.add_images(tmp_x, tmp_y, False)
if self.data.pixel_type == GREYSCALE:
return result.to_greyscale()
if self.data.pixel_type == GREY16:
return result.to_grey16()
return result
def __call__(self, other, in_place=False):
if self.data.pixel_type == ONEBIT:
return _logical.or_image(self, other, in_place)
return _arithmetic.add_images(self, other, in_place)
def __call__(self, other, in_place=False):
if self.data.pixel_type == ONEBIT:
return _logical.or_image(self, other, in_place)
return _arithmetic.add_images(self, other, in_place)