def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Sobel: %s",
self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size,
sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Prewitt: %s",
self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value
or self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold, high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented edge detection method: %s" % self.method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape,bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Sobel: %s",self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4)+1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size, sigma)
elif self.method == M_VARIANCE:
sigma = self.get_sigma()
size = int(sigma)+1
output_pixels = variance_transform(orig_pixels, size, mask)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Prewitt: %s",self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value or
self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels,mask, self.get_sigma(),
low_threshold,
high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented edge detection method: %s"%
self.method.value)
output_image = cpi.Image(output_pixels, parent_image = image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def test_02_03_sobel_all(self):
'''Test the Sobel transform'''
np.random.seed(0)
image = np.random.uniform(size=(20,20)).astype(np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_SOBEL
module.direction.value = F.E_ALL
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == FIL.sobel(image)))
def test_02_03_sobel_all(self):
'''Test the Sobel transform'''
np.random.seed(0)
image = np.random.uniform(size=(20,20)).astype(np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_SOBEL
module.direction.value = F.E_ALL
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == FIL.sobel(image)))
def run(self, workspace):
image_set = workspace.image_set
image = image_set.get_image(self.input_image_name.value)
pixel_data = image.pixel_data
if self.filter_choice == S_GAUSSIAN:
pixel_data = gaussian_filter(pixel_data, sigma=self.sigma.value)
else:
pixel_data = sobel(pixel_data)
output = cpi.Image(pixel_data, parent_image = image)
image_set.add(self.output_image_name.value, output)
if self.show_window:
workspace.display_data.input_image = image.pixel_data
workspace.display_data.output_image = pixel_data
def run(self, workspace):
image_set = workspace.image_set
image = image_set.get_image(self.input_image_name.value)
pixel_data = image.pixel_data
if self.filter_choice == S_GAUSSIAN:
pixel_data = gaussian_filter(pixel_data, sigma=1)
else:
pixel_data = sobel(pixel_data)
output = cpi.Image(pixel_data, parent_image = image)
image_set.add(self.output_image_name.value, output)
if self.show_window:
workspace.display_data.input_image = image.pixel_data
workspace.display_data.output_image = pixel_data
def test_06_01_canny(self):
'''Test the canny method'''
i,j = np.mgrid[-20:20,-20:20]
image = np.logical_and(i > j, i**2+j**2 < 300).astype(np.float32)
np.random.seed(0)
image = image *.5 + np.random.uniform(size=image.shape)*.3
image = np.ascontiguousarray(image, np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_CANNY
module.wants_automatic_threshold.value = True
module.wants_automatic_low_threshold.value = True
module.wants_automatic_sigma.value = True
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
t1,t2 = otsu3(FIL.sobel(image))
result = FIL.canny(image, np.ones(image.shape,bool), 1.0, t1, t2)
self.assertTrue(np.all(output.pixel_data == result))
def test_06_01_canny(self):
'''Test the canny method'''
i,j = np.mgrid[-20:20,-20:20]
image = np.logical_and(i > j, i**2+j**2 < 300).astype(np.float32)
np.random.seed(0)
image = image *.5 + np.random.uniform(size=image.shape)*.3
image = np.ascontiguousarray(image, np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_CANNY
module.wants_automatic_threshold.value = True
module.wants_automatic_low_threshold.value = True
module.wants_automatic_sigma.value = True
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
t1,t2 = otsu3(FIL.sobel(image))
result = FIL.canny(image, np.ones(image.shape,bool), 1.0, t1, t2)
self.assertTrue(np.all(output.pixel_data == result))
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape,bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Sobel: %s",self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4)+1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size, sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Prewitt: %s",self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value or
self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels,mask, self.get_sigma(),
low_threshold,
high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented edge detection method: %s"%
self.method.value)
if not workspace.frame is None:
figure = workspace.create_or_find_figure(title="EnhanceEdges, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(2,2))
figure.subplot_imshow_grayscale(0,0, orig_pixels,
"Original: %s"%
self.image_name.value)
if self.method == M_CANNY:
# Canny is binary
figure.subplot_imshow_bw(0,1, output_pixels,
self.output_image_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
else:
figure.subplot_imshow_grayscale(0,1,output_pixels,
self.output_image_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
color_image = np.zeros((output_pixels.shape[0],
output_pixels.shape[1],3))
color_image[:,:,0] = stretch(orig_pixels)
color_image[:,:,1] = stretch(output_pixels)
figure.subplot_imshow(1,0, color_image,"Composite image",
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
output_image = cpi.Image(output_pixels, parent_image = image)
workspace.image_set.add(self.output_image_name.value, output_image)
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Sobel: %s",
self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size,
sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Prewitt: %s",
self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value
or self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold, high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented edge detection method: %s" % self.method.value)
if not workspace.frame is None:
figure = workspace.create_or_find_figure(
title="EnhanceEdges, image cycle #%d" %
(workspace.measurements.image_set_number),
subplots=(2, 2))
figure.subplot_imshow_grayscale(
0, 0, orig_pixels, "Original: %s" % self.image_name.value)
if self.method == M_CANNY:
# Canny is binary
figure.subplot_imshow_bw(0,
1,
output_pixels,
self.output_image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
else:
figure.subplot_imshow_grayscale(0,
1,
output_pixels,
self.output_image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
color_image = np.zeros(
(output_pixels.shape[0], output_pixels.shape[1], 3))
color_image[:, :, 0] = stretch(orig_pixels)
color_image[:, :, 1] = stretch(output_pixels)
figure.subplot_imshow(1,
0,
color_image,
"Composite image",
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)