def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value)-1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
result = white_tophat(pixel_data, radius, mask)
elif self.enhance_method == E_NEURITES:
if self.neurite_choice == N_GRADIENT:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
else:
sigma = self.smoothing.value
smoothed = gaussian_filter(pixel_data, sigma)
L = hessian(smoothed, return_hessian = False,
return_eigenvectors = False)
#
# The positive values are darker pixels with lighter
# neighbors. The original ImageJ code scales the result
# by sigma squared - I have a feeling this might be
# a first-order correction for e**(-2*sigma), possibly
# because the hessian is taken from one pixel away
# and the gradient is less as sigma gets larger.
#
result = -L[:, :, 0] * (L[:, :, 0] < 0) * sigma * sigma
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1,int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max+1)/2)
result = enhance_dark_holes(pixel_data, min_radius,
max_radius, mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask = mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data,
self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s"%
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s"%self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if not workspace.frame is None:
figure = workspace.create_or_find_figure(title="EnhanceOrSuppressFeatures, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(2,1))
figure.subplot_imshow_grayscale(0, 0, image.pixel_data,
"Original: %s" %
self.image_name.value)
figure.subplot_imshow_grayscale(1, 0, result,
"Filtered: %s" %
self.filtered_image_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value) - 1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
result = white_tophat(pixel_data, radius, mask)
elif self.enhance_method == E_NEURITES:
if self.neurite_choice == N_GRADIENT:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
else:
sigma = self.smoothing.value
smoothed = gaussian_filter(pixel_data, sigma)
L = hessian(smoothed,
return_hessian=False,
return_eigenvectors=False)
#
# The positive values are darker pixels with lighter
# neighbors. The original ImageJ code scales the result
# by sigma squared - I have a feeling this might be
# a first-order correction for e**(-2*sigma), possibly
# because the hessian is taken from one pixel away
# and the gradient is less as sigma gets larger.
#
result = -L[:, :, 0] * (L[:, :, 0] < 0) * sigma * sigma
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1, int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max + 1) / 2)
result = enhance_dark_holes(pixel_data, min_radius, max_radius,
mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask=mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data, self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s" %
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s" % self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if self.show_window:
workspace.display_data.image = image.pixel_data
workspace.display_data.result = result
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value)-1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
result = white_tophat(pixel_data, radius, mask)
elif self.enhance_method == E_NEURITES:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1,int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max+1)/2)
result = enhance_dark_holes(pixel_data, min_radius,
max_radius, mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask = mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data,
self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s"%
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s"%self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if not workspace.frame is None:
figure = workspace.create_or_find_figure(title="EnhanceOrSuppressFeatures, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(2,1))
figure.subplot_imshow_grayscale(0, 0, image.pixel_data,
"Original: %s" %
self.image_name.value)
figure.subplot_imshow_grayscale(1, 0, result,
"Filtered: %s" %
self.filtered_image_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
def run_function(self, function_name, pixel_data, mask, count, scale,
custom_repeats):
'''Apply the function once to the image, returning the result'''
is_binary = pixel_data.dtype.kind == 'b'
strel = morph.strel_disk(scale / 2.0)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SPUR, F_THICKEN,
F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SPUR, F_THICKEN, F_THIN, F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
img_max = np.max(image)
if img_max > 0:
image = image / img_max
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
def run_function(self, function, pixel_data, mask):
'''Apply the function once to the image, returning the result'''
count = function.repeat_count
function_name = function.function.value
scale = function.scale.value
custom_repeats = function.custom_repeats.value
is_binary = pixel_data.dtype.kind == 'b'
if function.structuring_element == SE_ARBITRARY:
strel = np.array(function.strel.get_matrix())
elif function.structuring_element == SE_DISK:
strel = morph.strel_disk(scale / 2.0)
elif function.structuring_element == SE_DIAMOND:
strel = morph.strel_diamond(scale / 2.0)
elif function.structuring_element == SE_LINE:
strel = morph.strel_line(scale, function.angle.value)
elif function.structuring_element == SE_OCTAGON:
strel = morph.strel_octagon(scale / 2.0)
elif function.structuring_element == SE_PAIR:
strel = morph.strel_pair(function.x_offset.value,
function.y_offset.value)
elif function.structuring_element == SE_PERIODIC_LINE:
xoff = function.x_offset.value
yoff = function.y_offset.value
n = max(scale / 2.0 / np.sqrt(float(xoff*xoff+yoff*yoff)), 1)
strel = morph.strel_periodicline(
xoff, yoff, n)
elif function.structuring_element == SE_RECTANGLE:
strel = morph.strel_rectangle(
function.width.value, function.height.value)
else:
strel = morph.strel_square(scale)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SKELPE, F_SPUR,
F_THICKEN, F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SKELPE, F_SPUR, F_THICKEN, F_THIN,
F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
if function.rescale_values.value:
image = image / np.max(image)
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SKELPE:
return morph.skeletonize(
pixel_data, mask,
scind.distance_transform_edt(pixel_data) *
poisson_equation(pixel_data))
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
def run_function(self, function_name, pixel_data, mask, count, scale,
custom_repeats):
'''Apply the function once to the image, returning the result'''
is_binary = pixel_data.dtype.kind == 'b'
strel = morph.strel_disk(scale / 2.0)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SPUR, F_THICKEN,
F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SPUR, F_THICKEN, F_THIN, F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
img_max = np.max(image)
if img_max > 0:
image = image / img_max
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
def run_function(self, function, pixel_data, mask):
'''Apply the function once to the image, returning the result'''
count = function.repeat_count
function_name = function.function.value
scale = function.scale.value
custom_repeats = function.custom_repeats.value
is_binary = pixel_data.dtype.kind == 'b'
if function.structuring_element == SE_ARBITRARY:
strel = np.array(function.strel.get_matrix())
elif function.structuring_element == SE_DISK:
strel = morph.strel_disk(scale / 2.0)
elif function.structuring_element == SE_DIAMOND:
strel = morph.strel_diamond(scale / 2.0)
elif function.structuring_element == SE_LINE:
strel = morph.strel_line(scale, function.angle.value)
elif function.structuring_element == SE_OCTAGON:
strel = morph.strel_octagon(scale / 2.0)
elif function.structuring_element == SE_PAIR:
strel = morph.strel_pair(function.x_offset.value,
function.y_offset.value)
elif function.structuring_element == SE_PERIODIC_LINE:
xoff = function.x_offset.value
yoff = function.y_offset.value
n = max(scale / 2.0 / np.sqrt(float(xoff*xoff+yoff*yoff)), 1)
strel = morph.strel_periodicline(
xoff, yoff, n)
elif function.structuring_element == SE_RECTANGLE:
strel = morph.strel_rectangle(
function.width.value, function.height.value)
else:
strel = morph.strel_square(scale)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SKELPE, F_SPUR,
F_THICKEN, F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SKELPE, F_SPUR, F_THICKEN, F_THIN,
F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
if function.rescale_values.value:
image = image / np.max(image)
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SKELPE:
return morph.skeletonize(
pixel_data, mask,
scind.distance_transform_edt(pixel_data) *
poisson_equation(pixel_data))
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value)-1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
if self.speckle_accuracy == S_SLOW:
result = white_tophat(pixel_data, radius, mask)
else:
#
# white_tophat = img - opening
# = img - dilate(erode)
# = img - median_filter(median_filter(0%) 100%)
result = pixel_data - median_filter(
median_filter(pixel_data, mask, radius, percent = 0),
mask, radius, percent = 100)
if mask is not None:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_NEURITES:
if self.neurite_choice == N_GRADIENT:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
else:
sigma = self.smoothing.value
smoothed = gaussian_filter(pixel_data, sigma)
L = hessian(smoothed, return_hessian = False,
return_eigenvectors = False)
#
# The positive values are darker pixels with lighter
# neighbors. The original ImageJ code scales the result
# by sigma squared - I have a feeling this might be
# a first-order correction for e**(-2*sigma), possibly
# because the hessian is taken from one pixel away
# and the gradient is less as sigma gets larger.
#
result = -L[:, :, 0] * (L[:, :, 0] < 0) * sigma * sigma
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1,int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max+1)/2)
result = enhance_dark_holes(pixel_data, min_radius,
max_radius, mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask = mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data,
self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s"%
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s"%self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if self.show_window:
workspace.display_data.image = image.pixel_data
workspace.display_data.result = result
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value)-1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
result = white_tophat(pixel_data, radius, mask)
elif self.enhance_method == E_NEURITES:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1,int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max+1)/2)
result = enhance_dark_holes(pixel_data, min_radius,
max_radius, mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask = mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data,
self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s"%
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s"%self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if not workspace.frame is None:
figure = workspace.create_or_find_figure(title="EnhanceOrSuppressFeatures, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(2,1))
figure.subplot_imshow_grayscale(0, 0, image.pixel_data,
"Original: %s" %
self.image_name.value)
figure.subplot_imshow_grayscale(1, 0, result,
"Filtered: %s" %
self.filtered_image_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
