def run(self,workspace):
first_image_set = workspace.measurements.get_current_image_measurement(
cpmeas.GROUP_INDEX) == 1
image_set_list = workspace.image_set_list
d = self.get_dictionary(image_set_list)
orig_image = workspace.image_set.get_image(self.image_name.value)
recalculate_flag = (self.shape not in (SH_ELLIPSE, SH_RECTANGLE) or
self.individual_or_once == IO_INDIVIDUALLY or
first_image_set or
workspace.pipeline.test_mode)
save_flag = (self.individual_or_once == IO_FIRST and first_image_set)
if not recalculate_flag:
if d[D_FIRST_CROPPING].shape != orig_image.pixel_data.shape[:2]:
recalculate_flag = True
logger.warning("""Image, "%s", size changed from %s to %s during cycle %d, recalculating""",
self.image_name.value,
str(d[D_FIRST_CROPPING].shape),
str(orig_image.pixel_data.shape[:2]),
workspace.image_set.image_number)
mask = None # calculate the mask after cropping unless set below
cropping = None
masking_objects = None
if not recalculate_flag:
cropping = d[D_FIRST_CROPPING]
mask = d[D_FIRST_CROPPING_MASK]
elif self.shape == SH_CROPPING:
cropping_image = workspace.image_set.get_image(self.cropping_mask_source.value)
cropping = cropping_image.crop_mask
elif self.shape == SH_IMAGE:
source_image = workspace.image_set.get_image\
(self.image_mask_source.value).pixel_data
if self.use_plate_fix.value:
source_image = self.plate_fixup(source_image)
cropping = source_image > 0
elif self.shape == SH_OBJECTS:
masking_objects = workspace.get_objects(self.objects_source.value)
cropping = masking_objects.segmented > 0
elif self.crop_method == CM_MOUSE:
cropping = self.ui_crop(workspace,orig_image)
elif self.shape == SH_ELLIPSE:
cropping = self.get_ellipse_cropping(workspace,orig_image)
elif self.shape == SH_RECTANGLE:
cropping = self.get_rectangle_cropping(workspace,orig_image)
if self.remove_rows_and_columns == RM_NO:
cropped_pixel_data = orig_image.pixel_data.copy()
if cropped_pixel_data.ndim == 3:
cropped_pixel_data[~cropping,:]=0
else:
cropped_pixel_data[np.logical_not(cropping)] = 0
if mask is None:
mask = cropping
if orig_image.has_mask:
image_mask = mask & orig_image.get_mask()
else:
image_mask = mask
else:
internal_cropping = self.remove_rows_and_columns == RM_ALL
cropped_pixel_data = cpi.crop_image(orig_image.pixel_data,
cropping,
internal_cropping)
if mask is None:
mask = cpi.crop_image(cropping, cropping, internal_cropping)
if orig_image.has_mask:
image_mask = cpi.crop_image(
orig_image.get_mask(), cropping, internal_cropping) & mask
else:
image_mask = mask
if cropped_pixel_data.ndim == 3:
cropped_pixel_data[~mask,:] = 0
else:
cropped_pixel_data[~mask] = 0
if self.shape == SH_OBJECTS:
# Special handling for objects - masked objects instead of
# mask and crop mask
output_image = cpi.Image(image=cropped_pixel_data,
masking_objects = masking_objects,
parent_image = orig_image)
else:
output_image=cpi.Image(image=cropped_pixel_data,
mask=image_mask,
parent_image = orig_image,
crop_mask = cropping)
#
# Display the image
#
if self.show_window:
workspace.display_data.orig_image_pixel_data = orig_image.pixel_data
workspace.display_data.cropped_pixel_data = cropped_pixel_data
workspace.display_data.image_set_number = workspace.measurements.image_set_number
if save_flag:
d[D_FIRST_CROPPING_MASK] = mask
d[D_FIRST_CROPPING] = cropping
#
# Save the image / cropping / mask
#
workspace.image_set.add(self.cropped_image_name.value, output_image)
#
# Save the old and new image sizes
#
original_image_area = np.product(orig_image.pixel_data.shape[:2])
area_retained_after_cropping = np.sum(cropping)
feature = FF_AREA_RETAINED%(self.cropped_image_name.value)
m = workspace.measurements
m.add_measurement('Image', feature,
np.array([area_retained_after_cropping]))
feature = FF_ORIGINAL_AREA%(self.cropped_image_name.value)
m.add_measurement('Image', feature,
np.array([original_image_area]))
def run(self,workspace):
first_image_set = workspace.measurements.get_current_image_measurement(
cpmeas.GROUP_INDEX) == 1
image_set_list = workspace.image_set_list
d = self.get_dictionary(image_set_list)
orig_image = workspace.image_set.get_image(self.image_name.value)
recalculate_flag = (self.shape not in (SH_ELLIPSE, SH_RECTANGLE) or
self.individual_or_once == IO_INDIVIDUALLY or
first_image_set or
workspace.pipeline.test_mode)
save_flag = (self.individual_or_once == IO_FIRST and first_image_set)
if not recalculate_flag:
if d[D_FIRST_CROPPING].shape != orig_image.pixel_data.shape[:2]:
recalculate_flag = True
logger.warning("""Image, "%s", size changed from %s to %s during cycle %d, recalculating""",
self.image_name.value,
str(d[D_FIRST_CROPPING].shape),
str(orig_image.pixel_data.shape[:2]),
workspace.image_set.image_number)
mask = None # calculate the mask after cropping unless set below
cropping = None
masking_objects = None
if not recalculate_flag:
cropping = d[D_FIRST_CROPPING]
mask = d[D_FIRST_CROPPING_MASK]
elif self.shape == SH_CROPPING:
cropping_image = workspace.image_set.get_image(self.cropping_mask_source.value)
cropping = cropping_image.crop_mask
elif self.shape == SH_IMAGE:
source_image = workspace.image_set.get_image\
(self.image_mask_source.value).pixel_data
if self.use_plate_fix.value:
source_image = self.plate_fixup(source_image)
cropping = source_image > 0
elif self.shape == SH_OBJECTS:
masking_objects = workspace.get_objects(self.objects_source.value)
cropping = masking_objects.segmented > 0
elif self.crop_method == CM_MOUSE:
cropping = self.ui_crop(workspace,orig_image)
elif self.shape == SH_ELLIPSE:
cropping = self.get_ellipse_cropping(workspace,orig_image)
elif self.shape == SH_RECTANGLE:
cropping = self.get_rectangle_cropping(workspace,orig_image)
if self.remove_rows_and_columns == RM_NO:
cropped_pixel_data = orig_image.pixel_data.copy()
if cropped_pixel_data.ndim == 3:
cropped_pixel_data[~cropping,:]=0
else:
cropped_pixel_data[np.logical_not(cropping)] = 0
if mask is None:
mask = cropping
if orig_image.has_mask:
image_mask = mask & orig_image.get_mask()
else:
image_mask = mask
else:
internal_cropping = self.remove_rows_and_columns == RM_ALL
cropped_pixel_data = cpi.crop_image(orig_image.pixel_data,
cropping,
internal_cropping)
if mask is None:
mask = cpi.crop_image(cropping, cropping, internal_cropping)
if orig_image.has_mask:
image_mask = cpi.crop_image(
orig_image.get_mask(), cropping, internal_cropping) & mask
else:
image_mask = mask
if cropped_pixel_data.ndim == 3:
cropped_pixel_data[~mask,:] = 0
else:
cropped_pixel_data[~mask] = 0
if self.shape == SH_OBJECTS:
# Special handling for objects - masked objects instead of
# mask and crop mask
output_image = cpi.Image(image=cropped_pixel_data,
masking_objects = masking_objects,
parent_image = orig_image)
else:
output_image=cpi.Image(image=cropped_pixel_data,
mask=image_mask,
parent_image = orig_image,
crop_mask = cropping)
#
# Display the image
#
if self.show_window:
workspace.display_data.orig_image_pixel_data = orig_image.pixel_data
workspace.display_data.cropped_pixel_data = cropped_pixel_data
workspace.display_data.image_set_number = workspace.measurements.image_set_number
if save_flag:
d[D_FIRST_CROPPING_MASK] = mask
d[D_FIRST_CROPPING] = cropping
#
# Save the image / cropping / mask
#
workspace.image_set.add(self.cropped_image_name.value, output_image)
#
# Save the old and new image sizes
#
original_image_area = np.product(orig_image.pixel_data.shape[:2])
area_retained_after_cropping = np.sum(cropping)
feature = FF_AREA_RETAINED%(self.cropped_image_name.value)
m = workspace.measurements
m.add_measurement('Image', feature,
np.array([area_retained_after_cropping]))
feature = FF_ORIGINAL_AREA%(self.cropped_image_name.value)
m.add_measurement('Image', feature,
np.array([original_image_area]))