def recompute_selected_version(input_image, layers):
"""
Do the actual computation. Starting from the original image, for each sharpening layer
apply a Gaussian filter using the layer's parameters. Store the result in the central
data object for the selected version.
:return: -
"""
# Initialize the new image with the original image.
new_image = input_image
# Apply all sharpening layers. If the amount is positive, sharpen the image. A negative
# amount between -1 and 0 means that the image is to be softened with a Gaussian kernel.
# In this case If the sign of the amount is reversed and taken as the weight with which the
# softened image is mixed with the original one.
for layer in layers:
if layer.amount > 0.:
new_image = Miscellaneous.gaussian_sharpen(
new_image,
layer.amount,
layer.radius,
luminance_only=layer.luminance_only)
elif -1. <= layer.amount < 0.:
new_image = Miscellaneous.gaussian_blur(
new_image,
-layer.amount,
layer.radius,
luminance_only=layer.luminance_only)
# Store the result in the central data object.
return new_image
def execute_postprocess_image(self):
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start postprocessing +++",
self.attached_log_file)
self.my_timer.create_no_check('Conputing image postprocessing')
# Initialize the new image with the original image.
self.postprocessed_image = self.postproc_input_image
# Apply all sharpening layers of the postprocessing version selected last time.
version_index = self.configuration.postproc_data_object.version_selected
postproc_layers = self.configuration.postproc_data_object.versions[
version_index].layers
for layer in postproc_layers:
self.postprocessed_image = Miscellaneous.gaussian_sharpen(
self.postprocessed_image,
layer.amount,
layer.radius,
luminance_only=layer.luminance_only)
self.my_timer.stop('Conputing image postprocessing')
self.work_next_task_signal.emit("Save postprocessed image")