def workflow(input_name,
input_type='video',
roi=None,
automatic_ap_creation=True):
"""
Execute the whole stacking workflow for a test case. This can either use a video file (.avi .mov .mp4 .ser)
or still images stored in a single directory.
:param input_name: Video file (.avi .mov .mp4 .ser) or name of a directory containing still images
:param input_type: Either "video" or "image" (see "input_name")
:param roi: If specified, tuple (y_low, y_high, x_low, x_high) with pixel bounds for "region
of interest"
:return: average, [average_roi,] color_image_with_aps, stacked_image
with: - average: global mean frame
- average_roi: mean frame restricted to ROI (only if roi is specified)
- color_image_with_aps: mean frame overlaid with alignment points and their
boxes (white) and patches (green)
"""
# Initalize the timer object used to measure execution times of program sections.
my_timer = timer()
# Images can either be extracted from a video file or a batch of single photographs. Select
# the example for the test run.
# For video file input, the Frames constructor expects the video file name for "names".
if input_type == 'video':
names = input_name
# For single image input, the Frames constructor expects a list of image file names for "names".
else:
names = [
os.path.join(input_name, name) for name in os.listdir(input_name)
]
stacked_image_name = input_name + '.stacked.tiff'
# The name of the alignment point visualization file is derived from the input video name or
# the input directory name.
ap_image_name = input_name + ".aps.tiff"
print(
"\n" +
"*************************************************************************************\n"
+ "Start processing " + str(input_name) +
"\n*************************************************************************************"
)
my_timer.create('Execution over all')
# Get configuration parameters.
configuration = Configuration()
configuration.initialize_configuration()
# Read the frames.
print("+++ Start reading frames")
my_timer.create('Read all frames')
try:
frames = Frames(configuration, names, type=input_type)
print("Number of images read: " + str(frames.number))
print("Image shape: " + str(frames.shape))
except Error as e:
print("Error: " + str(e))
exit()
my_timer.stop('Read all frames')
# Rank the frames by their overall local contrast.
print("+++ Start ranking images")
my_timer.create('Ranking images')
rank_frames = RankFrames(frames, configuration)
rank_frames.frame_score()
my_timer.stop('Ranking images')
print("Index of best frame: " + str(rank_frames.frame_ranks_max_index))
# Initialize the frame alignment object.
align_frames = AlignFrames(frames, rank_frames, configuration)
if configuration.align_frames_mode == "Surface":
my_timer.create('Select optimal alignment patch')
# Select the local rectangular patch in the image where the L gradient is highest in both x
# and y direction. The scale factor specifies how much smaller the patch is compared to the
# whole image frame.
(y_low_opt, y_high_opt, x_low_opt,
x_high_opt) = align_frames.compute_alignment_rect(
configuration.align_frames_rectangle_scale_factor)
my_timer.stop('Select optimal alignment patch')
print("optimal alignment rectangle, y_low: " + str(y_low_opt) +
", y_high: " + str(y_high_opt) + ", x_low: " + str(x_low_opt) +
", x_high: " + str(x_high_opt))
# Align all frames globally relative to the frame with the highest score.
print("+++ Start aligning all frames")
my_timer.create('Global frame alignment')
try:
align_frames.align_frames()
except NotSupportedError as e:
print("Error: " + e.message)
exit()
except InternalError as e:
print("Warning: " + e.message)
my_timer.stop('Global frame alignment')
print("Intersection, y_low: " + str(align_frames.intersection_shape[0][0]) + ", y_high: "
+ str(align_frames.intersection_shape[0][1]) + ", x_low: " \
+ str(align_frames.intersection_shape[1][0]) + ", x_high: " \
+ str(align_frames.intersection_shape[1][1]))
# Compute the average frame.
print("+++ Start computing reference frame")
my_timer.create('Compute reference frame')
average = align_frames.average_frame()
my_timer.stop('Compute reference frame')
print("Reference frame computed from the best " +
str(align_frames.average_frame_number) + " frames.")
# If the ROI is to be set to a smaller size than the whole intersection, do so.
if roi:
print("+++ Start setting ROI and computing new reference frame")
my_timer.create('Setting ROI and new reference')
average_roi = align_frames.set_roi(roi[0], roi[1], roi[2], roi[3])
my_timer.stop('Setting ROI and new reference')
# Initialize the AlignmentPoints object.
my_timer.create('Initialize alignment point object')
alignment_points = AlignmentPoints(configuration, frames, rank_frames,
align_frames)
my_timer.stop('Initialize alignment point object')
if automatic_ap_creation:
# Create alignment points, and create an image with wll alignment point boxes and patches.
print("+++ Start creating alignment points")
my_timer.create('Create alignment points')
# If a ROI is selected, alignment points are created in the ROI window only.
alignment_points.create_ap_grid()
my_timer.stop('Create alignment points')
print("Number of alignment points selected: " +
str(len(alignment_points.alignment_points)) +
", aps dropped because too dim: " +
str(alignment_points.alignment_points_dropped_dim) +
", aps dropped because too little structure: " +
str(alignment_points.alignment_points_dropped_structure))
else:
# Open the alignment point editor.
app = QtWidgets.QApplication(sys.argv)
alignment_point_editor = AlignmentPointEditorWidget(
None, configuration, align_frames, alignment_points, None)
alignment_point_editor.setMinimumSize(800, 600)
alignment_point_editor.showMaximized()
app.exec_()
print("After AP editing, number of APs: " +
str(len(alignment_points.alignment_points)))
count_updates = 0
for ap in alignment_points.alignment_points:
if ap['reference_box'] is not None:
continue
count_updates += 1
AlignmentPoints.set_reference_box(ap, alignment_points.mean_frame)
print("Buffers allocated for " + str(count_updates) +
" alignment points.")
# Produce an overview image showing all alignment points.
if roi:
color_image_with_aps = alignment_points.show_alignment_points(
average_roi)
else:
color_image_with_aps = alignment_points.show_alignment_points(average)
# For each alignment point rank frames by their quality.
my_timer.create('Rank frames at alignment points')
print("+++ Start ranking frames at alignment points")
alignment_points.compute_frame_qualities()
my_timer.stop('Rank frames at alignment points')
# Allocate StackFrames object.
stack_frames = StackFrames(configuration, frames, rank_frames,
align_frames, alignment_points, my_timer)
# Stack all frames.
print("+++ Start stacking frames")
stack_frames.stack_frames()
# Merge the stacked alignment point buffers into a single image.
print("+++ Start merging alignment patches")
stacked_image = stack_frames.merge_alignment_point_buffers()
# If the drizzle factor is 1.5, reduce the pixel resolution of the stacked image buffer
# to half the size used in stacking.
if configuration.drizzle_factor_is_1_5:
print("+++ Start reducing image buffer size")
stack_frames.half_stacked_image_buffer_resolution()
# Save the stacked image as 16bit int (color or mono).
my_timer.create('Saving the final image')
Frames.save_image(stacked_image_name,
stacked_image,
color=frames.color,
header=configuration.global_parameters_version)
my_timer.stop('Saving the final image')
# Print out timer results.
my_timer.stop('Execution over all')
my_timer.print()
# Write the image with alignment points.
Frames.save_image(ap_image_name,
color_image_with_aps,
color=True,
header=configuration.global_parameters_version)
# If a ROI is selected, return both the original and the reduced-size average frame.
if roi:
return average, average_roi, color_image_with_aps, stacked_image
else:
return average, color_image_with_aps, stacked_image
configuration = Configuration()
my_timer.create('Read all frames')
try:
frames = Frames(configuration, names, type=type)
print("Number of images read: " + str(frames.number))
print("Image shape: " + str(frames.shape))
except Error as e:
print("Error: " + e.message)
exit()
my_timer.stop('Read all frames')
# Rank the frames by their overall local contrast.
my_timer.create('Ranking images')
rank_frames = RankFrames(frames, configuration)
rank_frames.frame_score()
my_timer.stop('Ranking images')
print("Index of maximum: " + str(rank_frames.frame_ranks_max_index))
print("Frame scores: " + str(rank_frames.frame_ranks))
print("Frame scores (sorted): " + str([
rank_frames.frame_ranks[i] for i in rank_frames.quality_sorted_indices
]))
print("Sorted index list: " + str(rank_frames.quality_sorted_indices))
# Initialize the frame alignment object.
my_timer.create('Select optimal alignment patch')
align_frames = AlignFrames(frames, rank_frames, configuration)
if configuration.align_frames_mode == "Surface":
# Select the local rectangular patch in the image where the L gradient is highest in both x
class Workflow(QtCore.QObject):
master_dark_created_signal = QtCore.pyqtSignal(bool)
master_flat_created_signal = QtCore.pyqtSignal(bool)
work_next_task_signal = QtCore.pyqtSignal(str)
work_current_progress_signal = QtCore.pyqtSignal(str, int)
set_main_gui_busy_signal = QtCore.pyqtSignal(bool)
set_status_bar_signal = QtCore.pyqtSignal(str, str)
create_image_window_signal = QtCore.pyqtSignal()
update_image_window_signal = QtCore.pyqtSignal(object)
terminate_image_window_signal = QtCore.pyqtSignal()
def __init__(self, main_gui):
super(Workflow, self).__init__()
self.main_gui = main_gui
self.configuration = main_gui.configuration
self.my_timer = None
self.frames = None
self.rank_frames = None
self.align_frames = None
self.alignment_points = None
self.stack_frames = None
self.stacked_image_name = None
self.postprocessed_image_name = None
self.postprocessed_image = None
self.postproc_input_image = None
self.postproc_input_name = None
self.job_type = None
self.attached_log_name = None
self.attached_log_file = None
self.stdout_saved = None
self.output_redirected = False
self.protocol_file = None
# Switch alignment point debugging on / off.
self.debug_AP = False
# The following code works on Windows and Linux systems only. It is not necessary, though.
try:
if platform.system() == 'Windows':
mkl_rt = CDLL('mkl_rt.dll')
else:
mkl_rt = CDLL('libmkl_rt.so')
mkl_get_max_threads = mkl_rt.mkl_get_max_threads
def mkl_set_num_threads(cores):
mkl_rt.mkl_set_num_threads(byref(c_int(cores)))
mkl_set_num_threads(mkl_get_max_threads())
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol("Number of threads used by mkl: " +
str(mkl_get_max_threads()),
self.attached_log_file,
precede_with_timestamp=True)
except Exception as e:
Miscellaneous.protocol(
"Warning: mkl_rt.dll / libmkl_rt.so does not work (not a Windows or Linux system, "
"or Intel Math Kernel Library not installed?). " + str(e),
self.attached_log_file,
precede_with_timestamp=True)
# Create the calibration object, used for potential flat / dark corrections.
self.calibration = Calibration(self.configuration)
@QtCore.pyqtSlot(list)
def execute_create_master_dark(self, dark_names):
# Create a new master dark.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Creating a new master dark frame +++",
self.attached_log_file,
precede_with_timestamp=True)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(" Input frames: " + dark_names[0],
self.attached_log_file,
precede_with_timestamp=False)
try:
self.set_main_gui_busy_signal.emit(True)
self.calibration.create_master_dark(dark_names[0])
# if self.configuration.global_parameters_protocol_level > 0 and \
# self.calibration.master_flat_removed:
# Miscellaneous.protocol(" A non-matching master flat was de-activated",
# self.attached_log_file, precede_with_timestamp=False)
self.master_dark_created_signal.emit(True)
except Exception as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
" Error in creating master dark frame: " +
str(e),
self.attached_log_file,
precede_with_timestamp=False)
self.master_dark_created_signal.emit(False)
@QtCore.pyqtSlot(list)
def execute_create_master_flat(self, flat_names):
# Create a new master flat.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Creating a new master flat frame +++",
self.attached_log_file,
precede_with_timestamp=True)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(" Input frames: " + flat_names[0],
self.attached_log_file,
precede_with_timestamp=False)
try:
self.set_main_gui_busy_signal.emit(True)
self.calibration.create_master_flat(flat_names[0])
# if self.configuration.global_parameters_protocol_level > 0 and \
# self.calibration.master_dark_removed:
# Miscellaneous.protocol(" A non-matching master dark was de-activated",
# self.attached_log_file, precede_with_timestamp=False)
self.master_flat_created_signal.emit(True)
except Error as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
" Error in creating master flat frame: " +
str(e) + ", flat frame calibration de-activated",
self.attached_log_file,
precede_with_timestamp=False)
self.master_flat_created_signal.emit(False)
@QtCore.pyqtSlot()
def execute_reset_masters(self):
# De-activate master frames.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ De-activating master frames +++",
self.attached_log_file,
precede_with_timestamp=True)
self.calibration.reset_masters()
@QtCore.pyqtSlot(str, str, bool)
def execute_frames(self, input_name, input_type, convert_to_grayscale):
# If objects are left over from previous run, delete them.
for obj in [
self.frames, self.rank_frames, self.align_frames,
self.alignment_points, self.stack_frames
]:
if obj is not None:
del obj
# Force the garbage collector to release unreferenced objects.
gc.collect()
# Update the status bar in the main GUI.
self.input_name = input_name
self.set_status_bar_processing_phase("reading frames")
# A jobs can either "stack" images or "postprocess" a single image. In the latter case,
# input is a single image file.
#
# Images for stacking can either be extracted from a video file or a batch of single
# photographs. In the first case, input_type is set to 'video', in the second case to
# 'image'.
if input_type == 'postproc':
self.job_type = 'postproc'
self.postproc_input_name = input_name
# Reset the postprocessed image to None. This way, in saving the postprocessing result,
# it can be checked if an image was computed in the workflow thread.
self.postprocessed_image = None
self.postprocessed_image_name = PostprocDataObject.set_file_name_processed(
input_name, self.configuration.postproc_suffix,
self.configuration.global_parameters_image_format)
self.attached_log_name = splitext(
input_name)[0] + '_postproc-log.txt'
# For video file input, the Frames constructor expects the video file name for "names".
elif input_type == 'video':
self.job_type = 'stacking'
names = input_name
self.stacked_image_name = splitext(input_name)[0] + \
self.configuration.stack_frames_suffix + '.' + \
self.configuration.global_parameters_image_format
self.attached_log_name = splitext(
input_name)[0] + '_stacking-log.txt'
# For single image input, the Frames constructor expects a list of image file names for
# "names".
else: # input_type = 'image'
self.job_type = 'stacking'
names = [join(input_name, name) for name in listdir(input_name)]
self.stacked_image_name = input_name + self.configuration.stack_frames_suffix + '.' + \
self.configuration.global_parameters_image_format
self.attached_log_name = input_name + '_stacking-log.txt'
# Redirect stdout to a file if requested.
if self.configuration.global_parameters_write_protocol_to_file != self.output_redirected:
# Output currently redirected. Reset to stdout.
if self.output_redirected:
sys.stdout = self.stdout_saved
self.output_redirected = False
# Currently set to stdout, redirect to file now.
else:
try:
self.stdout_saved = sys.stdout
sys.stdout = open(self.configuration.protocol_filename,
'a+')
self.output_redirected = True
except IOError:
pass
# Create logfile if requested to store the log with the stacked file.
if self.attached_log_file:
self.attached_log_file.close()
if self.configuration.global_parameters_store_protocol_with_result:
self.attached_log_file = open(self.attached_log_name, "w+")
else:
self.attached_log_file = None
# Write a header to stdout and optionally to the logfile.
if self.configuration.global_parameters_protocol_level > 0:
decorator_line = (len(input_name) + 28) * "*"
Miscellaneous.protocol(decorator_line,
self.attached_log_file,
precede_with_timestamp=False)
Miscellaneous.protocol("Start processing " + input_name,
self.attached_log_file)
Miscellaneous.protocol(decorator_line,
self.attached_log_file,
precede_with_timestamp=False)
# Initalize the timer object used to measure execution times of program sections.
self.my_timer = timer()
self.my_timer.create('Execution over all')
if self.job_type == 'stacking':
# Write parameters to the protocol.
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.print_stacking_parameters(
self.configuration, self.attached_log_file)
# Decide on the objects to be buffered, depending on configuration parameter.
buffer_original, buffer_monochrome, buffer_gaussian, buffer_laplacian = \
Frames.set_buffering(self.configuration.global_parameters_buffering_level)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
"+++ Buffering level is " +
str(self.configuration.global_parameters_buffering_level) +
" +++", self.attached_log_file)
if buffer_original:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start reading frames +++",
self.attached_log_file)
try:
self.frames = Frames(
self.configuration,
names,
type=input_type,
calibration=self.calibration,
convert_to_grayscale=convert_to_grayscale,
progress_signal=self.work_current_progress_signal,
buffer_original=buffer_original,
buffer_monochrome=buffer_monochrome,
buffer_gaussian=buffer_gaussian,
buffer_laplacian=buffer_laplacian)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(" Number of images: " +
str(self.frames.number) +
", image shape: " +
str(self.frames.shape),
self.attached_log_file,
precede_with_timestamp=False)
if self.frames.calibration_matches:
if self.calibration.master_dark_frame_adapted is not None and \
self.calibration.inverse_master_flat_frame is not None:
Miscellaneous.protocol(
" Dark / flat frame calibration is active",
self.attached_log_file,
precede_with_timestamp=False)
elif self.calibration.master_dark_frame_adapted is not None:
Miscellaneous.protocol(
" Dark frame calibration is active",
self.attached_log_file,
precede_with_timestamp=False)
elif self.calibration.inverse_master_flat_frame is not None:
Miscellaneous.protocol(
" Flat frame calibration is active",
self.attached_log_file,
precede_with_timestamp=False)
else:
Miscellaneous.protocol(
" No matching master dark / flat frames found, "
"calibration de-activated",
self.attached_log_file,
precede_with_timestamp=False)
except Error as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + e.message + ", continue with next job\n",
self.attached_log_file)
self.work_next_task_signal.emit("Next job")
return
except Exception as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + str(e) + ", continue with next job\n",
self.attached_log_file)
self.work_next_task_signal.emit("Next job")
return
# Look up the available RAM (without paging)
virtual_memory = dict(psutil.virtual_memory()._asdict())
available_ram = virtual_memory['available'] / 1e9
# Compute the approximate RAM usage of this job at the selected buffering level.
needed_ram = self.frames.compute_required_buffer_size(
self.configuration.global_parameters_buffering_level)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(" RAM required (Gbytes): " +
str(needed_ram) + ", available: " +
str(available_ram),
self.attached_log_file,
precede_with_timestamp=False)
# If the required RAM is not available, test if lowering the buffering level would help.
if needed_ram > available_ram:
recommended_level = None
for level in range(
self.configuration.global_parameters_buffering_level -
1, -1, -1):
alternative_ram = self.frames.compute_required_buffer_size(
level)
if alternative_ram < available_ram:
recommended_level = level
break
# If an appropriate level was found, write it as a recommendation to the protocol.
if self.configuration.global_parameters_protocol_level > 0:
if recommended_level is not None:
Miscellaneous.protocol(
"Error: Too little RAM for chosen buffering level,"
" recommended level: " + str(recommended_level) +
", continuing with next job\n",
self.attached_log_file)
else:
Miscellaneous.protocol(
"Error: Too little RAM for this job, "
"continuing with the next one\n",
self.attached_log_file)
# Continue with the next job.
self.work_next_task_signal.emit("Next job")
return
# The RAM seems to be sufficient, continue with ranking frames.
self.work_next_task_signal.emit("Rank frames")
# Job type is 'postproc'.
else:
try:
self.postproc_input_image = Frames.read_image(
self.postproc_input_name)
except Error as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + e.message + ", continue with next job\n",
self.attached_log_file)
self.work_next_task_signal.emit("Next job")
return
except Exception as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + str(e) + ", continue with next job\n",
self.attached_log_file)
self.work_next_task_signal.emit("Next job")
return
# Convert 8 bit to 16 bit.
if self.postproc_input_image.dtype == uint8:
self.postproc_input_image = self.postproc_input_image.astype(
uint16) * 256
self.work_next_task_signal.emit("Postprocessing")
@QtCore.pyqtSlot()
def execute_rank_frames(self):
self.set_status_bar_processing_phase("ranking frames")
# Rank the frames by their overall local contrast.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start ranking images +++",
self.attached_log_file)
self.my_timer.create_no_check('Ranking images')
try:
self.rank_frames = RankFrames(self.frames, self.configuration,
self.work_current_progress_signal)
self.rank_frames.frame_score()
self.my_timer.stop('Ranking images')
except Error as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + e.message + ", continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Ranking images')
self.work_next_task_signal.emit("Next job")
return
except Exception as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + str(e) + ", continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Ranking images')
self.work_next_task_signal.emit("Next job")
return
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(" Index of best frame: " +
str(self.rank_frames.frame_ranks_max_index),
self.attached_log_file,
precede_with_timestamp=False)
self.work_next_task_signal.emit("Align frames")
@QtCore.pyqtSlot(int, int, int, int)
def execute_align_frames(self, y_low_opt, y_high_opt, x_low_opt,
x_high_opt):
self.set_status_bar_processing_phase("aligning frames")
# Initialize the frame alignment object.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Initializing frame alignment +++",
self.attached_log_file)
self.align_frames = AlignFrames(
self.frames,
self.rank_frames,
self.configuration,
progress_signal=self.work_current_progress_signal)
if self.configuration.align_frames_mode == "Surface":
auto_execution = False
if y_low_opt == 0 and y_high_opt == 0 and x_low_opt == 0 and x_high_opt == 0:
auto_execution = True
elif (y_high_opt - y_low_opt) / self.frames.shape[0] > \
self.configuration.align_frames_max_stabilization_patch_fraction or \
(x_high_opt - x_low_opt) / self.frames.shape[1] > \
self.configuration.align_frames_max_stabilization_patch_fraction and \
self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
" Stabilization patch selected manually is "
"too large, switch to automatic mode",
self.attached_log_file,
precede_with_timestamp=False)
auto_execution = True
elif (y_high_opt - y_low_opt) / self.frames.shape[0] < \
self.configuration.align_frames_min_stabilization_patch_fraction or \
(x_high_opt - x_low_opt) / self.frames.shape[1] < \
self.configuration.align_frames_min_stabilization_patch_fraction and \
self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
" Stabilization patch selected manually is "
"too small, switch to automatic mode",
self.attached_log_file,
precede_with_timestamp=False)
auto_execution = True
# Compute the local rectangular patch in the image where the L gradient is highest
# in both x and y direction. The scale factor specifies how much smaller the patch
# is compared to the whole image frame. In batch mode, variable "auto_execution" is
# set to "True", and the automatic patch computation is the only option.
if auto_execution or self.configuration.align_frames_automation:
self.my_timer.create_no_check('Select optimal alignment patch')
(y_low_opt, y_high_opt, x_low_opt, x_high_opt) = \
self.align_frames.compute_alignment_rect(
self.configuration.align_frames_rectangle_scale_factor)
self.my_timer.stop('Select optimal alignment patch')
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" Alignment rectangle, computed automatically: "
+ str(y_low_opt) + "<y<" + str(y_high_opt) + ", " +
str(x_low_opt) + "<x<" + str(x_high_opt),
self.attached_log_file,
precede_with_timestamp=False)
# As an alternative, set the coordinates of the rectangular patch explicitly.
else:
# The image displayed in the stabilization patch editor was shrunk on all four
# sides by a number of pixels given by the alignment search width parameter.
# Therefore, the resulting coordinates of the stabilization patch have to be
# corrected by this offset now.
y_low_opt += self.configuration.align_frames_search_width
y_high_opt += self.configuration.align_frames_search_width
x_low_opt += self.configuration.align_frames_search_width
x_high_opt += self.configuration.align_frames_search_width
self.align_frames.set_alignment_rect(y_low_opt, y_high_opt,
x_low_opt, x_high_opt)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" Alignment rectangle, set by the user: "******"<y<" + str(y_high_opt) + ", " +
str(x_low_opt) + "<x<" + str(x_high_opt),
self.attached_log_file,
precede_with_timestamp=False)
# Align all frames globally relative to the frame with the highest score.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start aligning all frames +++",
self.attached_log_file)
self.my_timer.create_no_check('Global frame alignment')
# Align all frames in "Surface" mode.
if self.configuration.align_frames_mode == "Surface":
# Try the frame alignment using the alignment patch with the highest quality first. If
# for at least one frame no valid shift can be found, try the next alignment patch. If
# valid shifts cannot be computed with any patch, abort processing of this job and go to
# the next one.
number_patches = len(self.align_frames.alignment_rect_qualities)
for patch_index in range(number_patches):
self.align_frames.select_alignment_rect(patch_index)
try:
self.align_frames.align_frames()
# Everything is fine, no need to try another stabilization patch.
break
except (NotSupportedError, ArgumentError) as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + e.message +
", continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Global frame alignment')
self.work_next_task_signal.emit("Next job")
return
# For some frames no valid shift could be computed. This would create problems later
# in the workflow. Therefore, try again with another stabilization patch.
except InternalError as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Warning: " + e.message + ", will try another"
" stabilization patch", self.attached_log_file)
# If there is no more patch available, skip this job.
if patch_index == number_patches - 1:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: No alternative stabilization patch"
" available, continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Global frame alignment')
self.work_next_task_signal.emit("Next job")
return
# Continue with the next best stabilization patch.
else:
y_low_opt, y_high_opt, x_low_opt, x_high_opt = \
self.align_frames.alignment_rect_bounds[patch_index + 1]
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
" Next alignment rectangle tried: " +
str(y_low_opt) + "<y<" + str(y_high_opt) +
", " + str(x_low_opt) + "<x<" +
str(x_high_opt),
self.attached_log_file,
precede_with_timestamp=False)
# Align all frames in "Planet" mode.
else:
try:
self.align_frames.align_frames()
except Error as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + e.message + ", continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Global frame alignment')
self.work_next_task_signal.emit("Next job")
return
except Exception as e:
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"Error: " + str(e) + ", continue with next job\n",
self.attached_log_file)
self.my_timer.stop('Global frame alignment')
self.work_next_task_signal.emit("Next job")
return
self.my_timer.stop('Global frame alignment')
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" Pixel range common to all frames: " +
str(self.align_frames.intersection_shape[0][0]) + "<y<" +
str(self.align_frames.intersection_shape[0][1]) + ", " +
str(self.align_frames.intersection_shape[1][0]) + "<x<" +
str(self.align_frames.intersection_shape[1][1]),
self.attached_log_file,
precede_with_timestamp=False)
# Compute the average frame.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start computing the average frame +++",
self.attached_log_file)
self.my_timer.create_no_check('Compute reference frame')
self.align_frames.average_frame()
self.my_timer.stop('Compute reference frame')
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" The average frame was computed using the best " +
str(self.align_frames.average_frame_number) + " frames.",
self.attached_log_file,
precede_with_timestamp=False)
self.work_next_task_signal.emit("Select stack size")
@QtCore.pyqtSlot(int, int, int, int)
def execute_set_roi(self, y_min, y_max, x_min, x_max):
self.set_status_bar_processing_phase("setting the ROI")
if self.configuration.global_parameters_protocol_level > 0 and y_min != 0 or y_max != 0:
Miscellaneous.protocol(
"+++ Start setting a ROI and computing a new average frame +++",
self.attached_log_file)
self.my_timer.create_no_check('Setting ROI and new reference')
self.align_frames.set_roi(y_min, y_max, x_min, x_max)
self.my_timer.stop('Setting ROI and new reference')
if self.configuration.global_parameters_protocol_level > 1 and y_min != 0 or y_max != 0:
Miscellaneous.protocol(" ROI, set by the user: "******"<y<" + str(y_max) + ", " +
str(x_min) + "<x<" + str(x_max),
self.attached_log_file,
precede_with_timestamp=False)
self.work_next_task_signal.emit("Set alignment points")
@QtCore.pyqtSlot()
def execute_set_alignment_points(self):
# If not executing in "automatic" mode, the APs are created on the main_gui thread.
if self.main_gui.automatic:
self.set_status_bar_processing_phase("creating alignment points")
# Initialize the AlignmentPoints object.
self.my_timer.create_no_check('Initialize alignment point object')
self.alignment_points = AlignmentPoints(
self.configuration,
self.frames,
self.rank_frames,
self.align_frames,
progress_signal=self.work_current_progress_signal)
self.my_timer.stop('Initialize alignment point object')
# Create alignment points, and create an image with wll alignment point boxes and patches.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"+++ Start creating alignment points +++",
self.attached_log_file)
self.my_timer.create_no_check('Create alignment points')
# If a ROI is selected, alignment points are created in the ROI window only.
self.alignment_points.create_ap_grid()
self.my_timer.stop('Create alignment points')
self.work_next_task_signal.emit("Compute frame qualities")
@QtCore.pyqtSlot()
def execute_compute_frame_qualities(self):
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" Number of alignment points selected: " +
str(len(self.alignment_points.alignment_points)) +
", aps dropped because too dim: " +
str(self.alignment_points.alignment_points_dropped_dim) +
", aps dropped because too little structure: " +
str(self.alignment_points.alignment_points_dropped_structure),
self.attached_log_file,
precede_with_timestamp=False)
self.set_status_bar_processing_phase(
"ranking all frames at all alignment points")
# For each alignment point rank frames by their quality.
self.my_timer.create_no_check('Rank frames at alignment points')
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"+++ Start ranking all frames at all alignment points +++",
self.attached_log_file)
self.alignment_points.compute_frame_qualities()
self.my_timer.stop('Rank frames at alignment points')
self.work_next_task_signal.emit("Stack frames")
@QtCore.pyqtSlot()
def execute_stack_frames(self):
self.set_status_bar_processing_phase("stacking frames")
# Allocate StackFrames object.
self.stack_frames = StackFrames(
self.configuration,
self.frames,
self.align_frames,
self.alignment_points,
self.my_timer,
progress_signal=self.work_current_progress_signal,
debug=self.debug_AP,
create_image_window_signal=self.create_image_window_signal,
update_image_window_signal=self.update_image_window_signal,
terminate_image_window_signal=self.terminate_image_window_signal)
# Stack all frames.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"+++ Start stacking " + str(self.alignment_points.stack_size) +
" frames +++", self.attached_log_file)
self.stack_frames.stack_frames()
if self.configuration.global_parameters_protocol_level > 1 and \
len(self.alignment_points.alignment_points) > 0:
Miscellaneous.protocol(
"\n Distribution of shifts at alignment points:",
self.attached_log_file,
precede_with_timestamp=False)
Miscellaneous.protocol(self.stack_frames.print_shift_table() +
"\n",
self.attached_log_file,
precede_with_timestamp=False)
self.set_status_bar_processing_phase("merging AP patches")
# Merge the stacked alignment point buffers into a single image.
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"+++ Start merging all alignment patches and the background +++",
self.attached_log_file)
self.stack_frames.merge_alignment_point_buffers()
self.work_next_task_signal.emit("Save stacked image")
@QtCore.pyqtSlot()
def execute_save_stacked_image(self):
self.set_status_bar_processing_phase("saving result")
# Save the image as 16bit int (color or mono).
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol("+++ Start saving the stacked image +++",
self.attached_log_file)
self.my_timer.create_no_check('Saving the stacked image')
self.frames.save_image(self.stacked_image_name,
self.stack_frames.stacked_image,
color=self.frames.color,
avoid_overwriting=False)
self.my_timer.stop('Saving the stacked image')
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" The stacked image was written to: " +
self.stacked_image_name,
self.attached_log_file,
precede_with_timestamp=False)
# If postprocessing is included after stacking, set the stacked image as input.
if self.configuration.global_parameters_include_postprocessing:
self.postproc_input_image = self.stack_frames.stacked_image
self.postproc_input_name = self.stacked_image_name
self.postprocessed_image_name = PostprocDataObject.set_file_name_processed(
self.stacked_image_name, self.configuration.postproc_suffix,
self.configuration.global_parameters_image_format)
self.work_next_task_signal.emit("Postprocessing")
else:
self.work_next_task_signal.emit("Next job")
# Print timing info for this job.
self.my_timer.stop('Execution over all')
if self.configuration.global_parameters_protocol_level > 0:
self.my_timer.protocol(self.attached_log_file)
@QtCore.pyqtSlot()
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")
@QtCore.pyqtSlot(object)
def execute_save_postprocessed_image(self, postprocessed_image):
# The signal payload is None only if the editor was left with "cancel" in interactive mode.
# In this case, skip saving the result and proceed with the next job.
if postprocessed_image is not None:
self.set_status_bar_processing_phase("saving result")
# Save the image as 16bit int (color or mono).
if self.configuration.global_parameters_protocol_level > 0:
Miscellaneous.protocol(
"+++ Start saving the postprocessed image +++",
self.attached_log_file)
self.my_timer.create_no_check('Saving the postprocessed image')
Frames.save_image(self.postprocessed_image_name,
postprocessed_image,
color=(len(postprocessed_image.shape) == 3),
avoid_overwriting=False)
self.my_timer.stop('Saving the postprocessed image')
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.protocol(
" The postprocessed image was written to: " +
self.postprocessed_image_name,
self.attached_log_file,
precede_with_timestamp=False)
if self.configuration.global_parameters_protocol_level > 1:
Miscellaneous.print_postproc_parameters(
self.configuration.postproc_data_object.versions[
self.configuration.postproc_data_object.
version_selected].layers, self.attached_log_file)
self.work_next_task_signal.emit("Next job")
# Print timing info for this job.
self.my_timer.stop('Execution over all')
if self.configuration.global_parameters_protocol_level > 0:
self.my_timer.protocol(self.attached_log_file)
def set_status_bar_processing_phase(self, phase):
"""
Put a text of the form
"Processing < job name >, < processing step >."
on the main window status bar.
:param phase: Processing phase (string)
:return: -
"""
self.set_status_bar_signal.emit(
"Processing " + self.input_name + ", " + phase + ".", "black")