Mercurial > repos > bgruening > cp_measure_image_intensity
diff track_objects.py @ 6:b64af54c5c09 draft default tip
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
| author | bgruening |
|---|---|
| date | Sun, 05 Nov 2023 09:28:54 +0000 |
| parents | 8536b1ae0254 |
| children |
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--- a/track_objects.py Fri Feb 26 14:08:05 2021 +0000 +++ b/track_objects.py Sun Nov 05 09:28:54 2023 +0000 @@ -3,68 +3,95 @@ import argparse import json -from cp_common_functions import get_json_value -from cp_common_functions import get_pipeline_lines -from cp_common_functions import get_total_number_of_modules -from cp_common_functions import INDENTATION -from cp_common_functions import update_module_count -from cp_common_functions import write_pipeline +from cp_common_functions import (get_json_value, + get_pipeline_lines, + get_total_number_of_modules, + INDENTATION, update_module_count, + write_pipeline) MODULE_NAME = "TrackObjects" OUTPUT_FILENAME = "output.cppipe" def build_header(module_name, module_number): - result = "|".join([f"{module_name}:[module_num:{module_number}", - "svn_version:\\'Unknown\\'", - "variable_revision_number:7", - "show_window:True", - "notes:\\x5B\\'Track the embryos across images using the Overlap method\\x3A tracked objects are identified by the amount of frame-to-frame overlap. Save an image of embryos labeled with a unique number across time.\\'\\x5D", - "batch_state:array(\\x5B\\x5D, dtype=uint8)", - "enabled:True", - "wants_pause:False]\n"]) + result = "|".join( + [ + f"{module_name}:[module_num:{module_number}", + "svn_version:\\'Unknown\\'", + "variable_revision_number:7", + "show_window:True", + "notes:\\x5B\\'Track the embryos across images using the Overlap method\\x3A tracked objects are identified by the amount of frame-to-frame overlap. Save an image of embryos labeled with a unique number across time.\\'\\x5D", + "batch_state:array(\\x5B\\x5D, dtype=uint8)", + "enabled:True", + "wants_pause:False]\n", + ] + ) return result def build_main_block(input_params): - result = INDENTATION.join([f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n", - f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n" - ]) + result = INDENTATION.join( + [ + f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n", + f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n", + ] + ) - tracking_method = get_json_value(input_params, 'con_tracking_method.tracking_method') + tracking_method = get_json_value( + input_params, "con_tracking_method.tracking_method" + ) obj_measurement = "None" # default value if tracking_method == "Measurements": - measurement_category = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement_category') - measurement = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement') + measurement_category = get_json_value( + input_params, + "con_tracking_method.con_measurement_category.measurement_category", + ) + measurement = get_json_value( + input_params, "con_tracking_method.con_measurement_category.measurement" + ) if measurement_category == "Intensity" or measurement_category == "Location": - img_measure = get_json_value(input_params, 'con_tracking_method.con_measurement_category.img_measure') + img_measure = get_json_value( + input_params, "con_tracking_method.con_measurement_category.img_measure" + ) obj_measurement = f"{measurement_category}_{measurement}_{img_measure}" else: obj_measurement = f"{measurement_category}_{measurement}" - result += INDENTATION.join([f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"]) + result += INDENTATION.join( + [ + f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n" + ] + ) if tracking_method == "LAP": # no max distance required, set default for pipeline max_distance = 50 else: - max_distance = get_json_value(input_params, 'con_tracking_method.max_distance') + max_distance = get_json_value(input_params, "con_tracking_method.max_distance") - result += INDENTATION.join([f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"]) + result += INDENTATION.join( + [f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"] + ) - display_option = get_json_value(input_params, 'con_tracking_method.display_option') + display_option = get_json_value(input_params, "con_tracking_method.display_option") output_img_name = "TrackedCells" # default value, required by cppipe regardless of its presence in UI - save = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.save_coded_img') + save = get_json_value( + input_params, "con_tracking_method.con_save_coded_img.save_coded_img" + ) if save == "Yes": - output_img_name = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.name_output_img') + output_img_name = get_json_value( + input_params, "con_tracking_method.con_save_coded_img.name_output_img" + ) result += INDENTATION.join( - [f"{INDENTATION}Select display option:{display_option}\n", - f"Save color-coded image?:{save}\n", - f"Name the output image:{output_img_name}\n" - ]) + [ + f"{INDENTATION}Select display option:{display_option}\n", + f"Save color-coded image?:{save}\n", + f"Name the output image:{output_img_name}\n", + ] + ) # LAP method default values movement_model = "Both" @@ -85,67 +112,113 @@ # LAP method if tracking_method == "LAP": - movement_model = get_json_value(input_params, 'con_tracking_method.movement_method') - no_std = get_json_value(input_params, 'con_tracking_method.no_std_radius') - radius_limit_max = get_json_value(input_params, 'con_tracking_method.max_radius') - radius_limit_min = get_json_value(input_params, 'con_tracking_method.min_radius') + movement_model = get_json_value( + input_params, "con_tracking_method.movement_method" + ) + no_std = get_json_value(input_params, "con_tracking_method.no_std_radius") + radius_limit_max = get_json_value( + input_params, "con_tracking_method.max_radius" + ) + radius_limit_min = get_json_value( + input_params, "con_tracking_method.min_radius" + ) radius = f"{radius_limit_min},{radius_limit_max}" - run_second = get_json_value(input_params, 'con_tracking_method.con_second_lap.second_lap') + run_second = get_json_value( + input_params, "con_tracking_method.con_second_lap.second_lap" + ) if run_second == "Yes": - gap_closing = get_json_value(input_params, 'con_tracking_method.con_second_lap.gap_closing') - split_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.split_alt') - merge_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.merge_alt') - max_gap_displacement = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_gap_displacement') - max_split = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_split') - max_merge = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_merge') - max_temporal = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_temporal') - max_mitosis_dist = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_mitosis_distance') - mitosis_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.mitosis_alt') + gap_closing = get_json_value( + input_params, "con_tracking_method.con_second_lap.gap_closing" + ) + split_alt = get_json_value( + input_params, "con_tracking_method.con_second_lap.split_alt" + ) + merge_alt = get_json_value( + input_params, "con_tracking_method.con_second_lap.merge_alt" + ) + max_gap_displacement = get_json_value( + input_params, "con_tracking_method.con_second_lap.max_gap_displacement" + ) + max_split = get_json_value( + input_params, "con_tracking_method.con_second_lap.max_split" + ) + max_merge = get_json_value( + input_params, "con_tracking_method.con_second_lap.max_merge" + ) + max_temporal = get_json_value( + input_params, "con_tracking_method.con_second_lap.max_temporal" + ) + max_mitosis_dist = get_json_value( + input_params, "con_tracking_method.con_second_lap.max_mitosis_distance" + ) + mitosis_alt = get_json_value( + input_params, "con_tracking_method.con_second_lap.mitosis_alt" + ) result += INDENTATION.join( - [f"{INDENTATION}Select the movement model:{movement_model}\n", - f"Number of standard deviations for search radius:{no_std}\n", - f"Search radius limit, in pixel units (Min,Max):{radius}\n", - f"Run the second phase of the LAP algorithm?:{run_second}\n", - f"Gap closing cost:{gap_closing}\n", - f"Split alternative cost:{split_alt}\n", - f"Merge alternative cost:{merge_alt}\n", - f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n", - f"Maximum split score:{max_split}\n", - f"Maximum merge score:{max_merge}\n", - f"Maximum temporal gap, in frames:{max_temporal}\n" - ]) + [ + f"{INDENTATION}Select the movement model:{movement_model}\n", + f"Number of standard deviations for search radius:{no_std}\n", + f"Search radius limit, in pixel units (Min,Max):{radius}\n", + f"Run the second phase of the LAP algorithm?:{run_second}\n", + f"Gap closing cost:{gap_closing}\n", + f"Split alternative cost:{split_alt}\n", + f"Merge alternative cost:{merge_alt}\n", + f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n", + f"Maximum split score:{max_split}\n", + f"Maximum merge score:{max_merge}\n", + f"Maximum temporal gap, in frames:{max_temporal}\n", + ] + ) # common section - filter_by_lifetime = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.filter_by_lifetime') + filter_by_lifetime = get_json_value( + input_params, "con_tracking_method.con_filter_by_lifetime.filter_by_lifetime" + ) use_min = "Yes" # default min_life = 1 # default use_max = "No" # default max_life = 100 # default if filter_by_lifetime == "Yes": - use_min = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.use_min') + use_min = get_json_value( + input_params, + "con_tracking_method.con_filter_by_lifetime.con_use_min.use_min", + ) if use_min == "Yes": - min_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime') + min_life = get_json_value( + input_params, + "con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime", + ) - use_max = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.use_max') + use_max = get_json_value( + input_params, + "con_tracking_method.con_filter_by_lifetime.con_use_max.use_max", + ) if use_max == "Yes": - max_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime') + max_life = get_json_value( + input_params, + "con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime", + ) result += INDENTATION.join( - [f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n", - f"Filter using a minimum lifetime?:{use_min}\n", - f"Minimum lifetime:{min_life}\n", - f"Filter using a maximum lifetime?:{use_max}\n", - f"Maximum lifetime:{max_life}\n" - ]) + [ + f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n", + f"Filter using a minimum lifetime?:{use_min}\n", + f"Minimum lifetime:{min_life}\n", + f"Filter using a maximum lifetime?:{use_max}\n", + f"Maximum lifetime:{max_life}\n", + ] + ) # print 2 leftover from LAP result += INDENTATION.join( - [f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n", - f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n" - ]) + [ + f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n", + f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n", + ] + ) # Follow Neighbors # defaults @@ -155,32 +228,36 @@ weight_of_area_diff = 25.0 if tracking_method == "Follow Neighbors": - avg_cell_diameter = get_json_value(input_params, 'con_tracking_method.avg_diameter') - use_adv = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.adv_parameter') + avg_cell_diameter = get_json_value( + input_params, "con_tracking_method.avg_diameter" + ) + use_adv = get_json_value( + input_params, "con_tracking_method.con_adv_parameter.adv_parameter" + ) if use_adv == "Yes": - cost_of_cell = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.cost') - weight_of_area_diff = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.weight') + cost_of_cell = get_json_value( + input_params, "con_tracking_method.con_adv_parameter.cost" + ) + weight_of_area_diff = get_json_value( + input_params, "con_tracking_method.con_adv_parameter.weight" + ) result += INDENTATION.join( - [f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n", - f"Use advanced configuration parameters:{use_adv}\n", - f"Cost of cell to empty matching:{cost_of_cell}\n", - f"Weight of area difference in function matching cost:{weight_of_area_diff}\n" - ]) + [ + f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n", + f"Use advanced configuration parameters:{use_adv}\n", + f"Cost of cell to empty matching:{cost_of_cell}\n", + f"Weight of area difference in function matching cost:{weight_of_area_diff}\n", + ] + ) result = result.rstrip("\n") return result if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '-p', '--pipeline', - help='CellProfiler pipeline' - ) - parser.add_argument( - '-i', '--inputs', - help='JSON inputs from Galaxy' - ) + parser.add_argument("-p", "--pipeline", help="CellProfiler pipeline") + parser.add_argument("-i", "--inputs", help="JSON inputs from Galaxy") args = parser.parse_args() pipeline_lines = get_pipeline_lines(args.pipeline)