Mercurial > repos > bgruening > cp_gray_to_color
view track_objects.py @ 4:31d195bedf6d draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 35da2dcd86747c9bff138e100dbe08c6106f3780"
author | bgruening |
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date | Sat, 06 Feb 2021 09:56:58 +0000 |
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children | ad271f78a87a |
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#!/usr/bin/env python 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 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"]) 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" ]) 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') if measurement_category == "Intensity" or measurement_category == "Location": 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"]) 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') 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') 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') if save == "Yes": 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" ]) # LAP method default values movement_model = "Both" no_std = 3.0 radius_limit_max = 10.0 radius_limit_min = 2.0 radius = "2.0,10.0" run_second = "Yes" gap_closing = 40 split_alt = 40 merge_alt = 40 max_gap_displacement = 5 max_split = 50 max_merge = 50 max_temporal = 5 max_mitosis_dist = 40 mitosis_alt = 80 # 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') radius = f"{radius_limit_min},{radius_limit_max}" 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') 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" ]) # common section 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') if use_min == "Yes": 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') if use_max == "Yes": 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" ]) # 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" ]) # Follow Neighbors # defaults avg_cell_diameter = 35.0 use_adv = "No" cost_of_cell = 15.0 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') 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') 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" ]) 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' ) args = parser.parse_args() pipeline_lines = get_pipeline_lines(args.pipeline) inputs_galaxy = json.load(open(args.inputs, "r")) current_module_num = get_total_number_of_modules(pipeline_lines) current_module_num += 1 pipeline_lines = update_module_count(pipeline_lines, current_module_num) header_block = build_header(MODULE_NAME, current_module_num) main_block = build_main_block(inputs_galaxy) module_pipeline = f"\n{header_block}{main_block}\n" pipeline_lines.append(module_pipeline) write_pipeline(OUTPUT_FILENAME, pipeline_lines)