Mercurial > repos > bgruening > cp_mask_image

comparison track_objects.py @ 4:3a05117669cf draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 35da2dcd86747c9bff138e100dbe08c6106f3780"
author bgruening
date Sat, 06 Feb 2021 10:02:20 +0000
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children b178453ea8d1
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3:ca1421f7e3ed 4:3a05117669cf
1 #!/usr/bin/env python
2
3 import argparse
4 import json
5
6 from cp_common_functions import get_json_value
7 from cp_common_functions import get_pipeline_lines
8 from cp_common_functions import get_total_number_of_modules
9 from cp_common_functions import INDENTATION
10 from cp_common_functions import update_module_count
11 from cp_common_functions import write_pipeline
12
13 MODULE_NAME = "TrackObjects"
14 OUTPUT_FILENAME = "output.cppipe"
15
16
17 def build_header(module_name, module_number):
18 result = "|".join([f"{module_name}:[module_num:{module_number}",
19 "svn_version:\\'Unknown\\'",
20 "variable_revision_number:7",
21 "show_window:True",
22 "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",
23 "batch_state:array(\\x5B\\x5D, dtype=uint8)",
24 "enabled:True",
25 "wants_pause:False]\n"])
26 return result
27
28
29 def build_main_block(input_params):
30 result = INDENTATION.join([f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n",
31 f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n"
32 ])
33
34 tracking_method = get_json_value(input_params, 'con_tracking_method.tracking_method')
35
36 obj_measurement = "None" # default value
37 if tracking_method == "Measurements":
38 measurement_category = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement_category')
39 measurement = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement')
40
41 if measurement_category == "Intensity" or measurement_category == "Location":
42 img_measure = get_json_value(input_params, 'con_tracking_method.con_measurement_category.img_measure')
43 obj_measurement = f"{measurement_category}_{measurement}_{img_measure}"
44 else:
45 obj_measurement = f"{measurement_category}_{measurement}"
46
47 result += INDENTATION.join([f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"])
48
49 if tracking_method == "LAP": # no max distance required, set default for pipeline
50 max_distance = 50
51 else:
52 max_distance = get_json_value(input_params, 'con_tracking_method.max_distance')
53
54 result += INDENTATION.join([f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"])
55
56 display_option = get_json_value(input_params, 'con_tracking_method.display_option')
57
58 output_img_name = "TrackedCells" # default value, required by cppipe regardless of its presence in UI
59 save = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.save_coded_img')
60 if save == "Yes":
61 output_img_name = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.name_output_img')
62
63 result += INDENTATION.join(
64 [f"{INDENTATION}Select display option:{display_option}\n",
65 f"Save color-coded image?:{save}\n",
66 f"Name the output image:{output_img_name}\n"
67 ])
68
69 # LAP method default values
70 movement_model = "Both"
71 no_std = 3.0
72 radius_limit_max = 10.0
73 radius_limit_min = 2.0
74 radius = "2.0,10.0"
75 run_second = "Yes"
76 gap_closing = 40
77 split_alt = 40
78 merge_alt = 40
79 max_gap_displacement = 5
80 max_split = 50
81 max_merge = 50
82 max_temporal = 5
83 max_mitosis_dist = 40
84 mitosis_alt = 80
85
86 # LAP method
87 if tracking_method == "LAP":
88 movement_model = get_json_value(input_params, 'con_tracking_method.movement_method')
89 no_std = get_json_value(input_params, 'con_tracking_method.no_std_radius')
90 radius_limit_max = get_json_value(input_params, 'con_tracking_method.max_radius')
91 radius_limit_min = get_json_value(input_params, 'con_tracking_method.min_radius')
92 radius = f"{radius_limit_min},{radius_limit_max}"
93
94 run_second = get_json_value(input_params, 'con_tracking_method.con_second_lap.second_lap')
95 if run_second == "Yes":
96 gap_closing = get_json_value(input_params, 'con_tracking_method.con_second_lap.gap_closing')
97 split_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.split_alt')
98 merge_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.merge_alt')
99 max_gap_displacement = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_gap_displacement')
100 max_split = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_split')
101 max_merge = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_merge')
102 max_temporal = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_temporal')
103 max_mitosis_dist = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_mitosis_distance')
104 mitosis_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.mitosis_alt')
105
106 result += INDENTATION.join(
107 [f"{INDENTATION}Select the movement model:{movement_model}\n",
108 f"Number of standard deviations for search radius:{no_std}\n",
109 f"Search radius limit, in pixel units (Min,Max):{radius}\n",
110 f"Run the second phase of the LAP algorithm?:{run_second}\n",
111 f"Gap closing cost:{gap_closing}\n",
112 f"Split alternative cost:{split_alt}\n",
113 f"Merge alternative cost:{merge_alt}\n",
114 f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n",
115 f"Maximum split score:{max_split}\n",
116 f"Maximum merge score:{max_merge}\n",
117 f"Maximum temporal gap, in frames:{max_temporal}\n"
118 ])
119
120 # common section
121 filter_by_lifetime = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.filter_by_lifetime')
122 use_min = "Yes" # default
123 min_life = 1 # default
124 use_max = "No" # default
125 max_life = 100 # default
126
127 if filter_by_lifetime == "Yes":
128 use_min = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.use_min')
129 if use_min == "Yes":
130 min_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime')
131
132 use_max = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.use_max')
133 if use_max == "Yes":
134 max_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime')
135
136 result += INDENTATION.join(
137 [f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n",
138 f"Filter using a minimum lifetime?:{use_min}\n",
139 f"Minimum lifetime:{min_life}\n",
140 f"Filter using a maximum lifetime?:{use_max}\n",
141 f"Maximum lifetime:{max_life}\n"
142 ])
143
144 # print 2 leftover from LAP
145 result += INDENTATION.join(
146 [f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n",
147 f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n"
148 ])
149
150 # Follow Neighbors
151 # defaults
152 avg_cell_diameter = 35.0
153 use_adv = "No"
154 cost_of_cell = 15.0
155 weight_of_area_diff = 25.0
156
157 if tracking_method == "Follow Neighbors":
158 avg_cell_diameter = get_json_value(input_params, 'con_tracking_method.avg_diameter')
159 use_adv = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.adv_parameter')
160 if use_adv == "Yes":
161 cost_of_cell = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.cost')
162 weight_of_area_diff = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.weight')
163
164 result += INDENTATION.join(
165 [f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n",
166 f"Use advanced configuration parameters:{use_adv}\n",
167 f"Cost of cell to empty matching:{cost_of_cell}\n",
168 f"Weight of area difference in function matching cost:{weight_of_area_diff}\n"
169 ])
170
171 return result
172
173
174 if __name__ == "__main__":
175 parser = argparse.ArgumentParser()
176 parser.add_argument(
177 '-p', '--pipeline',
178 help='CellProfiler pipeline'
179 )
180 parser.add_argument(
181 '-i', '--inputs',
182 help='JSON inputs from Galaxy'
183 )
184 args = parser.parse_args()
185
186 pipeline_lines = get_pipeline_lines(args.pipeline)
187 inputs_galaxy = json.load(open(args.inputs, "r"))
188
189 current_module_num = get_total_number_of_modules(pipeline_lines)
190 current_module_num += 1
191 pipeline_lines = update_module_count(pipeline_lines, current_module_num)
192
193 header_block = build_header(MODULE_NAME, current_module_num)
194 main_block = build_main_block(inputs_galaxy)
195
196 module_pipeline = f"\n{header_block}{main_block}\n"
197 pipeline_lines.append(module_pipeline)
198
199 write_pipeline(OUTPUT_FILENAME, pipeline_lines)