Mercurial > repos > imgteam > superdsm
changeset 3:7fd8dba15bd3 draft
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/superdsm/ commit fea6c8161c4b3e6394fe035b12b69b73e6fa7d75
| author | imgteam |
|---|---|
| date | Thu, 16 Nov 2023 12:29:41 +0000 |
| parents | 244f67290d28 |
| children | dc5f72f6b1e9 |
| files | run-superdsm.py superdsm.xml test-data/cfg.tsv |
| diffstat | 3 files changed, 158 insertions(+), 22 deletions(-) [+] |
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--- a/run-superdsm.py Mon Nov 13 22:12:35 2023 +0000 +++ b/run-superdsm.py Thu Nov 16 12:29:41 2023 +0000 @@ -7,6 +7,7 @@ """ import argparse +import csv import imghdr import os import pathlib @@ -45,14 +46,25 @@ return cfg +def flatten_dict(d, sep='/'): + result = {} + for key, val in d.items(): + if isinstance(val, dict): + for sub_key, sub_val in flatten_dict(val, sep=sep).items(): + result[f'{key}{sep}{sub_key}'] = sub_val + else: + result[key] = val + return result + + if __name__ == "__main__": parser = argparse.ArgumentParser(description='Segmentation of cell nuclei in 2-D fluorescence microscopy images') - parser.add_argument('image', help='Path to the input image') - parser.add_argument('cfg', help='Path to the file containing the configuration') - parser.add_argument('masks', help='Path to the file containing the segmentation masks') - parser.add_argument('overlay', help='Path to the file containing the overlay of the segmentation results') - parser.add_argument('seg_border', type=int) + parser.add_argument('image', type=str, help='Path to the input image') parser.add_argument('slots', type=int) + parser.add_argument('--do-masks', type=str, default=None, help='Path to the file containing the segmentation masks') + parser.add_argument('--do-cfg', type=str, default=None, help='Path to the file containing the configuration') + parser.add_argument('--do-overlay', type=str, default=None, help='Path to the file containing the overlay of the segmentation results') + parser.add_argument('--do-overlay-border', type=int) for key, ptype in hyperparameters: parser.add_argument('--' + get_param_name(key), type=ptype, default=None) args = parser.parse_args() @@ -83,13 +95,26 @@ pipeline = superdsm.pipeline.create_default_pipeline() cfg = create_config(args) img = superdsm.io.imread(img_filepath) - data, cfg, _ = superdsm.automation.process_image(pipeline, cfg, img) - with open(args.cfg, 'w') as fp: - cfg.dump_json(fp) + if args.do_cfg: + print(f'Writing config to: {args.do_cfg}') + cfg, _ = superdsm.automation.create_config(pipeline, cfg, img) + with open(args.do_cfg, 'w') as fp: + tsv_out = csv.writer(fp, delimiter='\t') + tsv_out.writerow(['Hyperparameter', 'Value']) + for key, value in flatten_dict(cfg.entries).items(): + tsv_out.writerow([key, value]) - overlay = superdsm.render.render_result_over_image(data, border_width=args.seg_border, normalize_img=False) - superdsm.io.imwrite(args.overlay, overlay) + if args.do_overlay or args.do_masks: + print('Performing segmentation') + data, cfg, _ = pipeline.process_image(img, cfg) - masks = superdsm.render.rasterize_labels(data) - superdsm.io.imwrite(args.masks, masks) + if args.do_overlay: + print(f'Writing overlay to: {args.do_overlay}') + overlay = superdsm.render.render_result_over_image(data, border_width=args.do_overlay_border, normalize_img=False) + superdsm.io.imwrite(args.do_overlay, overlay) + + if args.do_masks: + print(f'Writing masks to: {args.do_masks}') + masks = superdsm.render.rasterize_labels(data) + superdsm.io.imwrite(args.do_masks, masks)
--- a/superdsm.xml Mon Nov 13 22:12:35 2023 +0000 +++ b/superdsm.xml Thu Nov 16 12:29:41 2023 +0000 @@ -1,5 +1,9 @@ -<tool id="ip_superdsm" name="Perform segmentation using deformable shape models" version="0.1.3+galaxy2" profile="20.05"> +<tool id="ip_superdsm" name="Perform segmentation using deformable shape models" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05"> <description>with SuperDSM</description> + <macros> + <token name="@TOOL_VERSION@">0.1.3</token> + <token name="@VERSION_SUFFIX@">3</token> + </macros> <edam_operations> <edam_operation>operation_3443</edam_operation> </edam_operations> @@ -18,11 +22,22 @@ <![CDATA[ python '$__tool_directory__/run-superdsm.py' '${dataset}' - 'cfg.json' - 'masks.png' - 'overlay.png' - $seg_border \${GALAXY_SLOTS:-4} + #if 'masks' in $outputs: + --do-masks 'masks.png' + #end if + #if 'cfg' in $outputs: + --do-cfg 'cfg.tsv' + #end if + #if 'overlay' in $outputs: + --do-overlay 'overlay.png' + #if $seg_border.value % 2 == 1: + #set $seg_border = "%d" % ($seg_border.value + 1) + --do-overlay-border $seg_border + #else: + --do-overlay-border $seg_border + #end if + #end if #if str($config.AF_scale) != '': --AF_scale '${config.AF_scale}' #end if @@ -72,7 +87,12 @@ </environment_variables> <inputs> <param name="dataset" type="data" format="tiff,png" label="Dataset" /> - <param name="seg_border" type="integer" min="1" value="8" label="Width of the outlines (in pixels) of the segmentation results (overlays)" /> + <param name="outputs" type="select" label="Outputs" multiple="true" optional="false"> + <option value="overlay" selected="true">Create a segmentation overlay</option> + <option value="masks">Create a label map (e.g., for further processing)</option> + <option value="cfg">Report all hyperparameters (manually set and automatically determined values)</option> + </param> + <param name="seg_border" type="integer" min="1" value="8" label="Width of the outlines (in pixels)" help="This parameter is only used for segmentation overlays (see above)." /> <section name="config" title="Hyperparameters" expanded="false"> <param argument="--AF_scale" optional="true" type="float" value="" min="0" label="scale σ" help="The scale of the objects to be segmented. Leave empty to use the automatically determined value." /> <param argument="--c2f_region_analysis_min_atom_radius" optional="true" type="float" value="" min="0" label="min_atom_radius" help="No region determined by the Coarse-to-fine region analysis scheme is smaller than a circle of this radius (in terms of the surface area). Leave empty to use the automatically determined value." /> @@ -91,14 +111,27 @@ </section> </inputs> <outputs> - <data format="json" name="cfg" from_work_dir="cfg.json" label="${tool.name} on ${on_string}: cfg" /> - <data format="png" name="masks" from_work_dir="masks.png" label="${tool.name} on ${on_string}: masks" /> - <data format="png" name="overlay" from_work_dir="overlay.png" label="${tool.name} on ${on_string}: overlay" /> + <data format="png" name="masks" from_work_dir="masks.png" label="${tool.name} on ${on_string}: masks"> + <filter>'masks' in outputs</filter> + </data> + <data format="tsv" name="cfg" from_work_dir="cfg.tsv" label="${tool.name} on ${on_string}: cfg"> + <filter>'cfg' in outputs</filter> + </data> + <data format="png" name="overlay" from_work_dir="overlay.png" label="${tool.name} on ${on_string}: overlay"> + <filter>'overlay' in outputs</filter> + </data> </outputs> <tests> - <test> + <test expect_num_outputs="3"> <param name="dataset" value="BBBC033_C2_z28.png" /> + <param name="outputs" value="overlay,masks,cfg" /> <output name="overlay" value="overlay.png" ftype="png" compare="sim_size" /> + <output name="cfg" value="cfg.tsv" ftype="tsv" compare="sim_size" /> + </test> + <test expect_num_outputs="1"> + <param name="dataset" value="BBBC033_C2_z28.png" /> + <param name="outputs" value="cfg" /> + <output name="cfg" value="cfg.tsv" ftype="tsv" compare="sim_size" /> </test> </tests> <help>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cfg.tsv Thu Nov 16 12:29:41 2023 +0000 @@ -0,0 +1,78 @@ +Hyperparameter Value +c2f_region_analysis_min_norm_energy_improvement 0.1 +c2f_region_analysis_max_atom_norm_energy 0.05 +c2f_region_analysis_max_cluster_marker_irregularity 0.2 +dsm_AF_alpha 0.0005 +global_energy_minimization_AF_beta 0.66 +postprocess_mask_max_distance 1 +postprocess_mask_stdamp 2.0 +postprocess_max_norm_energy 0.2 +postprocess_min_contrast 1.35 +postprocess_min_object_radius 0.0 +AF_scale +preprocess/AF_sigma2 1.0 +preprocess/sigma2 65.05382386916237 +preprocess/enabled True +preprocess/sigma1 1.4142135623730951 +preprocess/offset_clip 3 +preprocess/lower_clip_mean False +dsm/AF_alpha 0.0005 +dsm/alpha 2.116 +dsm/AF_smooth_amount 0.2 +dsm/smooth_amount 13 +dsm/AF_smooth_subsample 0.4 +dsm/smooth_subsample 26 +dsm/AF_background_margin 0.4 +dsm/background_margin 26 +dsm/enabled True +dsm/cachesize 1 +dsm/cachetest +dsm/sparsity_tol 0 +dsm/init elliptical +dsm/epsilon 1.0 +dsm/scale 1000 +dsm/gaussian_shape_multiplier 2 +dsm/smooth_mat_dtype float32 +dsm/smooth_mat_max_allocations inf +dsm/cp_timeout 300 +c2f-region-analysis/AF_min_atom_radius 0.33 +c2f-region-analysis/min_atom_radius 30 +c2f-region-analysis/enabled True +c2f-region-analysis/seed_connectivity 8 +c2f-region-analysis/max_atom_norm_energy 0.05 +c2f-region-analysis/min_norm_energy_improvement 0.1 +c2f-region-analysis/max_cluster_marker_irregularity 0.2 +global-energy-minimization/AF_beta 0.66 +global-energy-minimization/beta 2793.1200000000003 +global-energy-minimization/AF_max_seed_distance inf +global-energy-minimization/max_seed_distance inf +global-energy-minimization/enabled True +global-energy-minimization/strict True +global-energy-minimization/max_iter 5 +global-energy-minimization/gamma 0.8 +global-energy-minimization/max_work_amount 1000000 +postprocess/AF_min_object_radius 0.0 +postprocess/min_object_radius 0.0 +postprocess/AF_max_object_radius inf +postprocess/max_object_radius inf +postprocess/AF_min_glare_radius inf +postprocess/min_glare_radius inf +postprocess/enabled True +postprocess/max_norm_energy 0.2 +postprocess/discard_image_boundary False +postprocess/min_boundary_obj_radius 0 +postprocess/max_eccentricity 0.99 +postprocess/max_boundary_eccentricity inf +postprocess/exterior_scale 5 +postprocess/exterior_offset 5 +postprocess/min_contrast 1.35 +postprocess/contrast_epsilon 0.0001 +postprocess/mask_stdamp 2 +postprocess/mask_max_distance 1 +postprocess/mask_smoothness 3 +postprocess/fill_holes True +postprocess/glare_detection_smoothness 3 +postprocess/glare_detection_num_layers 5 +postprocess/glare_detection_min_layer 0.5 +postprocess/min_boundary_glare_radius inf +histological False