Blobdetect lc

docs/getting_started/installation.md

@@ -30,13 +30,6 @@ pip install -e git+https://github.com/khanlab/spimquant#egg=spimquant
 
 Note: you can re-run this command to re-install with the latest version
 
-Before running the app, you need to specify a config file to use. "SPIMquant/examples/snakebids_template.yml" 
-provides a starting point for specifying a config. If you are using the example dataset provided in the 
-above section, then a config file is also included in the zip file.
-
-To specify the config, copy the config file into SPIMquant/spimquant/config/snakebids.yml, and change the 
-properties in the config file to ensure paths to the directory are properly set.
-
 ## Running the app
 
 Do a dry-run first (`-n`) and simply print (`-p`) what would be run:

docs/usage/cli.md

@@ -1,8 +1,43 @@
-## Core command-line interface
+## The `spimquant` Interface
 
 The spimquant tool command-line interface is a composition of the core 
 (app-based) arguments and options, and the options related to Snakemake. 
 
+As an end user, command line interface (CLI) is recommended as a way to specify 
+the input/output data paths and configuration settings. The arguments passed 
+in this way will be used to overwrite some of the default options defined in 
+the config file `spimquant/config/snakebids.yml` and adapt to different datasets. 
+
+Underlying SPIMquant, we use `snakemake` to manage workflow and config file. 
+`spimquant` is a program that writes to the config file, and supports 
+all Snakemake arguments. 
+
+Steps to run a typical job looks like the following:
+```bash
+spimquant bids_dir output_dir {participant} [snakemake_args]
+# now since we run spimquant, contents in config/snakebids.yml has changed
+# and we can run snakemake with the updated configuration
+snakemake result_file_path
+```
+All three arguments above to `spimquant` are not optional but necessary. 
+The above first command will do the following:
+1. Log the configuration used to run snakemake in the location output_dir, 
+   including the snakebids.yml file and logs.
+2. Pass bids_dir over to overwrite the bids_dir defined in the default 
+   snakebids.yml. 
+3. Start snakemake using the snakemake_args provided
+
+A more concrete example:
+```bash
+spimquant test_bids_dir test_out participant
+snakemake results/result_image.nii -n
+```
+Here we use -n so you can run this command but snakemake will only output a 
+plan without generating any file. In an actual run, you can use `--cores all` 
+to specify to use all cores and `--use-apptainer` to download and use existing 
+container image for SPIMquant that have already installed all dependencies, 
+without having to set up the environment yourself.
+
 The core BIDS App arguments and app-specific options are listed below. 
 
 

spimquant/workflow/rules/blobdetect.smk

@@ -311,10 +311,9 @@ rule negatively_masked_counting:
     Work in progress
     """
     input:
-        zarr='/path_to_bids_root/bids/sub-onuska21/micr/sub-onuska21_sample_brain_acq-prestitched_SPIM.ome.zarr?slices=[1]',
-        neg_mask='/path_to_bids_root/resources/onuska21_patched.tiff',
+        neg_mask='C:/ProgrammingTools/ComputerVision/RobartsResearch/data/lightsheet/spimquant_ubuntu/resources/onuska21_patched.tiff',
     params:
-        neg_mask_scale=(1, 1, 1),
+        zarr='C:/ProgrammingTools/ComputerVision/RobartsResearch/data/lightsheet/spimquant_ubuntu/bids/sub-onuska21/micr/sub-onuska21_sample_brain_acq-prestitched_SPIM.ome.zarr?slices=[1]',
         tmp_path=f'{config["output_dir"]}/tmp'
     output:
         found_lc=directory(f'{config["output_dir"]}/found_lc')

spimquant/workflow/scripts/contour_counting/negatively_masked_counting.py

@@ -19,6 +19,7 @@
     import cvpl_tools.im.process.bs_to_os as sp_bs_to_os
     import cvpl_tools.im.process.os_to_cc as sp_os_to_cc
     import cvpl_tools.im.process.any_to_any as sp_any_to_any
+    import cvpl_tools.im.algs.dask_resize as im_resize
     import cvpl_tools.ome_zarr.io as ome_io
     import cvpl_tools.im.ndblock as ndblock
     import dask.array as da
@@ -52,10 +53,11 @@ def forward(self, im, cptr, viewer_args: dict = None):
                              client_args=dict(threads_per_worker=12, n_workers=1),
                              viewer_args=dict(use_viewer=False)) as plc:
         # DO DASK COMPUTATION, AND SHOW RESULTS IN plc.viewer
-        src_im = ome_io.load_dask_array_from_path(snakemake.input.zarr, mode='r', level=0)
+        src_im = ome_io.load_dask_array_from_path(snakemake.params.zarr, mode='r', level=0)
         pipeline = Pipeline()
         src_im = da.clip(src_im / 1000, 0., 1.)
         assert src_im.ndim == 3
+        print(f'Saving results in {plc.cache_root.abs_path}')
         print(f'Computing centroids size and location. Masking the image, imshape={src_im.shape}.')
         src_im = src_im.rechunk(chunks=(128, 256, 256))
         storage_options = dict(
@@ -72,12 +74,14 @@ def forward(self, im, cptr, viewer_args: dict = None):
             storage_options=storage_options
         )
 
-        up_sampler = sp_any_to_any.UpsamplingByIntFactor(factor=snakemake.params.neg_mask_scale, order=0)
-
         def compute_masking():
             neg_mask = da.from_array(tifffile.imread(snakemake.input.neg_mask), chunks=(64, 64, 64))
-            neg_mask = up_sampler.forward(neg_mask, cptr=plc.cache_root.cache('neg_mask_upsampling'),
-                                          viewer_args=viewer_args | dict(is_label=True))
+            neg_mask = im_resize.upsample_pad_crop_fit(
+                src_arr=neg_mask,
+                tgt_arr=src_im,
+                cptr=plc.cache_root.cache('neg_mask_upsampling'),
+                viewer_args=viewer_args | dict(is_label=True),
+            )
             return src_im * (1 - neg_mask)
         plc.cache_root.cache_im(compute_masking, cid='masked_src_im', viewer_args=viewer_args)
         cc = pipeline.forward(src_im, plc.cache_root.cache(cid='global_label'), viewer_args=viewer_args)