quick start

docs/GettingStarted/quick_start.rst

@@ -0,0 +1,102 @@
+.. _quick_start:
+
+Quickstart
+##########
+
+Installation
+************
+
+Create an Anaconda environment, and install `torch <https://pytorch.org/get-started/locally/>`_ (with GPU
+if you need training besides prediction). Check the following environment are installed as well
+(use Python 3.11 if you work with SPIMquant environment):
+
+.. code-block::
+    python = ">=3.9"
+    numpy = ">=1.23"
+    nibabel = ">=5.2.1"
+    pillow = ">=7.1.0"
+    scipy = ">=1.12.0"
+    matplotlib = ">=3.9"
+    scikit-image = ">=0.22.0"
+    napari = ">=0.4.19"
+    zarr = ">=2.17.0"
+    dask = ">=2024.2.0"
+    dask-image = ">=2024.5.3"
+    ome-zarr = ">=0.9.0"
+    fsspec = ">=2024.6.1"
+    nnunetv2 = ">=2.5.1"
+
+Then :code:`pip install cvpl_tools` to finish the installation.
+
+OME ZARR
+********
+
+Create an example OME ZARR, write it to disk and read back:
+
+.. code-block:: Python
+    import dask.array as da
+    import cvpl_tools.ome_zarr.io as ome_io
+    import napari
+    import numpy as np
+    import asyncio
+
+    viewer = napari.Viewer(ndisplay=2)
+    da_array = da.from_array(np.arange(16).reshape((4, 4)))
+    print(f'print array:\n{da_array.compute()}')
+    asyncio.run(ome_io.write_ome_zarr_image('test.ome.zarr', da_arr=da_array))
+    read_back = ome_io.load_dask_array_from_path('test.ome.zarr', mode='r', level=0)  # always use level=0 for original resolution
+    print(f'read back:\n{read_back.compute()}')  # should print the same content
+    viewer.add_image(read_back, contrast_limits=[0, 15])
+    viewer.show(block=True)
+
+Read and write can be done on network location. An example of read and display:
+
+.. code-block:: Python
+    import cvpl_tools.ome_zarr.io as ome_io
+    import cvpl_tools.ome_zarr.napari.add as nadd  # read but only for display purpose
+    import napari
+
+    viewer = napari.Viewer(ndisplay=2)
+    OME_ZARR_PATH = 'gcs://khanlab-lightsheet/data/mouse_appmaptapoe/bids/sub-F1A1Te4/micr/sub-F1A1Te4_sample-brain_acq-blaze_SPIM.ome.zarr'
+    read_back = ome_io.load_dask_array_from_path(OME_ZARR_PATH, mode='r', level=0)
+    print(f'read back shape:{read_back.shape}')  # Reading metadata
+    nadd.group_from_path(viewer, OME_ZARR_PATH)
+    viewer.show(block=True)  # Displaying the image in Napari
+
+nn-UNet
+*******
+
+Download the training (o22)/testing (o23) annotations from Google Drive as follows:
+
+1. `o22 <https://drive.google.com/file/d/1S8xNdWD1pznAMaKsrjncBySkHVO5b3HR/view?usp=drive_link>`_
+
+2. `o23 <https://drive.google.com/file/d/1xhxLA0RnxoL3c1ojSGTCAIB5yp-Wfrgh/view?usp=drive_link>`_
+
+Put the canvas_o22.tiff file in the same folder as your script, then pair o22 annotation tiff file with the
+corresponding training input image volume and start training:
+
+.. code-block:: Python
+
+    from cvpl_tools.examples.mousebrain_processing import main, get_subject
+    import cvpl_tools.nnunet.triplanar as triplanar
+
+    SUBJECT_ID = 'o22'
+    SUBJECTS_DIR = f'subjects'
+    NNUNET_CACHE_DIR = f'nnunet_250epoch'
+    subject = get_subject(SUBJECT_ID, SUBJECTS_DIR, NNUNET_CACHE_DIR)
+    main(subject=subject, run_nnunet=False, run_coiled_process=False)
+
+    train_args = {
+        "cache_url": NNUNET_CACHE_DIR,
+        "train_im": subject.SECOND_DOWNSAMPLE_CORR_PATH,  # image
+        "train_seg": 'canvas_o22.tiff',  # label
+        "nepoch": 250,
+        "stack_channels": 0,
+        "triplanar": False,
+        "dataset_id": 1,
+        "fold": '0',
+        "max_threshold": 7500.,
+    }
+    triplanar.train_triplanar(train_args)
+
+

docs/index.rst

@@ -30,6 +30,7 @@ or on cloud.
    :caption: Contents:
 
    Introduction <self>
+   Quickstart <GettingStarted/quick_start>
    Viewing and IO of OME Zarr <GettingStarted/ome_zarr>
    Setting Up the Script <GettingStarted/setting_up_the_script>
    Defining Segmentation Pipeline <GettingStarted/segmentation_pipeline>

pyproject.toml

@@ -18,9 +18,17 @@ scikit-image = ">=0.22.0"
 napari = ">=0.4.19"
 zarr = ">=2.17.0"
 dask = ">=2024.2.0"
+dask-image = ">=2024.5.3"
 ome-zarr = ">=0.9.0"
 fsspec = ">=2024.6.1"
 
+# optional dependencies
+nnunetv2 = { version = ">=2.5.1", optional = true }
+
+
+[tool.poetry.extras]
+nnunet = ["nnunetv2"]
+
 
 [tool.poetry.dev-dependencies]
 mypy = ">=1.11.1"

src/cvpl_tools/examples/mousebrain_processing.py

@@ -70,9 +70,9 @@ def get_subject(SUBJECT_ID, SUBJECTS_DIR, NNUNET_CACHE_DIR):
         OME_ZARR_PATH = f'Z:/projects/lightsheet_lifecanvas/bids/sub-{MINIMUM_SUBJECT_ID}/micr/sub-{MINIMUM_SUBJECT_ID}_sample-brain_acq-prestitched_SPIM.ome.zarr'
         BA_CHANNEL = np.s_[1]
 
-    RUN_ON_FULL_IM = False
-    if not RUN_ON_FULL_IM:
-        BA_CHANNEL = np.s_[BA_CHANNEL, 256:512, :, :]  # **CHANGE THIS**
+    # RUN_ON_FULL_IM = False
+    # if not RUN_ON_FULL_IM:
+    #     BA_CHANNEL = np.s_[BA_CHANNEL, 256:512, :, :]  # **CHANGE THIS**
 
     subject.MINIMUM_SUBJECT_ID = MINIMUM_SUBJECT_ID
     subject.OME_ZARR_PATH = OME_ZARR_PATH

src/cvpl_tools/examples/test_cvpl_tools.py

@@ -0,0 +1,21 @@
+from cvpl_tools.examples.mousebrain_processing import main, get_subject
+import cvpl_tools.nnunet.triplanar as triplanar
+
+SUBJECT_ID = 'o22'
+SUBJECTS_DIR = f'subjects'
+NNUNET_CACHE_DIR = f'nnunet_250epoch'
+subject = get_subject(SUBJECT_ID, SUBJECTS_DIR, NNUNET_CACHE_DIR)
+main(subject=subject, run_nnunet=False, run_coiled_process=False)
+
+train_args = {
+    "cache_url": NNUNET_CACHE_DIR,
+    "train_im": subject.SECOND_DOWNSAMPLE_CORR_PATH,  # image
+    "train_seg": 'canvas_o22.tiff',  # label
+    "nepoch": 250,
+    "stack_channels": 0,
+    "triplanar": False,
+    "dataset_id": 1,
+    "fold": '0',
+    "max_threshold": 7500.,
+}
+triplanar.train_triplanar(train_args)