diff --git a/docs/GettingStarted/quick_start.rst b/docs/GettingStarted/quick_start.rst new file mode 100644 index 0000000..2bbf3f7 --- /dev/null +++ b/docs/GettingStarted/quick_start.rst @@ -0,0 +1,102 @@ +.. _quick_start: + +Quickstart +########## + +Installation +************ + +Create an Anaconda environment, and install `torch `_ (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 `_ + +2. `o23 `_ + +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) + + diff --git a/docs/index.rst b/docs/index.rst index c3df58f..4a22c5f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -30,6 +30,7 @@ or on cloud. :caption: Contents: Introduction + Quickstart Viewing and IO of OME Zarr Setting Up the Script Defining Segmentation Pipeline diff --git a/pyproject.toml b/pyproject.toml index e04c907..7b74a92 100644 --- a/pyproject.toml +++ b/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" diff --git a/src/cvpl_tools/examples/mousebrain_processing.py b/src/cvpl_tools/examples/mousebrain_processing.py index bdf5fb7..ebb0ee6 100644 --- a/src/cvpl_tools/examples/mousebrain_processing.py +++ b/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 diff --git a/src/cvpl_tools/examples/test_cvpl_tools.py b/src/cvpl_tools/examples/test_cvpl_tools.py new file mode 100644 index 0000000..294983f --- /dev/null +++ b/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)