feat: Add CARE dataset

.napari-hub/DESCRIPTION.md

@@ -5,3 +5,21 @@ you wish not to do any customization for the napari hub listing.
 If you need some help writing a good description, check out our 
 [guide](https://github.com/chanzuckerberg/napari-hub/wiki/Writing-the-Perfect-Description-for-your-Plugin)
 -->
+CAREamics is a PyTorch library aimed at simplifying the use of Noise2Void and its many
+variants and cousins (CARE, Noise2Noise, N2V2, P(P)N2V, HDN, muSplit etc.).
+
+## Why CAREamics?
+
+Noise2Void is a widely used denoising algorithm, and is readily available from the `n2v`
+python package. However, `n2v` is based on TensorFlow, while more recent methods
+denoising methods (PPN2V, DivNoising, HDN) are all implemented in PyTorch, but are
+lacking the extra features that would make them usable by the community.
+
+The aim of CAREamics is to provide a PyTorch library reuniting all the latest methods
+in one package, while providing a simple and consistent API. The library relies on
+PyTorch Lightning as a back-end. In addition, we will provide extensive documentation and
+tutorials on how to best apply these methods in a scientific context.
+
+## Installation and use
+
+Check out the [documentation](https://careamics.github.io/) for installation instructions and guides!

src/careamics_napari/napari.yaml

@@ -5,13 +5,25 @@ contributions:
     - id: careamics-napari.make_train_widget
       python_name: careamics_napari.training_plugin:TrainPluginWrapper
       title: Train CAREamics
-    - id: careamics-napari.data_SEM
+    - id: careamics-napari.data_SEM_N2V
       python_name: careamics_napari.sample_data:n2v_sem_data
       title: Noise2Void SEM
+    - id: careamics-napari.data_SEM_N2N
+      python_name: careamics_napari.sample_data:n2n_sem_data
+      title: Noise2Noise SEM
+    - id: careamics-napari.data_U2OS_CARE
+      python_name: careamics_napari.sample_data:care_u2os_data
+      title: CARE U2OS
   widgets:
     - command: careamics-napari.make_train_widget
       display_name: Train CAREamics
   sample_data:
-    - command: careamics-napari.data_SEM
-      display_name: Download data (SEM)
+    - command: careamics-napari.data_SEM_N2V
+      display_name: Download data for N2V (SEM)
       key: careamics_n2v_SEM
+    - command: careamics-napari.data_SEM_N2N
+      display_name: Download data for N2N (SEM)
+      key: careamics_n2n_SEM
+    - command: careamics-napari.data_U2OS_CARE
+      display_name: Download data for CARE (U2OS)
+      key: careamics_care_U2OS

src/careamics_napari/sample_data.py

@@ -1,5 +1,6 @@
 """Sample data for the careamics napari plugin."""
 
+import numpy as np
 from careamics.utils import get_careamics_home
 from careamics_portfolio import PortfolioManager
 from napari.types import LayerDataTuple
@@ -36,8 +37,37 @@ def _load_sem_n2n() -> list[tuple[NDArray, dict[str, str]]]:
     files = [f for f in download if f.endswith("tif")]
     files.sort()
 
-    img_train = imread(files[2])
-    img_target = imread(files[3])
+    imgs_train = imread(files[1])
+    img_train = imgs_train[2]
+    img_target = imgs_train[3]
+
+    return [(img_train, {"name": "train"}), (img_target, {"name": "target"})]
+
+
+def _load_u2os_care() -> list[tuple[NDArray, dict[str, str]]]:
+    """Download CARE U2OS data.
+
+    Returns
+    -------
+    list of (numpy.ndarray, dict)
+        List of data and layer name.
+    """
+    root_path = get_careamics_home()
+    PortfolioManager().denoising.CARE_U2OS.download(root_path)
+
+    # path to the training data
+    root_path = root_path / "denoising-CARE_U2OS.unzip" / "data" / "U2OS"
+    train_path = root_path / "train" / "low"
+    target_path = root_path / "train" / "GT"
+
+    # load the training data
+    files_gt = list(target_path.glob("*.tif"))
+    files_gt.sort()
+    img_target = np.array([imread(f) for f in files_gt])
+
+    files_low = list(train_path.glob("*.tif"))
+    files_low.sort()
+    img_train = np.array([imread(f) for f in files_low])
 
     return [(img_train, {"name": "train"}), (img_target, {"name": "target"})]
 
@@ -88,3 +118,26 @@ def n2n_sem_data() -> LayerDataTuple:
     """
     ntf.show_info("Downloading data might take a few minutes.")
     return _load_sem_n2n()
+
+
+def care_u2os_data() -> LayerDataTuple:
+    """Load CARE U2OS data.
+
+    Returns
+    -------
+    LayerDataTuple
+        Data and layer name.
+    """
+    ntf.show_info("Downloading data might take a few minutes.")
+    return _load_u2os_care()
+
+
+if __name__ == "__main__":
+
+    import napari
+
+    # create a Viewer
+    viewer = napari.Viewer()
+
+    # start UI
+    napari.run()

tests/test_samples.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+from careamics_napari.sample_data import n2n_sem_data, n2v_sem_data, u2os_care_data
+
+
+def test_n2v_sem_data():
+    data = n2v_sem_data()
+    assert len(data) == 2
+    assert all(isinstance(d, np.ndarray) for d, _ in data)
+
+
+def test_n2n_sem_data():
+    data = n2n_sem_data()
+    assert len(data) == 2
+    assert all(isinstance(d, np.ndarray) for d, _ in data)
+
+
+def test_u2os_care_data():
+    data = u2os_care_data()
+    assert len(data) == 2
+    assert all(isinstance(d, np.ndarray) for d, _ in data)