Basic tutorial (#308)

Add a basic in-memory postprocessor for watershed.

It doesn't seem to be working that well because prediction seems broken.
Here's a picture:

![Figure_1](https://github.com/user-attachments/assets/01c209e3-914e-44a2-85b4-70343b1a984f)

I expect the postprocessor to work significantly better if the
predictions are done with enough context to avoid the block artifacts
and the predictions are post processed properly so that high affinity
within objects leads to an affinity of 1 (it looks like its set to 0
here), and a low affinity in the background leads to an affinity of 0
(looks like it is set to 0.5 here)

dacapo/experiments/tasks/post_processors/watershed_post_processor.py

@@ -3,6 +3,10 @@
 from dacapo.blockwise.scheduler import segment_blockwise
 from dacapo.experiments.datasplits.datasets.arrays import ZarrArray
 from dacapo.store.array_store import LocalArrayIdentifier
+from dacapo.utils.array_utils import to_ndarray, save_ndarray
+from funlib.persistence import open_ds
+import daisy
+import mwatershed as mws
 
 from .watershed_post_processor_parameters import WatershedPostProcessorParameters
 from .post_processor import PostProcessor
@@ -123,29 +127,15 @@ def process(
             np.uint64,
             block_size * self.prediction_array.voxel_size,
         )
+        input_array = open_ds(
+            self.prediction_array_identifier.container.path,
+            self.prediction_array_identifier.dataset,
+        )
 
-        read_roi = Roi((0, 0, 0), self.prediction_array.voxel_size * block_size)
-        # run blockwise prediction
-        pars = {
-            "offsets": self.offsets,
-            "bias": parameters.bias,
-            "context": parameters.context,
-        }
-        segment_blockwise(
-            segment_function_file=str(
-                Path(Path(dacapo.blockwise.__file__).parent, "watershed_function.py")
-            ),
-            context=parameters.context,
-            total_roi=self.prediction_array.roi,
-            read_roi=read_roi.grow(parameters.context, parameters.context),
-            write_roi=read_roi,
-            num_workers=num_workers,
-            max_retries=2,  # TODO: make this an option
-            timeout=None,  # TODO: make this an option
-            ######
-            input_array_identifier=self.prediction_array_identifier,
-            output_array_identifier=output_array_identifier,
-            parameters=pars,
+        data = to_ndarray(input_array, output_array.roi)
+        segmentation = mws.agglom(
+            data - parameters.bias, offsets=self.offsets, randomized_strides=True
         )
+        save_ndarray(segmentation, self.prediction_array.roi, output_array)
 
-        return output_array
+        return output_array_identifier

docs/source/notebooks/minimal_tutorial.py

@@ -376,3 +376,42 @@
     ax[snapshot, 2].imshow(prediction[prediction.shape[0] // 2])
     ax[snapshot, 0].set_ylabel(f"Snapshot {snapshot_it}")
 plt.show()
+
+# %%
+# Visualize validations
+import zarr
+
+num_validations = run_config.num_iterations // run_config.validation_interval
+fig, ax = plt.subplots(num_validations, 4, figsize=(10, 2 * num_validations))
+
+# Set column titles
+column_titles = ["Raw", "Ground Truth", "Prediction", "Segmentation"]
+for col in range(len(column_titles)):
+    ax[0, col].set_title(column_titles[col])
+
+for validation in range(1, num_validations + 1):
+    dataset = run.datasplit.validate[0].name
+    validation_it = validation * run_config.validation_interval
+    # break
+    raw = zarr.open(
+        f"/Users/pattonw/dacapo/example_run/validation.zarr/inputs/{dataset}/raw"
+    )[:]
+    gt = zarr.open(
+        f"/Users/pattonw/dacapo/example_run/validation.zarr/inputs/{dataset}/gt"
+    )[0]
+    pred_path = f"/Users/pattonw/dacapo/example_run/validation.zarr/{validation_it}/ds_{dataset}/prediction"
+    out_path = f"/Users/pattonw/dacapo/example_run/validation.zarr/{validation_it}/ds_{dataset}/output/WatershedPostProcessorParameters(id=2, bias=0.5, context=(32, 32, 32))"
+    output = zarr.open(
+        out_path
+    )[:]
+    prediction = zarr.open(pred_path)[0]
+    print(raw.shape, gt.shape, output.shape)
+    c = (raw.shape[1] - gt.shape[1]) // 2
+    if c != 0:
+        raw = raw[:, c:-c, c:-c]
+    ax[validation - 1, 0].imshow(raw[raw.shape[0] // 2])
+    ax[validation - 1, 1].imshow(gt[gt.shape[0] // 2])
+    ax[validation - 1, 2].imshow(prediction[prediction.shape[0] // 2])
+    ax[validation - 1, 3].imshow(output[output.shape[0] // 2])
+    ax[validation - 1, 0].set_ylabel(f"Validation {validation_it}")
+plt.show()

pyproject.toml

@@ -78,6 +78,8 @@ docs = [
     "sphinx-click",
     "sphinx-rtd-theme",
     "myst-parser",
+    "matplotlib",
+    "pooch",
 ]
 examples = [
     "ipython",