Update 2d annotator to use automatic SAM instance segmentation by default

Author: constantinpape

examples/sam_annotator_2d.py

@@ -2,22 +2,30 @@
 from micro_sam.sam_annotator import annotator_2d
 
 
-# TODO describe how to get the data and don't use hard-coded system path
+# TODO describe how to get the data and don't use hard-coded path
 def livecell_annotator():
     im = imageio.imread(
         "/home/pape/Work/data/incu_cyte/livecell/images/livecell_test_images/A172_Phase_C7_1_01d04h00m_4.tif"
     )
     embedding_path = "./embeddings/embeddings-livecell_cropped.zarr"
-    annotator_2d(im, embedding_path, show_embeddings=True)
+    annotator_2d(im, embedding_path, show_embeddings=False)
+
+
+# This runs interactive 2d annotation for data from the cell tracking challenge:
+# It uses the training data for the HeLA dataset. You can download the data from
+# http://data.celltrackingchallenge.net/training-datasets/DIC-C2DH-HeLa.zip
+def hela_2d_annotator():
+    im = imageio.imread("./data/DIC-C2DH-HeLa/train/01/t011.tif")
+    embedding_path = "./embeddings/embeddings-hela2d.zarr"
+    annotator_2d(im, embedding_path, show_embeddings=False)
 
 
 def main():
     # 2d annotator for livecell data
-    livecell_annotator()
+    # livecell_annotator()
 
-    # TODO
     # 2d annotator for cell tracking challenge hela data
-    # hela_2d_annotator()
+    hela_2d_annotator()
 
 
 if __name__ == "__main__":

examples/sam_annotator_tracking.py

@@ -3,7 +3,7 @@
 
 
 # This runs the interactive tracking annotator for data from the cell tracking challenge:
-# It uses the training data for the HeLA dataset. You can download the data via
+# It uses the training data for the HeLA dataset. You can download the data from
 # http://data.celltrackingchallenge.net/training-datasets/DIC-C2DH-HeLa.zip
 def track_ctc_data():
     path = "./data/DIC-C2DH-HeLa/train/01"

micro_sam/sam_annotator/annotator_2d.py

@@ -5,8 +5,8 @@
 from napari import Viewer
 
 from .. import util
+from .. import segment_instances
 from ..visualization import project_embeddings_for_visualization
-from ..segment_instances import segment_from_embeddings
 from ..segment_from_prompts import segment_from_points
 from .util import (
     commit_segmentation_widget, create_prompt_menu, prompt_layer_to_points, toggle_label, LABEL_COLOR_CYCLE
@@ -21,20 +21,26 @@ def segment_wigdet(v: Viewer):
     v.layers["current_object"].refresh()
 
 
-# TODO enable choosing setting the segmentation method and setting other params
-@magicgui(call_button="Segment All Objects")
-def autosegment_widget(v: Viewer):
-    # choose if we segment with/without tiling based on the image shape
-    seg = segment_from_embeddings(PREDICTOR, IMAGE_EMBEDDINGS)
+# TODO expose more parameters
+@magicgui(call_button="Segment All Objects", method={"choices": ["default", "sam", "embeddings"]})
+def autosegment_widget(v: Viewer, method: str = "default"):
+    if method in ("default", "sam"):
+        print("Run automatic segmentation with SAM. This can take a few minutes ...")
+        image = v.layers["raw"].data
+        seg = segment_instances.segment_instances_sam(SAM, image)
+    elif method == "embeddings":
+        seg = segment_instances.segment_instances_from_embeddings(PREDICTOR, IMAGE_EMBEDDINGS)
+    else:
+        raise ValueError
     v.layers["auto_segmentation"].data = seg
     v.layers["auto_segmentation"].refresh()
 
 
 def annotator_2d(raw, embedding_path=None, show_embeddings=False, segmentation_result=None):
     # for access to the predictor and the image embeddings in the widgets
-    global PREDICTOR, IMAGE_EMBEDDINGS
+    global PREDICTOR, IMAGE_EMBEDDINGS, SAM
 
-    PREDICTOR = util.get_sam_model()
+    PREDICTOR, SAM = util.get_sam_model(return_sam=True)
     IMAGE_EMBEDDINGS = util.precompute_image_embeddings(PREDICTOR, raw, save_path=embedding_path, ndim=2)
     util.set_precomputed(PREDICTOR, IMAGE_EMBEDDINGS)