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neurodata · Nov 8, 2023 · 19efbaf · 19efbaf
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diff --git a/experiments/BrainLine/axon_analysis.ipynb b/experiments/BrainLine/axon_analysis.ipynb
diff --git a/experiments/BrainLine/data/axon_data.json b/experiments/BrainLine/data/axon_data.json
@@ -232,7 +232,8 @@
                 "url": "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=30O_uxkW67rv2Q",
                 "layer": "val"
             },
-            "subtype": "gad2 vgat"
+            "subtype": "gad2 vgat",
+            "transformed_mask": "precomputed://s3://smartspim-precomputed-volumes/2023_05_24/MS9/axon_mask_transformed"
         },
         "MS33": {
             "base": "precomputed://s3://smartspim-precomputed-volumes/2023_09_18/MS33/",
@@ -245,7 +246,8 @@
                 "url": "/Users/thomasathey/Documents/mimlab/mouselight/ailey/detection_axon/brainMS33/train.json",
                 "layer": "train"
             },
-            "subtype": "tph2 gad2"
+            "subtype": "tph2 gad2",
+            "transformed_mask": "precomputed://s3://smartspim-precomputed-volumes/2023_09_18/MS33/axon_mask_transformed"
         },
         "pytest": {
             "subtype": "test_type"

diff --git a/experiments/BrainLine/data/soma_data.json b/experiments/BrainLine/data/soma_data.json
@@ -305,7 +305,8 @@
                 "somas_layer": "soma_val",
                 "nonsomas_layer": "nonsoma_val"
             },
-            "subtype": "gad2 vgat"
+            "subtype": "gad2 vgat",
+            "somas_atlas_url": "https://ara.viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=ebbZ0XEKgFjHKg"
         }
     },
     "brain2centers": {

diff --git a/experiments/BrainLine/scripts/upload_mask.py b/experiments/BrainLine/scripts/upload_mask.py
@@ -0,0 +1,66 @@
+from cloudvolume import CloudVolume
+from brainlit.BrainLine.apply_ilastik import ApplyIlastik_LargeImage
+from brainlit.BrainLine.util import _get_corners
+import json
+import numpy as np
+from pathlib import Path
+import os
+from tqdm import tqdm
+
+local_vol = CloudVolume("precomputed://file:///mnt/data/Neuroglancer_Data/2023_04_10/MS12/axon_mask")
+
+brain_id = "MS12"
+antibody_layer = "Ch_647"
+background_layer = "Ch_561"
+endogenous_layer = "Ch_488"
+
+brainline_exp_dir = Path(os.getcwd()) / Path(__file__).parents[1]
+data_dir = (
+    brainline_exp_dir / "data" / "brain_temp"
+)  # data_dir = "/data/tathey1/matt_wright/brain_temp/"  # directory to store temporary subvolumes for segmentation
+data_file = brainline_exp_dir / "data" / "axon_data.json"
+layer_names = [antibody_layer, background_layer, endogenous_layer]
+chunk_size = [1024, 1024, 1024]  # [256, 256, 300]
+ncpu = 2
+
+
+ilastik_path = "/Applications/ilastik-1.4.0b21-OSX.app/Contents/ilastik-release/run_ilastik.sh"  # "/data/tathey1/matt_wright/ilastik/ilastik-1.4.0rc5-Linux/run_ilastik.sh"  # path to ilastik executable
+ilastik_project = brainline_exp_dir / "data" / "models" / "axon" / "axon_segmentation.ilp"  # "/data/tathey1/matt_wright/ilastik/model1/axon_segmentation.ilp"  # path to ilastik
+ilastik_path = "/home/user/Documents/ilastik-1.4.0-Linux/run_ilastik.sh"
+
+alli = ApplyIlastik_LargeImage(
+    ilastik_path=ilastik_path,
+    ilastik_project=ilastik_project,
+    ncpu=ncpu,
+    data_file=data_file,
+)
+
+
+
+with open(data_file) as f:
+    data = json.load(f)
+object_type = data["object_type"]
+brain2paths = data["brain2paths"]
+
+volume_base_dir_write = brain2paths[brain_id]["base_s3"]
+volume_base_dir_read = brain2paths[brain_id]["base_local"]
+mask_dir = volume_base_dir_write + "axon_mask"
+
+sample_path = volume_base_dir_read + layer_names[1]
+vol = CloudVolume(sample_path, parallel=True, mip=0, fill_missing=True)
+
+
+try:
+    CloudVolume(mask_dir)
+except:
+    assert np.all([c_ilastik % c_vol == 0 for c_ilastik, c_vol in zip(chunk_size, [128, 128, 2])])
+    alli._make_mask_info(mask_dir, vol, [128, 128, 2])
+
+s3_vol = CloudVolume(mask_dir)
+
+corners = _get_corners(local_vol.shape, local_vol.chunks)
+
+for corner in tqdm(corners):
+    s3_vol[corner[0][0]:corner[1][0],corner[0][1]:corner[1][1],corner[0][2]:corner[1][2]] = local_vol[corner[0][0]:corner[1][0],corner[0][1]:corner[1][1],corner[0][2]:corner[1][2]]
+
+