replace 11-5 with 7-13

Author: linok-bc

ves_seg/README.md

@@ -21,21 +21,25 @@ To continue retraining from a checkpoint, use:
 
 Inference tools are available in `tools/process.py`. To do inference, in a Python shell or file, run:
 
-`> do_inference(<im_path>, <pred_path>, [<base_config.yaml>, <bcd_config.yaml>], <checkpoint_path>)`
+`do_inference(<im_path>, <pred_path>, [<base_config.yaml>, <bcd_config.yaml>], <checkpoint_path>)`
 
 To also get adapted rand, precison, and recall metrics, run:
 
-`> infer_and_stats([<im_path1>, ...], [<pred_path1>, ...], [<mask_path1, ...>], [<base_config.yaml>, <bcd_config.yaml>], <checkpoint_path>), 
+`infer_and_stats([<im_path1>, ...], [<pred_path1>, ...], [<mask_path1, ...>], [<base_config.yaml>, <bcd_config.yaml>], <checkpoint_path>), 
 `
 
 Our final model checkpoint is provided at `outputs/checkpoint_1000000.pth.tar`.
 
 ## Visualiztion
 
-Visualing predictions is done with neuroglancer. To load a volume and its segmentation, modify the file `scripts/ng.py`, then run `python -i scripts/ng.py`. A link to view neuroglancer will open. If running on a remote cluster, please note that port forwarding will be necessary to view on your machine. The `screnshot()` function can be used to take high-resolution screenshots of the neuroglancer representation.
+Visualing predictions is done with neuroglancer. To load a volume and its segmentation, modify the file `scripts/ng.py`, then run `python -i scripts/ng.py`. A link to view neuroglancer will open. If running on a remote cluster, please note that port forwarding will be necessary to view on your machine. The `screenshot()` function can be used to take high-resolution screenshots of the neuroglancer representation.
 
 Further documentation for both functions is contained in `tools/process.py`
 
 ## Sample
 
-A small sample is prepared in the `sample` directory. `11-5` shows a small inter-neuron region with an assortment of large vesicles. Neuroglancer scripts that display the CLAHE-enhanced images, neuron mask, ground truth, and predictions with the final model checkpoints are prepared in `sample/11-5_ng.py`.
+A small sample is prepared in the `sample` directory. `7-13` shows a small soma region with large vesicles. To generate large vesicle predictions in the regionm, run the following command from within the root directory:
+
+`python tools/process.py`
+
+After inference, a neuroglancer script that display the CLAHE-enhanced images, neuron mask, ground truth, and predictions with the final model checkpoints can be viewed by running `sample/ng.py` from within the root directory.

ves_seg/sample/11-5_mask.h5

@@ -8,9 +8,8 @@
 from skimage import morphology
 
 import neuroglancer
-ip = 'localhost' # f'{hostname}.cm.cluster'
-port = 9098 # change to an unused port number
-neuroglancer.set_server_bind_address(bind_address=ip,bind_port=port)
+port = 9098
+neuroglancer.set_server_bind_address(bind_address='localhost',bind_port=port)
 viewer=neuroglancer.Viewer()
 
 def screenshot(path='temp.png', save=True, show=True, size=None):
@@ -24,8 +23,8 @@ def screenshot(path='temp.png', save=True, show=True, size=None):
         plt.imshow(ss)
         plt.show()
 
-data_dir = '.'
-vol_name = '11-5'
+data_dir = 'sample'
+vol_name = '7-13'
 
 clahe = readvol(os.path.join(data_dir, vol_name+"_clahe.h5"))
 mask = readvol(os.path.join(data_dir, vol_name+"_mask.h5"))

ves_seg/sample/11-5_pred.h5

@@ -281,3 +281,6 @@ def infer_and_stats(
 
     # return the list of metrics
     return metric_list
+
+if __name__ == '__main__':
+   do_inference('sample/7-13_clahe.h5', 'sample/7-13_pred.h5', ['configs/03_base.yaml', 'configs/bcd_config.yaml'], 'outputs/checkpoint_1000000.pth.tar', show=True)