Clean up

Author: constantinpape

doc/start_page.md

@@ -19,7 +19,7 @@ Please cite our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2024.
 ## Requirements & Installation
 
 SynapseNet was tested on all operating systems (Linux, Mac, Windows).
-SynapseNet requires a GPU or a Macbooc with M chipset for the segmentation of 3D volumes. 
+SynapseNet requires a GPU or a Macbook with M chipset for the segmentation of 3D volumes.
 
 You need a [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) or [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) installation. Follow the instruction at the respective links if you have installed neither. We assume you have `conda` for the rest of the instructions. After installing it, you can use the `conda` command.
 

synapse_net/inference/util.py

@@ -366,7 +366,6 @@ def inference_helper(
         assert len(input_files) == len(mask_files)
 
     for i, img_path in tqdm(enumerate(input_files), total=len(input_files), desc="Processing files"):
-        print("!!!!!!!!!!!!!!!!!1 start segmentation for", img_path)
         # Determine the output file name.
         input_folder, input_name = os.path.split(img_path)
 
@@ -419,7 +418,6 @@ def inference_helper(
             with open_file(output_path, "a") as f:
                 f.create_dataset(output_key, data=segmentation, compression="gzip")
 
-        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11 segmentation done")
         print(f"Saved segmentation to {output_path}.")