Update README.md

Author: anna-grim

README.md

@@ -66,34 +66,19 @@ Note: Misalignments between the ground truth graphs and prediction segmentation
 Here is a simple example of evaluating a predicted segmentation.
 
 ```python
-from segmentation_skeleton_metrics.utils import util
 from segmentation_skeleton_metrics.skeleton_metric import SkeletonMetric
 from segmentation_skeleton_metrics.utils.img_util import TiffReader
 
 
 def evaluate():
-    # Initializations
     segmentation = TiffReader(segmentation_path)
     skeleton_metric = SkeletonMetric(
         groundtruth_pointer,
         segmentation,
         fragments_pointer=fragments_pointer,
         output_dir=output_dir,
     )
-    full_results, avg_results = skeleton_metric.run()
-
-    # Report results
-    print(f"\nAveraged Results...")
-    for key in avg_results.keys():
-        print(f"   {key}: {round(avg_results[key], 4)}")
-
-    print(f"\nTotal Results...")
-    print("# splits:", skeleton_metric.count_total_splits())
-    print("# merges:", skeleton_metric.count_total_merges())
-
-    # Save results
-    path = f"{output_dir}/evaluation_results.xls"
-    util.save_results(path, full_results)
+    full_results, avg_results = skeleton_metric.run(results_path)
 
 
 if __name__ == "__main__":
@@ -102,6 +87,7 @@ if __name__ == "__main__":
     segmentation_path = "./pred_labels.tif"
     fragments_pointer = "./pred_swcs.zip"
     groundtruth_pointer = "./target_swcs.zip"
+    results_path = path = f"{output_dir}/results.xls"
 
     # Run
     evaluate()