update readme

README.md

@@ -35,9 +35,10 @@ This is how you can quickly evaluate your horizon prediction models using SEA-AI
 
 ```python
 
+
 import evaluate
 
-# Use artificial data for testing or
+#Use artificial data for testing or
 ground_truth_points = [[[0.0, 0.5384765625], [1.0, 0.4931640625]],
                        [[0.0, 0.53796875], [1.0, 0.4928515625]],
                        [[0.0, 0.5374609375], [1.0, 0.4925390625]],
@@ -50,20 +51,20 @@ prediction_points = [[[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
                      [[0.0, 0.5200016849393765], [1.0, 0.4728554579177664]],
                      [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]]]
 
-# Load data from fiftyone
+#Load data from fiftyone
 sequence = "Sentry_2023_02_Portugal_2023_01_24_19_15_17"
 dataset_name = "SENTRY_VIDEOS_DATASET_QA"
 sequence_view = fo.load_dataset(dataset_name).match(F("sequence") == sequence)
 sequence_view = sequence_view.select_group_slices("thermal_wide")
 
-# Get the ground truth points
+#Get the ground truth points
 polylines_gt = sequence_view.values("frames.ground_truth_pl")
 ground_truth_points = [
     line["polylines"][0]["points"][0] for line in polylines_gt[0]
     if line is not None
 ]
 
-# Get the predicted points
+#Get the predicted points
 polylines_pred = sequence_view.values(
     "frames.ahoy-IR-b2-whales__XAVIER-AGX-JP46_pl")
 prediction_points = [