add fiftyone example in readme

README.md

@@ -34,25 +34,46 @@ To get started with horizon-metrics, make sure you have the necessary dependenci
 This is how you can quickly evaluate your horizon prediction models using SEA-AI/horizon-metrics:
 
 ```python
+
 import evaluate
 
+# 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]],
-                      [[0.0, 0.536953125], [1.0, 0.4922265625]],
-                      [[0.0, 0.5364453125], [1.0, 0.4919140625]]]
+                       [[0.0, 0.53796875], [1.0, 0.4928515625]],
+                       [[0.0, 0.5374609375], [1.0, 0.4925390625]],
+                       [[0.0, 0.536953125], [1.0, 0.4922265625]],
+                       [[0.0, 0.5364453125], [1.0, 0.4919140625]]]
 
 prediction_points = [[[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
-                    [[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
-                    [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]],
-                    [[0.0, 0.5200016849393765], [1.0, 0.4728554579177664]],
-                    [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]]]
-
+                     [[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
+                     [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]],
+                     [[0.0, 0.5200016849393765], [1.0, 0.4728554579177664]],
+                     [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]]]
+
+# 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
+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
+polylines_pred = sequence_view.values(
+    "frames.ahoy-IR-b2-whales__XAVIER-AGX-JP46_pl")
+prediction_points = [
+    line["polylines"][0]["points"][0] for line in polylines_pred[0]
+    if line is not None
+]
 
 module = evaluate.load("SEA-AI/horizon-metrics")
 module.add(predictions=ground_truth_points, references=prediction_points)
 module.compute()
-
 ```
 
 This is output the evalutaion metrics for your horizon prediciton model: