update _compute arguments and logic

iliauniiccocrevaluation.py

@@ -17,7 +17,7 @@ import datasets
 import evaluate
 
 # TODO: Add BibTeX citation
-from ocr_evaluation.evaluate.metrics import evaluate_by_words
+from ocr_evaluation.evaluate.metrics import evaluate_by_symbols, evaluate_by_words, evaluate_by_word_groups,
 from ocr_evaluation.ocr.fiftyone import FiftyOneOcr
 
 _CITATION = """\
@@ -181,16 +181,28 @@ class IliauniIccOCREvaluation(evaluate.Metric):
         # TODO: Download external resources if needed
         pass
 
-    def _compute(self, predictions, references):
+    def _compute(self, *, predictions=None, references=None, **kwargs):
         """Returns the scores"""
 
+        eval_method = "word"
+        if kwargs.get("eval_method", "word") in ["symbol", "word", "word_group"]:
+            eval_method = kwargs["eval_method"]
+
         assert len(predictions) == len(references)
         results = []
         for prediction, reference in zip(predictions, references):
-            prediction_df = FiftyOneOcr(data=prediction).get_word_annotations(convert_bbox=True)
-            reference_df = FiftyOneOcr(data=reference).get_word_annotations(convert_bbox=True)
+            prediction_df = FiftyOneOcr(data=prediction).get_detections(convert_bbox=True)
+            reference_df = FiftyOneOcr(data=reference).get_detections(convert_bbox=True)
+
+            if eval_method == "symbol":
+                result = evaluate_by_symbols(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
+            elif eval_method == "word":
+                result = evaluate_by_words(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
+            elif eval_method == "word_group":
+                result = evaluate_by_word_groups(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
+            else:
+                result = {}
 
-            result = evaluate_by_words(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
             results.append(result)
 
         return results