Merge pull request #75 from clamsproject/develop

Releasing 4.0
clamsproject · Feb 27, 2024 · 710f527 · 710f527
2 parents 573077a + 448142e
commit 710f527
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Showing 2 changed files with 4 additions and 16 deletions.
diff --git a/app.py b/app.py
@@ -68,16 +68,14 @@ def _annotate(self, mmif: Union[str, dict, Mmif], **parameters) -> Mmif:
             timeframe_annotation = new_view.new_annotation(AnnotationTypes.TimeFrame)
             timeframe_annotation.add_property("label", tf.label),
             timeframe_annotation.add_property('classification', {tf.label: tf.score})
-            #timeframe_annotation.add_property("score", tf.score)
-            #timeframe_annotation.add_property("scores", tf.scores)
             timepoint_annotations = []
             for prediction in tf.targets:
                 timepoint_annotation = new_view.new_annotation(AnnotationTypes.TimePoint)
                 prediction.annotation = timepoint_annotation
                 scores = [prediction.score_for_label(lbl) for lbl in prediction.labels]
-                label = self._label_with_highest_score(prediction.labels, scores)
                 classification = {l:s for l, s in zip(prediction.labels, scores)}
                 classification = self._transform(classification, bins)
+                label = max(classification, key=classification.get)
                 timepoint_annotation.add_property('timePoint', prediction.timepoint)
                 timepoint_annotation.add_property('label', label)
                 timepoint_annotation.add_property('classification', classification)
@@ -107,14 +105,6 @@ def _export_parameters(self, parameters: dict):
             elif parameter == "minFrameCount":
                 self.stitcher.min_frame_count = value
 
-    @staticmethod
-    def _label_with_highest_score(labels: list, scores: list) -> str:
-        """Return the label associated with the highest scores. The score for 
-        labels[i] is scores[i]."""
-        # TODO: now the NEG scores are included, perhaps not do that
-        sorted_scores = list(sorted(zip(scores, labels), reverse=True))
-        return sorted_scores[0][1]
-
     @staticmethod
     def _transform(classification: dict, bins: dict):
         """Take the raw classification and turn it into a classification of user

diff --git a/metadata.py b/metadata.py
@@ -27,12 +27,10 @@ def appmetadata() -> AppMetadata:
         url="https://github.com/clamsproject/app-swt-detection"
     )
 
+    labels = ['bars', 'slate', 'chyron', 'credits', 'NEG']
     metadata.add_input(DocumentTypes.VideoDocument, required=True)
-    metadata.add_output(AnnotationTypes.TimeFrame, timeUnit='milliseconds', frameType='bars')
-    metadata.add_output(AnnotationTypes.TimeFrame, timeUnit='milliseconds', frameType='slate')
-    metadata.add_output(AnnotationTypes.TimeFrame, timeUnit='milliseconds', frameType='chyron')
-    metadata.add_output(AnnotationTypes.TimeFrame, timeUnit='milliseconds', frameType='credits')
-    metadata.add_output(AnnotationTypes.TimePoint, timeUnit='milliseconds')
+    metadata.add_output(AnnotationTypes.TimeFrame, timeUnit='milliseconds', labelset=labels)
+    metadata.add_output(AnnotationTypes.TimePoint, timeUnit='milliseconds', labelset=labels)
 
     # TODO: defaults are the same as in modeling/config/classifier.yml, which is possibly
     # not a great idea, should perhaps read defaults from the configuration file. There is