windows fix hanging thread

Author: arch

funscript_editor/algorithms/funscriptgenerator.py

@@ -75,30 +75,7 @@ class FunscriptGeneratorParameter:
     max_threshold: float = float(HYPERPARAMETER['max_threshold'])
 
 
-def merge_score(item: list, number_of_trackers: int) -> list:
-    """ Merge score for given number of trackers
 
-    Note:
-        Python multiprocessing methods use a mp.SimpleQueue to pass tasks to the worker processes.
-        Everything that goes through the mp.SimpleQueue must be pickable.
-        In python functions are only picklable if they are defined at the top-level of a module.
-
-    Args:
-        item (list): score for each tracker
-        number_of_trackers (int): number of used tracker (pairs)
-
-    Returns:
-        list: merged score
-    """
-    if number_of_trackers == 1:
-        return item[0] if len(item) > 0 else []
-    else:
-        max_frame_number = max([len(item[i]) for i in range(number_of_trackers)])
-        arr = np.ma.empty((max_frame_number,number_of_trackers))
-        arr.mask = True
-        for tracker_number in range(number_of_trackers):
-            arr[:item[tracker_number].shape[0],tracker_number] = item[tracker_number]
-        return list(filter(None.__ne__, arr.mean(axis=1).tolist()))
 
 
 class FunscriptGeneratorThread(QtCore.QThread):
@@ -413,17 +390,26 @@ def calculate_score(self, bboxes) -> None:
                 score['y'][tracker_number] = np.array([max([x[1] for x in bboxes['Woman'][tracker_number]]) - w[1] for w in bboxes['Woman'][tracker_number]])
 
         self.logger.info("Merge Scores")
-        """
-        pool, queue = {}, {}
-        for metric in score.keys():
-            queue[metric] = mp.Queue()
-            pool[metric] = threading.Thread(target=merge_score, args=(score[metric], self.params.number_of_trackers, queue[metric], ))
-            pool[metric].start()
 
-        for metric in score.keys():
-            pool[metric].join()
-            score[metric] = queue[metric].get()
-        """
+        def merge_score(item: list, number_of_trackers: int) -> list:
+            """ Merge score for given number of trackers
+
+            Args:
+                item (list): score for each tracker
+                number_of_trackers (int): number of used tracker (pairs)
+
+            Returns:
+                list: merged score
+            """
+            if number_of_trackers == 1:
+                return item[0] if len(item) > 0 else []
+            else:
+                max_frame_number = max([len(item[i]) for i in range(number_of_trackers)])
+                arr = np.ma.empty((max_frame_number,number_of_trackers))
+                arr.mask = True
+                for tracker_number in range(number_of_trackers):
+                    arr[:item[tracker_number].shape[0],tracker_number] = item[tracker_number]
+                return list(filter(None.__ne__, arr.mean(axis=1).tolist()))
 
         for metric in score.keys():
             score[metric] = merge_score(score[metric], self.params.number_of_trackers)