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main.py

@@ -73,15 +73,16 @@ class TransNetV2Torch:
 
         predictions = []
 
-        for inp in input_iterator():
-            single_frame_pred, all_frames_pred = self.predict_raw(inp)
-            predictions.append((single_frame_pred[0, 25:75, 0],
-                                all_frames_pred[0, 25:75, 0]))
-
-            print("\r[TransNetV2] Processing video frames {}/{}".format(
-                min(len(predictions) * 50, total), total
-            ), end="")
-        print("")
+        with tqdm(total=total, desc="[TransNetV2] Processing video frames", unit="frames") as pbar:
+            for inp in input_iterator():
+                single_frame_pred, all_frames_pred = self.predict_raw(inp)
+                predictions.append((single_frame_pred[0, 25:75, 0],
+                                    all_frames_pred[0, 25:75, 0]))
+
+                processed = min(len(predictions) * 50, total)
+                pbar.n = processed
+                pbar.last_print_n = processed
+                pbar.refresh()
 
         single_frame_pred = np.concatenate([single_ for single_, _ in predictions])
         all_frames_pred = np.concatenate([all_ for _, all_ in predictions])