Update

run_opencv.py

@@ -35,6 +35,10 @@ class ArgParser(Tap):
 
     id2label: Optional[str] = "labels/kinetics_400.json"
 
+    threshold: Optional[float] = 10.0
+
+    max_confidence: Optional[float] = 20.0  # Set None if not scale
+
 
 class ActivityModel:
     def __init__(self, args: ArgParser):
@@ -77,9 +81,12 @@ class ActivityModel:
         max_index = logits.argmax(-1).item()
         predicted_label = self.model.config.id2label[max_index]
 
-        img_container.frame_rate.label = (
-            f"{predicted_label}_{logits[0][max_index]:.2f}%"
-        )
+        confidence = logits[0][max_index]
+
+        if (self.args.threshold is None) or (
+            self.args.threshold is not None and confidence >= self.args.threshold
+        ):
+            img_container.frame_rate.label = f"{predicted_label}_{confidence:.2f}%"
 
         # logits = np.squeeze(logits)
         logits = logits.squeeze().numpy()
@@ -105,7 +112,7 @@ class ActivityModel:
 
 def main(args: ArgParser):
     activity_model = ActivityModel(args)
-    img_container = ImgContainer(activity_model.frames_per_video)
+    img_container = ImgContainer(activity_model.frames_per_video, args.is_recording)
 
     num_skips = 0
 

utils/img_container.py

@@ -7,13 +7,13 @@ from .frame_rate import FrameRate
 
 
 class ImgContainer:
-    def __init__(self, frames_per_video: int = 8) -> None:
+    def __init__(self, frames_per_video: int = 8, is_recording: bool = False) -> None:
         self.img: Optional[np.ndarray] = None  # raw image
         self.frame_rate: FrameRate = FrameRate()
         self.imgs: List[np.ndarray] = []
         self.frames_per_video = frames_per_video
         self.rs: Optional[DataFrame] = None
-        self.is_recording: bool = False
+        self.is_recording = is_recording
 
     def add_frame(self, frame: np.ndarray) -> None:
         if len(self.imgs) >= self.frames_per_video: