Update app.py

app.py

@@ -11,7 +11,6 @@ import gradio as gr
 # from huggingface_hub import snapshot_download
 import os
 import cv2
-import time
 
 
 PATH_TO_LABELS = 'data/label_map.pbtxt'   
@@ -41,7 +40,7 @@ def predict(image_np):
         category_index,
         use_normalized_coordinates=True,
         max_boxes_to_draw=200,
-        min_score_thresh=.60,
+        min_score_thresh=.7,
         agnostic_mode=False,
         line_thickness=2
     )
@@ -53,6 +52,7 @@ def predict_on_video(video_in_filepath, video_out_filepath, detection_model, cat
     frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
     frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
     fps = video_reader.get(cv2.CAP_PROP_FPS)
+    
     video_writer = cv2.VideoWriter(
         video_out_filepath,
         cv2.VideoWriter_fourcc(*'mp4v'),
@@ -64,20 +64,14 @@ def predict_on_video(video_in_filepath, video_out_filepath, detection_model, cat
         if not ret:
             break  # Break the loop if the video is finished
         
-        start_time = time.time()
         processed_frame = predict(frame)
-        end_time = time.time()
-        processing_time = end_time - start_time
         processed_frame_np = np.array(processed_frame)
         video_writer.write(processed_frame_np)
         
-        # Adjust the delay based on processing time
-        delay_time = max(1, int((1 / fps - processing_time) * 1000))
-        cv2.waitKey(delay_time)
     video_reader.release()
     video_writer.release()
     cv2.destroyAllWindows()
-    cv2.waitKey(5)
+    cv2.waitKey(1)
 
 # Function to process a video
 def process_video(video_path):