Update app.py

app.py

@@ -60,31 +60,31 @@ def predict_on_video(video_in_filepath, video_out_filepath, detection_model, cat
         fps,
         (frame_w, frame_h)
     )
-    # while True:
-    #     ret, frame = video_reader.read()
-    #     if not ret:
-    #         break  # Break the loop if the video is finished
+    while True:
+        ret, frame = video_reader.read()
+        if not ret:
+            break  # Break the loop if the video is finished
         
-    #     processed_frame = predict(frame)
-    #     processed_frame_np = np.array(processed_frame)
-    #     video_writer.write(processed_frame_np)
-    for i in tqdm(range(nb_frames)):
-        ret, image_np = video_reader.read()
-        input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
-        results = detection_model(input_tensor)
-        viz_utils.visualize_boxes_and_labels_on_image_array(
-                  image_np,
-                  results['detection_boxes'][0].numpy(),
-                  (results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
-                  results['detection_scores'][0].numpy(),
-                  category_index,
-                  use_normalized_coordinates=True,
-                  max_boxes_to_draw=200,
-                  min_score_thresh=.50,
-                  agnostic_mode=False,
-                  line_thickness=2)
+        processed_frame = predict(frame)
+        processed_frame_np = np.array(processed_frame)
+        video_writer.write(processed_frame_np)
+    # for i in tqdm(range(nb_frames)):
+    #     ret, image_np = video_reader.read()
+    #     input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
+    #     results = detection_model(input_tensor)
+    #     viz_utils.visualize_boxes_and_labels_on_image_array(
+    #               image_np,
+    #               results['detection_boxes'][0].numpy(),
+    #               (results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
+    #               results['detection_scores'][0].numpy(),
+    #               category_index,
+    #               use_normalized_coordinates=True,
+    #               max_boxes_to_draw=200,
+    #               min_score_thresh=.50,
+    #               agnostic_mode=False,
+    #               line_thickness=2)
 
-        video_writer.write(np.uint8(image_np))
+    #     video_writer.write(np.uint8(image_np))
                 
     # Release camera and close windows
     video_reader.release()