clean up

app.py

@@ -97,6 +97,7 @@ def inference(stream_url, start_time, end_time, count_only_api, api_key,
     seconds = length / fps
     all_frames = []
     frame_i = 1
+    print('Reading frames...')
     while cap.isOpened():
         ret, frame = cap.read()
         if ret is False:
@@ -107,6 +108,7 @@ def inference(stream_url, start_time, end_time, count_only_api, api_key,
         all_frames.append(img)
         frame_i += 1
     cap.release()
+    print('Done!')
 
     length = len(all_frames)
     period_lengths = np.zeros(len(all_frames) + seq_len + stride_length)
@@ -123,7 +125,6 @@ def inference(stream_url, start_time, end_time, count_only_api, api_key,
     for i in tqdm(range(0, length + stride_length - stride_pad, stride_length)):
         batch = all_frames[i:i + seq_len]
         Xlist = []
-        print('Preprocessing...')
         for img in batch:
             frameTensor = preprocess(img).unsqueeze(0)
             Xlist.append(frameTensor)
@@ -136,7 +137,6 @@ def inference(stream_url, start_time, end_time, count_only_api, api_key,
         X *= 255
         batch_list.append(X.unsqueeze(0))
         idx_list.append(i)
-        print('Running inference...')
         if len(batch_list) == batch_size:
             batch_X = torch.cat(batch_list)
             outputs = ort_sess.run(None, {'video': batch_X.numpy()})