adding failsafe for high resolution images

app.py

@@ -135,6 +135,7 @@ def draw_cockpit(frame, top_pred,cnt):
     
 
 def process_video(input_video, out_fps = 'auto', skip_frames = 7):
+    print('Processing video: ')
     cap = cv2.VideoCapture(input_video)
 
     output_path = "output.mp4"
@@ -147,6 +148,10 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
 
     width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    if width > 1920 or height > 1080:
+        width = int(width//4)
+        height = int(height//4)
 
     video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
 
@@ -157,6 +162,7 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
         print('overall count ', cnt)
         
         if (cnt % skip_frames) == 0:
+            frame = cv2.resize(frame, (int(width), int(height)))
             print('starting Frame: ', cnt)
             # flip frame vertically
             display_frame, result = inference_frame_serial(frame)
@@ -189,6 +195,7 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
             print(pred_dashbord.shape)
             print(frame.shape)
             print(prediction_frame.shape)
+            print(width, height)
             yield frame , None
         
         cnt += 1
@@ -217,4 +224,4 @@ demo.queue()
 if os.getenv('SYSTEM') == 'spaces':
     demo.launch(width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD')))
 else: 
-    demo.launch()
+    demo.launch(debug=True)

metrics.py

@@ -106,7 +106,7 @@ def add_class_sizes(top_pred = {}, class_sizes = None):
 
         top_pred['pred_above_thresh_sizes'] = size_list
 
-        if top_pred['shark_n'] > 0:
+        if top_pred['shark_n'] > 0 and len(shark_size_list) > 0:
             top_pred['biggest_shark_size'] = np.max(shark_size_list)
         else:
             top_pred['biggest_shark_size'] = None
@@ -132,7 +132,7 @@ def add_class_weights(top_pred = {}, class_weights = None):
 
         top_pred['pred_above_thresh_weights'] = weight_list
 
-        if top_pred['shark_n'] > 0:
+        if top_pred['shark_n'] > 0 and len(shark_weight_list) > 0:
             top_pred['biggest_shark_weight'] = np.max(shark_weight_list)
         else:
             top_pred['biggest_shark_weight'] = None