skipping frames

app.py

@@ -215,35 +215,38 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 5):
 
             frame = cv2.resize(frame, (int(width), int(height)))
             print('starting Frame: ', cnt)
-            # flip frame vertically
-            display_frame, result = inference_frame_serial(frame)
-            
-            #print(result)
-            top_pred = process_results_for_plot(predictions = result.numpy(),
-                                                classes = classes,
-                                                class_sizes = class_sizes_lower)
-
-            # add to last 5   
-            last_5_shark_detected[drawn_count % 5] = int(top_pred['shark_n'] > 0)
-            last_5_human_detected[drawn_count % 5] = int(top_pred['human_n'] > 0)
-            last_5_dangerous_dist[drawn_count % 5] = int(top_pred['dangerous_dist'] > 0)
-
-            top_pred['shark_sighted'] = int(np.sum(last_5_shark_detected) > 3)
-            top_pred['human_sighted'] = int(np.sum(last_5_human_detected) > 3)
-            top_pred['dangerous_dist_confirmed'] = int(np.sum(last_5_dangerous_dist) > 3)
-
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            prediction_frame = cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB)
-            
-            #
-            #video.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-            
             if cnt*skip_frames %2==0: 
+                
+                # flip frame vertically
+                display_frame, result = inference_frame_serial(frame)
+                
+                #print(result)
+                top_pred = process_results_for_plot(predictions = result.numpy(),
+                                                    classes = classes,
+                                                    class_sizes = class_sizes_lower)
+
+                # add to last 5   
+                last_5_shark_detected[drawn_count % 5] = int(top_pred['shark_n'] > 0)
+                last_5_human_detected[drawn_count % 5] = int(top_pred['human_n'] > 0)
+                last_5_dangerous_dist[drawn_count % 5] = int(top_pred['dangerous_dist'] > 0)
+
+                top_pred['shark_sighted'] = int(np.sum(last_5_shark_detected) > 3)
+                top_pred['human_sighted'] = int(np.sum(last_5_human_detected) > 3)
+                top_pred['dangerous_dist_confirmed'] = int(np.sum(last_5_dangerous_dist) > 3)
+
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                prediction_frame = cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB)
+                
+                #
+                #video.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+                
+            
                 prediction_frame = cv2.resize(prediction_frame, (int(width), int(height)))
                 frame = prediction_frame
+                frame = draw_cockpit(frame, top_pred,cnt*skip_frames)
                 
             #if  top_pred['shark_sighted'] or top_pred['shark_suspected']: 
-                frame = draw_cockpit(frame, top_pred,cnt*skip_frames)
+                
             
             
             frame = cv2.resize(frame, (int(width), int(height)))

inference.py

@@ -44,7 +44,7 @@ classes = ['Beach',
  'Sea cow',
  'Stingray',
  'Person',
- 'Surfer',
+ 'ocean',
  'Surfer',
  'Surfer',
  'Fish',