change metrics towards using running averages

app.py

@@ -43,7 +43,6 @@ theme = gr.themes.Soft(
 )
 
 
-
 def add_border(frame, color = (255, 0, 0), thickness = 2):
     # Add a red border to the image
     relative = max(frame.shape[0],frame.shape[1])
@@ -157,11 +156,17 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
 
     iterating, frame = cap.read()
     cnt = 0
-    
+    drawn_count = 0
+    last_5_shark_detected = np.array([0, 0, 0, 0, 0])
+    last_5_human_detected = np.array([0, 0, 0, 0, 0])
+    last_5_dangerous_dist = np.array([0, 0, 0, 0, 0])
+
     while iterating:
         print('overall count ', cnt)
         
         if (cnt % skip_frames) == 0:
+            drawn_count += 1
+
             frame = cv2.resize(frame, (int(width), int(height)))
             print('starting Frame: ', cnt)
             # flip frame vertically
@@ -171,16 +176,25 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
             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 and top_pred['shark_sighted']: 
                 prediction_frame = cv2.resize(prediction_frame, (int(width), int(height)))
-                frame =prediction_frame
+                frame = prediction_frame
                 
             if  top_pred['shark_sighted']: 
                 frame = draw_cockpit(frame, top_pred,cnt*skip_frames)
@@ -190,6 +204,8 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
                 
             
             pred_dashbord = prediction_dashboard(top_pred = top_pred)
+
+            drawn_count += 1
             #print('sending frame')
             print('finalizing frame:',cnt)
             print(pred_dashbord.shape)

metrics.py

@@ -213,8 +213,8 @@ def process_results_for_plot(predictions = None, threshold = 0.5, classes = None
                     'dangerous_dist': False}
 
     return {'min_dist_str': min_dist['min_dist'], 
-            'shark_sighted': top_pred['shark_n'] > 0,
-            'human_sighted': top_pred['human_n'] > 0,
+            'shark_suspected': top_pred['shark_n'] > 0,
+            'human_suspected': top_pred['human_n'] > 0,
             'shark_n': top_pred['shark_n'],
             #'human_n': max(top_pred['human_n'],len(bboxes)-top_pred['shark_n']),
             'human_n': top_pred['human_n'],
@@ -227,19 +227,30 @@ def process_results_for_plot(predictions = None, threshold = 0.5, classes = None
 
 def prediction_dashboard(top_pred = None):
     # Bullet points:
-    shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted'])
-    human_sighted = 'Number of Humans: ' + str(top_pred['human_n'])
+    if top_pred['shark_sighted'] > 0:
+        shark_suspected = 'Shark Sighted !'
+    elif top_pred['shark_suspected'] > 0:
+        shark_suspected = 'Shark Suspected !'
+    else:
+        shark_suspected = 'No Sharks ...'
+
+    if top_pred['human_sighted'] > 0:
+        human_suspected = 'Human Sighted !'
+    elif top_pred['human_suspected'] > 0:
+        human_suspected = 'Human Suspected !'
+    else:
+        human_suspected = 'No Humans ...'
 
     shark_size_estimate = 'Biggest shark size: ' + str(top_pred['biggest_shark_size'])
     shark_weight_estimate = 'Biggest shark weight: ' + str(top_pred['biggest_shark_weight'])
 
     danger_level = 'Danger Level: ' 
-    danger_level += 'High' if top_pred['dangerous_dist'] else 'Low'
+    danger_level += 'High' if top_pred['dangerous_dist_confirmed'] else 'Low'
 
-    danger_color = 'orangered' if top_pred['dangerous_dist'] else 'yellowgreen'
+    danger_color = 'orangered' if top_pred['dangerous_dist_confirmed'] else 'yellowgreen'
 
     # Create a list of strings to plot
-    strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level]
+    strings = [shark_suspected, human_suspected, shark_size_estimate, shark_weight_estimate, danger_level]
 
     # Create a figure and axis
     fig, ax = plt.subplots()