fixing labels, putting border again

app.py

@@ -65,28 +65,31 @@ def overlay_text_on_image(image, text_list, font=cv2.FONT_HERSHEY_SIMPLEX, font_
         cv2.putText(image, line, (image.shape[1] - text_width - margin, y), font, font_size, color, font_thickness, lineType=cv2.LINE_AA)
     return image
 
-def draw_cockpit(frame, top_pred, cnt):
-#     # Bullet points:
-#     high_danger_color = (255,0,0)
-#     low_danger_color = yellowgreen = (154,205,50)
-
-#     shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted'])
-#     human_sighted = 'Number of Humans: ' + str(top_pred['human_n'])
-#     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_color = high_danger_color if top_pred['dangerous_dist'] else low_danger_color # 'orangered' for high danger , 'yellowgreen' for low danger'
-   
-#    # Create a list of strings to plot
-#     strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level]
-#     relative = max(frame.shape[0],frame.shape[1])
-#     if top_pred['shark_sighted'] and (cnt % 2) == 0:
-#         relative = max(frame.shape[0],frame.shape[1])
-#         frame  = add_border(frame, color=danger_color, thickness=int(relative*0.025))
-#     frame = overlay_text_on_image(frame, strings, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=1.5, font_thickness=3, margin=int(relative*0.05), color=(255, 255, 255))
+def draw_cockpit(frame, top_pred,cnt):
+    # Bullet points:
+    high_danger_color = (255,0,0)
+    low_danger_color = yellowgreen = (154,205,50)
+    shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted'])
+    human_sighted = 'Number of Humans: ' + str(top_pred['human_n'])
+    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_color = 'orangered' if top_pred['dangerous_dist'] else 'yellowgreen'
+    # Create a list of strings to plot
+    strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level]
+    relative = max(frame.shape[0],frame.shape[1])
+    if top_pred['shark_sighted'] and top_pred['dangerous_dist'] and cnt%2 == 0:
+        relative = max(frame.shape[0],frame.shape[1])
+        frame  = add_border(frame, color=high_danger_color, thickness=int(relative*0.025))
+    elif top_pred['shark_sighted'] and not top_pred['dangerous_dist'] and cnt%2 == 0:
+         relative = max(frame.shape[0],frame.shape[1])
+         frame  = add_border(frame, color=low_danger_color, thickness=int(relative*0.025))
+    overlay_text_on_image(frame, strings, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=relative*0.0007, font_thickness=1, margin=int(relative*0.05), color=(255, 255, 255))
     return frame
     
+    
+
 def process_video(input_video, out_fps = 'auto', skip_frames = 7):
     cap = cv2.VideoCapture(input_video)
 
@@ -107,46 +110,38 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
     cnt = 0
     
     while iterating:
-        print(cnt)
         if (cnt % skip_frames) == 0:
-            # Get inference results and output masks
+            print('starting Frame: ', cnt)
+            # flip frame vertically
             display_frame, result = inference_frame_serial(frame)
-            video.write(cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB))
             
             #print(result)
-            print('start top_pred')
             top_pred = process_results_for_plot(predictions = result.numpy(),
                                                 classes = classes,
                                                 class_sizes = class_sizes_lower)
-            
             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             prediction_frame = cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB)
             
-            frame = cv2.resize(frame, (int(width*4), int(height*4)))
             
-            # Only show the prediction frame every other frame
-            # print('first if')
-            # if ((cnt*skip_frames) % 2 == 0): # and top_pred['shark_sighted']: 
-            #     prediction_frame = cv2.resize(prediction_frame, (int(width*4), int(height*4)))
-            #     frame = prediction_frame
-             
-            # # Add cockpit to frame
-            # print('cockput if')
-            # if  top_pred['shark_sighted']: 
-            #     frame = draw_cockpit(frame, top_pred, cnt*skip_frames)
+            #frame = cv2.resize(frame, (int(width), int(height)))
             
-            print('dashboard')
+            if cnt*skip_frames %2==0 and top_pred['shark_sighted']: 
+                #prediction_frame = cv2.resize(prediction_frame, (int(width), int(height)))
+                frame =prediction_frame
+             
+            if  top_pred['shark_sighted']: 
+                frame = draw_cockpit(frame, top_pred,cnt*skip_frames)
+            video.write(frame)
             pred_dashbord = prediction_dashboard(top_pred = top_pred)
             #print('sending frame')
-            print('finished dashboard')
-            print(cnt)
-            # if cnt == 119:
-            #     import ipdb; ipdb.set_trace()
-            
-            yield prediction_frame, frame , None, pred_dashbord
+            print('finalizing frame:',cnt)
+            print(pred_dashbord.shape)
+            print(frame.shape)
+            print(prediction_frame.shape)
+            yield prediction_frame,frame , None, pred_dashbord
+        print('overall count ', cnt)
         cnt += 1
         iterating, frame = cap.read()
-        print('interating: ', iterating)
     
     video.release()
     yield None, None, output_path, None
@@ -154,15 +149,12 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
 with gr.Blocks(theme=theme) as demo:
     with gr.Row().style(equal_height=True,height='50%'):
         input_video = gr.Video(label="Input")
-        output_video = gr.Video(label="Output Video")
-        
-    with gr.Row():
         processed_frames = gr.Image(label="Shark Engine")
+        output_video = gr.Video(label="Output Video")
         dashboard = gr.Image(label="Dashboard")
-        original_frames = gr.Image(label="Original Frame") #, width='100%', height='100%')
     
-    #with gr.Row(height='100%',width='100%'):
-        #original_frames = gr.Image(label="Original Frame", width='100%', height='100%')
+    with gr.Row():
+        original_frames = gr.Image(label="Original Frame").style( height=768)
         
     with gr.Row():
         paths = sorted(pathlib.Path('videos_example/').rglob('*.mp4'))
@@ -174,6 +166,6 @@ with gr.Blocks(theme=theme) as demo:
 
 demo.queue()
 if os.getenv('SYSTEM') == 'spaces':
-    demo.launch(width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD')), debug = True)
+    demo.launch(width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD')))
 else: 
-    demo.launch(debug = True)
+    demo.launch()

inference.py

@@ -53,11 +53,11 @@ classes = ['Beach',
  'Dolphin',
  'Miscellaneous',
  'Unidentifiable shark',
- 'Carpet shark',
+ 'C Shark',
  'Dusty shark',
  'Blue shark',
  'Great white shark',
- 'Copper shark',
+ 'Shark',
  'Nurse shark',
  'Silky shark',
  'Leopard shark',
@@ -106,11 +106,11 @@ class_sizes = {'Beach': None,
                'Dolphin': {'feet':[6.6, 13.1], 'meter': [2, 4], 'kg': [150, 650], 'pounds': [330, 1430]},
                'Miscellaneous': None,
                'Unidentifiable shark': {'feet': [2, 15], 'meter': [0.6, 4.5], 'kg': [50, 1000], 'pounds': [110, 2200]},
-               'Carpet shark': {'feet': [4, 10], 'meter': [1.25, 3], 'kg': [50, 1000], 'pounds': [110, 2200]}, # Prob incorrect
+               'C Shark': {'feet': [4, 10], 'meter': [1.25, 3], 'kg': [50, 1000], 'pounds': [110, 2200]}, # Prob incorrect
                'Dusty shark': {'feet': [9, 14], 'meter': [3, 4.25], 'kg': [160, 180], 'pounds': [350, 400]},
                'Blue shark': {'feet': [7.9, 12.5], 'meter': [2.4, 3], 'kg': [60, 120], 'pounds': [130, 260]}, 
                'Great white shark': {'feet': [13.1, 20], 'meter': [4, 6], 'kg': [680, 1800], 'pounds': [1500, 4000]},
-               'Copper shark': {'feet': [7.2, 10.8], 'meter': [2.2, 3.3], 'kg': [130, 300], 'pounds': [290, 660]},
+               'Shark': {'feet': [7.2, 10.8], 'meter': [2.2, 3.3], 'kg': [130, 300], 'pounds': [290, 660]},
                'Nurse shark': {'feet': [7.9, 9.8], 'meter': [2.4, 3], 'kg': [90, 115], 'pounds': [200, 250]},
                'Silky shark': {'feet': [6.6, 8.2], 'meter': [2, 2.5], 'kg': [300, 380], 'pounds': [660, 840]},
                'Leopard shark': {'feet': [3.9, 4.9], 'meter': [1.2, 1.5], 'kg': [11, 20], 'pounds': [22, 44]},

metrics.py

@@ -49,6 +49,7 @@ def add_class_sizes(top_pred = {}, class_sizes = None):
                 tmp_class_sizes = class_sizes[tmp_pred.lower()]
                 if tmp_class_sizes == None:
                     size_list.append(None)
+                    continue 
                 else:
                     size_list.append(tmp_class_sizes['feet'])
 
@@ -74,6 +75,7 @@ def add_class_weights(top_pred = {}, class_weights = None):
                 tmp_class_weights = class_weights[tmp_pred.lower()]
                 if tmp_class_weights == None:
                     weight_list.append(None)
+                    continue
                 else:
                     weight_list.append(tmp_class_weights['pounds'])
 

pre-requeriments.txt

@@ -1,5 +1,4 @@
 
-
 numpy==1.22.4
 opencv-python-headless==4.5.5.64
 openmim==0.1.5