add bb text

app.py

@@ -123,6 +123,7 @@ def crop_animal_detections(img_in,
 
     ## Extract animal crops
     #print(yolo_results)
+    #pdb.set_trace()
     list_labels_as_str = [i for i in yolo_results.names.values()]  # ['animal', 'person', 'vehicle'] 
     list_np_animal_crops = []
 
@@ -160,20 +161,22 @@ def crop_animal_detections(img_in,
 
     return list_np_animal_crops
 
-def draw_rectangle_text(img,results,font_style='amiko',font_size=8,):
+def draw_rectangle_text(img,results,font_style='amiko',font_size=8, keypt_color="white",):
     bbxyxy = results.xyxy[0].tolist()[0]
     w, h = bbxyxy[2], bbxyxy[3]
     shape = [(bbxyxy[0], bbxyxy[1]), (w , h)]
     imgR = ImageDraw.Draw(img)  
-    imgR.rectangle(shape,  outline ="red",width=5)
+    imgR.rectangle(shape,  outline ="red",width=5) ##bb for animal
 
     confidence = bbxyxy[4]
-    print(confidence)
+    string_bb = 'animal ' + str(round(confidence, 2))
     font = ImageFont.truetype(FONTS[font_style], font_size) 
-    imgR.text((keypoint_x + marker_size, keypoint_y + marker_size),#(0.5*im_width, 0.5*im_height), #-------
-                      map_label_id_to_str[i],
-                      ImageColor.getcolor(keypt_color, "RGB"), # rgb
-                      font=font)
+
+    text_size = font.getsize(string_bb) # (h,w)
+    position = (bbxyxy[0],bbxyxy[1] - text_size[1] -2 )
+    left, top, right, bottom = imgR.textbbox(position, string_bb, font=font)
+    imgR.rectangle((left, top-5, right+5, bottom+5), fill="red")
+    imgR.text((bbxyxy[0] + 3 ,bbxyxy[1] - text_size[1] -2 ), string_bb, font=font, fill="black")
 
     return imgR
 
@@ -304,7 +307,8 @@ def predict_pipeline(img_input,
             img_background.paste(img_crop,
                                  box = tuple([int(t) for t in md_results.xyxy[0][ic,:2]]))
 
-            draw_rectangle_text(img_background, md_results)                                                    
+            draw_rectangle_text(img_background, md_results ,font_style=font_style,font_size=font_size, keypt_color=keypt_color)       
+            #draw_rectangle_text(img,results,font_style='amiko',font_size=8, keypt_color="#ff0000",):                                             
         return img_background
 
 #############################################
@@ -377,9 +381,9 @@ gr_description = "Contributed by Sofia Minano, Neslihan Wittek, Nirel Kadzo, Vic
 
 # article = "<p style='text-align: center'>This app makes predictions using a YOLOv5x6 model that was trained to detect animals, humans, and vehicles in camera trap images; find out more about the project on <a href='https://github.com/microsoft/CameraTraps'>GitHub</a>. This app was built by Henry Lydecker but really depends on code and models developed by <a href='http://ecologize.org/'>Ecologize</a> and <a href='http://aka.ms/aiforearth'>Microsoft AI for Earth</a>. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and developed by Ultralytics, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
 
-examples = [['example/monkey_full.jpg', 'md_v5a','full_macaque', False, True, 0.5, 0.3, 'amiko', 5, 'blue', 3],
-            ['example/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.05, 'amiko', 5, 'yellow', 3],
-            ['example/cat.jpg', 'md_v5a', 'full_cat', False, True, 0.5, 0.05, 'amiko', 5, 'purple', 3]]
+examples = [['example/monkey_full.jpg', 'md_v5a','full_macaque', False, True, 0.5, 0.3, 'amiko', 9, 'blue', 3],
+            ['example/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.05, 'amiko',9, 'yellow', 3],
+            ['example/cat.jpg', 'md_v5a', 'full_cat', False, True, 0.5, 0.05, 'amiko', 9, 'purple', 3]]
 
 ################################################
 # %% Define and launch gradio interface