Update app.py

app.py

@@ -167,7 +167,7 @@ async def predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
     
 
-async def detect_multiple_dogs(image, conf_threshold=0.4, iou_threshold=0.55):
+async def detect_multiple_dogs(image, conf_threshold=0.35, iou_threshold=0.55):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     boxes = []
@@ -195,56 +195,6 @@ async def detect_multiple_dogs(image, conf_threshold=0.4, iou_threshold=0.55):
     return dogs
 
 
-# async def detect_multiple_dogs(image, conf_threshold=0.35, iou_threshold=0.5):
-#     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
-#     dogs = []
-#     boxes = []
-    
-#     for box in results.boxes:
-#         if box.cls == 16:  # COCO dataset class for dog is 16
-#             xyxy = box.xyxy[0].tolist()
-#             confidence = box.conf.item()
-#             boxes.append((xyxy, confidence))
-    
-#     if not boxes:
-#         dogs.append((image, 1.0, [0, 0, image.width, image.height]))
-#     else:
-#         nms_boxes = non_max_suppression(boxes, iou_threshold)
-        
-#         for box, confidence in nms_boxes:
-#             x1, y1, x2, y2 = [int(coord) for coord in box]
-#             cropped_image = image.crop((x1, y1, x2, y2))
-#             dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
-    
-#     # 應用過濾器來移除可能的錯誤檢測
-#     dogs = filter_detections(dogs, (image.width, image.height))
-    
-#     return dogs
-
-# def filter_detections(dogs, image_size):
-#     filtered_dogs = []
-#     image_area = image_size[0] * image_size[1]
-#     num_dogs = len(dogs)
-
-#     # 根據檢測到的狗的數量動態調整閾值
-#     if num_dogs > 5:
-#         min_ratio, max_ratio = 0.003, 0.5
-#     elif num_dogs > 2:
-#         min_ratio, max_ratio = 0.005, 0.6
-#     else:
-#         min_ratio, max_ratio = 0.01, 0.7
-
-#     for dog in dogs:
-#         _, confidence, box = dog
-#         dog_area = (box[2] - box[0]) * (box[3] - box[1])
-#         area_ratio = dog_area / image_area
-        
-#         if min_ratio < area_ratio < max_ratio:
-#             filtered_dogs.append(dog)
-    
-#     return filtered_dogs
-
-
 def non_max_suppression(boxes, iou_threshold):
     keep = []
     boxes = sorted(boxes, key=lambda x: x[1], reverse=True)
@@ -385,7 +335,6 @@ async def predict(image):
         return error_msg, None, gr.update(visible=False, choices=[]), None
 
 
-
 def show_details(choice, previous_output, initial_state):
     if not choice:
         return previous_output, gr.update(visible=True), initial_state